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Observational Research – Methods and Guide

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Observational Research

Definition:

Observational research is a type of research method where the researcher observes and records the behavior of individuals or groups in their natural environment. In other words, the researcher does not intervene or manipulate any variables but simply observes and describes what is happening.

Observation

Observation is the process of collecting and recording data by observing and noting events, behaviors, or phenomena in a systematic and objective manner. It is a fundamental method used in research, scientific inquiry, and everyday life to gain an understanding of the world around us.

Types of Observational Research

Observational research can be categorized into different types based on the level of control and the degree of involvement of the researcher in the study. Some of the common types of observational research are:

Naturalistic Observation

In naturalistic observation, the researcher observes and records the behavior of individuals or groups in their natural environment without any interference or manipulation of variables.

Controlled Observation

In controlled observation, the researcher controls the environment in which the observation is taking place. This type of observation is often used in laboratory settings.

Participant Observation

In participant observation, the researcher becomes an active participant in the group or situation being observed. The researcher may interact with the individuals being observed and gather data on their behavior, attitudes, and experiences.

Structured Observation

In structured observation, the researcher defines a set of behaviors or events to be observed and records their occurrence.

Unstructured Observation

In unstructured observation, the researcher observes and records any behaviors or events that occur without predetermined categories.

Cross-Sectional Observation

In cross-sectional observation, the researcher observes and records the behavior of different individuals or groups at a single point in time.

Longitudinal Observation

In longitudinal observation, the researcher observes and records the behavior of the same individuals or groups over an extended period of time.

Data Collection Methods

Observational research uses various data collection methods to gather information about the behaviors and experiences of individuals or groups being observed. Some common data collection methods used in observational research include:

Field Notes

This method involves recording detailed notes of the observed behavior, events, and interactions. These notes are usually written in real-time during the observation process.

Audio and Video Recordings

Audio and video recordings can be used to capture the observed behavior and interactions. These recordings can be later analyzed to extract relevant information.

Surveys and Questionnaires

Surveys and questionnaires can be used to gather additional information from the individuals or groups being observed. This method can be used to validate or supplement the observational data.

Time Sampling

This method involves taking a snapshot of the observed behavior at pre-determined time intervals. This method helps to identify the frequency and duration of the observed behavior.

Event Sampling

This method involves recording specific events or behaviors that are of interest to the researcher. This method helps to provide detailed information about specific behaviors or events.

Checklists and Rating Scales

Checklists and rating scales can be used to record the occurrence and frequency of specific behaviors or events. This method helps to simplify and standardize the data collection process.

Observational Data Analysis Methods

Observational Data Analysis Methods are:

Descriptive Statistics

This method involves using statistical techniques such as frequency distributions, means, and standard deviations to summarize the observed behaviors, events, or interactions.

Qualitative Analysis

Qualitative analysis involves identifying patterns and themes in the observed behaviors or interactions. This analysis can be done manually or with the help of software tools.

Content Analysis

Content analysis involves categorizing and counting the occurrences of specific behaviors or events. This analysis can be done manually or with the help of software tools.

Time-series Analysis

Time-series analysis involves analyzing the changes in behavior or interactions over time. This analysis can help identify trends and patterns in the observed data.

Inter-observer Reliability Analysis

Inter-observer reliability analysis involves comparing the observations made by multiple observers to ensure the consistency and reliability of the data.

Multivariate Analysis

Multivariate analysis involves analyzing multiple variables simultaneously to identify the relationships between the observed behaviors, events, or interactions.

Event Coding

This method involves coding observed behaviors or events into specific categories and then analyzing the frequency and duration of each category.

Cluster Analysis

Cluster analysis involves grouping similar behaviors or events into clusters based on their characteristics or patterns.

Latent Class Analysis

Latent class analysis involves identifying subgroups of individuals or groups based on their observed behaviors or interactions.

Social network Analysis

Social network analysis involves mapping the social relationships and interactions between individuals or groups based on their observed behaviors.

The choice of data analysis method depends on the research question, the type of data collected, and the available resources. Researchers should choose the appropriate method that best fits their research question and objectives. It is also important to ensure the validity and reliability of the data analysis by using appropriate statistical tests and measures.

Applications of Observational Research

Observational research is a versatile research method that can be used in a variety of fields to explore and understand human behavior, attitudes, and preferences. Here are some common applications of observational research:

• Psychology : Observational research is commonly used in psychology to study human behavior in natural settings. This can include observing children at play to understand their social development or observing people’s reactions to stress to better understand how stress affects behavior.
• Marketing : Observational research is used in marketing to understand consumer behavior and preferences. This can include observing shoppers in stores to understand how they make purchase decisions or observing how people interact with advertisements to determine their effectiveness.
• Education : Observational research is used in education to study teaching and learning in natural settings. This can include observing classrooms to understand how teachers interact with students or observing students to understand how they learn.
• Anthropology : Observational research is commonly used in anthropology to understand cultural practices and beliefs. This can include observing people’s daily routines to understand their culture or observing rituals and ceremonies to better understand their significance.
• Healthcare : Observational research is used in healthcare to understand patient behavior and preferences. This can include observing patients in hospitals to understand how they interact with healthcare professionals or observing patients with chronic illnesses to better understand their daily routines and needs.
• Sociology : Observational research is used in sociology to understand social interactions and relationships. This can include observing people in public spaces to understand how they interact with others or observing groups to understand how they function.
• Ecology : Observational research is used in ecology to understand the behavior and interactions of animals and plants in their natural habitats. This can include observing animal behavior to understand their social structures or observing plant growth to understand their response to environmental factors.
• Criminology : Observational research is used in criminology to understand criminal behavior and the factors that contribute to it. This can include observing criminal activity in a particular area to identify patterns or observing the behavior of inmates to understand their experience in the criminal justice system.

Observational Research Examples

Here are some real-time observational research examples:

• A researcher observes and records the behaviors of a group of children on a playground to study their social interactions and play patterns.
• A researcher observes the buying behaviors of customers in a retail store to study the impact of store layout and product placement on purchase decisions.
• A researcher observes the behavior of drivers at a busy intersection to study the effectiveness of traffic signs and signals.
• A researcher observes the behavior of patients in a hospital to study the impact of staff communication and interaction on patient satisfaction and recovery.
• A researcher observes the behavior of employees in a workplace to study the impact of the work environment on productivity and job satisfaction.
• A researcher observes the behavior of shoppers in a mall to study the impact of music and lighting on consumer behavior.
• A researcher observes the behavior of animals in their natural habitat to study their social and feeding behaviors.
• A researcher observes the behavior of students in a classroom to study the effectiveness of teaching methods and student engagement.
• A researcher observes the behavior of pedestrians and cyclists on a city street to study the impact of infrastructure and traffic regulations on safety.

How to Conduct Observational Research

Here are some general steps for conducting Observational Research:

• Define the Research Question: Determine the research question and objectives to guide the observational research study. The research question should be specific, clear, and relevant to the area of study.
• Choose the appropriate observational method: Choose the appropriate observational method based on the research question, the type of data required, and the available resources.
• Plan the observation: Plan the observation by selecting the observation location, duration, and sampling technique. Identify the population or sample to be observed and the characteristics to be recorded.
• Train observers: Train the observers on the observational method, data collection tools, and techniques. Ensure that the observers understand the research question and objectives and can accurately record the observed behaviors or events.
• Conduct the observation : Conduct the observation by recording the observed behaviors or events using the data collection tools and techniques. Ensure that the observation is conducted in a consistent and unbiased manner.
• Analyze the data: Analyze the observed data using appropriate data analysis methods such as descriptive statistics, qualitative analysis, or content analysis. Validate the data by checking the inter-observer reliability and conducting statistical tests.
• Interpret the results: Interpret the results by answering the research question and objectives. Identify the patterns, trends, or relationships in the observed data and draw conclusions based on the analysis.
• Report the findings: Report the findings in a clear and concise manner, using appropriate visual aids and tables. Discuss the implications of the results and the limitations of the study.

When to use Observational Research

Here are some situations where observational research can be useful:

• Exploratory Research: Observational research can be used in exploratory studies to gain insights into new phenomena or areas of interest.
• Hypothesis Generation: Observational research can be used to generate hypotheses about the relationships between variables, which can be tested using experimental research.
• Naturalistic Settings: Observational research is useful in naturalistic settings where it is difficult or unethical to manipulate the environment or variables.
• Human Behavior: Observational research is useful in studying human behavior, such as social interactions, decision-making, and communication patterns.
• Animal Behavior: Observational research is useful in studying animal behavior in their natural habitats, such as social and feeding behaviors.
• Longitudinal Studies: Observational research can be used in longitudinal studies to observe changes in behavior over time.
• Ethical Considerations: Observational research can be used in situations where manipulating the environment or variables would be unethical or impractical.

Purpose of Observational Research

Observational research is a method of collecting and analyzing data by observing individuals or phenomena in their natural settings, without manipulating them in any way. The purpose of observational research is to gain insights into human behavior, attitudes, and preferences, as well as to identify patterns, trends, and relationships that may exist between variables.

The primary purpose of observational research is to generate hypotheses that can be tested through more rigorous experimental methods. By observing behavior and identifying patterns, researchers can develop a better understanding of the factors that influence human behavior, and use this knowledge to design experiments that test specific hypotheses.

Observational research is also used to generate descriptive data about a population or phenomenon. For example, an observational study of shoppers in a grocery store might reveal that women are more likely than men to buy organic produce. This type of information can be useful for marketers or policy-makers who want to understand consumer preferences and behavior.

In addition, observational research can be used to monitor changes over time. By observing behavior at different points in time, researchers can identify trends and changes that may be indicative of broader social or cultural shifts.

Overall, the purpose of observational research is to provide insights into human behavior and to generate hypotheses that can be tested through further research.

