similarities between case study and naturalistic observation

Observation Method in Psychology: Naturalistic, Participant and Controlled

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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The observation method in psychology involves directly and systematically witnessing and recording measurable behaviors, actions, and responses in natural or contrived settings without attempting to intervene or manipulate what is being observed.

Used to describe phenomena, generate hypotheses, or validate self-reports, psychological observation can be either controlled or naturalistic with varying degrees of structure imposed by the researcher.

There are different types of observational methods, and distinctions need to be made between:

1. Controlled Observations 2. Naturalistic Observations 3. Participant Observations

In addition to the above categories, observations can also be either overt/disclosed (the participants know they are being studied) or covert/undisclosed (the researcher keeps their real identity a secret from the research subjects, acting as a genuine member of the group).

In general, conducting observational research is relatively inexpensive, but it remains highly time-consuming and resource-intensive in data processing and analysis.

The considerable investments needed in terms of coder time commitments for training, maintaining reliability, preventing drift, and coding complex dynamic interactions place practical barriers on observers with limited resources.

Controlled Observation

Controlled observation is a research method for studying behavior in a carefully controlled and structured environment.

The researcher sets specific conditions, variables, and procedures to systematically observe and measure behavior, allowing for greater control and comparison of different conditions or groups.

The researcher decides where the observation will occur, at what time, with which participants, and in what circumstances, and uses a standardized procedure. Participants are randomly allocated to each independent variable group.

Rather than writing a detailed description of all behavior observed, it is often easier to code behavior according to a previously agreed scale using a behavior schedule (i.e., conducting a structured observation).

The researcher systematically classifies the behavior they observe into distinct categories. Coding might involve numbers or letters to describe a characteristic or the use of a scale to measure behavior intensity.

The categories on the schedule are coded so that the data collected can be easily counted and turned into statistics.

For example, Mary Ainsworth used a behavior schedule to study how infants responded to brief periods of separation from their mothers. During the Strange Situation procedure, the infant’s interaction behaviors directed toward the mother were measured, e.g.,

• Proximity and contact-seeking
• Contact maintaining
• Avoidance of proximity and contact
• Resistance to contact and comforting

The observer noted down the behavior displayed during 15-second intervals and scored the behavior for intensity on a scale of 1 to 7.

Sometimes participants’ behavior is observed through a two-way mirror, or they are secretly filmed. Albert Bandura used this method to study aggression in children (the Bobo doll studies ).

A lot of research has been carried out in sleep laboratories as well. Here, electrodes are attached to the scalp of participants. What is observed are the changes in electrical activity in the brain during sleep ( the machine is called an EEG ).

Controlled observations are usually overt as the researcher explains the research aim to the group so the participants know they are being observed.

Controlled observations are also usually non-participant as the researcher avoids direct contact with the group and keeps a distance (e.g., observing behind a two-way mirror).

• Controlled observations can be easily replicated by other researchers by using the same observation schedule. This means it is easy to test for reliability .
• The data obtained from structured observations is easier and quicker to analyze as it is quantitative (i.e., numerical) – making this a less time-consuming method compared to naturalistic observations.
• Controlled observations are fairly quick to conduct which means that many observations can take place within a short amount of time. This means a large sample can be obtained, resulting in the findings being representative and having the ability to be generalized to a large population.

Limitations

• Controlled observations can lack validity due to the Hawthorne effect /demand characteristics. When participants know they are being watched, they may act differently.

Naturalistic Observation

Naturalistic observation is a research method in which the researcher studies behavior in its natural setting without intervention or manipulation.

It involves observing and recording behavior as it naturally occurs, providing insights into real-life behaviors and interactions in their natural context.

Naturalistic observation is a research method commonly used by psychologists and other social scientists.

This technique involves observing and studying the spontaneous behavior of participants in natural surroundings. The researcher simply records what they see in whatever way they can.

In unstructured observations, the researcher records all relevant behavior with a coding system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.

Compared with controlled observations, it is like the difference between studying wild animals in a zoo and studying them in their natural habitat.

With regard to human subjects, Margaret Mead used this method to research the way of life of different tribes living on islands in the South Pacific. Kathy Sylva used it to study children at play by observing their behavior in a playgroup in Oxfordshire.

Collecting Naturalistic Behavioral Data

Technological advances are enabling new, unobtrusive ways of collecting naturalistic behavioral data.

The Electronically Activated Recorder (EAR) is a digital recording device participants can wear to periodically sample ambient sounds, allowing representative sampling of daily experiences (Mehl et al., 2012).

Studies program EARs to record 30-50 second sound snippets multiple times per hour. Although coding the recordings requires extensive resources, EARs can capture spontaneous behaviors like arguments or laughter.

EARs minimize participant reactivity since sampling occurs outside of awareness. This reduces the Hawthorne effect, where people change behavior when observed.

The SenseCam is another wearable device that passively captures images documenting daily activities. Though primarily used in memory research currently (Smith et al., 2014), systematic sampling of environments and behaviors via the SenseCam could enable innovative psychological studies in the future.

• By being able to observe the flow of behavior in its own setting, studies have greater ecological validity.
• Like case studies , naturalistic observation is often used to generate new ideas. Because it gives the researcher the opportunity to study the total situation, it often suggests avenues of inquiry not thought of before.
• The ability to capture actual behaviors as they unfold in real-time, analyze sequential patterns of interactions, measure base rates of behaviors, and examine socially undesirable or complex behaviors that people may not self-report accurately.
• These observations are often conducted on a micro (small) scale and may lack a representative sample (biased in relation to age, gender, social class, or ethnicity). This may result in the findings lacking the ability to generalize to wider society.
• Natural observations are less reliable as other variables cannot be controlled. This makes it difficult for another researcher to repeat the study in exactly the same way.
• Highly time-consuming and resource-intensive during the data coding phase (e.g., training coders, maintaining inter-rater reliability, preventing judgment drift).
• With observations, we do not have manipulations of variables (or control over extraneous variables), meaning cause-and-effect relationships cannot be established.

Participant Observation

Participant observation is a variant of the above (natural observations) but here, the researcher joins in and becomes part of the group they are studying to get a deeper insight into their lives.

If it were research on animals , we would now not only be studying them in their natural habitat but be living alongside them as well!

Leon Festinger used this approach in a famous study into a religious cult that believed that the end of the world was about to occur. He joined the cult and studied how they reacted when the prophecy did not come true.

Participant observations can be either covert or overt. Covert is where the study is carried out “undercover.” The researcher’s real identity and purpose are kept concealed from the group being studied.

The researcher takes a false identity and role, usually posing as a genuine member of the group.

On the other hand, overt is where the researcher reveals his or her true identity and purpose to the group and asks permission to observe.

• It can be difficult to get time/privacy for recording. For example, researchers can’t take notes openly with covert observations as this would blow their cover. This means they must wait until they are alone and rely on their memory. This is a problem as they may forget details and are unlikely to remember direct quotations.
• If the researcher becomes too involved, they may lose objectivity and become biased. There is always the danger that we will “see” what we expect (or want) to see. This problem is because they could selectively report information instead of noting everything they observe. Thus reducing the validity of their data.

Recording of Data

With controlled/structured observation studies, an important decision the researcher has to make is how to classify and record the data. Usually, this will involve a method of sampling.

In most coding systems, codes or ratings are made either per behavioral event or per specified time interval (Bakeman & Quera, 2011).

The three main sampling methods are:

Event-based coding involves identifying and segmenting interactions into meaningful events rather than timed units.

For example, parent-child interactions may be segmented into control or teaching events to code. Interval recording involves dividing interactions into fixed time intervals (e.g., 6-15 seconds) and coding behaviors within each interval (Bakeman & Quera, 2011).

Event recording allows counting event frequency and sequencing while also potentially capturing event duration through timed-event recording. This provides information on time spent on behaviors.

Coding Systems

The coding system should focus on behaviors, patterns, individual characteristics, or relationship qualities that are relevant to the theory guiding the study (Wampler & Harper, 2014).

Codes vary in how much inference is required, from concrete observable behaviors like frequency of eye contact to more abstract concepts like degree of rapport between a therapist and client (Hill & Lambert, 2004). More inference may reduce reliability.

Macroanalytic coding systems

Macroanalytic coding systems involve rating or summarizing behaviors using larger coding units and broader categories that reflect patterns across longer periods of interaction rather than coding small or discrete behavioral acts. 

For example, a macroanalytic coding system may rate the overall degree of therapist warmth or level of client engagement globally for an entire therapy session, requiring the coders to summarize and infer these constructs across the interaction rather than coding smaller behavioral units.

These systems require observers to make more inferences (more time-consuming) but can better capture contextual factors, stability over time, and the interdependent nature of behaviors (Carlson & Grotevant, 1987).

Microanalytic coding systems

Microanalytic coding systems involve rating behaviors using smaller, more discrete coding units and categories.

For example, a microanalytic system may code each instance of eye contact or head nodding during a therapy session. These systems code specific, molecular behaviors as they occur moment-to-moment rather than summarizing actions over longer periods.

Microanalytic systems require less inference from coders and allow for analysis of behavioral contingencies and sequential interactions between therapist and client. However, they are more time-consuming and expensive to implement than macroanalytic approaches.

Mesoanalytic coding systems

Mesoanalytic coding systems attempt to balance macro- and micro-analytic approaches.

In contrast to macroanalytic systems that summarize behaviors in larger chunks, mesoanalytic systems use medium-sized coding units that target more specific behaviors or interaction sequences (Bakeman & Quera, 2017).

For example, a mesoanalytic system may code each instance of a particular type of therapist statement or client emotional expression. However, mesoanalytic systems still use larger units than microanalytic approaches coding every speech onset/offset.

The goal of balancing specificity and feasibility makes mesoanalytic systems well-suited for many research questions (Morris et al., 2014). Mesoanalytic codes can preserve some sequential information while remaining efficient enough for studies with adequate but limited resources.

For instance, a mesoanalytic couple interaction coding system could target key behavior patterns like validation sequences without coding turn-by-turn speech.

In this way, mesoanalytic coding allows reasonable reliability and specificity without requiring extensive training or observation. The mid-level focus offers a pragmatic compromise between depth and breadth in analyzing interactions.

Preventing Coder Drift

Coder drift results in a measurement error caused by gradual shifts in how observations get rated according to operational definitions, especially when behavioral codes are not clearly specified.

This type of error creeps in when coders fail to regularly review what precise observations constitute or do not constitute the behaviors being measured.

Preventing drift refers to taking active steps to maintain consistency and minimize changes or deviations in how coders rate or evaluate behaviors over time. Specifically, some key ways to prevent coder drift include:

• Operationalize codes : It is essential that code definitions unambiguously distinguish what interactions represent instances of each coded behavior. 
• Ongoing training : Returning to those operational definitions through ongoing training serves to recalibrate coder interpretations and reinforce accurate recognition. Having regular “check-in” sessions where coders practice coding the same interactions allows monitoring that they continue applying codes reliably without gradual shifts in interpretation.
• Using reference videos : Coders periodically coding the same “gold standard” reference videos anchors their judgments and calibrate against original training. Without periodic anchoring to original specifications, coder decisions tend to drift from initial measurement reliability.
• Assessing inter-rater reliability : Statistical tracking that coders maintain high levels of agreement over the course of a study, not just at the start, flags any declines indicating drift. Sustaining inter-rater agreement requires mitigating this common tendency for observer judgment change during intensive, long-term coding tasks.
• Recalibrating through discussion : Having meetings for coders to discuss disagreements openly explores reasons judgment shifts may be occurring over time. Consensus on the application of codes is restored.
• Adjusting unclear codes : If reliability issues persist, revisiting and refining ambiguous code definitions or anchors can eliminate inconsistencies arising from coder confusion.

Essentially, the goal of preventing coder drift is maintaining standardization and minimizing unintentional biases that may slowly alter how observational data gets rated over periods of extensive coding.

Through the upkeep of skills, continuing calibration to benchmarks, and monitoring consistency, researchers can notice and correct for any creeping changes in coder decision-making over time.

Reducing Observer Bias

Observational research is prone to observer biases resulting from coders’ subjective perspectives shaping the interpretation of complex interactions (Burghardt et al., 2012). When coding, personal expectations may unconsciously influence judgments. However, rigorous methods exist to reduce such bias.

Coding Manual

A detailed coding manual minimizes subjectivity by clearly defining what behaviors and interaction dynamics observers should code (Bakeman & Quera, 2011).

High-quality manuals have strong theoretical and empirical grounding, laying out explicit coding procedures and providing rich behavioral examples to anchor code definitions (Lindahl, 2001).

Clear delineation of the frequency, intensity, duration, and type of behaviors constituting each code facilitates reliable judgments and reduces ambiguity for coders. Application risks inconsistency across raters without clarity on how codes translate to observable interaction.

Coder Training

Competent coders require both interpersonal perceptiveness and scientific rigor (Wampler & Harper, 2014). Training thoroughly reviews the theoretical basis for coded constructs and teaches the coding system itself.

Multiple “gold standard” criterion videos demonstrate code ranges that trainees independently apply. Coders then meet weekly to establish reliability of 80% or higher agreement both among themselves and with master criterion coding (Hill & Lambert, 2004).

Ongoing training manages coder drift over time. Revisions to unclear codes may also improve reliability. Both careful selection and investment in rigorous training increase quality control.

Blind Methods

To prevent bias, coders should remain unaware of specific study predictions or participant details (Burghardt et al., 2012). Separate data gathering versus coding teams helps maintain blinding.

Coders should be unaware of study details or participant identities that could bias coding (Burghardt et al., 2012).

Separate teams collecting data versus coding data can reduce bias.

In addition, scheduling procedures can prevent coders from rating data collected directly from participants with whom they have had personal contact. Maintaining coder independence and blinding enhances objectivity.

Bakeman, R., & Quera, V. (2017). Sequential analysis and observational methods for the behavioral sciences. Cambridge University Press.

Burghardt, G. M., Bartmess-LeVasseur, J. N., Browning, S. A., Morrison, K. E., Stec, C. L., Zachau, C. E., & Freeberg, T. M. (2012). Minimizing observer bias in behavioral studies: A review and recommendations. Ethology, 118 (6), 511-517.

