distinguish between a case study and a survey

Distinguishing Between Case Study & Survey Methods

Key Difference – Case Study vs Survey

When carrying out research, case studies and surveys are two methods used by researchers. Although both are used to collect information, there is a key difference between a case study and a survey. A case study involves researching an individual, group, or specific situation in-depth, usually over a long period of time. On the other hand, a survey involves gathering data from an entire population or a very large sample to understand opinions on a specific topic. The main difference between the two methods is that case studies produce rich, descriptive data, while surveys do not; instead, the data collected from surveys is more statistically significant.

Key Takeaways

• Case studies involve in-depth research of an individual, group, or specific situation, while surveys gather data from an entire population or a large sample.
• Case studies produce rich, descriptive data, while surveys produce data that is more statistically significant.
• Case studies are used in qualitative research, while surveys are mostly used in quantitative research.

What is a Case Study?

A case study refers to an in-depth study in which an individual, group, or a particular situation is studied. This is used in both natural and social sciences. In the natural sciences, a case study can be used to validate a theory or even a hypothesis. In the social sciences, case studies are used extensively to study human behavior and comprehend various social aspects. For example, in psychology, case studies are conducted to comprehend individual behavior. In such cases, the researcher records the entire history of the individual so that it enables him to identify various patterns of behavior. One of the classic examples of a case study is Sigmund Freud’s study of Anna O.

Case studies typically produce rich descriptive data. However, they cannot be used to provide generalizations on an entire population since the sample of a case study is usually limited to a single individual or a few individuals. Various research techniques, such as interviews, direct and participatory observation, and documents can be used for case studies.

What is a Survey?

A survey refers to research where data is gathered from an entire population or a very large sample to understand the opinions on a particular matter. In modern society, surveys are often used in politics and marketing. For example, imagine a situation where an organization wishes to understand the opinions of consumers on their latest product. Naturally, the organization would conduct a survey to comprehend the opinions of the consumer.

One of the most powerful research techniques used for surveys is the questionnaire. For this, the researcher creates a set of questions on the topic for which he will gather information from the participants. Unlike case studies, the data gathered from surveys is not very descriptive. Instead, they are statistically significant.

What is the difference between Case Study and Survey?

Definitions of Case Study and Survey: Case Study: A case study refers to an in-depth study in which an individual, group, or a particular situation is studied. Survey: A survey refers to research where data is gathered from an entire population or a very large sample to understand the opinions on a particular matter. Characteristics of Case Study and Survey: Research Type: Case Study: Case studies are used in qualitative research. Survey: Surveys are mostly used in quantitative research. Data: Case Study: Case studies produce rich in-depth data. Survey: Surveys produce numerical data. Sample: Case Study: For a case study, a relatively small population is chosen. This can vary from a few individuals to groups. Survey: For a survey, a large population can be used as the sample.

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Case Study vs. Survey: What's the Difference?

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Difference Between Case Study and Survey

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CC0006 Basics of Report Writing

Structure of a report (case study, literature review or survey).

• Structure of report (Site visit)
• Citing Sources
• Tips and Resources

The information in the report has to be organised in the best possible way for the reader to understand the issue being investigated, analysis of the findings and recommendations or implications that relate directly to the findings. Given below are the main sections of a standard report. Click on each section heading to learn more about it.

• Tells the reader what the report is about
• Informative, short, catchy

Example - Sea level rise in Singapore : Causes, Impact and Solution

The title page must also include group name, group members and their matriculation numbers.

Content s Page

• Has headings and subheadings that show the reader where the various sections of the report are located
• Written on a separate page
• Includes the page numbers of each section
• Briefly summarises the report, the process of research and final conclusions
• Provides a quick overview of the report and describes the main highlights
• Short, usually not more than 150 words in length
• Mention briefly why you choose this project, what are the implications and what kind of problems it will solve

Usually, the abstract is written last, ie. after writing the other sections and you know the key points to draw out from these sections. Abstracts allow readers who may be interested in the report to decide whether it is relevant to their purposes.