Advantages of Observational Research

There are several advantages to using observational research in different fields, including:

• Naturalistic observation: Observational research allows researchers to observe behavior in a naturalistic setting, which means that people are observed in their natural environment without the constraints of a laboratory. This helps to ensure that the behavior observed is more representative of the real-world situation.
• Unobtrusive : Observational research is often unobtrusive, which means that the researcher does not interfere with the behavior being observed. This can reduce the likelihood of the research being affected by the observer’s presence or the Hawthorne effect, where people modify their behavior when they know they are being observed.
• Cost-effective : Observational research can be less expensive than other research methods, such as experiments or surveys. Researchers do not need to recruit participants or pay for expensive equipment, making it a more cost-effective research method.
• Flexibility: Observational research is a flexible research method that can be used in a variety of settings and for a range of research questions. Observational research can be used to generate hypotheses, to collect data on behavior, or to monitor changes over time.
• Rich data : Observational research provides rich data that can be analyzed to identify patterns and relationships between variables. It can also provide context for behaviors, helping to explain why people behave in a certain way.
• Validity : Observational research can provide high levels of validity, meaning that the results accurately reflect the behavior being studied. This is because the behavior is being observed in a natural setting without interference from the researcher.

Disadvantages of Observational Research

While observational research has many advantages, it also has some limitations and disadvantages. Here are some of the disadvantages of observational research:

• Observer bias: Observational research is prone to observer bias, which is when the observer’s own beliefs and assumptions affect the way they interpret and record behavior. This can lead to inaccurate or unreliable data.
• Limited generalizability: The behavior observed in a specific setting may not be representative of the behavior in other settings. This can limit the generalizability of the findings from observational research.
• Difficulty in establishing causality: Observational research is often correlational, which means that it identifies relationships between variables but does not establish causality. This can make it difficult to determine if a particular behavior is causing an outcome or if the relationship is due to other factors.
• Ethical concerns: Observational research can raise ethical concerns if the participants being observed are unaware that they are being observed or if the observations invade their privacy.
• Time-consuming: Observational research can be time-consuming, especially if the behavior being observed is infrequent or occurs over a long period of time. This can make it difficult to collect enough data to draw valid conclusions.
• Difficulty in measuring internal processes: Observational research may not be effective in measuring internal processes, such as thoughts, feelings, and attitudes. This can limit the ability to understand the reasons behind behavior.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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On This Page:

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.

• Interval recording is common in microanalytic coding to sample discrete behaviors in brief time samples across an interaction. The time unit can range from seconds to minutes to whole interactions. Interval recording requires segmenting interactions based on timing rather than events (Bakeman & Quera, 2011).
• Instantaneous sampling provides snapshot coding at certain moments rather than summarizing behavior within full intervals. This allows quicker coding but may miss behaviors in between target times.

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.

Coding schemes can vary in their level of detail or granularity. Micro-level schemes capture fine-grained behaviors, such as specific facial movements, while macro-level schemes might code broader behavioral states or interactions. The appropriate level of granularity depends on the research questions and the practical constraints of the study.

Another important consideration is the concreteness of the codes. Some schemes use physically based codes that are directly observable (e.g., “eyes closed”), while others use more socially based codes that require some level of inference (e.g., “showing empathy”). While physically based codes may be easier to apply consistently, socially based codes often capture more meaningful behavioral constructs.

Most coding schemes strive to create sets of codes that are mutually exclusive and exhaustive (ME&E). This means that for any given set of codes, only one code can apply at a time (mutual exclusivity), and there is always an applicable code (exhaustiveness). This property simplifies both the coding process and subsequent data analysis.

For example, a simple ME&E set for coding infant state might include: 1) Quiet alert, 2) Crying, 3) Fussy, 4) REM sleep, and 5) Deep sleep. At any given moment, an infant would be in one and only one of these states.

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. 

Macroanalytic coding systems focus on capturing overarching themes, global qualities, or general patterns of behavior rather than specific, discrete actions.

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).

Examples of Macroanalytic Coding Systems:

• Emotional Availability Scales (EAS) : This system assesses the quality of emotional connection between caregivers and children across dimensions like sensitivity, structuring, non-intrusiveness, and non-hostility.
• Classroom Assessment Scoring System (CLASS) : Evaluates the quality of teacher-student interactions in classrooms across domains like emotional support, classroom organization, and instructional support.

Microanalytic coding systems

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

These systems focus on capturing specific, discrete behaviors or events as they occur moment-to-moment. Behaviors are often coded second-by-second or in very short time intervals.

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.

Examples of Microanalytic Coding Systems:

• Facial Action Coding System (FACS) : Codes minute facial muscle movements to analyze emotional expressions.
• Specific Affect Coding System (SPAFF) : Used in marital interaction research to code specific emotional behaviors.
• Noldus Observer XT : A software system that allows for detailed coding of behaviors in real-time or from video recordings.

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.

Examples of Mesoanalytic Coding Systems:

• Feeding Scale for Mother-Infant Interaction : Assesses feeding interactions in 5-minute episodes, coding specific behaviors and overall qualities.
• Couples Interaction Rating System (CIRS): Codes specific behaviors and rates overall qualities in segments of couple interactions.
• Teaching Styles Rating Scale : Combines frequency counts of specific teacher behaviors with global ratings of teaching style in classroom segments.

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.

Data Analysis Approaches

Data analysis in behavioral observation aims to transform raw observational data into quantifiable measures that can be statistically analyzed.

It’s important to note that the choice of analysis approach is not arbitrary but should be guided by the research questions, study design, and nature of the data collected.

Interval data (where behavior is recorded at fixed time points), event data (where the occurrence of behaviors is noted as they happen), and timed-event data (where both the occurrence and duration of behaviors are recorded) may require different analytical approaches.

Similarly, the level of measurement (categorical, ordinal, or continuous) will influence the choice of statistical tests.

Researchers typically start with simple descriptive statistics to get a feel for their data before moving on to more complex analyses. This stepwise approach allows for a thorough understanding of the data and can often reveal unexpected patterns or relationships that merit further investigation.

simple descriptive statistics

Descriptive statistics give an overall picture of behavior patterns and are often the first step in analysis.

• Frequency counts tell us how often a particular behavior occurs, while rates express this frequency in relation to time (e.g., occurrences per minute).
• Duration measures how long behaviors last, offering insight into their persistence or intensity.
• Probability calculations indicate the likelihood of a behavior occurring under certain conditions, and relative frequency or duration statistics show the proportional occurrence of different behaviors within a session or across the study.

These simple statistics form the foundation of behavioral analysis, providing researchers with a broad picture of behavioral patterns. 

They can reveal which behaviors are most common, how long they typically last, and how they might vary across different conditions or subjects.

For instance, in a study of classroom behavior, these statistics might show how often students raise their hands, how long they typically stay focused on a task, or what proportion of time is spent on different activities.

contingency analyses

Contingency analyses help identify if certain behaviors tend to occur together or in sequence.

• Contingency tables , also known as cross-tabulations, display the co-occurrence of two or more behaviors, allowing researchers to see if certain behaviors tend to happen together.
• Odds ratios provide a measure of the strength of association between behaviors, indicating how much more likely one behavior is to occur in the presence of another.
• Adjusted residuals in these tables can reveal whether the observed co-occurrences are significantly different from what would be expected by chance.

For example, in a study of parent-child interactions, contingency analyses might reveal whether a parent’s praise is more likely to follow a child’s successful completion of a task, or whether a child’s tantrum is more likely to occur after a parent’s refusal of a request.

These analyses can uncover important patterns in social interactions, learning processes, or behavioral chains.

sequential analyses

Sequential analyses are crucial for understanding processes and temporal relationships between behaviors.

• Lag sequential analysis looks at the likelihood of one behavior following another within a specified number of events or time units.
• Time-window sequential analysis examines whether a target behavior occurs within a defined time frame after a given behavior.

These methods are particularly valuable for understanding processes that unfold over time, such as conversation patterns, problem-solving strategies, or the development of social skills.

observer agreement

Since human observers often code behaviors, it’s important to check reliability . This is typically done through measures of observer agreement.

• Cohen’s kappa is commonly used for categorical data, providing a measure of agreement between observers that accounts for chance agreement.
• Intraclass correlation coefficient (ICC) : Used for continuous data or ratings.

Good observer agreement is crucial for the validity of the study, as it demonstrates that the observed behaviors are consistently identified and coded across different observers or time points.

advanced statistical approaches

As researchers delve deeper into their data, they often employ more advanced statistical techniques.

• For instance, an ANOVA might reveal differences in the frequency of aggressive behaviors between children from different socioeconomic backgrounds or in different school settings.
• This approach allows researchers to account for dependencies in the data and to examine how behaviors might be influenced by factors at different levels (e.g., individual characteristics, group dynamics, and situational factors).
• This method can reveal trends, cycles, or patterns in behavior over time, which might not be apparent from simpler analyses. For instance, in a study of animal behavior, time series analysis might uncover daily or seasonal patterns in feeding, mating, or territorial behaviors.

representation techniques

Representation techniques help organize and visualize data:

• Many researchers use a code-unit grid, which represents the data as a matrix with behaviors as rows and time units as columns.
• This format facilitates many types of analyses and allows for easy visualization of behavioral patterns.
• Standardized formats like the Sequential Data Interchange Standard (SDIS) help ensure consistency in data representation across studies and facilitate the use of specialized analysis software.
• Indeed, the complexity of behavioral observation data often necessitates the use of specialized software tools. Programs like GSEQ, Observer, and INTERACT are designed specifically for the analysis of observational data and can perform many of the analyses described above efficiently and accurately.

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.

What is an Observational Research: Steps, Types, Pros and Cons

Observational research refers to qualitative and non-experimental studies that seek to systematically observe, record, and analyse a particular society, culture, behaviours and attitudes. It is non-experimental in its observation as it does not manipulate any variables.

The steps are undertaken in conducting Observation research usually include:

Observational research is typically dichotomized on the basis of  :.