Hill, C. E., & Lambert, M. J. (2004). Methodological issues in studying psychotherapy processes and outcomes. In M. J. Lambert (Ed.), Bergin and Garfield’s handbook of psychotherapy and behavior change (5th ed., pp. 84–135). Wiley.

Lindahl, K. M. (2001). Methodological issues in family observational research. In P. K. Kerig & K. M. Lindahl (Eds.), Family observational coding systems: Resources for systemic research (pp. 23–32). Lawrence Erlbaum Associates.

Mehl, M. R., Robbins, M. L., & Deters, F. G. (2012). Naturalistic observation of health-relevant social processes: The electronically activated recorder methodology in psychosomatics. Psychosomatic Medicine, 74 (4), 410–417.

Morris, A. S., Robinson, L. R., & Eisenberg, N. (2014). Applying a multimethod perspective to the study of developmental psychology. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (2nd ed., pp. 103–123). Cambridge University Press.

Smith, J. A., Maxwell, S. D., & Johnson, G. (2014). The microstructure of everyday life: Analyzing the complex choreography of daily routines through the automatic capture and processing of wearable sensor data. In B. K. Wiederhold & G. Riva (Eds.), Annual Review of Cybertherapy and Telemedicine 2014: Positive Change with Technology (Vol. 199, pp. 62-64). IOS Press.

Traniello, J. F., & Bakker, T. C. (2015). The integrative study of behavioral interactions across the sciences. In T. K. Shackelford & R. D. Hansen (Eds.), The evolution of sexuality (pp. 119-147). Springer.

Wampler, K. S., & Harper, A. (2014). Observational methods in couple and family assessment. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (2nd ed., pp. 490–502). Cambridge University Press.

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What Is Naturalistic Observation?

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Illustration by Brianna Gilmartin, Verywell

• How Naturalistic Observation Works
• Pros and Cons
• Data Collection Methods

How Often Is Data Collected?

Naturalistic observation is a research method that involves observing subjects in their natural environment. This approach is often used by psychologists and other social scientists. It is a form of qualitative research , which focuses on collecting, evaluating, and describing non-numerical data.

It can be useful if conducting lab research would be unrealistic, cost-prohibitive, or would unduly affect the subject's behavior. The goal of naturalistic observation is to observe behavior as it occurs in a natural setting without interference or attempts to manipulate variables.

This article discusses how naturalistic observation works and the pros and cons of doing this type of research. It also covers how data is collected and examples of when this method might be used in psychology research.

How Does Naturalistic Observation Work?

People do not necessarily behave in a lab setting the way they would in a natural environment. Researchers sometimes want to observe their subject's behavior as it happens ("in the wild," so to speak). Psychologists can get a better idea of how and why people react the way that they do by watching how they respond to situations and stimuli in real life.

Naturalistic observation is different than structured observation because it involves looking at a subject's behavior as it occurs in a natural setting, with no attempts at intervention on the part of the researcher.

For example, a researcher interested in aspects of classroom behavior (such as the interactions between students or teacher-student dynamics) might use naturalistic observation as part of their research.

Performing these observations in a lab would be difficult because it would involve recreating a classroom environment. This would likely influence the behavior of the participants, making it difficult to generalize the observations made.

By observing the subjects in their natural setting (the classroom where they work and learn), the researchers can more fully observe the behavior they are interested in as it occurs in the real world.

Naturalistic Observation Pros and Cons 

Like other research methods, naturalistic observation has advantages and disadvantages.

More realistic

More affordable

Can detect patterns

Inability to manipulate or control variables

Cannot explain why behaviors happen

Risk of observer bias

An advantage of naturalistic observation is that it allows the investigators to directly observe the subject in a natural setting. The method gives scientists a first-hand look at social behavior and can help them notice things that they might never have encountered in a lab setting.

The observations can also serve as inspiration for further investigations. The information gleaned from naturalistic observation can lead to insights that can be used to help people overcome problems and lead to healthier, happier lives.

Other advantages of naturalistic observation include:

• Allows researchers to study behaviors or situations that cannot be manipulated in a lab due to ethical concerns . For example, it would be unethical to study the effects of imprisonment by actually confining subjects. But researchers can gather information by using naturalistic observation in actual prison settings.
• Can support the external validity of research . Researchers might believe that the findings of a lab study can be generalized to a larger population, but that does not mean they would actually observe those findings in a natural setting. They may conduct naturalistic observation to make that confirmation.

Naturalistic observation can be useful in many cases, but the method also has some downsides. Some of these include:

• Inability to draw cause-and-effect conclusions : The biggest disadvantage of naturalistic observation is that determining the exact cause of a subject's behavior can be difficult.
• Lack of control : Another downside is that the experimenter cannot control for outside variables .
• Lack of validity : While the goal of naturalistic observation is to get a better idea of how it occurs in the real world, experimental effects can still influence how people respond. The Hawthorne effect and other demand characteristics can play a role in people altering their behavior simply because they know they are being observed.
• Observer bias : The biases of the people observing the natural behaviors can influence the interpretations that experimenters make.

It is also important to note that naturalistic observation is a type of correlational research (others include surveys and archival research). A correlational study is a non-experimental approach that seeks to find statistical relationships between variables. Naturalistic observation is one method that can be used to collect data for correlational studies.

While such methods can look at the direction or strength of a relationship between two variables, they cannot determine if one causes the other. As the saying goes, correlation does not imply causation.

Data Collection Methods 

Researchers use different techniques to collect and record data from naturalistic observation. For example, they might write down how many times a certain behavior occurred in a specific period of time or take a video recording of subjects.

• Audio or video recordings : Depending on the type of behavior being observed, the researchers might also decide to make audio or videotaped recordings of each observation session. They can then later review the recordings.
• Observer narrative : The observer might take notes during the session that they can refer back to. They can collect data and discern behavior patterns from these notes.
• Tally counts : The observer writes down when and how many times certain behaviors occurred.

It is rarely practical—or even possible—to observe  every  moment of a subject's life. Therefore, researchers often use sampling to gather information through naturalistic observation.

The goal is to make sure that the sample of data is representative of the subject's overall behavior. A representative sample is a selection that accurately depicts the characteristics that are present in the total subject of interest. A  representative sample  can be obtained through:

• Time sampling : This involves taking samples at different intervals of time (random or systematic). For example, a researcher might observe a person in the workplace to notice how frequently they engage in certain behaviors and to determine if there are patterns or trends.
• Situation sampling : This type of sampling involves observing behavior in different situations and settings. An example of this would be observing a child in a classroom, home, and community setting to determine if certain behaviors only occur in certain settings.
• Event sampling : This approach involves observing and recording each time an event happens. This allows the researchers to better identify patterns that might be present. For example, a researcher might note every time a subject becomes agitated. By noting the event and what was occurring around the time of each event, researchers can draw inferences about what might be triggering those behaviors.

Examples of Naturalistic Observation

Imagine that you want to study risk-taking behavior in teenagers. You might choose to observe behavior in different settings, such as a sledding hill, a rock-climbing wall, an ice-skating rink, and a bumper car ride. After you operationally define "risk-taking behavior," you would observe your teen subjects in these settings and record every incidence of what you have defined as risky behavior.

Famous examples of naturalistic observations include Charles Darwin's journey aboard the  HMS Beagle , which served as the basis for his theory of natural selection, and Jane Goodall's work studying the behavior of chimpanzees in their natural habitat.

Naturalistic observation can play an important role in the research process. It offers a number of advantages, including often being more affordable and less intrusive than other types of research.

In some cases, researchers may utilize naturalistic observation as a way to learn more about something that is happening in a certain population. Using this information, they can then formulate a hypothesis that can be tested further.

Mehl MR, Robbins ML, Deters FG. Naturalistic observation of health-relevant social processes: the electronically activated recorder methodology in psychosomatics . Psychosom Med. 2012;74(4):410-7. doi:10.1097/PSY.0b013e3182545470

U.S. National Library of Medicine. Rewriting the book of nature - Darwin and the Beagle voyage .

Angrosino MV. Naturalistic Observation . Left Coast Press.

DiMercurio A, Connell JP, Clark M, Corbetta D. A naturalistic observation of spontaneous touches to the body and environment in the first 2 months of life . Front Psychol . 2018;9:2613. doi:10.3389/fpsyg.2018.02613

Pierce K, Pepler D. A peek behind the fence: observational methods 25 years later . In: Smith PK, Norman JO, eds. The Wiley Blackwell Handbook of Bullying. 1st ed . Wiley; 2021:215-232. doi:10.1002/9781118482650.ch12

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Naturalistic Observation: Definition, Examples, and Advantages

Categories Research Methods

Naturalistic observation is a psychological research method that involves observing and recording behavior in the natural environment. Unlike experiments, researchers do not manipulate variables. This research method is frequently used in psychology to help researchers investigate human behavior.

This article explores how naturalistic observation is used in psychology. It offers examples and the potential advantages and disadvantages of this type of research. 

Table of Contents

What Is Naturalistic Observation?

In naturalistic observation, the researcher observes the participants’ behavior in their natural setting, taking notes on their behavior and interactions. The researcher may use various tools, such as video or audio recordings, to help capture the behavior accurately. The researcher may also use coding systems or other quantitative measures to systematically record observed behavior.

Naturalistic observation can be used to investigate a wide range of psychological phenomena, such as social interaction patterns, parental behavior, or animal behavior. 

Types of Naturalistic Observation

Naturalistic observation can be:

Unstructured or Structured

The observer can either watch and record everything that happens, or they can have a checklist or form to guide their observations.

Participant or Non-Participant

The observer can be an active participant, or they can remain separate from the subject and view from the sidelines.

Overt or Covert

The observer can either openly watch and record the subjects’ behaviors, or they can keep their presence hidden from the individual or group.

The specific type of naturalistic observation that researchers use depends on the situation, what they are researching, and the resources available. No matter the type, the observation must occur in a natural setting rather than in an experimental lab.

How to Collect Data in Naturalistic Observation

There are a number of methods that researchers might utilize to record data about the behaviors and events they observe. Some of these include:

• Note-taking : Research may opt to take notes about what they witness. This approach tends to be unstructured, allowing the observers to determine what they think is relevant and to include insights that may be helpful.
• Tally counts : In other cases, research may take a more structured approach where they count the frequency of a behavior.
• Audiovisual recordings : In other cases, research may want recordings of participant behavior. This not only allows researchers to refer to the recordings later, it can also be useful for sharing with others.

How Data Is Sampled in Naturalistic Observation

While naturalistic observation is not an experimental design, researchers still want to ensure that the data they collect represents what is happening in the group. To do this, researchers must collect a representative sample. When a sample is representative, it means that it accurately reflects what is happening in a given population.

To do this, researchers may utilize three primary sampling approaches:

Event Sampling

Event sampling involves the researcher creating a set of predefined categories and behaviors they will observe. This method is useful when the researcher wants to collect data on specific behaviors or events, allowing for more precise data collection.

Using this approach, the research would note every occurrence of a specific behavior.

Situation Sampling

Situation sampling involves observing participants in more than one situation. This approach can give researchers more insight and allow them to determine if certain behaviors only occur in specific contexts or settings. 

Time Sampling

Time sampling is a type of systematic observation that involves the researcher observing and recording the subjects’ behavior at predetermined intervals. This method is useful when the researcher wants to collect data on the frequency and duration of specific behaviors.

Each method of data collection has its strengths and weaknesses, and the choice of method depends on the research question and the nature of the subjects being observed.

Examples of Naturalistic Observation

It can be helpful to look at a few different examples to learn more about how naturalistic observation can be used:

• Researchers might observe children in a classroom to learn more about their social interaction patterns. 
• Naturalistic observation can also be used to study animal behavior in their natural habitat, such as observing chimpanzees in the wild to understand their social behavior.

Researchers use this research method in various fields, including animal researchers and anthropologists. 

The work of zoologist Konrad Lorenz, for example, relied on the use of naturalistic observation. Lorenz observed the behavior of ducklings after they hatched and noted that they became attached to the first possible parent figure they saw, a phenomenon known as imprinting. Once imprinted on a parent figure, the duckling would follow and learn from their parent.

From his naturalistic observations, Lorenz hypothesized that there was a critical period immediately after hatching where ducklings needed to imprint on a parent. Based on his observations, Lorenz conducted further experiments that confirmed his hypothesis.

More Examples of Naturalistic Observation

Naturalistic observation is a research method commonly used in various areas of psychology. 

Social Psychology

Naturalistic observation can provide valuable insights into people’s behavior in different social situations. By observing people’s behavior in a crowded public place like a shopping mall or train station, researchers can better understand how social norms are established and maintained and how people interact in various social groups.

Consumer Research

Consumer research is another area where naturalistic observation can be used effectively. By observing shoppers in a grocery store or shopping mall, researchers can study how people make purchasing decisions in real-life situations.

Researchers can gain valuable insights into consumer behavior by analyzing what catches their attention, how they interact with different products, and how they decide what to buy.

Developmental Psychology

Observing children playing in a playground or a classroom can help researchers understand how children develop and learn new skills in natural settings.

Researchers can gain insights into the developmental process by observing children as they interact with each other and learn social skills or as they learn new concepts and skills in a classroom.

Cognitive Psychology

Naturalistic observation can be used to study how people think and process information in real-life situations. For example, observing people using a computer program can help researchers understand how people navigate through it and solve problems.

Similarly, observing people in a conversation can provide insights into how they process and respond to information in real time.

Advantages of Naturalistic Observation

Naturalistic observation offers a number of benefits that can make it a good choice for research. 

Ecological Validity

One of the strengths of naturalistic observation is its ability to capture behavior in a natural setting, providing a more accurate and comprehensive picture of how people or animals behave in their everyday environment.

It is often more realistic than lab research, so it can give insight into how people behave authentically in everyday settings and situations.

Inspiration for Additional Research

Naturalistic observation can also generate new hypotheses and insights that may not be captured in other research methods. 

Research That Can’t Be Done in a Lab

Naturalistic observation allows the study of behaviors that cannot be replicated in a lab. Naturalistic observation is sometimes the only approach for studying behaviors that cannot be reproduced in a lab due to ethical reasons.