Introduction

• Discusses the background and sets the context
• Introduces the topic, significance of the problem, and the purpose of research
• Gives the scope ie shows what it includes and excludes

In the introduction, write about what motivates your project, what makes it interesting, what questions do you aim to answer by doing your project. The introduction lays the foundation for understanding the research problem and should be written in a way that leads the reader from the general subject area of the topic to the particular topic of research.

Literature Review

• Helps to gain an understanding of the existing research in that topic
• To develop on your own ideas and build your ideas based on the existing knowledge
• Prevents duplication of the research done by others

Search the existing literature for information. Identify the data pertinent to your topic. Review, extract the relevant information for eg how the study was conducted and the findings. Summarise the information. Write what is already known about the topic and what do the sources that you have reviewed say. Identify conflicts in previous studies, open questions, or gaps that may exist. If you are doing

• Case study - look for background information and if any similar case studies have been done before.
• Literature review - find out from literature, what is the background to the questions that you are looking into
• Site visit - use the literature review to read up and prepare good questions before hand.
• Survey - find out if similar surveys have been done before and what did they find?

Keep a record of the source details of any information you want to use in your report so that you can reference them accurately.

Methodology

Methodology is the approach that you take to gather data and arrive at the recommendation(s). Choose a method that is appropriate for the research topic and explain it in detail.

In this section, address the following: a) How the data was collected b) How it was analysed and c) Explain or justify why a particular method was chosen.

Usually, the methodology is written in the past tense and can be in the passive voice. Some examples of the different methods that you can use to gather data are given below. The data collected provides evidence to build your arguments. Collect data, integrate the findings and perspectives from different studies and add your own analysis of its feasibility.

• Explore the literature/news/internet sources to know the topic in depth
• Give a description of how you selected the literature for your project
• Compare the studies, and highlight the findings, gaps or limitations.
• An in-depth, detailed examination of specific cases within a real-world context.
• Enables you to examine the data within a specific context.
• Examine a well defined case to identify the essential factors, process and relationship.
• Write the case description, the context and the process involved.
• Make sense of the evidence in the case(s) to answer the research question
• Gather data from a predefined group of respondents by asking relevant questions
• Can be conducted in person or online
• Why you chose this method (questionnaires, focus group, experimental procedure, etc)
• How you carried out the survey. Include techniques and any equipment you used
• If there were participants in your research, who were they? How did you select them and how may were there?
• How the survey questions address the different aspects of the research question
• Analyse the technology / policy approaches by visiting the required sites
• Make a detailed report on its features and your understanding of it

Results and Analysis

• Present the results of the study. You may consider visualising the results in tables and graphs, graphics etc.
• Analyse the results to obtain answer to the research question.
• Provide an analysis of the technical and financial feasibility, social acceptability etc

Discussion, Limitation(s) and Implication(s)

• Discuss your interpretations of the analysis and the significance of your findings
• Explain any new understanding or insights that emerged as a result of your research
• Consider the different perspectives (social, economic and environmental)in the discussion
• Explain the limitation(s)
• Explain how could what you found be used to make a difference for sustainability

Conclusion and Recommendations

• Summarise the significance and outcome of the study highlighting the key points.
• Come up with alternatives and propose specific actions based on the alternatives
• Describe the result or improvement it would achieve
• Explain how it will be implemented

Recommendations should have an innovative approach and should be feasible. It should make a significant difference in solving the issue under discussion.

• List the sources you have referred to in your writing
• Use the recommended citation style consistently in your report

Appendix (if necessary/any)

Include any material relating to the report and research that does not fit in the body of the report, in the appendix. For example, you may include survey questionnaire and results in the appendix.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Research Writing and Analysis

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• Problem Statement
• Purpose Statement
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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• Last Updated: May 29, 2024 8:05 AM
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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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2.2 Approaches to Research

Learning objectives.

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

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

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

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

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

Clinical or Case Studies

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

Link to Learning

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

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

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

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

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

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

Naturalistic Observation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Archival Research

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

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

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

Longitudinal and Cross-Sectional Research

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

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

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

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

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

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

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

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Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

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Understanding the difference between survey and experiment: a student's guide.