Degree of Structure of the Environment:

Other types of Observation research include:

Observation research, irrespective of type, come with a plethora of advantages and disadvantages as described below.

Advantages :

Disadvantages.

Observation research comes with a myriad of advantages and disadvantages. Obviously, not all pros and cons listed above apply to every research project but several do and it is important to note that this research method must be tailored to the phenomena that you want to study. Each research question will call for a different approach and the observation research style can be moulded to satisfy the studies’ research objectives.

References:

McLeod, S. A. (2015, June 06). Observation methods. Simply Psychology. https://www.simplypsychology.org/observation.html

Ciesielska, Malgorzata, et al. “Observation Methods.” Qualitative Methodologies in Organization Studies , 2017, pp. 33–52., doi:10.1007/978-3-319-65442-3_2.

Bailey, K. (1994). Observation in Methods of social research. Simon and

Schuster, 4th ed. The Free Press, New York NY10020. Ch 10. Pp.241-273.

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups

What is observational research?

Uses for observational research, observations in research, the different types of observational research, conducting observational studies, uses with other methods, challenges of observational studies.

• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Observational research

Observational research is a social research technique that involves the direct observation of phenomena in their natural setting.

An observational study is a non-experimental method to examine how research participants behave. Observational research is typically associated with qualitative methods , where the data ultimately require some reorganization and analysis .

Contemporary research is often associated with controlled experiments or randomized controlled trials, which involve testing or developing a theory in a controlled setting. Such an approach is appropriate for many physical and material sciences that rely on objective concepts such as the melting point of substances or the mass of objects. On the other hand, observational studies help capture socially constructed or subjective phenomena whose fundamental essence might change when taken out of their natural setting.

What is an example of observational research?

For example, imagine a study where you want to understand the actions and behaviors of single parents taking care of children. A controlled experiment might prove challenging, given the possibility that the behaviors of parents and their children will change if you isolate them in a lab or an otherwise unfamiliar context.

Instead, researchers pursuing such inquiries can observe participants in their natural environment, collecting data on what people do, say, and behave in interaction with others. Non-experimental research methods like observation are less about testing theories than learning something new to contribute to theories.

The goal of the observational study is to collect data about what people do and say. Observational data is helpful in several fields:

• market research
• health services research
• educational research
• user research

Observational studies are valuable in any domain where researchers want to learn about people's actions and behaviors in a natural setting. For example, observational studies in market research might seek out information about the target market of a product or service by identifying the needs or problems of prospective consumers. In medical contexts, observers might be interested in how patients cope with a particular medical treatment or interact with doctors and nurses under certain conditions.

Researchers may still be hung up on science being all about experiments to the point where they may overlook the empirical contribution that observations bring to research and theory. With that in mind, let's look at the strengths and weaknesses of observations in research .

Strengths of observational research

Observational research, especially those conducted in natural settings, can generate more insightful knowledge about social processes or rituals that one cannot fully understand by reading a plain-text description in a book or an online resource. Think about a cookbook with recipes, then think about a series of videos showing a cook making the same recipes. Both are informative, but the videos are often easier to understand as the cook can describe the recipe and show how to follow the steps at the same time. When you can observe what is happening, you can emulate the process for yourself.

Observing also allows researchers to create rich data about phenomena that cannot be explained through numbers. The quality of a theatrical performance, for example, cannot easily be reduced to a set of numbers. Qualitatively, a researcher can analyze aspects gleaned from observing that performance and create a working theory about the quality of that performance. Through data analysis, the researcher can identify patterns related to the aesthetics and creativity of the performance to provide a framework to judge the quality of other performances.

Weaknesses of observational research

Science is about organizing knowledge for the purposes of identifying the aspects of a concept or of determining cause-and-effect relationships between different phenomena. Experiments look to empirically accomplish these tasks by controlling certain variables to determine how other variables change under changing conditions. Those conducting observational research, on the other hand, exert no such control, which makes replication by other researchers difficult or even impossible when observing dynamic environments.

Observational studies take on various forms. There are various types of observational research, each of which has strengths and weaknesses. These types are organized below by the extent to which an experimenter intrudes upon or controls the environment.

Naturalistic observation

Naturalistic observation refers to a method where researchers study participants in their natural environment without manipulating variables or intervening in any way. It provides a realistic snapshot of behavior as it occurs in real-life settings, thereby enhancing ecological validity.

Examples of naturalistic observation include people-watching in public places, observing animal behaviors in the wild, and longitudinally studying children's social development at school. This method can reveal insights about behavior and relationships that might not surface in experimental designs, such as patterns of social interaction, routines, or responses to environmental changes.

Participant observation

Participant observation is similar to naturalistic observation, except that the researcher is part of the natural environment they are observing. In such studies, the researcher is also interested in rituals or cultural practices where they can only determine their value by actually experiencing them firsthand. For example, any individual can understand the basic rules of baseball by watching a game or following a team. Participant observation, on the other hand, allows for direct participation to develop a better sense of team dynamics and relationships among fellow players.

Most commonly, this process involves the researcher inserting themselves into a group to observe behavior that otherwise would not be accessible by observing from afar. Participant observation can capture rich data from the interactions with those who are observed to the reflections of the researchers themselves.

Controlled observation

A more structured observation involves capturing the behaviors of research participants in an isolated environment. Case-control studies have a greater resemblance to experimental research while still relying on observational research methods. Researchers may utilize a case-control study when they want to establish the causation of a particular phenomenon.

For example, a researcher may want to establish a structured observation of a control group and an experimental group, each with randomly assigned research participants, to observe the effects of variables such as distractions on people completing a particular task. By subjecting the experimental group to distractions such as noise and lights, researchers can observe the time it takes participants to complete a task and determine causation accordingly.

Longitudinal study

Among the different types of observational research, this observational method is quite arduous and time-consuming as it requires observation of people or events over extended periods. Researchers should consider longitudinal observations when their inquiry involves variables that can only be observed over time. After all, variables such as literacy development or weight loss cannot be fully captured in any particular moment of observation. Longitudinal studies keep track of the same research participants or events through multiple observations to document changes to or patterns in behavior.

A cohort study is a specific type of longitudinal study where researchers observe participants with similar traits (e.g., a similar risk factor or biological characteristic). Cohort studies aim to observe multiple participants over time to identify a relationship between observed phenomena and a common characteristic.

All forms of observational or field research benefit extensively from the special capabilities of qualitative research tools like ATLAS.ti . Our software can accommodate the major forms of data , such as text, audio, video, and images . The ATLAS.ti platform can help you organize all your observations , whatever method you employ.

Whatever your research, make it happen with ATLAS.ti.

Powerful analytical tools at your fingertips. Try for free by clicking here.

Like any other study design, observational studies begin by posing research questions . Inquiries common when employing observational methods include the study of different cultures, interactions between people from different communities, or people in particular circumstances warranting further study (e.g., people coping with a rare disease).

Generally, a research question that seeks to learn more about a relatively unfamiliar phenomenon would be best suited for observational research. On the other hand, quantitative methods or experimental research methods may be more suitable for inquiries where the theory about a social phenomenon is fairly established.

Study design

Study design for observational research involves thinking about who to observe, where they should be observed, and what the researcher should look for during observation. Many events can occur in a natural, dynamic environment in a short period, so it is challenging to document everything. If the researcher knows what they want to observe, they can pursue a structured observation which involves taking notes on a limited set of phenomena.

The actual data collection for an observational study can take several forms. Note-taking is common in observational research, where the researcher writes down what they see during the course of their observation. The goal of this method is to provide a record of the events that are observed to determine patterns and themes useful for theoretical development.

Observation can also involve taking pictures or recording audio for a richer understanding of social phenomena. Video recorded from observations can also provide data that the researcher can use to document the facial expressions, gestures, and other body language of research participants.

Note that there are ethical considerations when conducting observational research. Researchers should respect the privacy and confidentiality of their research participants to ensure they are not adversely affected by the research. Researchers should obtain informed consent from participants before any observation where possible.

Observational studies can be supplemented with other methods to further contextualize the research inquiry. Researchers can conduct interviews or focus groups with research participants to gather data about what they recall about their actions and behaviors in a natural setting. Focus groups, in particular, provide further opportunities to observe participants interacting with each other. In both cases, these research methods are ideal where the researcher needs to follow up with research participants about the evidence they've collected regarding their behaviors or actions.

As with many other methods in qualitative research , conducting an observational study is time-consuming. While experimental methods can quickly generate data , observational research relies on documenting events and interactions in detail that can be analyzed for theoretical development.

Unstructured data

One common critique of observational research is that it lacks the structure inherent to experimental research, which has concepts such as selection bias and interrater reliability to ensure research quality. On the other hand, qualitative research relies on the assumption that the study and its data are presented transparently and honestly . Under this principle, researchers are responsible for convincing their audiences that the assertions they make are connected empirically to the observations they have made and the data they have collected.

Researcher bias

In most qualitative research, but especially in observational research, the most important data collection instrument is the researcher themselves. This raises issues of bias and subjectivity influencing the collection and interpretation of the data.

Later in this guide, there will be discussion of reflexivity , a concept where the researcher comprehensively accounts for their place in the research relative to others in the environment. For now, it's important to know that social science researchers can and do adequately address critiques of researcher bias to maintain the empirical nature of their observational research.

Conduct your observational study with ATLAS.ti

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• Knowledge Base
• Methodology
• 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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Non-Experimental Research

32 Observational Research

Learning objectives.

• List the various types of observational research methods and distinguish between each.
• Describe the strengths and weakness of each observational research method. 