For example, researchers might use this approach to research prison behavior or the social impact of domestic violence on emotional health. Those are not situations they can manipulate in a lab, but they can observe the impact on people who have had those experiences.

Disadvantages of Naturalistic Observation

While naturalistic can be a valuable tool, it is not appropriate for every situation. Some potential downsides include: 

Bias and Lack of Control

Naturalistic observation is limited by its lack of environmental control and the potential for observer bias. Researchers must be careful to minimize the influence of their presence on the behavior being observed and to use systematic and objective methods for recording and analyzing the data. 

Inability to Infer Cause and Effect

Naturalistic observation is also limited by its inability to establish causality between variables.

Naturalistic Observation vs. Case Study

Naturalistic observation and case studies are both research methods used in psychology but differ in their approach and purpose. Naturalistic observation involves observing and recording the behavior of individuals or groups in their natural environment without any intervention or manipulation by the researcher.

On the other hand, a case study is an in-depth analysis of a single individual or a small group of individuals, often conducted through interviews, surveys, and other forms of data collection.

The key difference between naturalistic observation and a case study is that the former focuses more on observing and recording behaviors and interactions as they occur naturally, while the latter focuses on gathering detailed information about a specific individual or group.

Naturalistic observation is often used to study social interactions, group dynamics, and other natural behaviors in real-world settings. In contrast, case studies often explore complex psychological phenomena such as mental illness, personality disorders, or unusual behaviors.

Both naturalistic observation and case studies have their strengths and limitations. The choice of method depends on the research question, the level of detail needed, and the feasibility of conducting the study in a particular setting.

Naturalistic Observation Ideas

There are many potential ideas for studies that involve naturalistic observation. A few ideas include:

• Observe the behavior of animals in their natural habitats, studying their patterns of movement, foraging, and communication
• Observe human behavior in public spaces, such as parks or coffee shops, documenting patterns of social interaction and communication
• Focus on the behavior of individuals within specific social groups or communities, studying their interactions and relationships over time
• Watch the behavior of children in a classroom setting could provide insights into their learning and socialization processes

Frequently Asked Questions

Why do we use naturalistic observation.

Naturalistic observation is important because it allows researchers to better understand how individuals behave in their everyday lives. By observing behavior in a natural setting, researchers can obtain a more accurate representation of how people act and interact with each other in their normal environment. 

This method is particularly useful when studying social behavior, as it allows researchers to capture the complexity and nuances of social interactions that might not be apparent in a laboratory setting.

Naturalistic observation can also offer valuable insights into the development of certain behaviors, such as those related to child development or the formation of social groups.

What is the most famous example of naturalistic observation?

The most famous example of naturalistic observation is probably Jane Goodall’s study of chimpanzees in the wild. Goodall spent years observing the behavior of chimpanzees in Tanzania, documenting their social interactions, tool use, and other aspects of their lives. Her work helped to revolutionize our understanding of these animals and their place in the natural world.

In conclusion, naturalistic observation is a powerful research method that can be used effectively in various areas within psychology. Researchers can gain valuable insights into human behavior and cognition by observing people’s behavior in natural settings.

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.2 Correlational Research Methods

Learning objectives.

By the end of this section, you will be able to:

• Describe the different research methods used by psychologists
• Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research
• Compare longitudinal and cross-sectional approaches to research

   There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied. These can be a series of simple questions, in-depth interviews, or well-controlled experiments.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data, which is discussed later. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

All of the above research methods are correlational designs. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships because there is no experimental manipulation. In experimental research, which will be discussed later in this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the generalizability of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

A few of the research designs which psychologists use are discussed in the next few sections. Each research method has it’s own advantages and drawbacks, and is best suited for different types of research questions. When designing a study, it’s important to consider the strengths and weaknesses to design the most valid and reliable study possible.

CLINICAL OR CASE STUDIES

   In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

   This connection means that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

NATURALISTIC OBSERVATION

   If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous so perhaps they will stand at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation: observing behavior in its natural setting.

For example, to better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

When people know they are being watched, they are less likely to behave naturally. Think about how your driving behavior down a deserted highway during the middle of the day might change if you are suddenly being followed by a police car (figure below).

Seeing a police car behind you would probably affect your driving behavior. (credit: Michael Gil)

   It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another.

The anthropologist Jane Goodall , for example, spent nearly five decades observing the behavior of chimpanzees in Africa (figure below). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

(a) Jane Goodall made a career of conducting naturalistic observations of (b) chimpanzee behavior. (credit “Jane Goodall”: modification of work by Erik Hersman; “chimpanzee”: modification of work by “Afrika Force”/Flickr.com)

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the chapter on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias. Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability: a measure of reliability that assesses the consistency of observations by different observers.

The final drawback of naturalistic observation is the issue of consent to participation in the study. While informed consent will be discussed further in the ethics section of this chapter, participants may not know that they are being observed and thus, do not have the ability to agree or not to being part of the research. In the bathroom example, what if the researcher observed a doctor in a white coat leave without washing their hands? Should the researcher do something? What are the rights of that participant who never consented to being observed in the restroom by a stranger?

   Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (figure below). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

Surveys can be administered in a number of ways, including as electronically administered research, like the survey shown here. (credit: Robert Nyman) 

   There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: People don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

ARCHIVAL RESEARCH

   Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students (figure below).

   In comparing archival research to other research methods, there are several important distinctions.  For one, the researcher employing archival research never directly interacts with the research participants.  Therefore, the investment of time and money to collect data is considerably less with archival research.  Additionally, researchers have no control over what information was originally collected.  Therefore, their research questions must be tailored so they can be answers within the structure of the existing data sets.  There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

LONGITUDINAL AND CROSS-SECTIONAL RESEARCH

   Sometimes we want to see how people change over time, as in studies of human development and lifespan. We can use a variety of study designs (surveys, observations, experiments, etc.) at either one time point ( cross-sectional ) or several different times (longitudinal) to answer different questions about how things are at a single moment or how they might change over time.

When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

   The clinical or case study involves studying just a few individuals for an extended period of time. While this approach provides an incredible depth of information, the ability to generalize these observations to the larger population is problematic.

Naturalistic observation involves observing behavior in a natural setting and allows for the collection of valid, true-to-life information from realistic situations. However, naturalistic observation does not allow for much control and often requires quite a bit of time and money to perform.

Surveys can be administered in a number of ways and make it possible to collect large amounts of data quickly. However, the depth of information that can be collected through surveys is somewhat limited compared to a clinical or case study.

Archival research involves studying existing data sets to answer research questions. However, researchers have no control over what data was collected and how it was collected.

Longitudinal research has been incredibly helpful to researchers who need to collect data on how people change over time, but is a major investment of time and money.

Cross-sectional research compares multiple segments of a population at a single time, but can be confounded by generational effects.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions: 1. Sigmund Freud developed his theory of human personality by conducting in-depth interviews over an extended period of time with a few clients. This type of research approach is known as a(n): ________.

a. archival research

b. case study

c. naturalistic observation

2. ________ involves observing behavior in individuals in their natural environments.

3. The major limitation of case studies is ________.

a. the superficial nature of the information collected in this approach

b. the lack of control that the researcher has in this approach

c. the inability to generalize the findings from this approach to the larger population

d. the absence of inter-rater reliability

4. The benefit of naturalistic observation studies is ________.

a. the honesty of the data that is collected in a realistic setting

b. how quick and easy these studies are to perform

c. the researcher’s capacity to make sure that data is collected as efficiently as possible

d. the ability to determine cause and effect in this particular approach

5. Using existing records to try to answer a research question is known as ________.

a. naturalistic observation

b. survey research

c. longitudinal research

d. archival research

6. ________ involves following a group of research participants for an extended period of time.

b. longitudinal research

d. cross-sectional research

7. A(n) ________ is a list of questions developed by a researcher that can be administered in paper form.

8. Longitudinal research is complicated by high rates of ________.

a. deception

b. observation

c. attrition

d. generalization

Critical Thinking Questions:

1. In this section, conjoined twins, Krista and Tatiana, were described as being potential participants in a case study. In what other circumstances would you think that this particular research approach would be especially helpful and why?

2. Presumably, reality television programs aim to provide a realistic portrayal of the behavior displayed by the characters featured in such programs. This section pointed out why this is not really the case. What changes could be made in the way that these programs are produced that would result in more honest portrayals of realistic behavior?

3. Which of the research methods discussed in this section would be best suited to research the effectiveness of the D.A.R.E. program in preventing the use of alcohol and other drugs? Why?

4. Aside from biomedical research, what other areas of research could greatly benefit by both longitudinal and archival research?

Personal Application Questions: 

1. A friend of your s is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

2. As a college student, you are no doubt concerned about the grades that you earn while completing your coursework. If you wanted to know how overall GPA is related to success in life after college, how would you choose to approach this question and what kind of resources would you need to conduct this research?

archival research

clinical or case study 

cross-sectional research 

generalize 

inter-rater reliability 

longitudinal research 

naturalistic observation 

observer bias 

population 

Answers to Exercises

Review Questions:

1.  Case studies might prove especially helpful using individuals who have rare conditions. For instance, if one wanted to study multiple personality disorder then the case study approach with individuals diagnosed with multiple personality disorder would be helpful.

2. The behavior displayed on these programs would be more realistic if the cameras were mounted in hidden locations, or if the people who appear on these programs did not know when they were being recorded.

3. Longitudinal research would be an excellent approach in studying the effectiveness of this program because it would follow students as they aged to determine if their choices regarding alcohol and drugs were affected by their participation in the program.

4. Answers will vary. Possibilities include research on hiring practices based on human resource records, and research that follows former prisoners to determine if the time that they were incarcerated provided any sort of positive influence on their likelihood of engaging in criminal behavior in the future.

archival research:  method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships

attrition:  reduction in number of research participants as some drop out of the study over time

clinical or case study:  observational research study focusing on one or a few people

cross-sectional research:  compares multiple segments of a population at a single time

generalize:  inferring that the results for a sample apply to the larger population

inter-rater reliability:  measure of agreement among observers on how they record and classify a particular event

longitudinal research:  studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time

naturalistic observation:  observation of behavior in its natural setting

observer bias:  when observations may be skewed to align with observer expectations

population:  overall group of individuals that the researchers are interested in

sample:  subset of individuals selected from the larger population

survey:  list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

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2.2 Approaches to Research

Learning objectives.

By the end of this section, you will be able to:

• Describe the different research methods used by psychologists
• Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research
• Compare longitudinal and cross-sectional approaches to research
• Compare and contrast correlation and causation

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected. All of the methods described thus far are correlational in nature. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

Clinical or Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

Watch this CBC video about Krista's and Tatiana's lives to learn more.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

Over time, it has become clear that while Krista and Tatiana share some sensory experiences and motor control, they remain two distinct individuals, which provides invaluable insight for researchers interested in the mind and the brain (Egnor, 2017).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a precious amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this chapter: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway ( Figure 2.7 ).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall , for example, spent nearly five decades observing the behavior of chimpanzees in Africa ( Figure 2.8 ). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity , or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the chapter on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally ( Figure 2.9 ). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: People don't always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research . Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students ( Figure 2.10 ).

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research. In cross-sectional research , a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of studying a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals that make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) ( Figure 2.11 ).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increase over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

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2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behavior are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 2.2 “Characteristics of the Three Research Designs” , are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Table 2.2 Characteristics of the Three Research Designs

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behavior of individuals. This section reviews three types of descriptive research: case studies , surveys , and naturalistic observation .

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behavior . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud (1909/1964).

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there is question about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviors of a sample of people of interest . The people chosen to participate in the research (known as the sample ) are selected to be representative of all the people that the researcher wishes to know about (the population ). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year,” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research—known as naturalistic observation —is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 2.5 “Height Distribution” , where most of the scores are located near the center of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 2.5 “Height Distribution” , the mean height of the students is 67.12 inches. The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 2.6 “Family Income Distribution” ), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 2.6 “Family Income Distribution” that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 2.6 “Family Income Distribution” that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency, like this:

Or they may be more spread out away from it, like this:

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 2.5 “Height Distribution” is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviors or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships Among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 2.10 “Examples of Scatter Plots” , a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line, as in parts (a) and (b) of Figure 2.10 “Examples of Scatter Plots” , the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable, as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 2.10 “Examples of Scatter Plots” shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 2.10 “Examples of Scatter Plots” show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 2.11 “Prediction of Job Performance From Three Predictor Variables” shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. The use of multiple regression analysis shows an important advantage of correlational research designs—they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

there are other possibilities. One alternate possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home:

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other:

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behavior might go away.

Common-causal variables in correlational research designs can be thought of as “mystery” variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: Correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behavior as it occurs in everyday life. And we can also use correlational designs to make predictions—for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behavior

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality:

Figure 2.2.3

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions , a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet—and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on 5 April 2022 by Tegan George . Revised on 20 March 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables impacting your analysis.

Table of contents

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs experiment, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies.

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyse a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analysing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for ethical or practical reasons, or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

• Are you determining what you want to observe beforehand, or going in open-minded?
• Is there another research method that would make sense in tandem with an observational study?
• If yes, make sure you conduct a covert observation.
• If not, think about whether observing from afar or actively participating in your observation is a better fit.
• How can you preempt confounding variables that could impact your analysis?
• You could observe the children playing at the playground in a naturalistic observation.
• You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
• You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organised. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

• Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
• Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
• Get informed consent from your participants (or their parents) if you want to record:  Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or ‘lurking’ variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyse your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive or deductive approach :

• If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
• If you had specific hypotheses prior to conducting your observations, a deductive approach analyses whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis.

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

• Observational studies can provide information about difficult-to-analyse topics in a low-cost, efficient manner.
• They allow you to study subjects that cannot be randomised safely, efficiently, or ethically .
• They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilise preexisting data.
• They’re often invaluable in informing later, larger-scale clinical trials or experiments.

Disadvantages

• Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables.
• They lack conclusive results, typically are not externally valid or generalisable, and can usually only form a basis for further research.
• They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomise your participants safely and your research question is definitely causal in nature, consider using an experiment.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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• Archival Research
• Clinical Study
• Cross-sectional Research
• Inter-Rater Reliability
• Longitudinal Research
• Naturalistic Observation
• Observer Bias
• Research Methods

Archival, Case Studies and Natural Observations

By the end of this section, you will be able to:

• Describe the different research methods used by psychologists
• Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected. All of the methods described thus far are correlational in nature. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

ARCHIVAL RESEARCH

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research . Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students ( Figure ).