In the realm of academic research, surveys and experiments are two fundamental methodologies that students often encounter. Understanding the difference between these two approaches is crucial for designing effective studies and interpreting data accurately. This guide will delve into the essentials of survey and experimental research, compare their applications, and provide practical advice for integrating them into academic projects.

Key Takeaways

• Survey research is a method for collecting data from a predefined group of respondents to gain information and insights on various topics of interest.
• Experiments involve manipulating one variable to determine if changes in one variable cause changes in another variable, establishing a cause-and-effect relationship.
• Surveys are typically used when collecting a large amount of data from a large sample size, while experiments are used when looking to control and measure the impact of specific variables.
• Both surveys and experiments have their own set of advantages and limitations, and the choice between them should be based on the research question and objectives.
• Combining surveys and experiments can provide a more comprehensive understanding of the research topic and can lead to more robust and actionable conclusions.

Fundamentals of Survey Research

Defining survey research and its purpose.

As you delve into the world of research, you'll find that survey research is a fundamental tool for gathering information. Surveys are primary research tools that provide data as part of overall research strategies, critical to getting the answers you need. At its core, survey research involves the collection of information from a sample of individuals through their responses to questions. This method is standardized and systematic , ensuring that the data collected is reliable and can be generalized to a larger population.

When considering survey research, it's important to understand its purpose. Surveys are most effective when you aim to collect brief and straightforward data points from a large, representative sample. They can be used to measure various elements within a population, from customer feedback to academic research. Here are some key reasons for using surveys:

• To gather qualitative and emotional feedback
• To collect comprehensive data efficiently
• To understand customer or public opinion

Remember, the choice of using a survey ultimately depends on the specific needs and constraints of your research project. By defining clear objectives and understanding the strengths of survey methodology, you can ensure that your research yields valuable insights.

Types of Surveys: Cross-Sectional and Longitudinal

When you embark on survey research, you'll encounter two primary types: cross-sectional and longitudinal studies. Cross-sectional surveys are snapshots, capturing data at a single point in time from a selected sample. They are particularly useful for assessing the current state of affairs, such as public opinion or consumer preferences. In contrast, longitudinal surveys are designed to track changes over time, collecting data from the same subjects at multiple intervals. This approach is invaluable for observing trends, patterns, and the long-term effects of interventions.

Choosing between these types hinges on your research objectives. If you aim to understand how variables may correlate at a specific time, a cross-sectional study might suffice. However, if you're interested in how relationships between variables evolve, a longitudinal survey will be more appropriate. Below is a list highlighting the distinct features of each type:

Cross-sectional surveys:

• Provide a quick overview of a situation
• Cost-effective and less time-consuming
• Ideal for descriptive research

Longitudinal surveys:

• Allow for the observation of developments and changes
• Can identify causal relationships
• Require more resources and commitment

Remember, the choice of survey type will significantly influence your study's insights and conclusions. Tools and resources, such as thesis worksheets and action plans , can assist in managing your data and maintaining the integrity of your research design.

Advantages and Limitations of Survey Methodology

When you embark on survey research, you're choosing a path with both significant benefits and notable challenges. Surveys are praised for their ease of implementation and the ability to collect large volumes of data quickly and at low cost. This is particularly true for remote data collection, where geographical constraints are virtually eliminated. The ability to reach a wide audience swiftly is a key advantage of surveys.

However, surveys come with limitations that must be carefully considered. They provide sampled data, not complete data, which means that the results are based on a subset of the population rather than the entire group. This can lead to survey fatigue , reducing response rates and potentially skewing the data. Moreover, the honesty and intention of respondents can impact the accuracy of the results, and unintentional biases in survey design can lead to incorrect conclusions.