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational methods that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation .  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. This type of reactivity is known as the Hawthorne effect . For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are flirting, having sex, wearing next to nothing, screaming at each other, and occasionally behaving in ways that are embarrassing.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that are collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation , the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

In a famous example of disguised participant observation, Leon Festinger and his colleagues infiltrated a doomsday cult known as the Seekers, whose members believed that the apocalypse would occur on December 21, 1954. Interested in studying how members of the group would cope psychologically when the prophecy inevitably failed, they carefully recorded the events and reactions of the cult members in the days before and after the supposed end of the world. Unsurprisingly, the cult members did not give up their belief but instead convinced themselves that it was their faith and efforts that saved the world from destruction. Festinger and his colleagues later published a book about this experience, which they used to illustrate the theory of cognitive dissonance (Festinger, Riecken, & Schachter, 1956) [1] .

In contrast with undisguised participant observation ,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second deception is being used. The researcher is deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [2]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [3] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researchers are in a much better position to understand the viewpoint and experiences of the people they are studying when they are a part of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation, additional concerns arise when researchers become active members of the social group they are studying because that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation . Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation. Often the setting in which the observations are made is not the natural setting. Instead, the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation.

Structured observation is very similar to naturalistic observation and participant observation in that in all three cases researchers are observing naturally occurring behavior; however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic or participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [4] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186).

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.  In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [5] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

In yet another example (this one in a laboratory environment), Dov Cohen and his colleagues had observers rate the emotional reactions of participants who had just been deliberately bumped and insulted by a confederate after they dropped off a completed questionnaire at the end of a hallway. The confederate was posing as someone who worked in the same building and who was frustrated by having to close a file drawer twice in order to permit the participants to walk past them (first to drop off the questionnaire at the end of the hallway and once again on their way back to the room where they believed the study they signed up for was taking place). The two observers were positioned at different ends of the hallway so that they could read the participants’ body language and hear anything they might say. Interestingly, the researchers hypothesized that participants from the southern United States, which is one of several places in the world that has a “culture of honor,” would react with more aggression than participants from the northern United States, a prediction that was in fact supported by the observational data (Cohen, Nisbett, Bowdle, & Schwarz, 1996) [6] .

When the observations require a judgment on the part of the observers—as in the studies by Kraut and Johnston and Cohen and his colleagues—a process referred to as   coding is typically required . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that guides different observers to code them in the same way. This difficulty with coding illustrates the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interest which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A  case study   is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individual’s depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [7] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [8] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, he believed that her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample of individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation.

However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods. The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with both internal and external validity. Case studies lack the proper controls that true experiments contain. As such, they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (a possibility suggested by the dissection of HM’s brain following his death) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So, as with all observational methods, case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically an abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity. With case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research involves analyzing archival data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [9] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [10] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Media Attributions

• What happens when you remove the hippocampus? – Sam Kean by TED-Ed licensed under a standard YouTube License
• Pappenheim 1882  by unknown is in the  Public Domain .
• Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
• Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An "experimental ethnography." Journal of Personality and Social Psychology, 70 (5), 945-960. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

An observational method that involves observing people’s behavior in the environment in which it typically occurs.

When researchers engage in naturalistic observation by making their observations as unobtrusively as possible so that participants are not aware that they are being studied.

Where the participants are made aware of the researcher presence and monitoring of their behavior.

Refers to when a measure changes participants’ behavior.

In the case of undisguised naturalistic observation, it is a type of reactivity when people know they are being observed and studied, they may act differently than they normally would.

Researchers become active participants in the group or situation they are studying.

Researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

Researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation.

When a researcher makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation.

A part of structured observation whereby the observers use a clearly defined set of guidelines to "code" behaviors—assigning specific behaviors they are observing to a category—and count the number of times or the duration that the behavior occurs.

An in-depth examination of an individual.

A family of systematic approaches to measurement using qualitative methods to analyze complex archival data.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Observational Research Method explained

Observational Research Method: this article explains the concept of Observational Research Method in a practical way. The article begins with an introduction and the general definition of the term, followed by an explanation of why observational research is important, its advantages and disadvantages, and a practical example. Enjoy reading!

What is observational research?

Observational research is a method of collecting data by simply observing and recording the behavior of individuals, animals or objects in their natural environment.

It offers researchers insights into human and animal behavior, revealing patterns and dynamics that would otherwise go unnoticed.

This article explores the definition, types, advantages, and disadvantages of observational research. Several examples, including its application in market research, will show you how this approach improves our human understanding of the world.

Observational research: collecting insights unobtrusively

Definition of observational research.

Observational studies serve as a means of answering research questions through careful observation of subjects, without any interference or manipulation by the researcher.

Unlike traditional experiments, these studies lack control and treatment groups, allowing researchers to collect data in a natural setting without imposing predetermined conditions.

Observational studies are generally of a qualitative nature, with both exploratory and explanatory purposes, providing insight into the complexity of particular phenomena.

While quantitative observational studies also exist, they are less common compared to the qualitative studies.

Observational research is widely used in disciplines such as the exact sciences, medicine and social sciences.

Often, ethical or practical considerations prohibit researchers from conducting controlled experiments, leading them to opt for observational studies instead.

The lack of control and treatment groups can pose challenges in drawing conclusions. The risk of confounding variables and observer bias affecting the analysis is high, highlighting the importance of careful interpretation.

Types of observational research

Figure 1 – Types of observational research

Some common types of observational research are:

Naturalistic observation

In naturalistic observation, researchers observe participants in their natural environment, without any interference or disturbance. The aim is to study the behavior and interactions of individuals or groups as they occur in their natural environment.

Structured observation

In structured observation, a predetermined set of behaviors or variables is observed and systematically recorded.

The researchers use specific behavioral categories or measurement tools to collect data .

Participant observation

Participant observation means that the researcher actively participates in the activities or interactions of the participants while they are being observed.

This gives the researcher a deeper insight into the experience and perspectives of the participants.

Covert observation

In the case of a covert observational study, the researcher tries to make himself known to the participants as little as possible.

They observe and record behavior without the participants being aware of the observation. This minimizes the risk of deviant behaviour.

Cross-sectional study

In cross-sectional studies, data is collected at a single point in time or over a short period of time.

The goal is to get a snapshot of the behavior or phenomenon being studied.

Longitudinal study

Longitudinal studies involve following and observing participants over a longer period of time. This makes it possible to identify and analyze changes in behavior or patterns over time.

Choosing the right type of observational study depends on the research question, the aim of the study and the available resources and time. Each type has its own strengths and weaknesses and can be adapted to the specific needs of the research.

Steps in observational research

Below you will find the steps that are followed when setting up an observational research.

Step 1: determine research topic and objectives

The first step involves determining the phenomenon to be observed and the reasons why it is important. Observational studies are especially suitable when an experiment is not an option for practical or ethical reasons. The research topic may also depend on natural behaviour.

As an example, let’s consider a researcher who is interested in the interactions of teens in their social situations. The researcher wants to investigate whether having a smartphone influences the social interactions of the teenagers. Conducting an experiment can be tricky because smartphone use should not be manipulated.

Step 2: choose the type of observation and techniques

Think about what needs to be observed. Does the researcher go in without preconceived notion? Is there another research method that makes more sense to use? Is it important for the analysis that the researcher is present during the observation? If so, a covert observation is already ruled out.

In the example described earlier, several options are possible. The observations could be performed by observing the teens in different situations. It may also be considered to have the observer join a social group and actively participate in their interactions while the group is being observed. Hidden cameras can also be used to record teens’ social interactions in a controlled environment.

Step 3: set up the observational study

There are a number of things to consider before starting the observation.

First, you need to plan ahead. If the participants are observed in a social setting such as community centers or schools, clear agreements should be made and permission should be given. Informed consent might be required. Decide in advance the observational research methods you will use for data collection. Are notes taken? Or video images or audio recordings?

Step 4: before the observation

Once the type of observation has been chosen, the research technique has been decided on and the correct time and place have been determined, it is time to conduct the observation.

In the example, it can be considered to observe two situations, for example one with smartphones and one without smartphones. When conducting the observation, it is important to take confounding variables into account.

Step 5: analyzing data

After completing the observation, it is important to immediately record the first clues, thoughts and impressions. If the observation has been recorded, this recording must be transcribed. Subsequently, a thematic or content analysis must be carried out.

Observations are often exploratory and have an open character. That is why this analysis fits well with this method.

Step 6: discuss next steps

Observational studies are generally exploratory in nature and therefore usually do not immediately yield definitive conclusions. This is mainly because of the risk of observational bias and confounding variables. If the researcher is satisfied with the conclusions that have been reached, it may be useful to switch to another research method, like an experiment.

Examples of observational research

Observational research has led to several revolutionary results that have forever changed our understanding of the world and human behavior.

Some examples of this are:

Development of Darwin’s theory of evolution

Charles Darwin used observational research during his travels on the ship HMS Beagle. Observations of various animal species in their natural environment, such as birds in the Galapagos Islands, allowed Darwin to gather evidence for his theory of evolution.

This revolutionary theory has completely changed the understanding of the origin and diversity of species of creatures.

Discovery of penicillin

Sir Alexander Fleming accidentally discovered the effect of penicillin, a revolutionary antibiotic, through observational research.

He observed that a fungus called Penicillium notatum destroyed bacteria in a petri dish.

This discovery laid the foundation for the development of modern antibiotics and has had an enormous impact on medicine and the treatment of infectious diseases.

Confirmation of Einstein’s theory of relativity

During a solar eclipse in 1919, Arthur Eddington and his team conducted observational research to test the predictions of Einstein’s general theory of relativity.

By observing the positions of stars during the eclipse, they were able to confirm the deflection of light by the sun’s gravity. This experimental evidence supported Einstein’s theory and marked a revolutionary breakthrough in physics.

Research into the effects of smoking on health

One of the most influential observational studies was the study of the relationship between smoking and health problems, particularly lung cancer.

By observing large groups of smokers over a long period of time and collecting data on their smoking behavior and health outcomes, it was shown that there is a strong association between smoking and the risk of lung cancer.

These findings have led to a better understanding of the harmful effects of smoking and have contributed to the promotion of anti-smoking measures and health education.