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

CLINICAL OR CASE STUDIES

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

To learn more about Krista and Tatiana, watch this New York Times video about their lives.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

NATURALISTIC OBSERVATION

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this chapter: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway ( Figure ).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall , for example, spent nearly five decades observing the behavior of chimpanzees in Africa ( Figure ). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity , or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the chapter on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

The clinical or case study involves studying just a few individuals for an extended period of time. While this approach provides an incredible depth of information, the ability to generalize these observations to the larger population is problematic. Naturalistic observation involves observing behavior in a natural setting and allows for the collection of valid, true-to-life information from realistic situations. However, naturalistic observation does not allow for much control and often requires quite a bit of time and money to perform. Researchers strive to ensure that their tools for collecting data are both reliable (consistent and replicable) and valid (accurate).

Surveys can be administered in a number of ways and make it possible to collect large amounts of data quickly. However, the depth of information that can be collected through surveys is somewhat limited compared to a clinical or case study.

Archival research involves studying existing data sets to answer research questions.

Longitudinal research has been incredibly helpful to researchers who need to collect data on how people change over time. Cross-sectional research compares multiple segments of a population at a single time.

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Observation Method in Psychology: Naturalistic, Participant and Controlled

Saul Mcleod, PhD

Educator, Researcher

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

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Observation (watching what people do) would seem to be an obvious method of carrying out research in psychology. However, there are different types of observational methods, and distinctions need to be made between:

1. Controlled Observations 2. Naturalistic Observations 3. Participant Observations

In addition to the above categories, observations can also be either overt/disclosed (the participants know they are being studied) or covert/undisclosed (the researcher keeps their real identity a secret from the research subjects, acting as a genuine member of the group).

In general, observations are relatively cheap to carry out, and the researcher needs few resources. However, they can often be very time-consuming and longitudinal.

Controlled Observation

Controlled observation is a research method for studying behavior in a carefully controlled and structured environment.

The researcher sets specific conditions, variables, and procedures to systematically observe and measure behavior, allowing for greater control and comparison of different conditions or groups.

The researcher decides where the observation will occur, at what time, with which participants, and in what circumstances, and uses a standardized procedure. Participants are randomly allocated to each independent variable group.

Rather than writing a detailed description of all behavior observed, it is often easier to code behavior according to a previously agreed scale using a behavior schedule (i.e., conducting a structured observation).

The researcher systematically classifies the behavior they observe into distinct categories. Coding might involve numbers or letters to describe a characteristic or the use of a scale to measure behavior intensity.

The categories on the schedule are coded so that the data collected can be easily counted and turned into statistics.

For example, Mary Ainsworth used a behavior schedule to study how infants responded to brief periods of separation from their mothers. During the Strange Situation procedure infant’s interaction behaviors directed toward the mother were measured, e.g.

• Proximity and contacting seeking
• Contact maintaining
• Avoidance of proximity and contact
• Resistance to contact and comforting

The observer noted down the behavior displayed during 15-second intervals and scored the behavior for intensity on a scale of 1 to 7.

Sometimes the behavior of participants is observed through a two-way mirror, or they are secretly filmed. Albert Bandura used this method to study aggression in children (the Bobo doll studies ).

A lot of research has been carried out in sleep laboratories as well. Here electrodes are attached to the scalp of participants. What is observed are the changes in electrical activity in the brain during sleep ( the machine is called an electroencephalogram – an EEG ).

Controlled observations are usually overt as the researcher explains the research aim to the group so the participants know they are being observed.

Controlled observations are also usually non-participant as the researcher avoids any direct contact with the group, keeping a distance (e.g., observing behind a two-way mirror).

• Controlled observations can be easily replicated by other researchers by using the same observation schedule. This means it is easy to test for reliability .
• The data obtained from structured observations is easier and quicker to analyze as it is quantitative (i.e., numerical) – making this a less time-consuming method compared to naturalistic observations.
• Controlled observations are fairly quick to conduct which means that many observations can take place within a short amount of time. This means a large sample can be obtained, resulting in the findings being representative and having the ability to be generalized to a large population.

Limitations

• Controlled observations can lack validity due to the Hawthorne effect /demand characteristics. When participants know they are being watched, they may act differently.

Naturalistic Observation

Naturalistic observation is a research method in which the researcher studies behavior in its natural setting without intervention or manipulation.

It involves observing and recording behavior as it naturally occurs, providing insights into real-life behaviors and interactions in their natural context.

Naturalistic observation is a research method commonly used by psychologists and other social scientists.

This technique involves observing and studying the spontaneous behavior of participants in natural surroundings. The researcher simply records what they see in whatever way they can.

In unstructured observations, the researcher records all relevant behavior without a system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.

Compared with controlled observations, it is like the difference between studying wild animals in a zoo and studying them in their natural habitat.

With regard to human subjects, Margaret Mead used this method to research the way of life of different tribes living on islands in the South Pacific. Kathy Sylva used it to study children at play by observing their behavior in a playgroup in Oxfordshire.

• By being able to observe the flow of behavior in its own setting, studies have greater ecological validity.
• Like case studies , naturalistic observation is often used to generate new ideas. Because it gives the researcher the opportunity to study the total situation, it often suggests avenues of inquiry not thought of before.
• These observations are often conducted on a micro (small) scale and may lack a representative sample (biased in relation to age, gender, social class, or ethnicity). This may result in the findings lacking the ability to generalize to wider society.
• Natural observations are less reliable as other variables cannot be controlled. This makes it difficult for another researcher to repeat the study in exactly the same way.
• A further disadvantage is that the researcher needs to be trained to be able to recognize aspects of a situation that are psychologically significant and worth further attention.
• With observations, we do not have manipulations of variables (or control over extraneous variables), meaning cause-and-effect relationships cannot be established.

Participant Observation

Participant observation is a variant of the above (natural observations) but here, the researcher joins in and becomes part of the group they are studying to get a deeper insight into their lives.

If it were research on animals , we would now not only be studying them in their natural habitat but be living alongside them as well!

Leon Festinger used this approach in a famous study into a religious cult that believed that the end of the world was about to occur. He joined the cult and studied how they reacted when the prophecy did not come true.

Participant observations can be either cover or overt. Covert is where the study is carried out “undercover.” The researcher’s real identity and purpose are kept concealed from the group being studied.

The researcher takes a false identity and role, usually posing as a genuine member of the group.

On the other hand, overt is where the researcher reveals his or her true identity and purpose to the group and asks permission to observe.

• It can be difficult to get time/privacy for recording. For example, researchers can’t take notes openly with covert observations as this would blow their cover. This means they must wait until they are alone and rely on their memory. This is a problem as they may forget details and are unlikely to remember direct quotations.
• If the researcher becomes too involved, they may lose objectivity and become biased. There is always the danger that we will “see” what we expect (or want) to see. This problem is because they could selectively report information instead of noting everything they observe. Thus reducing the validity of their data.

Recording of Data

With controlled/structured observation studies, an important decision the researcher has to make is how to classify and record the data. Usually, this will involve a method of sampling. The three main sampling methods are:

• Event sampling . The observer decides in advance what types of behavior (events) she is interested in and records all occurrences. All other types of behavior are ignored.
• Time sampling . The observer decides in advance that observation will occur only during specified time periods (e.g., 10 minutes every hour, 1 hour per day) and records the occurrence of the specified behavior during that period only.
• Instantaneous (target time) sampling . The observer decides in advance the pre-selected moments when observation will occur and records what is happening at that instant. Everything happening before or after is ignored.

Ethnography, Observational Research, and Narrative Inquiry

Qualitative observational research describes and classifies various cultural, racial and/or sociological groups by employing interpretive and naturalistic approaches. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity and generalizability). Connelly and Clandinin (1990) suggest that qualitative inquiry relies more on apparency, verisimilitude and transferability. On the other hand, Lincoln and Guba (1985) emphasize the importance of credibility, transferability, dependability and confirmability in qualitative studies. Because the field of qualitative research is still evolving, the criteria and terminology for its evaluation are not yet agreed upon.

What is agreed upon is that qualitative observational research is a systematic inquiry into the nature or qualities of observable group behaviors in order to learn what it means to be a member of that group. The researcher's job, rather than to describe a stable entity, is to give continually updated accounts of observations on multiple levels of group interactions that occur on both a temporal and continuous basis simultaneously.

Thus, this type of research attempts to identify and explain complex social structures within the study group. Typically, qualitative research methodologies are combined with each other in order to provide comparative results. A triangulation of methods (also called multiple methods), where three or more methodologies are used and the results compared against each other, is common and can provide a more complete understanding of the behavior of the study group.

Qualitative study lends itself to thick narrative description, and it may be intensive given the complexity of group interactions. It takes place on site, in the group's natural environment, and attempts to be non-manipulative of group behaviors. The purpose is to aim for objectivity, while it must take into account the views of the participants.

This guide attempts to acknowledge the broad categories of qualitative observational research. First, qualitative observational research is broken down into its most common approaches, including types of this research method, themes that guide researchers' study designs and other, secondary approaches. Next, a Methods section introduces steps and methods used in qualitative observational research, employing multiple methods and computer software for this field of research. Then, a Commentary section includes some of the advantages and disadvantages to qualitative observational research, a look at the ongoing qualitative vs. quantitative discussion and some of the ethical considerations of this form of research.

Types of Qualitative Observational Research

Qualitative observational research consists of over 30 different approaches which often overlap and whose distinctions are subtle. The type of approach used depends on the research question and/or the discipline the researcher belongs to. For instance, anthropologists commonly employ ethnomethodology and ethnography, while sociologists often use symbolic interaction and philosophers frequently use concept analysis (Marshall & Rossman 1995). This overview discusses five approaches frequently used in English studies and two others, phenomenology and kinesiology, that may prove useful to some researcher.

Ethnography

Ethnography is a long term investigation of a group (often a culture) that is based on immersion and, optimally, participation in that group. Ethnography provides a detailed exploration of group activity and may include literature about and/or by the group. It is an approach which employs multiple methodologies to arrive at a theoretically comprehensive understanding of a group or culture. The issue for the observer is how the particulars in a given situation are interrelated. In other words, ethnography attempts to explain the Web of interdependence of group behaviors and interactions.

Narrative Inquiry

Narrative inquiry is the process of gathering information for the purpose of research through storytelling. The researcher then writes a narrative of the experience. Connelly and Clandinin (1990) note that, "Humans are storytelling organisms who, individually and collectively, lead storied lives. Thus, the study of narrative is the study of the ways humans experience the world." In other words, people's lives consist of stories.

Field notes, interviews, journals, letters, autobiographies, and orally told stories are all methods of narrative inquiry. For example, a researcher might do a study on the way in which fourth grade girls define their social roles in school. A researcher might look at such things as notes and journal entries,and might also interview the girls and spend time observing them. After this, the researcher would then construct her own narrative of the study, using such conventions as scene and plot. As Connelly and Clandinin also note,"Research is a collaborative document, a mutually constructed story out of the lives of both researcher and participant."

Narrative inquiry is appropriate to many social science fields. The entire field of study is often used in disciplines such as literary theory, history, anthropology, drama, art, film, theology, philosophy, psychology, linguistics, education, politics, nutrition, medicine, and even aspects of evolutionary biological science.

Short Term Observation

Short term observational studies list or present findings of short term qualitative study based on recorded observation. Observation in the studied group's natural setting is a key aspect of qualitative research. The terms group and culture are used in a loose sense here because for the researcher, a group or culture may include populations such as an individual classroom of students, a set of employees in the workplace, or residents of similar geographical or cultural areas or backgrounds. Short term observational studies differ from ethnographies in that they focus more narrowly on specified categories of group behaviors. This type of research functions well as a means of fleshing out quantitative research that would otherwise do little more than list numerical data. Types of short term observational research run the spectrum from crossing the boundary into quantitative research to a very nearly ethnographic approach. Regardless of the group or culture under study, the observer/researcher studies a set of individuals in their natural setting as opposed to a clinical setting, hence this type of research is known as fieldwork.

Traditionally, the period of observation for a qualitative observational study has been from six months to two years or more (Fetterman 1989). Today, it is generally acceptable to study groups for less than six months, provided that the researcher triangulates the research methods. The more time spent in the field the more likely your results will be viewed as credible by the academic community.

Ethnomethodology

According to Coulon (1995), "ethnomethodology is the empirical study of methods that individuals use to give sense to and...to accomplish their daily actions: communicating, making decisions, and reasoning" (p. 15). This approach is actually a form of ethnography, which specifically studies activities of group members to see how they make sense of their surroundings. Usually an ethnomethodologist will see or hear things in a group that participants are not consciously aware of. For instance, in Ways with Words , Heath (1983) notices that in the Black community of Trackton, children learn how to become fast thinkers when playfully interacting with adults and other children. The participants may not be aware of this teaching and learning process, but Heath asserts that the learned wittiness of the children pays off when they have to defend themselves.

Grounded Theory

In this approach, researchers are responsible for developing other theories that emerge from observing a group. The theories are "grounded" in the group's observable experiences, but researchers add their own insight into why those experiences exist. In essence, grounded theory attempts to "reach a theory or conceptual understanding through step wise, inductive process" (Banning 1995).

Phenomenology

This approach, most often used by psychologists, seeks to explain the "structure and essence of the experiences" of a group of people (Banning 1995). A phenomenologist is concerned with understanding certain group behaviors from that group's point of view. For instance, a researcher might notice that in a certain group, all girls wear pink socks on Tuesdays. A true phenomenologist would not assume that pink is the girls' favorite color and Tuesdays are their favorite day to wear them. Instead, that researcher would try to find out what significance this phenomenon has. Phenomenological inquiry requires that researchers go through a series of steps in which they try to eliminate their own assumptions and biases, examine the phenomenon without presuppositions, and describe the "deep structure" of the phenomenon based on internal themes that are discovered (Marshall & Rossman, 1995). Phenomenology does greatly overlap with ethnography, but, as Bruyn (1970), points out, some phenomenologists assert that they "study symbolic meanings as they constitute themselves in human consciousness" (p. 286).