Here's a quick overview of the advantages and disadvantages of surveys:

• Easy to implement
• Fast data collection turnaround
• Effective for collecting large volumes of data
• Suitable for remote data collection

Disadvantages:

• Provides sampled data, not complete data
• Potential for survey fatigue
• Responses may not be entirely objective
• Risk of biases affecting accuracy

Designing Effective Surveys

Crafting clear and unbiased questions.

When you're tasked with crafting clear and unbiased questions , it's crucial to focus on the precision and neutrality of your language. The goal is to elicit responses that are reflective of the respondents' true opinions and experiences, not influenced by the wording of the question. To achieve this, you should use language that is neutral, natural, and clear , avoiding any jargon that might confuse respondents or lead to misinterpretation.

Here are some best practices to consider:

• Ensure each question focuses on a single topic to avoid confusion.
• Keep questions brief; longer questions can be more difficult to comprehend and may introduce bias.
• Avoid double-barrelled questions that ask about two things at once, as they can be answered in multiple ways.
• Use closed-ended questions when looking for specific, quantifiable data.

Remember, the validation of your survey questions is as important as their formulation. Testing your survey with a small group before full deployment can help identify issues with question clarity and structure. By adhering to these guidelines, you can minimize bias and maximize the reliability of your survey data.

Choosing the Right Survey Medium

Selecting the appropriate survey medium is crucial for the success of your research. The medium you choose should align with your research objectives, target population, and available resources. For instance, online surveys are cost-effective and can reach a broad audience quickly, making them ideal for large-scale quantitative research. In contrast, face-to-face interviews allow for deeper exploration of responses, suitable for qualitative insights.

When considering your options, reflect on the accessibility of the medium to your intended participants. A survey that is not easily accessible can lead to low response rates and potential biases in your data. Here are some common survey mediums and their attributes:

• Online : Wide reach, cost-effective, quick turnaround
• Telephone : Personal touch, higher response rates
• Mail : Tangible, can reach non-internet users
• In-person : Detailed responses, high engagement

Remember, the medium you select can also impact the quality of the data collected. It's essential to weigh the advantages and disadvantages of each option. For example, while online surveys offer tools for fast data collection, they may also lead to survey fatigue. On the other hand, in-person interviews can provide rich qualitative data but may be more time-consuming and costly. Ultimately, your choice should be informed by the specific needs and constraints of your research project.

Ensuring Ethical Standards in Survey Research

As you embark on survey research, it's imperative to uphold the highest ethical standards. Ethical considerations are not just a formality; they are central to the integrity and validity of your research. When designing your survey, you must ensure voluntary participation and obtain informed consent , guaranteeing that respondents are fully aware of the survey's purpose and their rights. Anonymity and confidentiality are also crucial to protect the identity and privacy of participants, especially when sensitive data is involved.

To adhere to these ethical principles, consider the following steps:

• Clearly communicate the social and clinical value of your research to participants.
• Assess and ensure the scientific validity of your survey.
• Employ fair subject selection to avoid biases.
• Evaluate the risk-benefit ratio to minimize potential harm.
• Maintain independence in data analysis and reporting.

Remember, ethical research is not only about following guidelines but also about respecting the dignity and rights of your participants. Tools and resources are available to assist you in maintaining research integrity , such as worksheets and templates that emphasize transparent reporting of results. Always be vigilant of the ethical questions that may arise and be prepared to address them responsibly.

Principles of Experimental Research

Understanding controlled experiments.

In the realm of experimental research, a controlled experiment is a cornerstone methodology that allows you to explore cause-and-effect relationships. By manipulating one or more independent variables , researchers can observe the impact on dependent variables, while controlling for extraneous factors. This rigorous approach ensures that the outcomes observed are indeed due to the manipulation of the independent variable and not some other unseen variable.

To conduct a controlled experiment effectively, you must follow a structured process:

• Identify the independent and dependent variables.
• Establish a control group that does not receive the experimental treatment.
• Randomly assign participants to groups to prevent selection bias.
• Apply the treatment to the experimental group(s) while keeping all other conditions constant.
• Collect and analyze the data to determine the effect of the independent variable.