Pros and cons

Observational research has several advantages and disadvantages that need to be considered before choosing the right research approach.

Advantages of observational research

Authentic behaviour.

By observing people, animals or objects in their natural environment, researchers can study authentic behavior.

That means that the observations take place in real situations and not artificial laboratory conditions.

This allows researchers to study behavior as it actually occurs. This increases scientific validity.

Detailed information

Observational research offers the opportunity to collect detailed information about behaviour, interactions and context.

Researchers can observe specific behaviors such as nonverbal cues, responses to stimuli, and social dynamics. This leads to a deep understanding of the phenomenon being studied.

Flexibility

Observational research can be adapted to different research questions and contexts. Researchers can tailor the observations to the specific situations and variables they want to study. This gives them the flexibility to focus on specific aspects of behaviour, for example.

Disadvantages of observational research

Limited control.

In observational research, researchers have limited control over the conditions and variables they observe. They cannot perform experimental manipulations or control specific environmental factors.

Observer bias

Observer bias refers to the subjective interpretation of the observations by the researcher. Researchers may unconsciously project their own biases, expectations, or interpretations onto the observed behaviors. This could jeopardize the objectivity of the investigation.

Time consuming

Now it’s your turn

What do you think? Do you recognize the explanation about observational research? Are you familiar with observational research? What do you think are the main benefits of observational research? Have you ever read or experienced an observational study that has given you new insights? Do you have tips or other comments?

Share your experience and knowledge in the comments box below.

More information

• Barick, R. (2021). Research Methods For Business Students . Retrieved 02/16/2024 from Udemy.
• Rosenbaum, P. R. (2005). Observational study . Encyclopedia of statistics in behavioral science.
• Altmann, J. (1974). Observational study of behavior: sampling methods . Behaviour, 49(3-4), 227-266.
• Jepsen, P., Johnsen, S. P., Gillman, M. W., & Sørensen, H. T. (2004). Interpretation of observational studies . Heart, 90(8), 956-960.
• Ligthelm, R. J., Borzì, V., Gumprecht, J., Kawamori, R., Wenying, Y., & Valensi, P. (2007). Importance of observational studies in clinical practice . Clinical therapeutics , 29(6), 1284-1292.

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Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

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Observational Research: What is, Types, Pros & Cons + Example

Researchers can gather customer data in a variety of ways, including surveys, interviews, and research. But not all data can be collected by asking questions because customers might not be conscious of their behaviors. 

It is when observational research comes in. This research is a way to learn about people by observing them in their natural environment. This kind of research helps researchers figure out how people act in different situations and what things in the environment affect their actions.

This blog will teach you about observational research, including types and observation methods. Let’s get started.

What is observational research?

Observational research is a broad term for various non-experimental studies in which behavior is carefully watched and recorded.

The goal of this research is to describe a variable or a set of variables. More broadly, the goal is to capture specific individual, group, or setting characteristics.

Since it is non-experimental and uncontrolled, we cannot draw causal research conclusions from it. The observational data collected in research studies is frequently qualitative observation , but it can also be quantitative or both (mixed methods).

Types of observational research

Conducting observational research can take many different forms. There are various types of this research. These types are classified below according to how much a researcher interferes with or controls the environment.

Naturalistic observation

Taking notes on what is seen is the simplest form of observational research. A researcher makes no interference in naturalistic observation. It’s just watching how people act in their natural environments. 

Importantly, there is no attempt to modify factors in naturalistic observation, as there would be when comparing data between a control group and an experimental group.

Case studiesCase studies

A case study is a sort of observational research that focuses on a single phenomenon. It is a naturalistic observation because it captures data in the field. But case studies focus on a specific point of reference, like a person or event, while other studies may have a wider scope and try to record everything that happens in the researcher’s eyes. 

For example, a case study of a single businessman might try to find out how that person deals with a certain disease’s ups and down or loss.

Participant observation

Participant observation is similar to naturalistic observation, except that the researcher is a part of the natural environment they are studying. In such research, the researcher is also interested in rituals or cultural practices that can only be evaluated by sharing experiences. 

For example, anyone can learn the basic rules of table Tennis by going to a game or following a team. Participant observation, on the other hand, lets people take part directly to learn more about how the team works and how the players relate to each other.

It usually includes the researcher joining a group to watch behavior they couldn’t see from afar. Participant observation can gather much information, from the interactions with the people being observed to the researchers’ thoughts.

Controlled observation

A more systematic structured observation entails recording the behaviors of research participants in a remote place. Case-control studies are more like experiments than other types of research, but they still use observational research methods. When researchers want to find out what caused a certain event, they might use a case-control study.

Longitudinal observation

This observational research is one of the most difficult and time-consuming because it requires watching people or events for a long time. Researchers should consider longitudinal observations when their research involves variables that can only be seen over time. 

After all, you can’t get a complete picture of things like learning to read or losing weight in a single observation. Longitudinal studies keep an eye on the same people or events over a long period of time and look for changes or patterns in behavior.

Observational research methods

When doing this research, there are a few observational methods to remember to ensure that the research is done correctly. Along with other research methods, let’s learn some key research methods of it:

Have a clear objective

For an observational study to be helpful, it needs to have a clear goal. It will help guide the observations and ensure they focus on the right things.

Get permission

Get permission from your participants. Getting explicit permission from the people you will be watching is essential. It means letting them know that they will be watched, the observation’s goal, and how their data will be used.

Unbiased observation

It is important to make sure the observations are fair and unbiased. It can be done by keeping detailed notes of what is seen and not putting any personal meaning on the data.

Hide your observers

In the observation method, keep your observers hidden. The participants should be unaware of the observers to avoid potential bias in their actions.

Documentation

It is important to document the observations clearly and straightforwardly. It will allow others to examine the information and confirm the observational research findings.

Data analysis

Data analysis is the last method. The researcher will analyze the collected data to draw conclusions or confirm a hypothesis.

Pros and cons of observational research

Observational studies are a great way to learn more about how your customers use different parts of your business. There are so many pros and cons of observational research. Let’s have a look at them.

• It provides a practical application for a hypothesis. In other words, it can help make research more complete.
• You can see people acting alone or in groups, such as customers. So, you can answer a number of questions about how people act as customers.
• There is a chance of researcher bias in observational research. Experts say that this can be a very big problem.
• Some human activities and behaviors can be difficult to understand. We are unable to see memories or attitudes. In other words, there are numerous situations in which observation alone is inadequate.

Example of observational research

The researcher observes customers buying products in a mall. Assuming the product is soap, the researcher will observe how long the customer takes to decide whether he likes the packaging or comes to the mall with his decision already made based on advertisements.

If the customer takes their time making a decision, the researcher will conclude that packaging and information on the package affect purchase behavior. If a customer makes a quick decision, the decision is likely predetermined. 

As a result, the researcher will recommend more and better advertisements in this case. All of these findings were obtained through simple observational research.

How to conduct observational research with QuestionPro?

QuestionPro can help with observational research by providing tools to collect and analyze data. It can help in the following ways:

Define the research goals and question types you want to answer with your observational study . Use QuestionPro’s customizable survey templates and questions to do a survey that fits your research goals and gets the necessary information. 

You can distribute the survey to your target audience using QuestionPro’s online platform or by sending a link to the survey. 

With QuestionPro’s real-time data analysis and reporting features, you can collect and look at the data as people fill out the survey. Use the advanced analytics tools in QuestionPro to see and understand the data and find insights and trends. 

If you need to, you can export the data from QuestionPro into the analysis tools you like to use. Draw conclusions from the collected and analyzed data and answer the research questions that were asked at the beginning of the research.

To summarize, observational research is an effective strategy for collecting data and getting insights into real-world phenomena. When done right, this research can give helpful information and help people make decisions. 

QuestionPro is a valuable tool that can help with observational research by letting you create online surveys, analyze data in real time, make surveys your own, keep your data safe, and use advanced analytics tools.

To do this research with QuestionPro, researchers need to define their research goals, do a survey that matches their goals, send the survey to participants, collect and analyze the data, visualize and explain the results, export data if needed, and draw conclusions from the data collected.

By keeping in mind what has been said above, researchers can use QuestionPro to help with their observational research and gain valuable data. Try out QuestionPro today!

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Frequently Asked Questions (FAQ)

Observational research is a method in which researchers observe and systematically record behaviors, events, or phenomena without directly manipulating them.

There are three main types of observational research: naturalistic observation, participant observation, and structured observation.

Naturalistic observation involves observing subjects in their natural environment without any interference.

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6.5 Observational Research

Learning objectives.

• List the various types of observational research methods and distinguish between each
• Describe the strengths and weakness of each observational research method. 

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational research designs that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation.  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity  refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are, flirting, having sex, wearing next to nothing, screaming at each other, and at times acting like complete fools in front of the entire nation.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that is collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation, the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers. In contrast with undisguised participant observation,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second passive deception is being used. The researcher is passively deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further,  disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [1]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins (published in  Social Psychology Quarterly ) on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [2] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researcher is in a much better position to understand the viewpoint and experiences of the people they are studying when they are apart of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation when researchers because active members of the social group they are studying, additional concerns arise that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation. Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic and participant observation. Often the setting in which the observations are made is not the natural setting, rather the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation. Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [3] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186).  Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.  In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [4] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as  coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This difficulty with coding is the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interested which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A  case study  is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individuals’ depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

www.youtube.com/watch?v=KkaXNvzE4pk

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [5] , who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [6] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 10.1 Anna O. “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: http://en.wikipedia.org/wiki/File:Pappenheim_1882.jpg

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample to individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation. However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods.