Kinesic analysis examines what is communicated through body movement. This approach is based on the assumption that all human beings, although they may be unaware of it, act and react to situations nonverbally as well as verbally. Kinesics can be especially useful when employed in conjunction with other qualitative methods such as interviews and narratives to triangulate data. Kinesics must be used thoughtfully and carefully, as movements and gestures can be easily misinterpreted and presenting findings without giving context renders the data useless (Marshall & Rossman, 1995).

Characteristics of Qualitative Observational Research

Qualitative observational research can be characterized by at least ten overlapping themes that researchers should be aware of when collecting and analyzing data. In Qualitative Evaluation Methods , Patton (1980) discusses these characteristics to help researchers design studies. These characteristics are explained below using examples relating to Black English Vernacular (BEV) and the African American rhetorical tradition. All of the examples below are based on Balester's 1993 text, Cultural divide: A study of African-American college-level writers .

Naturalistic Inquiry

Qualitative observational research is naturalistic because it studies a group in its natural setting. Patton explains, "Naturalistic inquiry is thus contrasted to experimental research where the investigator attempts to completely control the condition of the study" (p. 42). For example, if you wanted to study college students who were speakers of BEV, you would not conduct your research in a predominantly Caucasian college or university.

Inductive analysis

This characteristic is prevalent in qualitative research because it allows the observer to become immersed in a group. The researcher starts with answers, but forms questions throughout the research process. Hypotheses and theories can continuously change depending on what the observer wants to know. For instance, an observer might realize that the purpose of many of BEV speech acts is to build up the reputation of the speaker. Thus, the observer's job is to find out why. This could lead to further research into the rhetorical strategies and purposes of BEV.

Holistic perspective

Patton states, "[A] holistic approach assumes that the whole is greater than the sum of its parts" (p. 40). In other words, almost every action or communication must be taken as a part of the whole phenomenon of a certain community or culture. However, this characteristic of qualitative observational research can be bothersome because it can lead researchers into taking every little action into consideration when writing a narrative. For instance, a researcher might notice that many speakers of BEV employ a particular rhetorical strategy in their writing. However, this phenomenon might not have anything to do with BEV and its traditions or strategies. It might be linked to something else in their lives.

Personal contact and insight

The researcher is responsible for becoming a part of a group to get a more in-depth study. However, the researcher also has to be aware of biases (both good and bad). For example, researchers who do not consider BEV a legitimate form of discourse should be aware of and acknowledge that bias before studying BEV. In contrast, a researcher who speaks BEV might ignore some negative implications of this discourse.

Dynamic systems

Qualitative observational research is not concerned with having straightforward, right or wrong answers. In addition, change in a study is common because the researcher is not concerned with finding only one answer. For example, a researcher could gain a different perspective on BEV by observing and interviewing a wide range of BEV speakers; the researcher could study both male and female speakers and speakers from different educational and geographical locations.

Unique case orientation

Researchers must remember that every study is special and deserves in-depth attention. This is especially necessary for doing cultural comparisons. For instance, a researcher may believe that "Jive" (a way of talking in the 1970s) and BEV are the same because they both derive from African-American culture. This is untrue, and BEV should be considered a unique form of discourse, with its own history, conventions, and uses/contexts.

Context sensitivity

Researchers must realize the different variables, such as values and beliefs, that influence cultural behaviors. For example, knowing that the rhetorical strategies of BEV--signifying, running it down, putting down, putting on, etc.--are context specific, a researcher might examine what values and beliefs influence this context specificity.

Empathic neutrality

Ideally, researchers should be non-judgmental when compiling findings. Because complete neutrality is impossible, this characteristic is a controversial aspect of qualitative research. For instance, it would be difficult for a researcher not to judge students who completely stop speaking BEV upon coming to college, since BEV has strong roots in African-American culture and is strongly tied to speakers' identities.This example might illustrate the difficulties in remaining completely neutral.

Design flexibility

Researchers can continue to do research on other topics or questions that emerge from the initial research. Some topics that could emerge from studying college students who are speakers of BEV are student composing processes, their academic success, or their assimilation or accommodation to academic discourse.

Qualitative data

This is a detailed description of why a culture is the way it is. Triangulation, or the use of many data gathering methods, such as field notes, interviews, writing samples, and other data helps determine the cultural phenomenon of a group. For example, a researcher could collect personal letters from different BEV speakers to find a common bond that is inherent in all their personal letters. The researcher could then interview the participants about their letter writing to get diverse points of view.

In sum, the qualitative observational researcher must attempt to maintain a non-judgmental bias throughout the study. The researcher's goal is to observe and describe group patterns, similarities, and differences as they occur. Preconceptions or expectations of an individual or group's behavior interferes with the researcher's ability to tell the group or culture's story in a fair and accurate manner. In addition, preconceived expectations preclude the researcher from observing subtle nuances of character and speech that may be important to understand group behaviors or interactions. While absolute objectivity is impossible, it is paramount that researchers enter the field or study group with an open a mind, an awareness of their own biases, and a commitment to detach from those biases as much as possible while observing and representing the group.

Methods of Qualitative Observational Research

Qualitative observational research involves more than simply going out into the field and observing a given group or culture. Researchers must also consider such issues as their role, their research question, the theory driving their inquiry, how they will collect and analyze information, and how they will report their results. In this section, we address these issues in detail. We also consider the use of multiple methodologies for collecting information and the role of computer software in qualitative observational research.

Steps and Methods used in Qualitative Observational Research

Qualitative observational research involves more than simply going out into the field and observing a given group or culture. Researchers must also consider the following:

Observer's role

As Connelly and Clandinin (1990) point out, in all instances, qualitative observational research involves formulating a thoughtful and well-understood relationship between the researcher and research participants. It is essential for the researcher to determine what role(s) to play to ensure facilitation of the study and acceptance by the participants in the study group or culture. Some possibilities include observing-participant, participant-observer and neutral observer.

The observer's role is to record group interactions and behaviors as objectively as possible using various qualitative inquiry tools. Observing-participants already have a position in the society/community before taking on the role of observer. They must also examine their own subjectivity and consider that participating in the group might lead to sympathetic or antagonistic interpretations of group behaviors. Participant-observers, on the other hand, attempt to become part of community and to adopt roles as participants, but come to the study with their own culture or community inscriptions. They attempt to participate fully and take on participant roles, but must be careful to behave in a consistent manner as part of the setting so as not to cause significant changes in the community itself. Although neutral observers do not participate in the group they are studying, they still need to be aware of any presumptions they may hold that would influence their findings and what influence the act of observing the participants may have on their behavior.

It is the observer's responsibility to let readers of the research report know not only the role played in the research, but also the point of view of the observer.

Defining the research question

Unlike most scientific research methods, qualitative observational inquiry does not require the researcher to define a precise set of issues in the initial phases; these issues often emerge from the study over time. While some qualitative inquiries may begin with a set of questions, it is common for theories about group behavior and interactions to emerge as a result of the observer's exploratory work (emergent design). And, those theories may identify relevant questions for further research.

The goal of qualitative observational research is to define and answer a specific research problem or question, but this problem or question may or may not be defined at the time when the researcher first begins the study. Some researchers like to enter the field with a specific research problem already in mind. While such researchers still want to let events unfold as freely as possible once in the field, they believe that by defining the research problem in advance they are better able to observe the study group or culture and identify specific patterns of behavior.

Other qualitative observational researchers like to enter the field first and let the research questions or problems identify themselves. These researchers believe that entering the field with a specifically defined research question may bias their observations, and they may fail to notice relationships or behavior patterns that are important in understanding the study group or culture. Whatever approach is taken in determining the research question, the observer does need to be clear about the purpose, scope, and focus of the study and identify the subjects and the context in which they will be studied.

Identifying the theory that drives the inquiry

The qualitative observational researcher must determine what underlying theory or model should inform the research. This may mean replicating or building on an earlier study, or it may mean formulating a new model or theory by which to conduct the study. Either way, the theory or model chosen will help the researcher determine how to structure the study (i.e., whether to study participants in the classroom only or to study them outside of the classroom as well, and how and when to use interviews).

Selecting qualitative research tools

Selecting how and when data will be collected is an essential step in designing qualitative observational research studies. One of the primary tools of ethnographic study is the use of field notes. Observers may simply begin with a blank notebook and write down everything that goes on. Others may use audio and/or video tapes. Some observers begin with a list of categories of behavior to be noted. This works best when the research question is already defined; however, categories should be flexible and modifiable throughout the study.

The goals of note taking are to help ensure validity of the data collection and interpretation processes, to check data with members of context if possible, to weigh the evidence, and to check for researcher and subjects' effects on both patterned and outlying data.

Another useful tool, journal records, may be made by participants, researchers or practitioners. These records are collected through participant observation in a shared practical setting.

Written dialogue between researcher and participants is also used in narrative inquiry as a way of offering and responding to tentative narrative interpretations (Clandinin, 1986). Researchers may look at autobiographical and biographical writing, as well as documents such as plans, newsletters, course materials and student products, rules, laws, architecture, picturing, metaphors, poetry, clothing, foods, rituals, physical setting, and implements such as musical instruments, artifacts, logs--in short, anything within the context of the studied group that speaks of their experience.

Unstructured interviews may be used to collect data; personal stories tell us something of how group members perceive and experience their conditions. Structured interviews permit more focused information gathering, but may overlook aspects of the group that an unstructured interview might reveal. To facilitate truthful responses, the interview should be informal or conversational in nature. Interviewees may be selected with intent to uncover specific information or to gain a cross section of group members (for instance, both high achievers and those having difficultly with the material).

Researchers may need to use "stimulation recall" to prompt interviewees or participants in informal discussion concerning specific events. Another method, "simulation response," presents hypothetical situations to obtain responses from members of the community. While these methods are often helpful, they are not infallible. Members may inhibit access to information by concealing aspects of their lives or by telling researchers what they think they want to hear.

Analyzing and reporting data

The final steps to be taken by the qualitative observational researcher are analyzing the data and writing the research report. The researcher's work culminates in synthesizing and interpreting the data into an understandable and enlightening piece of writing. But, despite the fact that these steps mark the culmination of the researcher's work, it should not be assumed that they are reserved for the end of the study. Instead, it is common for the researcher to analyze data and write parts of the final report throughout the research process. In analyzing descriptive data, the researcher reviews what was witnessed and recorded, and synthesizes it with the observations and words of the participants themselves.

The observer begins with reading a situation as a text, applying as many critical techniques as possible without violating the sanctity of the text. It is important to avoid picking and choosing instances of behavior out of context. Analysis may reveal convergent data, metaphors that run throughout a language, culture, or group (thematic analysis). Key terms or key metaphors may be unpacked and examined for their significance and interrelationships among other aspects of group dynamics (content analysis). Dominant plots in the literature, films, and the text of daily life of the group aid in analysis of the data as a whole.

Writing the research report

The analyzing and writing stages of research also mark the point where researchers wed their stories with the stories of research participants. This marriage represents the ultimate goal of qualitative research: to produce a text that in the end provides a clearer understanding of the group or culture's behavior, and by doing so helps us better understand our own individual or group behaviors.

Often, the research report is written as an ethnography or a narrative. However, these two forms are not the only options for presenting qualitative observational research findings. Increasingly, the scope of qualitative observational research reporting is broadening to include elements of other genres, such as self-narratives, fiction, and performance texts (Alvermann, et al. 1996).

What researchers choose to include or exclude from the final text can have a tremendous effect on how their results are interpreted by others. Alvermann, et al. propose that conscientious qualitative researchers might pose the following questions when writing up their findings:

• How much information needs to be included in the text about theories that may have guided the research, disciplinary biases, personal hunches that were followed, etc.?
• Should I include my original research question and its changing forms as I conducted my research?
• How much background information abouth the topic and description of research processes do readers need to understand my findings?
• How much description of myself needs to be included to reveal possible biases or perspectives (gender, ethnicity, age, academic/social theories adhered to, etc.)?
• How can I ensure the report is interesting without compromising credibility?
• How can I fairly and accurately report my findings within the length limitations of where it will appear (journal, paper presentation, etc.)?
• Are the representations of myself and the studied group fair? Is it clear that these are mere representations or have I presented them as definite factual evidence?

Researchers who take the time to confront these possible problems will produce fairer, clearer reports of their research. Even when the report takes the form of a narrative, researchers must be sure that their "telling of the story" gives readers an accurate and complete picture of the research.

It is important to note that the order of presentation is not indicative of an essential or set pattern. Although some elements in the researcher's decision-making process will necessarily precede others (i.e., the determination of the researcher's role before data collection), most of the steps outlined below will significantly overlap and recur throughout the research process.

Employing Multiple Methods

It is important to underscore that one cannot point to a single clear definition of a qualitative study. Oftentimes researchers triangulate data by combining different types of qualitative approaches and even including quantitative elements. For example, Doheny-Farina (1985) conducted a study of the collaborative writing process in a new software company. He visited the company for three to five days a week over eight months. His visits ranged from one to eight hours. His key informants were the company's top five executives, two middle managers, and two outside consults. He took 400 pages of field notes of three types: observational, theoretical, and methodological. He tape-recorded meetings, and he also conducted 30 open-ended and discourse-based interviews.

Doheny-Farina analyzed the data by reviewing it chronologically and developing a coding scheme as he reviewed. From the data he discovered a major theme and sub-theme. His analysis describes the writing of the company's business plan within its organizational context.

Essentially, his data showed that the organizational context shaped the writing of the business plan while the writing of the business plan shaped the organizational context.

Although the article Doheny-Farina wrote about his study starts out much like a traditional research report, it reports its results as a story with a chronology and a discussion of themes. He also offers theoretical, pedagogical, and research implications. He concludes by allowing that he is offering a model that is not necessarily generalizable but nonetheless valuable.