Remember, the goal is to achieve reliable and valid results that contribute to the body of knowledge in your field. As you embark on this journey, resources like the ' Experimental Research Roadmap ' can provide guidance, ensuring that your study adheres to the highest standards of academic rigor.

Randomization and Its Role in Reducing Bias

In your journey to understand experimental research, you'll find that randomization is a cornerstone of robust study design. Randomization serves as a powerful tool to balance treatment groups , ensuring that each participant has an equal chance of being assigned to any given condition. This process helps to mitigate the influence of confounding variables—those pesky factors that could otherwise skew your results.

By randomizing participants, you effectively remove the effect of extraneous variables , such as age or injury history, and minimize bias associated with treatment assignment. The benefits of this technique are manifold; it balances the groups with respect to baseline variability and both known and unknown confounding factors, thus eliminating selection bias. Moreover, randomization enhances the quality of evidence-based studies by minimizing the selection bias that could affect outcomes.

Consider the following points when implementing randomization in your experiment:

• It ensures each participant has an equal chance of assignment to any group.
• It minimizes the impact of confounding variables.
• It increases the reliability of your results.
• It is a key factor in the ability to generalize findings to a larger population.

Interpreting Results from Experimental Studies

Once you've conducted your experiment and gathered the data, the next critical step is to interpret the results. Interpreting the findings involves comparing them to your initial hypotheses and understanding what they mean in the context of your research. It's essential to reiterate the research problem and assess whether the data support or refute your predictions.

When analyzing the results, look for trends, compare groups, and examine relationships among variables. Unexpected or statistically insignificant findings should not be disregarded; instead, they can provide valuable insights. For instance, if you encounter unexpected data , it's crucial to report these events and explain how they were handled during the analysis, ensuring the validity of your study is maintained.

Discussing the implications of your results is where you highlight the key findings and their significance. Here, you can articulate how your results fill gaps in understanding the research problem. However, be mindful of any limitations or unavoidable bias in your study and discuss how these did not inhibit effective interpretation of the results. Below is a structured approach to interpreting experimental data:

• Reiterate the research problem and compare findings with the research questions.
• Describe trends, group comparisons, and variable relationships.
• Highlight unexpected findings and their handling.
• Discuss the implications and significance of the results.
• Acknowledge limitations and biases, and their impact on interpretation.

Comparing Surveys and Experiments

When to use surveys vs. experiments.

Choosing between a survey and an experiment hinges on the nature of your research question and the type of data you need. Surveys are ideal for gathering a large volume of responses on attitudes, behaviors, or perceptions, allowing you to generalize findings to a broader population. They are particularly useful when you aim to describe characteristics of a large group or when you need to collect data at one point in time or track changes over time.

Experiments, on the other hand, are the gold standard for establishing cause-and-effect relationships. By manipulating one or more variables and controlling external factors, you can infer causality with greater confidence. Experiments are indispensable when testing hypotheses under controlled conditions is necessary to isolate the effects of specific variables.

Here's a quick guide to help you decide:

• Use a survey when you need to understand the prevalence of certain views or behaviors in a population.
• Opt for an experiment when you need to determine if one variable affects another in a controlled setting.
• Consider the resources available, including time, budget, and expertise, as experiments often require more of each.
• Reflect on ethical considerations; surveys may be less intrusive, but informed consent is crucial in both methods.

In summary, surveys are powerful tools for descriptive research, while experiments excel in explanatory research. Your choice should align with your research objectives, the questions you seek to answer, and the level of evidence required.

Impact of Research Design on Data Quality

The integrity of your research findings hinges on the quality of your research design. A robust design ensures that the conclusions drawn are valid and reliable. The quality of research designs can be defined in terms of four key design attributes : internal validity, external validity, construct validity, and statistical validity. These attributes are critical in determining whether the results can be generalized to other settings (external validity), if the study measures what it intends to (construct validity), and if the statistical conclusions are accurate (statistical validity).