The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with internal and external validity. Case studies lack the proper controls that true experiments contain. As such they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (indeed questioning into the possibility of a separate brain lesion began after HM’s death and dissection of his brain) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So as with all observational methods case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically a very abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity, with case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research is the use of  archival research  which involves analyzing data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [7] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [8] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

• There are several different approaches to observational research including naturalistic observation, participant observation, structured observation, case studies, and archival research.
• Naturalistic observation is used to observe people in their natural setting, participant observation involves becoming an active member of the group being observed, structured observation involves coding a small number of behaviors in a quantitative manner, case studies are typically used to collect in-depth information on a single individual, and archival research involves analysing existing data.
• Describe one problem related to internal validity.
• Describe one problem related to external validity.
• Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

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Qualitative Research: Observation

• Getting Started
• Focus Groups
• Observation
• Case Studies
• Data Collection
• Cleaning Text
• Analysis Tools
• Institutional Review

Participant Observation

Photo: https://slideplayer.com/slide/4599875/

Field Guide

• Participant Observation Field Guide

What is an observation?

A way to gather data by watching people, events, or noting physical characteristics in their natural setting. Observations can be overt (subjects know they are being observed) or covert (do not know they are being watched).

• Researcher becomes a participant in the culture or context being observed.
• Requires researcher to be accepted as part of culture being observed in order for success

Direct Observation

• Researcher strives to be as unobtrusive as possible so as not to bias the observations; more detached.
• Technology can be useful (i.e video, audiorecording).

Indirect Observation

• Results of an interaction, process or behavior are observed (for example, measuring the amount of plate waste left by students in a school cafeteria to determine whether a new food is acceptable to them).

Suggested Readings and Film

• Born into Brothels . (2004) Oscar winning documentary, an example of participatory observation, portrays the life of children born to prostitutes in Calcutta. New York-based photographer Zana Briski gave cameras to the children of prostitutes and taught them photography
• Davies, J. P., & Spencer, D. (2010).  Emotions in the field: The psychology and anthropology of fieldwork experience . Stanford, CA: Stanford University Press.
• DeWalt, K. M., & DeWalt, B. R. (2011).  Participant observation : A guide for fieldworkers .   Lanham, Md: Rowman & Littlefield.
• Reinharz, S. (2011).  Observing the observer: Understanding our selves in field research . NY: Oxford University Press.
• Schensul, J. J., & LeCompte, M. D. (2013).  Essential ethnographic methods: A mixed methods approach . Lanham, MD: AltaMira Press.
• Skinner, J. (2012).  The interview: An ethnographic approach . NY: Berg.
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• Next: Case Studies >>
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4 Types of Observational Research

Observational research typically happens in the users’ home, workplace, or natural environment and not in a lab or controlled setting.

With this research, you can understand how people naturally interact with products and people and the challenges they face.

It can provide inspiration and ideas for opportunities for improvement and innovation.

While it may seem like observation is as simple and uniform as watching and taking notes, there are some subtle differences that can affect the type of data you collect. The role the observer plays forms a continuum from completely removed to completely engaged with the participant.

As you plan your next observational research project and choose the right type for it to be successful, consider the following:

Ethics of Observing . On both ends of the spectrum (a fully detached or fully engaged observer), you face ethical considerations, as those being observed aren’t aware of it. For that reason, most observational research you’ll conduct falls somewhere in between. Think about quantifying the results . While observational research is typically associated with qualitative methods, you can still quantify the occurrences of behaviors or statements made by the participants to get an idea about the frequency of customer attitudes and actions. Improve the reliability and validity of your observations. Consider having multiple independent researchers observe and code their notes. Using multiple observers with differing perspectives (e.g. product manager and researcher) helps identify areas of agreement and disagreement and makes your observational data more trustworthy and reliable.

Keep these caveats in mind as you chose a role for an observational research project. The four types of observational roles we discuss here are based on the distinctions made by the sociologist Raymond Gold in 1958 but apply to any field of research.

1. Complete Observer

This is a detached observer where the researcher is neither seen nor noticed by participants. It’s one way of minimizing the Hawthorne Effect as participants are more likely to act natural when they don’t know they’re being observed.

While this was once considered an objective role for the ethnographer, it’s fallen out of favor because it’s the role most likely to raise ethical questions about possible deception. How would you feel if you found out someone was watching you, but you didn’t know? Sort of Big Brotherish, most likely.

However, in public places like coffee shops, office building lobbies, airports, subway stations, or even public bathrooms the complete observer role may be the only means to collect the type of data you need. And with the ubiquity of video cameras, remote observation remains a viable option.

2. Observer as Participant

Here the researcher is known and recognized by the participants and in many cases, the participants know the research goals of the observer.

There is some interaction with the participants but the interaction is limited. The researcher’s aim is to play a neutral role as much as possible.

This approach is generally used when “following a customer home” to understand how someone uses software products to accomplish goals.

3. Participant as Observer

Here the researcher is fully engaged with the participants. She is more of a friend or colleague than a neutral third party. While there is full interaction with participants, they still known that this is a researcher.

This method is often used when studying remote indigenous populations or inner-city cultures. There’s an anthropologist joke [pdf] that a household photo of a native village consists of a married couple, their parents, and a graduate student.

4. Complete Participant

This is a fully embedded researcher, almost like a spy. Here the observer fully engages with the participants and partakes in their activities.

Participants aren’t aware that observation and research is being conducted, even though they fully interact with the researcher. This has sometimes been referred to as “going native,” in reference to performing indigenous fieldwork.

In customer research, this is like a secret shopper or the show Undercover Boss . The idea is that the best way to understand a type of role, people, or culture is to experience it firsthand. Want to understand Burning Man ? Then go as a complete participant.

Gathering authentic qualitative data can be a challenge in UX research; one way to do so is with observation outside of a controlled environment where participants are more likely to act natural.

There are four types of observational research you can do, ranging from detached observation with no participation on your part (complete observer) to immersing yourself completely in the environment (complete participant). Which you choose depends on your goals, timeframe, and properly balancing the ethical considerations.

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Observation

Observation, as the name implies, is a way of collecting data through observing. This data collection method is classified as a participatory study, because the researcher has to immerse herself in the setting where her respondents are, while taking notes and/or recording. Observation data collection method may involve watching, listening, reading, touching, and recording behavior and characteristics of phenomena.

Observation as a data collection method can be structured or unstructured. In structured or systematic observation, data collection is conducted using specific variables and according to a pre-defined schedule. Unstructured observation, on the other hand, is conducted in an open and free manner in a sense that there would be no pre-determined variables or objectives.

Moreover, this data collection method can be divided into overt or covert categories. In overt observation research subjects are aware that they are being observed. In covert observation, on the other hand, the observer is concealed and sample group members are not aware that they are being observed. Covert observation is considered to be more effective because in this case sample group members are likely to behave naturally with positive implications on the authenticity of research findings.

Advantages of observation data collection method include direct access to research phenomena, high levels of flexibility in terms of application and generating a permanent record of phenomena to be referred to later. At the same time, this method is disadvantaged with longer time requirements, high levels of observer bias, and impact of observer on primary data, in a way that presence of observer may influence the behaviour of sample group elements.

It is important to note that observation data collection method may be associated with certain ethical issues. As it is discussed further below in greater details, fully informed consent of research participant(s) is one of the basic ethical considerations to be adhered to by researchers. At the same time, the behaviour of sample group members may change with negative implications on the level of research validity if they are notified about the presence of the observer.

This delicate matter needs to be addressed by consulting with dissertation supervisor, and commencing the primary data collection process only after ethical aspects of the issue have been approved by the supervisor.

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What is Observational Study Design and Types

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Table of Contents

Most people think of a traditional experimental design when they consider research and published research papers. There is, however, a type of research that is more observational in nature, and it is appropriately referred to as “observational studies.”

There are many valuable reasons to utilize an observational study design. But, just as in research experimental design, different methods can be used when you’re considering this type of study. In this article, we’ll look at the advantages and disadvantages of an observational study design, as well as the 3 types of observational studies.

What is Observational Study Design?

An observational study is when researchers are looking at the effect of some type of intervention, risk, a diagnostic test or treatment, without trying to manipulate who is, or who isn’t, exposed to it.

This differs from an experimental study, where the scientists are manipulating who is exposed to the treatment, intervention, etc., by having a control group, or those who are not exposed, and an experimental group, or those who are exposed to the intervention, treatment, etc. In the best studies, the groups are randomized, or chosen by chance.

Any evidence derived from systematic reviews is considered the best in the hierarchy of evidence, which considers which studies are deemed the most reliable. Next would be any evidence that comes from randomized controlled trials. Cohort studies and case studies follow, in that order.

Cohort studies and case studies are considered observational in design, whereas the randomized controlled trial would be an experimental study.

Let’s take a closer look at the different types of observational study design.

The 3 types of Observational Studies

The different types of observational studies are used for different reasons. Selecting the best type for your research is critical to a successful outcome. One of the main reasons observational studies are used is when a randomized experiment would be considered unethical. For example, a life-saving medication used in a public health emergency. They are also used when looking at aetiology, or the cause of a condition or disease, as well as the treatment of rare conditions.

Case Control Observational Study

Researchers in case control studies identify individuals with an existing health issue or condition, or “cases,” along with a similar group without the condition, or “controls.” These two groups are then compared to identify predictors and outcomes. This type of study is helpful to generate a hypothesis that can then be researched.

Cohort Observational Study

This type of observational study is often used to help understand cause and effect. A cohort observational study looks at causes, incidence and prognosis, for example. A cohort is a group of people who are linked in a particular way, for example, a birth cohort would include people who were born within a specific period of time. Scientists might compare what happens to the members of the cohort who have been exposed to some variable to what occurs with members of the cohort who haven’t been exposed.

Cross Sectional Observational Study

Unlike a cohort observational study, a cross sectional observational study does not explore cause and effect, but instead looks at prevalence. Here you would look at data from a particular group at one very specific period of time. Researchers would simply observe and record information about something present in the population, without manipulating any variables or interventions. These types of studies are commonly used in psychology, education and social science.