Computer Software for Qualitative Research

Qualitative observational research, by nature, involves the compilation of massive amounts of data. Because of this, many researchers have begun using computer software to help them organize and make sense of the volumes of information. There are many reasons for using computers in qualitative research, but according to Richards and Richards (1993), "Computers [offer] to address each of the obvious barriers to qualitative analysis by manual methods--limitations on size, flexibility and complexity of data records, and systems of theorizing about data." The authors also argue that using computers for qualitative research can give studies more credibility and status because of the association between computers and "hard" data. Research software can also help the researcher to analyze data that was previously too unwieldy for study. Finally, computers greatly speed up the process of retrieving and exploring data. In their text Computer Programs for Qualitative Analysis, Weitzman and Miles (1995), cite a list of the ways computer software can help the qualitative researcher (p. 5):

• Making notes in the field
• Writing up or transcribing field notes
• Editing: correcting, extending, or revising field notes
• Coding: attaching keywords or tags to segments of text to permit later retrieval
• Storage: keeping text in an organized database
• Search and retrieval: locating relevant segments of texts and making them available for inspection
• Data "linking": connecting relevant data segments to each other, forming categories, clusters, or networks of information
• Memoing: writing reflective commentaries on some aspect of the data as a basis for deeper understanding
• Content analysis: counting frequencies, sequence, or locations of words and phrases
• Data display: placing selected or reduced data in a condensed organized format, such as a matrix or network, for inspection
• Conclusion-drawing and verification: aiding the analyst in interpreting displayed data and testing findings
• Theory-building: developing systematic, conceptually coherent explanations of findings; testing hypotheses
• Graphic mapping: creating diagrams that depict findings or theories
• Preparing interim and final reports

Before choosing software for a qualitative study, researchers should not only be familiar with the types of software available, but they should also be well versed in the particular program functions and features they need. Flexibility and user friendliness are two more considerations addressed by Weitzman and Miles. They explain that before choosing software, researchers should find out if the software is designed to do what they need, and if not, can the software be adapted to meet the needs of a particular study. In addition, researchers should consider how complicated the software is to learn and use. Researchers should also find out if the software comes with a manual, has on-screen help, and/or has a technical support phone number.

Commentary on Ethnography, Observational Research, and Narrative Inquiry

Advantages of qualitative observational research.

Qualitative observational research, especially ethnographies, can:

• Account for the complexity of group behaviors
• Reveal interrelationships among multifaceted dimensions of group interactions
• Provide context for behaviors

Narrative inquiry,especially ethnographic, can:

• Reveal qualities of group experience in a way that other forms of research cannot
• Help determine questions and types of follow-up research

Observational study can:

• Reveal descriptions of behaviors in context by stepping outside the group
• Allow qualitative researchers to identify recurring patterns of behavior that participants may be unable to recognize

Qualitative research expands the range of knowledge and understanding of the world beyond the researchers themselves. It often helps us see why something is the way it is, rather than just presenting a phenomenon. For instance, a quantitative study may find that students who are taught composition using a process method receive higher grades on papers than students taught using a product method. However, a qualitative study of composition instructors could reveal why many of them still use the product method even though they are aware of the benefits of the process method.

Disadvantages of Qualitative Observational Research

• Researcher bias can bias the design of a study.
• Researcher bias can enter into data collection.
• Sources or subjects may not all be equally credible.
• Some subjects may be previously influenced and affect the outcome of the study.
• Background information may be missing.
• Study group may not be representative of the larger population.
• Analysis of observations can be biased.
• Any group that is studied is altered to some degree by the very presence of the researcher. Therefore, any data collected is somewhat skewed. (Heisenburg Uncertainty Principle)
• It takes time to build trust with participants that facilitates full and honest self-representation. Short term observational studies are at a particular disadvantage where trust building is concerned.

Ethnographic studies

• The quality of the data alone is problematic. (Lauer and Asher) (1988): Ethnographic research is time consuming, potentially expensive, and requires a well trained researcher
• Too little data can lead to false assumptions about behavior patterns. Conversely, a large quantity of data may not be effectively be processed
• Data Collector's first impressions can bias collection

Narrative Inquiries

• Narrative inquiries do not lend themselves well to replicability and are not generalizable.
• Narrative Inquiries are considered unreliable by experimentalists. However, ethnographies can be assessed and compared for certain variables to yield testable explanations; this is as close as ethnographic research gets to being empirical in nature.
• Qualitative research is neither prescriptive nor definite. While it provides significant data about groups or cultures and prompts new research questions, narrative studies do not attempt to answer questions, nor are they predictive of future behaviors.

The Qualitative/Quantitative Debate

In Miles and Huberman's 1994 book Qualitative Data Analysis , quantitative researcher Fred Kerlinger is quoted as saying, "There's no such thing as qualitative data. Everything is either 1 or 0" (p. 40). To this another researcher, D. T. Campbell, asserts, "All research ultimately has a qualitative grounding" (p. 40). This back and forth banter among qualitative and quantitative researchers is "essentially unproductive," according to Miles and Huberman. They and many other researchers agree that these two research methods need each other more often than not. But, because qualitative data typically involves words and quantitative data involves numbers, there are some researchers who feel that one is better (or more scientific) than the other. Another major difference between the two is that qualitative research is inductive and quantitative research is deductive. In qualitative research, a hypothesis is not needed to begin research. However, all quantitative research requires a hypothesis before research can begin.

Another major difference between qualitative and quantitative research deals with the underlying assumptions about the role of the researcher. In quantitative research, the researcher is ideally an objective observer who neither participates in nor influences what is being studied. In qualitative research, however, it is thought that the researcher can learn the most by participating and/or being immersed in a research situation. These basic underlying assumptions of both methodologies guide and sequence the types of data collection methods employed.

Although there are clear differences between qualitative and quantitative approaches, some researchers maintain that the choice between using qualitative or quantitative approaches actually has less to do with methodologies than it does with positioning oneself within a particular discipline or research tradition. The difficulty in choosing a method is compounded by the fact that research is often affiliated with universities and other institutions. The findings of research projects often guide important decisions about specific practices and policies. Choices about which approach to use may reflect the interests of those conducting or benefiting from the research and the purposes for which the findings will be applied. Decisions about which kind of research method to use may also be based on the researcher's own experience and preference, the population being researched, the proposed audience for findings, time, money and other resources available (Hathaway, 1995).

Some researchers believe that qualitative and quantitative methodologies cannot be combined because the assumptions underlying each tradition are so vastly different. Other researchers think they can be used in combination only by alternating between methods; qualitative research is appropriate to answer certain kinds of questions in certain conditions and quantitative is right for others. And some researchers think that both qualitative and quantitative methods can be used simultaneously to answer a research question.

To a certain extent, researchers on all sides of the debate are correct; each approach has its drawbacks. Quantitative research often "forces" responses or people into categories that might not "fit" in order to make meaning. Qualitative research, on the other hand, sometimes focuses too closely on individual results and fails to make connections to larger situations or possible causes of the results. Rather than discounting either approach for its drawbacks, researchers should find the most effective ways to incorporate elements of both to ensure that their studies are as accurate and thorough as possible.

It is important for researchers to realize that qualitative and quantitative methods can be used in conjunction with each other. In a study of computer-assisted writing classrooms, Snyder (1995) employed both qualitative and quantitative approaches. The study was constructed according to guidelines for quantitative studies; the computer classroom was the "treatment" group and the traditional pen and paper classroom was the "control" group. Both classes contained subjects with the same characteristics from the population sampled. Both classes followed the same lesson plan and were taught by the same teacher in the same semester. The only variable used was the absence or presence of the computers. Although Snyder set this study up as an "experiment," she used many qualitative approaches to supplement her findings. She observed both classrooms on a regular basis as a participant-observer and conducted several interviews with the teacher both during and after the semester. However, there were problems in using this approach. The strict adherence to the same syllabus and lesson plans for both classes and the restricted access of the control group to the computers may have put some students at a disadvantage. Snyder also notes that in retrospect she should have used case studies of the students to further develop her findings. Although her study had certain flaws, Snyder insists that researchers can simultaneously employ qualitative and quantitative methods if studies are planned carefully and carried out conscientiously.

Newkirk (1991) argues for qualitative research in English education from a political point of view. He says that not only can teachers more readily identify with and accept such particularized studies, but also the work of observing-participants, who report classroom "lore," gives practitioners a voice in the conversations informing their discipline. In addition, he asserts that experimental research tends to support the hierarchical structure of education policy, which discounts the experience of practitioners by privileging the alleged objectivity and generalizability of experimental designs and removing research from context. Additionally, Newkirk points out that "ethnographic...research works from fundamentally different assumptions about knowledge." Essentially, ethnography's epistemological orientation is phenomenological (observation based) while experimental research's is ontological (investigates the metaphysical or essential nature of something).

Ethical Considerations in Ethnography, Observational Research, and Narrative Inquiry

Ethical issues should always be considered when undertaking data analysis. Because the nature of qualitative observational research requires observation and interaction with groups, it is understandable why certain ethical issues may arise. Miles and Huberman (1994) list several issues that researchers should consider when analyzing data. They caution researchers to be aware of these and other issues before, during, and after the research had been conducted. Some of the issues involve the following:

• Informed consent (Do participants have full knowledge of what is involved?)
• Harm and risk (Can the study hurt participants?)
• Honesty and trust (Is the researcher being truthful in presenting data?)
• Privacy, confidentiality, and anonymity (Will the study intrude too much into group behaviors?)
• Intervention and advocacy (What should researchers do if participants display harmful or illegal behavior?)

Related Links

The following is a list of Internet links that are related to the field of qualitative observational research methods.

The Association of Qualitative Research Practitioners

http://www.aqrp.co.uk/

Nova Southeastern University’s School of Social and Systematic Studies (go to their Homepage and do a search on Qualitative Research)

http://www.nova.edu/

ISWorld Net page for research and scholarship

http://www.umich.edu/~isworld/reshome.html

Annotated Bibliography

Alvermann, D., O'Brien, D., & Dillon, D. (1996). On writing qualitative research. Reading research quarterly, 31 (1), 114-120.

This article presents a "conversation" among the authors about issues in writing qualitative research reports. They address potential problems researchers may face when reporting their findings and discuss how theory and methodology shape qualitative research write-ups.

Anderson, G. L. (1994). The cultural politics of qualitative research in education: Confirming and contesting the canon. Educational Theory, 44, 225-237.

This article looks at different approaches to qualitative field research. It is also a critical review of the Handbook of qualitative research in education .

Andreas, D. (1992). Ethnography of Biography: Student Teachers Reflecting on 'Life-Stories' of Experienced Teachers. Paper presented at the Annual Meeting of the American Educational Research Association (San Francisco, CA, April 20-24).

Explores the use of ethnographic biography as a source of information and reflection for student teachers.

Balester, V. M. (1993). Cultural divide: A study of African-American college-level writers. Portsmouth, NH: Boynton/Cook.

This book is based on research Balester conducted on the spoken and written texts of African-American students. For her study, Balester did case studies of eight African-American students, looking specifically at the students' attitudes toward their own language and the language of academia.

Banning, J. (1995, Sept. 19). Qualitative research. Personal interview with professor at Colorado State University, Fort Collins.

Dr. Banning, a professor in the School of Education at Colorado State University, discusses in detail the workshop he and colleague Jeff Gliner conducted on qualitative research.

Bishop, W. (1992). I-Witnessing in Composition: Turning Ethnographic Data into Narratives. Rhetoric Review ; v11 n1 p147-58 Fall.

Discusses problems with reconciling ethnographic research with positivistic methods.

Blair, K. (1995). Ethnography and the Internet: Research into Electronic Discourse Communities. Paper presented at the Annual Meeting of the Conference on College Composition and Communication (46th, Washington, DC, March 23-25, 1995).

Pros of electronic ethnography.

Borman, K. M. (1986). Ethnographic and qualitative research design and why it doesn't work. American Behavioral Scientist, 30, 43-57.

Borman identifies the characteristics of qualitative research and its weaknesses, then offers solutions.

Brophy, J. (Nov. 1995). Thoughts on the qualitative quantitative debate. Chicago, IL: National Council for the Social Studies. (ERIC Document Reproduction Service No. 392 734)

The focus of this paper is on the goals of both qualitative and quantative research and developing effective studies for the classroom. Brophy asserts that qualitative and quantitative methods are simply "tools" and should be evaluated from the standpoint of what questions they can answer best.

Bruyn, S. T. (1970). The new empiricists: The participant observer and phenomenologist. In W. J. Filstead (Ed.), Qualitative methodology: Firsthand involvement with the social world. Chicago: Markham, 283-287.

This article discusses the importance of phenomenology to qualitative research.

Bullock, R. (1995). Classroom Research in Graduate Methods Courses. Paper presented at the Annual Meeting of the Conference on College Composition and Communication (46th, Washington, DC, March 23-25, 1995).

Examines first year graduate student-teachers and why they are distrustful of narrative or ethnographic research as opposed to empirical research.

Burroughs-Lange, S. G., & Lange, J. (1993). Denuded data! Grounded theory using the NUDIST computer analysis program: In researching the challenge to teacher self-efficacy posed by students with learning disabilities in Australian education. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA. (ERIC Document Reproduction Service No. ED 364 193)

The authors evaluate the use of the NUDIST (Non-numerical, Unstructured Data Indexing, Searching and Theorising) computer program to organize coded, qualitative data. NUDIST was used in the authors' study to develop a theoretical understanding of the challenge that students with learning disablities pose to neophyte teachers' newly-formed images of effectiveness.

Collier, J., & Collier, M. (1986). Visual anthropology: Photography as a research method. Albuquerque: University of New Mexico Press.

This work discusses the benefits and possibilities of including photography in anthropological and ethnographic research. The book includes sections on the role of the photographer in documenting a culture or group, how photographs function in the interviewing process, analyzing images, and the psychological significance of photography and visual images in conveying meaning.

Connelly, F. M., & Clandinin, D. J. (1990). Stories of experience and narrative inquiry. Educational Researcher, 19 (5), 2-14.

This article is a theoretical work on conducting narrative inquiry that focuses on the issues of transferability and generalizability in this field of research.

Coulon, A. (1995). Ethnomethodology (J. Coulon & J. Katz, Trans.). London: Sage.

This text covers the history and issues related to ethnomethodology.

Cross, G. (1994). Ethnographic Research in Business and Technical Writing: Between Extremes and Margins. Journal of Business and Technical Communication ; v8 n1 p118-34 Jan.