When you embark on your master thesis research , choosing the right design is paramount. It involves identifying research gaps and collecting reliable data to contribute to existing knowledge. A poor design can lead to incorrect conclusions, undermining the value of your research. Conversely, a thoughtful and well-executed design bolsters the credibility of your findings.

Here are some considerations to keep in mind when designing your research:

• Ensure clarity and objectivity in your research questions.
• Select a sample size that is representative of the population.
• Employ appropriate randomization techniques to reduce bias.
• Plan for replication to test the study's reliability.

Remember, conducting organizational research via online surveys and experiments offers advantages in data collection, but it also requires careful attention to design to maintain data quality.

Combining Surveys and Experiments for Comprehensive Insights

When you aim to achieve a holistic understanding of your research topic, combining surveys and experiments can be a powerful strategy. Surveys allow you to gather a broad range of data from a large sample, providing a snapshot of attitudes, behaviors, or characteristics. Experiments, on the other hand, enable you to establish cause-and-effect relationships through controlled conditions and manipulation of variables.

By integrating both methods , you can enrich your quantitative findings with the depth of qualitative insights. This mixed-methods approach not only enhances the robustness of your data but also allows you to explore different dimensions of your research question.

Consider the following steps to effectively combine surveys and experiments:

• Begin with a survey to identify patterns and generate hypotheses.
• Use experimental research to test these hypotheses under controlled conditions.
• Re-administer the survey post-experiment to measure changes and gather additional feedback.

This sequential application ensures that each method informs and complements the other, leading to more comprehensive and reliable conclusions . Remember, the key to a successful combination is to maintain clarity and consistency in your research objectives throughout the process.

Applying Survey and Experimental Research in Academic Projects

Selecting appropriate methods for thesis research.

When embarking on your thesis, the choice between survey and experimental research hinges on the nature of your research question. Surveys are ideal for descriptive research , where the goal is to capture the characteristics of a population at a specific point in time. In contrast, experiments are suited for explanatory research that seeks to establish causal relationships through manipulation and control of variables.

To select the method that best aligns with your study, consider the following points:

• Define the purpose of your research: Is it exploratory, descriptive, explanatory, or evaluative?
• Determine the nature of the data required: Do you need quantitative measurements or qualitative insights?
• Assess the feasibility: What resources and time are available to you?

Remember, the methodology you choose will significantly impact the quality of your data and the credibility of your findings. It's essential to weigh the advantages and limitations of each method in the context of your research objectives.

Case Studies: Successful Survey and Experimental Designs

In your academic journey, understanding how to effectively design and implement research is crucial. Case studies of successful survey and experimental designs provide invaluable insights into the practical application of these methodologies. For instance, Sage Publications highlights the complexity of developing research designs for case studies, emphasizing the lack of a comprehensive catalog of research methods tailored to case studies. This underscores the importance of customizing your approach to fit the unique aspects of your research question.

When examining various case studies, you'll notice a common theme: the in-depth, multi-faceted exploration of complex issues within their real-life settings , as noted by BMC Medical Research Methodology. This approach allows for a rich understanding of the phenomena under study. To illustrate, consider the following bulleted list of key elements derived from successful research designs:

• A clear, well-defined research question
• Thoughtful selection of research methods
• Rigorous data collection and management techniques
• Ethical considerations and participant consent
• Detailed analysis and interpretation of data

These elements are echoed across various resources, including websites offering thesis resources, worksheets, and articles on interview research techniques and data management . By studying these case studies, you can glean strategies for excelling in your chosen field of study, translating complex academic procedures into actionable steps .

Translating Research Findings into Actionable Conclusions

Once you've navigated the complexities of your research and arrived at meaningful conclusions, the next critical step is to translate these findings into practical applications. Understanding the implications of your study is essential for making a tangible impact. Begin by synthesizing the key findings without delving into statistical minutiae; provide a narrative that captures what you've learned and how it adds to the existing body of knowledge.

Consider the broader context of your research and how it can inform policy decisions or professional practices. For instance, if your study identifies effective teaching strategies, these can be translated into recommendations for educational curriculum development. It's crucial to understand the problem first to ensure that your conclusions address real-world challenges effectively.