Advantages and Disadvantages of Observational Study Design

Observational study designs have the distinct advantage of allowing researchers to explore answers to questions where a randomized controlled trial, or RCT, would be unethical. Additionally, if the study is focused on a rare condition, studying existing cases as compared to non-affected individuals might be the most effective way to identify possible causes of the condition. Likewise, if very little is known about a condition or circumstance, a cohort study would be a good study design choice.

A primary advantage to the observational study design is that they can generally be completed quickly and inexpensively. A RCT can take years before the data is compiled and available. RCTs are more complex and involved, requiring many more logistics and details to iron out, whereas an observational study can be more easily designed and completed.

The main disadvantage of observational study designs is that they’re more open to dispute than an RCT. Of particular concern would be confounding biases. This is when a cohort might share other characteristics that affect the outcome versus the outcome stated in the study. An example would be that people who practice good sleeping habits have less heart disease. But, maybe those who practice effective sleeping habits also, in general, eat better and exercise more.

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• What Is Participant Observation? | Definition & Examples

What Is Participant Observation? | Definition & Examples

Published on March 10, 2023 by Tegan George .

Participant observation is a research method where the researcher immerses themself in a particular social setting or group, observing the behaviors, interactions, and practices of the participants. This can be a valuable method for any research project that seeks to understand the experiences of individuals or groups in a particular social context.

In participant observation, the researcher is called a participant-observer , meaning that they participate in the group’s activities while also observing the group’s behavior and interactions. There is flexibility in the level of participation, ranging from non-participatory (the weakest) to complete participation (the strongest but most intensive.) The goal here is to gain a deep understanding of the group’s culture, beliefs, and practices from an “insider” perspective.

You immerse yourself in this subculture by spending time at skateparks, attending skateboarding events, and engaging with skateboarders. Perhaps you may even learn to skateboard yourself, in order to better understand the experiences of your study participants.

As you observe, you take notes on the behavior, language, norms, and values you witness and also conduct informal unstructured interviews with individual skateboarders to gain further insight into their thoughts and lived experiences.

Typically used in fields like anthropology, sociology, and other social sciences, this method is often used to gather rich and detailed data about social groups or phenomena through ethnographies or other qualitative research .

Table of contents

When to use participant observation, examples of participant observation, how to analyze data from participant observation, advantages and disadvantages of participant observations, other types of research bias, frequently asked questions.

Participant observation is a type of observational study . Like most observational studies, these are primarily qualitative in nature, used to conduct both explanatory research and exploratory research . Participant observation is also often used in conjunction with other types of research, like interviews and surveys .

This type of study is especially well suited for studying social phenomena that are difficult to observe or measure through other methods. As the researcher observes, they typically take detailed notes about their observations and interactions with the group. These are then analyzed to identify patterns and themes using thematic analysis or a similar method.

A participant observation could be a good fit for your research if:

• You are studying subcultures or groups with unique practices or beliefs. Participant observation fosters a deep and intimate understanding of the beliefs, values, and practices of your group or subculture of interest from an insider’s perspective. This can be especially useful when studying marginalized groups or groups that are resistant to observation.
• You are studying complex social interactions . Participant observation can be a powerful tool for studying the complex social interactions that occur within a particular group or community. By immersing yourself in the group and observing these interactions firsthand, you can gain a much more nuanced understanding of how these interactions flow.
• You are studying behaviors or practices that may be difficult to self-report . In some cases, participants may be unwilling or unable to accurately report their own behaviors or practices. Participant observation allows researchers to observe these behaviors directly, allowing for more accuracy in the data collection phase.

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Participant observation is a common research method in social sciences, with findings often published in research reports used to inform policymakers or other stakeholders.

Over the course of several months, you observe and take notes on the social interactions, customs, and beliefs of the community members, conducting informal interviews with individual residents to gain further insight into their experiences and perspectives. Through your observations, you gain a deep understanding of the community’s culture, including its values, traditions, and social hierarchy.

Participant observations are often also used in sociology to study social groups and related phenomena, like group formation, stratification, or conflict resolution.

Through this participant observation, you soon see that the group is highly stratified, with certain individuals occupying positions of social power and others being marginalized or even largely excluded. You also observe patterns of conformity within the group, alongside complex interpersonal dynamics.

Data analysis in participant observation typically involves a step-by-step process of immersion, categorization, and interpretation.

• After finishing up your observations, you read through your field notes or transcripts multiple times in the immersion phase. This helps you reflect on what you studied, and is well paired with conducting data cleansing to ensure everything is clear and correct prior to proceeding.
• You then create categories or themes to organize the data. This helps with identifying patterns, behaviors, and interactions relevant to your research question or study aims. In turn, these categories help you to form a coding system that labels or “tags” the aspects of the data that you want to focus on. These can be specific behaviors, emotions, or social interactions—whatever helps you to identify connections between different elements of your data.
• Next, your data can be analyzed using a variety of qualitative research methods, such as thematic analysis , grounded theory, or discourse analysis using the coded categories you created. This helps you interpret the data and develop further theories. You may also want to use triangulation , comparing data from multiple sources or methods, to bolster the reliability and validity of your findings.
• Lastly, it’s always a good research practice to seek feedback on your findings from other researchers in your field of study, as well as members of the group you studied. This helps to ensure the accuracy and reliability of your analysis and can mitigate some potential research biases .

Participant observations are a strong fit for some research projects, but with their advantages come their share of disadvantages as well.

Advantages of participant observations

• Participant observations allow you to generate rich and nuanced qualitative data —particularly useful when seeking to develop a deep understanding of a particular social context or experience. By immersing yourself in the group, you can gain an unrivaled insider perspective on the group’s beliefs, values, and practices.
• Participant observation is a flexible research method that can be adapted to fit a variety of research questions and contexts. Metrics like level of participation in the group, the length of the observation period, and the types of data collected all can be adjusted based on research goals and timeline.
• Participant observation is often used in combination with other research methods, such as interviews or surveys , to provide a more complete picture of the phenomenon being studied. This triangulation can help to improve the reliability and validity of the research findings, as participant observations are not particularly strong as a standalone method.

Disadvantages of participant observations

• Like many observational studies, participant observations are at high risk for many research biases , particularly on the side of the researcher. Because participant observation involves the researcher immersing themselves in the group being studied, there is a risk that their own biases could influence the data they collect, leading to observer bias . Likewise, the presence of a researcher in the group being studied can potentially influence the behavior of the participants. This can lead to inaccurate or biased data if participants alter their behavior in response to the researcher’s presence, leading to a Hawthorne effect or social desirability bias .
• Participant observations can be very expensive, time-consuming, and challenging to carry out. They often require a long period of time to build trust and gather sufficient data, with the data usually collected in an intensive, in-person manner. Some participant observations take generations to complete, which can make it difficult to conduct studies with limited time or resources.
• Participant observation can raise ethical concerns , requiring measured ethical consideration on the part of the researcher with regard to informed consent, privacy, and confidentiality. The researcher must take care to protect the privacy and autonomy of the participants and ensure that they are not placed at undue risk by the research.

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• Confirmation bias
• Baader–Meinhof phenomenon
• Availability heuristic
• Halo effect
• Hindsight bias
• Ingroup bias
• Outgroup bias
• Perception bias
• Framing effect
• Self-serving bias
• Affect heuristic
• Representativeness heuristic
• Anchoring heuristic
• Primacy bias
• Optimism bias
• Sampling bias
• Ascertainment bias
• Attrition bias
• Self-selection bias
• Survivorship bias
• Nonresponse bias
• Undercoverage bias
• Hawthorne effect
• Observer bias
• Omitted variable bias
• Publication bias
• Conformity bias
• Pygmalion effect
• Recall bias
• Social desirability bias
• Placebo effect
• Actor-observer bias
• Ceiling effect
• Ecological fallacy
• Affinity bias

Ethical considerations in participant observation involve:

• Obtaining informed consent from all participants
• Protecting their privacy and confidentiality
• Ensuring that they are not placed at undue risk by the research, and
• Respecting their autonomy and agency as participants

Researchers should also consider the potential impact of their research on the community being studied and take steps to minimize any negative after-effects.

Participant observation is a type of qualitative research method . It involves active participation on the part of the researcher in the group being studied, usually over a longer period of time.

Other qualitative research methods, such as interviews or focus groups , do not involve the same level of immersion in the research and can be conducted in a less intense manner.

In participant observation , the researcher plays an active role in the social phenomenon, group, or social context being studied. They may move into the community, attend events or activities, or even take on specific roles within the group— fully joining the community over the course of the study. However, the researcher also maintains an observer role here, taking notes on the behavior and interactions of the participants to draw conclusions and guide further research.

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Observational studies and their utility for practice

Julia fm gilmartin-thomas.

2 Research Department of Practice and Policy, University College London, School of Pharmacy, London

1 Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne

Ingrid Hopper

Randomised controlled clinical trials are the best source of evidence for assessing the efficacy of drugs. Observational studies provide critical descriptive data and information on long-term efficacy and safety that clinical trials cannot provide, at generally much less expense.

Observational studies include case reports and case series, ecological studies, cross-sectional studies, case-control studies and cohort studies. New and ongoing developments in data and analytical technology, such as data linkage and propensity score matching, offer a promising future for observational studies. However, no study design or statistical method can account for confounders and bias in the way that randomised controlled trials can.

Clinical registries are gaining importance as a method to monitor and improve the quality of care in Australia. Although registries are a form of cohort study, clinical trials can be incorporated into them to exploit the routine follow-up of patients to capture relevant outcomes.

Introduction

Observational studies involve the study of participants without any forced change to their circumstances, that is, without any intervention. 1 Although the participants’ behaviour may change under observation, the intent of observational studies is to investigate the ‘natural’ state of risk factors, diseases or outcomes. For drug therapy, a group of people taking the drug can be compared to people not taking the drug.

The main types of observational studies used in health research, their purpose and main strengths and limitations are shown in the Table . 2 - 8

Their purpose may be descriptive, analytical or both.