Explores the phenomenal context, the site's cultural context, the research community context, and the researcher's interior context in business and technical writing.

Doheny-Farina, S. (1986). Writing in an emerging organization: An ethnographic study. Written Communication, 3, 158-85.

This article, gleaned from the author's doctoral dissertation, discusses his study of collaborative writing among executives at a new software firm. His methods included participant-observations, open-ended interviews, and Discourse-Based interviews.

Doheny-Farina, S. & Odell, L. (1985). Ethnographic research on writing: assumptions and methodology. In L. Odell &D. Goswami (Ed.), Writing in nonacademic settings. New York: Guilford, 503-535.

With a caution that researchers in English need to understand ethnography's basis in anthropology, this article outlines theoretical assumptions, methodologies, and the uses and limitations of ethnographic research.

Dyson, A. Haas. (1984). Learning to write/learning to do school: Emergent writers' interpretations of school literacy tasks. Research in the Teaching of English, 18, . 233-264.

This article is the report of an ethnographic study of kindergarten children which examined the relationship between their learning to write and their adapting to the culture of school. Data was collected several times per week over a fourteen week period. The researcher was a participant-observer who selected three case study children during the first phase of observation and studied them in context.

Ember,C. R., & Ember, M. (1973). Anthropology. New York: Appleton, Century, Crofts.

Fetterman, D. M. (1989). Ethnography: Step by step. Newbury Park, CA: Sage.

As the title suggests, this is a how-to book on ethnographies and ethnographic research. The book answers the question: what is ethnographic research and outlines a step by step approach to conducting this type of research. Chapter subjects include methods and techniques of ethnographic fieldwork, equipment needed for ethnographic research, how to analyze your findings, the writing process, and ethics in ethnographic research.

Fielding, N. G., & Lee, R. M. (Ed.). (1991). Using computers in qualitative research. London: Sage.

This anthology contains 11 essays on computers and qualitative research. The topics include general information about types of qualitative research and software, implications for research, and qualitative knowledge and computing. This text provides valuable information on both the positive and negative aspects of using computers for qualitative research.

Filstead, W. J. (Ed.). (1970). Qualitative methodology: Firsthand involvement with the social world. Chicago: Markham.

This text is a collection of essays on qualitative methodologies.

Firestone, W. A. & Dawson, J. A. (June 1981). To ethnograph or not to ethnograph? Varieties of qualitative research in education. Philadelphia, PA: Research for Better Schools, Inc. (ERIC Document Reproduction Service No. ED 222 985)

This paper addresses the advantages and disadvantages of using ethnographic studies and outlines six criteria for successfully using ethnographies in education studies. The authors also discuss five ways in which qualitative approaches can vary in terms of data collection.

Fitch, K. (1994). Criteria for Evidence in Qualitative Research. Western Journal of Communication ; v58 n1 p32-38 Win.

Contributions and limitations of conversation analysis and postmodernism toward the enterprise of ethnographic research. Criteria for qualitative data as evidence for claims about social life and for a qualitative study to count as evidence.

Flake, C. (1992). Ethnography for Teacher Education: An Innovative Elementary School Social Studies Program in South Carolina. Social Studies ; v83 n6 p253-57 Nov-Dec 1992.

Describes a teacher education program that utilizes an internship that includes an ethnographic research project. Explains that the teacher intern is required to conduct an in-depth analysis of the social studies being taught in their school as contrasted to that described in their textbooks. Includes resulting suggestions for improvement in the curriculum.

Gilbert, R. (1992). Text and context in qualitative educational research: Discourse analysis and the problem of contextual explanation. Linguistics and Education, 4, 37-57.

This article discusses methods of improving qualitative research in education.

Gilmore, D.D. (1991, Fall). Subjectivity and subjugation: Fieldwork in the stratified community. Human Organization, 215.

This article outlines an anthropologist's efforts to maintain scholarly neutrality in an agricultural town in Franco Spain where class conflict was severe.

Greenberg, J. H. (1954). A quantitative approach to the morphological typology of language. In R. F. Spencer (Ed.). Method and perspective in anthropology . Minneapolis, MN: University of Minnesota Press.

The author compares and contrasts typological methods of languages against the genetic-historical method.

Hammersley,M., & Atkinson, P. (1983). Ethnography: Principles in practice. London: Taveston.

This work deals with what ethnographic research is, what its strengths and weaknesses are, and how to go about conducting the research for your own project.

Hammersley, M. (1990). Reading ethnographic research: A critical guide. New York: Longman.

This book is a how-to manual on ethnographic research emphasizing understanding within unspoken contexts.

Hasselkus, B. R. (1995). Beyond ethnography: Expanding our understanding and criteria for qualitative research. Occupational Therapy Journal of Research, 15, 75-84.

Hasselkus discusses the different methods of qualitative research.

Hathaway, R. (1995). Assumptions underlying quantitative and qualitative research: Implications for institutional research. Research in higher education, 36 (5), 535-562.

Hathaway says that the choice between using qualitative or quantitative approaches is less about methodology and more about aligning oneself with particular theoretical and academic traditions. He concluded that the two approaches address questions in very different ways, each one having its own advantages and drawbacks.

Heath, S. B. (1983). Ways with words: Language, life, and work in communities and classrooms. New York: Cambridge University Press.

Heath studies two communities; one Black and one White, to analyze the citizens' language development.

Heath, S. B. (1993). The Madness(es) of Reading and Writing Ethnography. Anthropology and Education Quarterly ; v24 n3 p256-68 Sep.

Describes how these reactions have led the author to see things in the work that she had not seen before. Strengths and weaknesses of the book she identifies have implications for the conduct of future ethnographic research.

Hinsley, C. M. (1981). Savages and scientists: The Smithsonian Institution and the development of American anthropology. Washington, D.C.: Smithsonian Institution Press.

Hornberger, N. (1995). Ethnography in Linguistic Perspective: Understanding School Processes. Language and Education ; v9 n4 p233-48 .

Perspectives and methodologies that sociolinguistics brings to ethnographic research in schools. Methodological contributions arising from linguistics that interactional sociolinguistics and microethnograpy share, such as the use of naturally occurring language data, the consultation of native intuition, and discourse analysis.

This short Web site briefly describes qualitative research and gives an example of how it can be used to supplement quantitative studies in health care.

Journal of Contemporary Ethnography (formerly Urban Life ). Newbury Park, CA: Sage.

This is a quarterly publication containing recent ethnographic studies and what's new in ethnography. This publication is a good source of information on and examples of how other researchers are conducting their own ethnographic studies

Kamil, M. L., Langer, J. A., & Shanahan, T. (1985). Ethnographic methodologies. Understanding research in reading and writing. Boston: Allyn and Bacon, 71-91.

The chapter defines ethnographic research, examines its theoretical underpinnings, and contrasts it with experimental research. It includes an extended example from Heath's "Questioning at Home and at School: A Comparative Study."

Kirk, J. & Miller, M. (1986). Reliability and validity in qualitative research . Beverly Hills, CA: Sage.

This book investigates how realiability and validity in qualitative research help to evaluate the objectivity of particular studies. The authors assert that given the true meaning of validity, many studies, including "scientific" ones, are not really valid. Also included are guidelines for maintaining reliability in qualitative studies.

Lancy, D. E. (1993). Qualitative research in education. White Plains, NY: Longman.

This text explores the many issues of qualitative research.

Lauer, J. M., & Asher, J. W. (1988). Ethnographies. Composition research: Empirical designs. New York: Oxford University Press, 39-53.

This chapter provides an overview of ethnographic research applied to English. It includes examples from two studies, Florio and Clark's "The function of writing in an elementary classroom" and Lemke and Bridwell's "Assessing writing ability--an ethnographic study of consultant-teacher relationships."

Lawless, E.J. (1992, Summer). I was afraid someone like you...an outsider...would misunderstand: Negotiating interpretive differences between ethnographers and subjects. Journal of American Folklore, 302.

This article looks at the role of the ethnographer in the collection of field research and writing. A new approach called "reciprocal ethnography" allows for interaction with the ethnographer.

Lazerfeld, P. F. (1972). Qualitative analysis: Historical and critical essays. Boston: Allyn and Bacon.

This text deals with the issues of qualitative research.

LeCompte, M. D., Millroy, W. L., & Preissle, J. (Ed.). (1992). The handbook of qualitative research in education. San Diego: Academic Press.

This anthology contains 18 essays on qualitative research in education. The topics range from the future of qualitative research to issues of validity and subjectivity in qualitative research. This text is a good source for those interested in current theories about and research on qualitative research itself.

Lier, L. (1988). The classroom and the language learner. New York: Longman.

The author argues for collecting and interpreting of classroom data (L-2 learning) in the presence of only limited knowledge of the process of teaching and learning in second language classrooms. This book sets out to define problems of classroom research within second language acquisition study and within social science. And, it offers a well documented guide for conducting research in the context of the classroom.

Lincoln, Y.S., & Guba, E.G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.

This text outlines the positivist and naturalist research paradigms.

Linstead, S. (1993, Jan.). From postmodern anthropology to deconstructive ethnography. Human Relations, 97.

This article studies the effects of ethnography and postmodern influences on organizations. Derridian deconstruction theory is applied in order to get a new angle on social interactions within organizations.

Manwar, A., Johnson, B. D., & Dunlap, E. (1994). Qualitative data analysis with hypertext: A case of New York City crack dealers. Qualitative Sociology, 17, 283-292.

The authors describe some of the problems of data management and analysis faced by a team of ethnographers researching cocaine and crack distributuion in New York City. The researchers used FolioVIEWS, a hypertext software program, which proved to be more effective than other available programs in solving managment and analytical problems.

Marshall, C. & Rossman, G. (1995). Designing qualitative research . (2nd ed.). Thousand Oaks, CA: Sage.

This book explains different types of qualitative studies and provides thorough instruction on how to design, conduct and evaluate a qualitative study. It also includes helpful information on managing time, personnel and financial resources for qualitative research.

Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis. Thousand Oaks, CA: Sage.

This text covers data analysis issues related to qualitative research.

Minnich, R. G. (Ed.). (1987). Aspects of Polish folk culture. Bergen, Norway: Department of Social Anthropology, University of Bergen.

This text is a good source of examples for work done in the field of ethnography dealing with culture and literature. The work is a compilation of ethnographic studies by different authors done on topics ranging from the role played by gifts in Polish weddings to the role of art in Polish society. Through the reports included in this work, Minnich draws a clearer picture of Polish folk culture.

Minnis, J. R. (1985). Ethnography, case study, grounded theory, and distance education research. Distance Education, 6, 189-198.

Minnis explores the possibility of expanding the research base through the use of accepted qualitative methodologies.

Moores, S. (1993). Interpreting audiences. Thousand Oaks, CA: Sage.

This text characterizes features of ethnography as a method of cultural investigation. It provides a discussion of the opposing, alternative perspectives on various forms of media reception and how ethnographic practice best equips researchers to map the media's varied uses and meanings for particular social subjects in particular cultural contexts.

Mortensen, P. & Kirsch, G., Eds. (1996). Ethics and Representation in Qualitative Studies of Literacy. Urbana, IL: ERIC .

Fourteen essays address questions faced by qualitative researchers today: how to represent others and themselves in research narratives; how to address ethical dilemmas in research-participant relations; and how to deal with various rhetorical, institutional, and historical constraints on research.

Narayan, K. (1989). Storytellers, saints, scoundrels: Folk narrative in Hindu religious teaching. Philadelphia: University of Pennsylvania Press.

The author relates hindu stories and their significance to education, both moral and religious.

Newkirk, T. (1991). The politics of composition research: The conspiracy against experience. In R. Bullock & J. Trimbur (Eds.), The politics of writing instruction: Postsecondary. Portsmouth, NH: Heinemann, 119-135.

The author argues for the importance of ethnographic research in English education from a political perspective. He cites its key strengths over experimental research--particularity, involvement of the researcher, underlying ideology--the very characteristics which experimentalists criticize. Newkirk asserts that ethnographic research empowers practitioners.

Patton, M. Q. (1992). Ethnography and research: A qualitative view. Topics in Language Disorders, 12, 1-14.

This article describes the functions of ethnography in the fields of education and communication disorders.

Patton, M. Q. (1980). Qualitative evaluation methods. Beverly Hills: Sage.

This book is an in depth study of qualitative research from conceptual issues to data analysis.

Rice-Lively. (1994). Wired Warp and Woof: An Ethnographic Study of a Networking Class. Internet Research ; v4 n4 p20-35 Win.

Describes an ethnographic study of the electronic community comprised of masters and doctoral students involved in a seminar on networking. Ethnographic research facilitated observation and description of the networked learning community. The exploration of the cultural meaning of class events led to enhanced understanding of online education and the applicability of ethnographic research.    

Richards, L., & Richards, T. (1993). Qualitative computing: promises, problems, and implications for research process. Qualitative data analysis resources Home Page. [On-line]. Available WWW: address http://www.qsr.com.au/ftp/papers/qualprobs.txt.

Based on their experience with qualitative research software, the authors examine both the positive and negative aspects of this technology.

Rosen, M. (1991, Jan.). Coming to terms with the field: Understanding and doing organizational ethnography. Journal of Management Studies, 1.

Ethnography is not well understood or applied as a methodology for studying organization culture. This article highlights problems and offers tools for effective research in this arena.

Sanday, P. R. (1979). The ethnographic paradigms(s). Administrative Science Quarterly, 24, 527-538.

Three styles of ethnography are examined: holistic, semiotic, and behavioristic.

Saville-Troike, M. (1989). The ethnography of communication (2nd ed.). Oxford: Basil Blackwell.

This text is a synthesis of the field of ethnography of communication, which studies the norms of communicative conduct in different communities and deals with methods for studying these norms.

Schmid, T. (1992). Classroom-Based Ethnography: A Research Pedagogy. Teaching Sociology ; v20 n1 p28-35 Jan.

Discusses difficulties of classroom-based research and obstacles to conducting classroom-based ethnographic research. Identifies temporal obstacles, personnel, safety, and traditional classroom orientation. Suggests experiential approaches for fieldwork instructors such as individual projects, a choice of group projects, or a single designated class project. Describes a cooperative project on homelessness.