To ensure your research has a lasting influence, follow these steps:

• Reiterate the research problem and align your findings with the initial research questions.
• Discuss any unexpected trends or statistically insignificant findings and their implications.
• Acknowledge limitations and suggest areas for future research to address gaps in the literature.

Remember, the goal is not just to add to the academic conversation but to drive change and foster improvement in the relevant field. By effectively disseminating and translating your research into clinical practice or business insights, you contribute to the advancement of knowledge and the betterment of society.

Delving into the intricacies of survey and experimental research can significantly enhance the quality and impact of your academic projects. By applying these methodologies, you can uncover valuable insights and contribute to the body of knowledge in your field. To learn more about effectively integrating these research techniques into your work, visit our website . We provide comprehensive guides and resources to support your academic endeavors.

In summary, understanding the distinction between surveys and experiments is crucial for students embarking on research projects. Surveys are invaluable for collecting data from large populations, offering insights through a series of questions and enabling the analysis of trends and patterns within a sample. Experiments, on the other hand, allow researchers to establish causal relationships by manipulating variables and observing the outcomes in a controlled setting. Both methods have their unique advantages and limitations, and the choice between them should be guided by the research objectives, the nature of the hypothesis, and the practical constraints of the study. By grasping the differences and applications of each method, students can design more effective studies and contribute meaningful findings to their respective fields.

Frequently Asked Questions

What is the main difference between a survey and an experiment.

A survey is a research method used to collect data from a sample of individuals through their responses to questions. An experiment involves manipulating one variable to determine its effect on another, establishing a cause-and-effect relationship under controlled conditions.

When should I use a survey in my research?

Surveys are most appropriate when you need to collect data from a large group of people to understand trends, attitudes, or behaviors. They are useful for gathering both qualitative and quantitative information.

What are the advantages of experimental research over surveys?

Experimental research allows you to control variables and establish causality, making it possible to determine the effect of one variable on another. This level of control is not possible in survey research, which can only show correlations.

Can I combine surveys and experiments in my research project?

Yes, combining surveys and experiments can provide comprehensive insights. Surveys can gather preliminary data or post-experiment feedback, while experiments can test hypotheses generated from survey results.

How can I ensure my survey questions are unbiased?

To ensure unbiased survey questions, avoid leading or loaded language, ensure questions are clear and straightforward, offer balanced answer choices, and pretest your survey with a small sample to identify potential biases.

What is randomization in experimental research, and why is it important?

Randomization is the process of randomly assigning participants to different treatment groups in an experiment. It is crucial because it helps reduce selection bias and ensures that the groups are comparable, which enhances the validity of the results.

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• Key Differences

Know the Differences & Comparisons

Difference Between Survey and Experiment

While surveys collected data, provided by the informants, experiments test various premises by trial and error method. This article attempts to shed light on the difference between survey and experiment, have a look.

Content: Survey Vs Experiment

Comparison chart, definition of survey.

By the term survey, we mean a method of securing information relating to the variable under study from all or a specified number of respondents of the universe. It may be a sample survey or a census survey. This method relies on the questioning of the informants on a specific subject. Survey follows structured form of data collection, in which a formal questionnaire is prepared, and the questions are asked in a predefined order.

Informants are asked questions concerning their behaviour, attitude, motivation, demographic, lifestyle characteristics, etc. through observation, direct communication with them over telephone/mail or personal interview. Questions are asked verbally to the respondents, i.e. in writing or by way of computer. The answer of the respondents is obtained in the same form.

Definition of Experiment

The term experiment means a systematic and logical scientific procedure in which one or more independent variables under test are manipulated, and any change on one or more dependent variable is measured while controlling for the effect of the extraneous variable. Here extraneous variable is an independent variable which is not associated with the objective of study but may affect the response of test units.

In an experiment, the investigator attempts to observe the outcome of the experiment conducted by him intentionally, to test the hypothesis or to discover something or to demonstrate a known fact. An experiment aims at drawing conclusions concerning the factor on the study group and making inferences from sample to larger population of interest.