• Descriptive studies are primarily designed to describe the characteristics of a studied population.
• Analytical studies seek to address cause-and-effect questions.

Case reports and case series

Case reports and case series are strictly speaking not studies. However, they serve a useful role in describing new or notable events in detail. These events often warrant further formal investigation. Examples include reports of unexpected benefits or adverse events, such as a case report describing the use of high-dose quetiapine in treatment-resistant schizophrenia after intolerance to clozapine developed 9 and a case report of a medication error involving lookalike packaging. 10

Ecological studies

Ecological studies are based on analysis of aggregated data at group levels (for example populations), and do not involve data on individuals. These data can be analysed descriptively, but not definitively for causation. Typical examples include studies that examine patterns of drug use over time. One example is the comparison of the use of non-steroidal anti-inflammatory drugs and COX-2 inhibitors in Australia and Canada. 11 Sometimes ecological studies describe associations between drugs and outcomes, such as changes in the rates of upper gastrointestinal haemorrhage after the introduction of COX-2 inhibitors. 12 However, because individual-level data are not presented, causality is at best only implied in ecological studies. The 'ecological fallacy' refers to the error of assuming that associations observed in ecological studies are causal when they are not.

Cross-sectional studies

Cross-sectional studies collect data at a single point in time for each single individual, but the actual data collection may take place over a period of time or on more than one occasion. There is no longitudinal follow-up of individuals. Cross-sectional studies represent the archetypal descriptive study. 1 Typically, they provide a profile of a population of interest, which may be broad, like the Australian Health Survey undertaken intermittently by the Australian Bureau of Statistics, 13 or focused on specific populations, such as older Australians. 14

Case-control studies

Case-control studies focus on determining risk factors for an outcome of interest (such as a disease or a drug’s adverse effect) that has already occurred. 5

• those who already have the outcome (cases)
• those who do not have the outcome (controls), who are often matched to the cases to make them similar and reduce bias.

Second, data on previous exposure to selected risk factors are collected and compared to see if these risk factors are more (or less) common among cases versus controls. Case-control studies are useful for studying the risk factors of rare outcomes, as there is no need to wait for these to occur. Multiple risk factors can be studied, but each case-control study can involve only one outcome. 5 One example explored the relationship between the use of antiplatelet and anticoagulant drugs (risk factor) and the risk of hospitalisation for bleeding (outcome) in older people with a history of stroke. 15 Another case-control study explored the risk factors for the development of flucloxacillin-associated jaundice (outcome). 16

Cohort studies

Cohort studies compare outcomes between or among subgroups of participants defined on the basis of whether or not they are exposed to a particular risk or protective factor (defined as an exposure). They provide information on how these exposures are associated with changes in the risk of particular downstream outcomes. Compared to case-control studies, cohort studies take individuals with exposures and look for outcomes, rather than taking those with outcomes and looking for exposures. Cohort studies are longitudinal, that is they involve follow-up of a cohort of participants over time. This follow-up can be prospective or retrospective. Retrospective cohort studies are those for which follow-up has already occurred. They are typically used to estimate the incidence of outcomes of interest, including the adverse effects of drugs.

Cohort studies provide a higher level of evidence of causality than case-control studies because temporality (the explicit time relationship between exposures and outcomes) is preserved. They also have the advantage of not being limited to a single outcome of interest. Their main disadvantage, compared to case-control studies, has been that longitudinal data are more expensive and time-consuming to collect. However, with the availability of electronic data, it has become easier to collect longitudinal data.

One prospective cohort study explored the relationship between the continuous use of antipsychotic drugs (exposure) and mortality (outcome) and hospitalisation (outcome) in older people. 17 In another older cohort, a retrospective study was used to explore the relationship between long-term treatment adherence (exposure) and hospital readmission (outcome). 18

Observational studies versus randomised controlled trials

Compared to randomised controlled trials, observational studies are relatively quick, inexpensive and easy to undertake. Observational studies can be much larger than randomised controlled trials so they can explore a rare outcome. They can be undertaken when a randomised controlled trial would be unethical. However, observational studies cannot control for bias and confounding to the extent that clinical trials can. Randomisation in clinical trials remains the best way to control for confounding by ensuring that potential confounders (such as age, sex and comorbidities) are evenly matched between the groups being compared. In observational studies, adjustment for potential confounders can be undertaken, but only for a limited number of confounders, and only those that are known. Randomisation in clinical trials also minimises selection bias, while blinding (masking) controls for information bias. Hence, for questions regarding drug efficacy, randomised controlled trials provide the most robust evidence.

New and upcoming developments

New methods of analysis and advances in technology are changing the way observational studies are performed.

Clinical registries

Clinical registries are essentially cohort studies, and are gaining importance as a method to monitor and improve the quality of care. 19 These registries systematically collect a uniform longitudinal dataset to evaluate specific outcomes for a population that is identified by a specific disease, condition or exposure. This allows for the identification of variations in clinical practice 20 and benchmarking across practitioners or institutions. These data can then be used to develop initiatives to improve evidence-based care and patient outcomes. 21

An example of a clinical registry in Australia is the Australian Rheumatology Association Database, 22 which collects data on the biologic disease-modifying antirheumatic drugs used for inflammatory arthritis. Clinical data from treating specialists are combined with patient-reported quality of life data and linked to national databases such as Medicare and the National Death Index. This registry has provided insight into the safety and efficacy of drugs and their effect on quality of life. It was used by the Pharmaceutical Benefits Advisory Committee to assess cost-effectiveness of these drugs. 23

Another example is the Haemostasis Registry. It was used to determine the thromboembolic adverse effects of off-label use of recombinant factor VII. 24

Clinical registries can also be used to undertake clinical trials which are nested within the registry architecture. Patients within a registry are randomised to interventions and comparators of interest. Their outcome data are then collected as part of the routine operation of the registry. The key advantages are convenience, reduced costs and greater representativeness of registry populations as opposed to those of traditional clinical trials.

One of the first registry-based trials was nested within the SWEDEHEART registry. 25 This prospectively examined manual aspiration of thrombus at the time of percutaneous coronary intervention in over 7000 patients. 26 The primary endpoint of all-cause mortality was ascertained through linkage to another Swedish registry. The cost of the trial was estimated to be US$400 000, which was a fraction of the many millions that a randomised controlled trial would have cost.

Propensity score matching

Even without randomising people within cohorts, methods have emerged in recent years that allow for less biased comparisons of two or more subgroups. Propensity score matching is a way to assemble two or more groups for comparison so that they appear like they had been randomised to an intervention or a comparator. 27 In short, the method involves logistic regression analyses to determine the likelihood (propensity) of each person within a cohort being on the intervention, and then matching people who were on the intervention to those who were not on the basis of propensity scores. Outcomes are then compared between the groups. Propensity score analysis of a large cohort of patients with relapsing remitting multiple sclerosis found that natalizumab was superior to interferon beta and glatiramer acetate in terms of improved outcomes. 28

Data technology

Increasing sophistication in techniques for data collection will lead to ongoing improvements in the capacity to undertake observational studies (and also clinical trials). Data linkage already offers a convenient way to capture outcomes, including retrospectively. However, ethical considerations must be taken into account, such as the possibility that informed consent might be required before linking data. Machine learning will soon allow for easy analyses of unstructured text (such as free text entries in an electronic prescription). 29 Patient-reported outcome measures are important and in future will be greatly facilitated by standardised, secure hardware and software platforms that allow for their capture, processing and analyses.

While clinical trials remain the best source of evidence regarding the efficacy of drugs, observational studies provide critical descriptive data. Observational studies can also provide information on long-term efficacy and safety that is usually lacking in clinical trials. New and ongoing developments in data and analytical technology offer a promising future for observational studies in pharmaceutical research.

Conflict of interest: Julia Gilmartin-Thomas is a Dementia research development fellow with the National Health and Medical Research Council (NHMRC) - Australian Research Council (ARC). Ingrid Hopper is supported by an NHMRC Early Career Fellowship.

The impact of different types of feedback on pre-service teachers’ microteaching practice and perceptions

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• Mengke Wang 1 ,
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• Yawen Shi 1 &
• Zengzhao Chen 1  

Feedback plays an indispensable role in pre-service teachers’ microteaching practice. It provides essential information about their microteaching performance, which is of great significance in their reflection and improvement. As AI and teaching analytics advance, feedback is no longer exclusively human-generated. AI technologies are increasingly capable of delivering feedback on microteaching performance. Yet, the effects of differing feedback types on the microteaching practices of pre-service teachers are not well documented. This study examines the impact of three types of feedback—observation-based, teaching analytics-based, and combined (a combination of both)—on pre-service teachers’ microteaching performance, scope of reflection, perceived usefulness, and satisfaction through an experimental research design. Sixty-five pre-service teachers voluntarily participated and were randomly assigned to three groups: observation-based feedback ( N  = 21), teaching analytics-based feedback ( N  = 23), and combined feedback ( N  = 21). The findings indicate that combined feedback was most effective in enhancing pre-service teachers’ scope of teaching reflection, perceived usefulness of feedback, and satisfaction, but not on microteaching performance. However, when only teaching analytics-based feedback was provided, pre-service teachers perceived it as least useful and were least satisfied. The study discusses the implications of different types of feedback in teacher education.

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Acknowledgements

We would like to express our sincere gratitude to all the colleagues, teachers, and pre-service teachers who participated in this study.

This work was supported by the National Natural Science Foundation of China (Project No. 62077022), Central China Normal University (Project No. CCNU24ai013), the Postgraduate Education Innovation Funding Project of Central China Normal University (Project No. 30106230470), and the China Scholarship Council (CSC202306770066).

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Mengke Wang, Taotao Long, Yawen Shi & Zengzhao Chen

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Wang, M., Long, T., Li, N. et al. The impact of different types of feedback on pre-service teachers’ microteaching practice and perceptions. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-13024-z

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