Shanahan, T., Ed. Teachers Thinking, Teachers Knowing: Reflections on Literacy and Language Education . Urbana, IL: ERIC.

Thirteen essays share the insights of leading scholars and teacher-researchers regarding the re-emergence of teacher education as a central focus in the field of English education. Discusses methods of supporting teacher development such as the study of cases, teacher groups, ethnographic research in the classroom and community, and teacher lore.

Smith, G.W. (1990, Nov.) Political activist as ethnographer. Social Problems, 629.

Two studies that use Dorothy E. Smith's reflexive materialist method of sociology are presented; the studies examine the social organization of ruling regimes with an aim toward changing them.

Snyder, I. (1995). Multiple perspectives in literacy research: Integrating the quantitative and qualitative. Language and Education, 9 (1), 45-59.

This article explains a study in which the author employed quantitative and qualitative methods simultaneously to compare computer composition classrooms and traditional classrooms. Although there were some problems with integrating both approaches, Snyder says they can be used together if researchers plan carefully and use their methods thoughtfully.

Tallerico, M. (1992). Computer technology for qualitative research: Hope and humbug. Journal of Educational Administration, 30 (2), 32-40.

The author describes how computer technology offers new options for the qulitative researcher in education. Tallerico also identifies both the potential benefits and limitations of research software, drawing on a study of local educational governance. She also decribes the ETHNOGRAPH, a data analysis program.

Tesch, R. (1991). Software for qualitative researchers: Analysis needs and program capabilities. In N. G. Fielding & R. M. Lee (Ed.), Using computers in qualitative research. London: Sage, 16-37.

Tesch begins by explaining the different types of qualitative research. She goes on to define the general categories of computer software available to qualitative researchers and gives advice on what functions and features to look for when choosing software.

Thornton, S. & Garrett, K. (1995). Ethnography as a Bridge to Multicultural Practice. Journal of Social Work Education; v31 n1 p67-74 Win.

Ethnographic research method taught as a way of studying different cultural groups in a social work curriculum.

Turner, E. (1992). Experiencing ritual: A new interpretation of African healing. Philadelphia: University of Pennsylvania Press.

This text reports an anthropology, the story of a "visible spirit" from among the Ndembu of Zambia. This work gives an account of the ethnographer's experience living with the Ndembu and attempting to parallel Ndembu life.

Van Maanen, J. (1979). The fact of fiction in organizational ethnography. Administrative Science Quarterly, 24, 539-550.

Van Maanen discusses the need to distinguish whether the point of view reported is that of informant or of researcher.

Van Maanen, J. (1988). Tales of the field. Chicago: The University of Chicago Press.

In this book, the author provides an informal introduction to ethnography addressed to fieldworkers of sociology or anthropology.

Weitzman, E. A., & Miles, M. B. (1995). Computer programs for qualitative data analysis. Thousand Oaks, CA: Sage.

Weitzman and Miles discuss the different functions of qualitative research software. They also categorize the software currently available and explain and review each program. This text provides valuable information for any researcher who is choosing software for qualitative research.

Wu, R. (1994). Writing In and Writing Out: Some Reflections on the Researcher's Dual Role in Ethnographic Research. Paper presented at the Annual Penn State Conference on Rhetoric and Composition (University Park, PA, July 13-16).

Proposes "a more fluid, process-oriented definition of the ethnographer's role based on feminist standpoint theories to acknowledge the complexity of multicultural observers and observed."

Zaharlick, A. (1992). Ethnography in anthropology and its value for education. Theory into Practice, 31, 116-125.

This article examines the role of ethnography in anthropology.

Citation Information

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2024). Ethnography, Observational Research, and Narrative Inquiry. The WAC Clearinghouse. Colorado State University. Available at https://wac.colostate.edu/repository/writing/guides/.

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Research Methods (Case Studies & Observation Studies) 0 Pages | Leaving School | 05/06/2024

• Case Studies & Observation Studies

——————————————————

Observation Studies

Categories of behaviour: If the researcher is undertaking a natural observation , he may need to divide the behaviour he sees into categories so that a quick record can be made. If the researcher wants to understand how the public respond to a woman collapsing in the street, for example, his categories might include 1.) Ignores and walks on. 2.) Hesitates and walks on. 3.) Checks to see if the woman is ok. 4.) Calls 999.

Inter-observer reliability: In order to test the reliability of an observer’s records, it might be sensible to have two observers who are working to exactly the same category and score sheet, so that they can compare their results at the end of the observation period. If these observations closely match each other then it can be assumed their observations have been accurate. If there is a significant difference it may be necessary to start the observation over again.

Advantages of natural observation Natural observations are high in ecological validity . A string of natural actions can be observed. In a laboratory situation people are often asked to complete unnatural tasks.

Disadvantages of natural observation In the absence ofcontrolled variables it is difficult to establish why someone behaved in a certain way. This type of study is reliant on the accuracy of the observation. There are ethical issues involved in an observation of this kind i.e. the people being observed may not know that this is the case. Should they be told? And if they are told, would their behaviour still be natural? Natural observations can be awkward to plan as well as time consuming.

• Research Methods
• Hypotheses and Experimental Designs
• Standardised Procedures & Instructions
• Ecological Validity & Sampling Methods
• Making Sense of Data & Anomalous Results
• Survey Methods & Ethical Considerations
• Remember it, Test it!

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Naturalistic Observations vs. Survey

What's the difference.

Naturalistic observations and surveys are both research methods used in psychology and other social sciences to gather data. However, they differ in their approach and the type of information they provide. Naturalistic observations involve observing and recording behavior in its natural setting without any manipulation or intervention. This method allows researchers to study behavior as it naturally occurs, providing rich and detailed information about real-life situations. On the other hand, surveys involve collecting data through self-report measures, where participants answer a series of questions about their thoughts, feelings, or behaviors. Surveys are efficient in gathering large amounts of data from a large number of participants, but they rely on participants' ability to accurately recall and report their experiences. Overall, naturalistic observations provide a more authentic and in-depth understanding of behavior, while surveys offer a broader perspective by collecting data from a larger sample size.

Further Detail

Introduction.

When conducting research, it is essential to choose the most appropriate method to gather data and gain insights. Two commonly used methods in social sciences are naturalistic observations and surveys. Both approaches have their unique attributes and can provide valuable information, but they differ in terms of data collection, participant involvement, and potential biases. In this article, we will explore the characteristics of naturalistic observations and surveys, highlighting their strengths and limitations.

Naturalistic Observations

Naturalistic observations involve observing and recording behavior in its natural setting without any intervention or manipulation by the researcher. This method aims to capture real-life behaviors and interactions as they naturally occur, providing a rich and authentic understanding of the phenomenon under study.

One of the key attributes of naturalistic observations is the high ecological validity they offer. By observing behavior in its natural context, researchers can ensure that the findings are applicable to real-world situations. This method allows for the collection of data that might not be easily obtained through other means, such as self-report measures.

Furthermore, naturalistic observations provide an opportunity to study behavior in a non-intrusive manner. Participants are not aware that they are being observed, which reduces the likelihood of their behavior being influenced by the presence of a researcher or the awareness of being studied. This minimizes the potential for demand characteristics, where participants alter their behavior to match perceived expectations.

However, naturalistic observations also have limitations. One significant challenge is the lack of control over variables. Since the researcher cannot manipulate the environment or conditions, it becomes difficult to establish cause-and-effect relationships. Additionally, the presence of an observer might still introduce some bias, as the researcher's interpretations and observations can be subjective.

In summary, naturalistic observations offer high ecological validity and the ability to study behavior in a non-intrusive manner. However, the lack of control over variables and potential observer bias are important considerations.

Surveys involve the collection of data through self-report measures, typically in the form of questionnaires or interviews. This method allows researchers to gather information directly from participants, providing insights into their thoughts, opinions, and behaviors.

One of the primary advantages of surveys is their ability to collect large amounts of data from a diverse range of participants. Surveys can be administered to a large sample size, making it possible to generalize findings to a larger population. This makes surveys particularly useful for studying attitudes, beliefs, and preferences.

Surveys also offer a structured approach to data collection. Researchers can design specific questions and response options, ensuring consistency across participants. This allows for easy comparison and analysis of responses, facilitating quantitative data analysis. Surveys can also be conducted anonymously, which may encourage participants to provide more honest and accurate responses, especially when addressing sensitive topics.

However, surveys also have limitations. One potential drawback is the reliance on self-report measures, which can be subject to response biases. Participants may provide socially desirable responses or may not accurately recall or report their behaviors. This can introduce measurement error and affect the validity of the findings.

Another limitation of surveys is the potential for sampling bias. Surveys often rely on convenience sampling, where participants are selected based on their accessibility or willingness to participate. This can result in a non-representative sample, limiting the generalizability of the findings to the larger population.

In summary, surveys offer the advantage of collecting large amounts of data from diverse participants in a structured manner. However, the reliance on self-report measures and the potential for sampling bias are important considerations.

Comparing Naturalistic Observations and Surveys

While naturalistic observations and surveys differ in their approach to data collection, they share some commonalities. Both methods aim to gather information about human behavior and provide insights into various phenomena. Additionally, both approaches have their strengths and limitations, which researchers must carefully consider when selecting the most appropriate method for their study.

One key difference between naturalistic observations and surveys is the level of participant involvement. In naturalistic observations, participants are unaware that they are being observed, ensuring that their behavior remains unaffected by the presence of a researcher. On the other hand, surveys require active participation from participants, as they are directly involved in providing responses to the questions posed.

Another distinction lies in the type of data collected. Naturalistic observations focus on capturing real-life behaviors and interactions, providing qualitative data that can offer rich descriptions and insights. Surveys, on the other hand, primarily collect quantitative data, allowing for statistical analysis and the identification of patterns and trends.

Furthermore, naturalistic observations offer high ecological validity, as they occur in real-world settings. This means that the findings can be applied to similar situations outside of the research context. Surveys, while lacking the same level of ecological validity, can still provide valuable information about attitudes, beliefs, and preferences, particularly when conducted with a representative sample.

Both methods also have potential biases that researchers need to be aware of. Naturalistic observations can be subject to observer bias, as the researcher's interpretations and observations may be influenced by their own biases and preconceptions. Surveys, on the other hand, can be affected by response biases, where participants may provide socially desirable responses or inaccurately recall or report their behaviors.

In conclusion, naturalistic observations and surveys are two valuable research methods that offer unique attributes and insights. Naturalistic observations provide high ecological validity and the ability to study behavior in a non-intrusive manner, while surveys allow for the collection of large amounts of data and structured analysis. Researchers must carefully consider the strengths and limitations of each method to select the most appropriate approach for their research question and objectives.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

• Science & Math
• Sociology & Philosophy
• Law & Politics

Psychology: Survey Method & Naturalistic Observation

• Psychology: Survey Method & Naturalistic…

SURVEY METHOD

The survey method involves handing out questionnaires to try to get an idea to establish people’s attitude, beliefs and behavior.

You have a population of interest; who you are interested in surveying.

E.g. You want to study Canadian teenagers about their attitude on the legalization of marijuana

Ideally, you would give a survey to every single teenager; however, this is not practical.

What should you do instead? Pick a sample from the population hoping that, the results that you have gathered from the sample population will be true of the entire teenage population.

You want to generalize your findings from the sample to the population . In order to generalize from a sample to a population, the sample must be representative of the population of interest. This is essential. Your sample must include a wide range of teenagers, varying from their age to where they live, and only then, will you be able to generalize your findings.

If you were to only survey a population from Toronto, this would be a biased sample . This data would not be representative of the entire Canadian teenage population because Toronto teenagers might have different attitudes toward the legalization of marijuana than teenagers from Vancouver or Calgary.

How do you ensure that your sample is representative? You do what is called random selection/sampling . You want to randomly pick your sample from the population so that every person in the population has an equal chance of being in the sample .

NATURALISTIC OBSERVATION

This method is used when you want to describe or measure people’s behavior as they are behaving naturally in a natural setting .

This is when you want to measure exactly what is going on, and/or describe what you are actually seeing and draw your own conclusions.

• In naturalistic observation, you can generalize your results. Typically, when you are observing people in a natural setting, subjects are unaware that they are being observed. Therefore, you can reasonably conclude that the way they are behaving in their real-life environment is the way they would naturally behave, whether you are observing them or not.

Disadvantage:

• You cannot infer cause and effect – you can’t go any further in terms of explaining why things are happening or why people are behaving in a certain way. You cannot say what is causing that behavior because of the fact that you have little control over what is going on.
• You can also misinterpret the situation. How can you prevent this? You can have multiple observers – this is called inter observable/ inter rater reliability . Reliability in this case means consistency .

E.g. 2 kids in a playground and one is head locking the other and you assume that there is aggressive behavior going on but in reality the kids could be talking about what they saw on T.V last night; again, you cannot infer cause and effect.

• It is also time-consuming; you have little control over the situation.

E.g. You want to study bullying in a schoolyard but for days nothing has been happening. You cannot tell a kid to attack another child in order to get a reaction to get some data. You can’t force things to happen if they’re not happening. It is time consuming and this can be frustrating for a researcher.

• The experimenter can tire and loss of attention.
• You can get what is called expectancy effect . This can be problematic with many different methods of research.

E.g. You are conducting a study and trying to prove your hypothesis so you are expecting to see results. Because of your expectations, what you are expecting to happen might influence how you are going to see and interpret your data. Expectations can and do influence your behavior even if you are a researcher.

• Another potential disadvantage of naturalistic observation is subject reactivity . You do not want subjects to be aware that they’re being watched. You do not want the subject to be reacting to your presence because you want them to behave naturally.

In naturalistic observations, sometimes you have what is called a “ participant observer ”. The researcher who is the observer will act as though they are a participant; in other words, they go “under cover”.

E.g. A researcher wants to study how a leader emerges from a group. The researcher will become a part of the social group to be studied to collect information by pretending to be part of the group. However, the researcher must not interfere and/or manipulate the situation to ascertain particular results.

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Case studies can often be thought of as natural tests and are frequently used by clinical physcologist? True What is meant by natural tests in the statement? PLEASE ANSWER!!!

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