Key Differences Between Survey and Experiment

The differences between survey and experiment can be drawn clearly on the following grounds:

• A technique of gathering information regarding a variable under study, from the respondents of the population, is called survey. A scientific procedure wherein the factor under study is isolated to test hypothesis is called an experiment.
• Surveys are performed when the research is of descriptive nature, whereas in the case of experiments are conducted in experimental research.
• The survey samples are large as the response rate is low, especially when the survey is conducted through mailed questionnaire. On the other hand, samples required in the case of experiments is relatively small.
• Surveys are considered suitable for social and behavioural science. As against this, experiments are an important characteristic of physical and natural sciences.
• Field research refers to the research conducted outside the laboratory or workplace. Surveys are the best example of field research. On the contrary, Experiment is an example of laboratory research. A laboratory research is nothing but research carried on inside the room equipped with scientific tools and equipment.
• In surveys, the data collection methods employed can either be observation, interview, questionnaire, or case study. As opposed to experiment, the data is obtained through several readings of the experiment.

While survey studies the possible relationship between data and unknown variable, experiments determine the relationship. Further, Correlation analysis is vital in surveys, as in social and business surveys, the interest of the researcher rests in understanding and controlling relationships between variables. Unlike experiments, where casual analysis is significant.

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sanjay kumar yadav says

November 17, 2016 at 1:08 am

Ishika says

September 9, 2017 at 9:30 pm

The article was quite helpful… Thank you.

May 21, 2018 at 3:26 pm

Can you develop your Application for Android

Surbhi S says

May 21, 2018 at 4:21 pm

Yeah, we will develop android app soon.

October 31, 2018 at 12:32 am

If I was doing an experiment with Poverty and Education level, which do you think would be more appropriate for me?

Thanks, Chris

Ndaware M.M says

January 7, 2021 at 2:29 am

So interested,

Victoria Addington says

May 18, 2023 at 5:31 pm

Thank you for explaining the topic

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What is the difference between a Schmidt Hammer and other rebound hammers?

Not all rebound hammers are created equal! What are the key differences and which one will be best for your project? Find out in this quick guide to the differences between Schmidt hammers and other rebound hammers for concrete strength testing. 

Infrastructure & Asset Inspection of Concrete Structures

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Compressive strength estimation is one of the most widely-used non-destructive tests for concrete. While rebound hammers are the number one method of determining the compressive strength, not all rebound hammers are equal.

This article takes a look at the differences between original Schmidt hammers and other rebound hammers, so you can make a more informed decision and get better results from your compressive strength estimations.

Although the term “Schmidt hammer” is often used interchangably with “rebound hammer” there are key distinctions that set the Original Schmidt hammers apart from the rest.

Let’s take a look at some of the main differentiators.

Cost-effectiveness

Although the initial price for Schmidt hammers is more expensive, the Original Schmidt OS8000 is arguably the most cost-effective rebound hammer available. Given that this is a high-end Swiss product, you may find that statement surprising, but let’s take a look in detail. Most manufacturers and some of the major standards call for calibration or servicing after around 2000 impacts and here you can see the reason why:

The Proceq Original Schmidt hammers consistently perform well in durability testing. This saves you money on servicing costs, down-time due to instrument availability, and potentially having to repeat site testing done with a hammer that was out of tolerance.

Original Schmidt hammers also deliver massive time savings in data acquisition and reporting compared with mechanical hammers. Moreover, Schmidt hammers benefit from our digital ecosystem that enables instant data back-up, collaboration, reporting and traceability.

Verification

Verification of the hammer is required by every major standard and is also important in achieving reliable strength estimates. The Schmidt app provides the necessary tools to keep on top of this with an on-board impact counter that gives a warning if the verification is overdue. Standardized verification procedures are fully automated on the app, and it generates a report which can be added to the test report.

Original Schmidt is the only rebound hammer that tracks calibration status according to standards. This enables you to confidently test according to standards with:

• Automatic calculation of the rebound value plus series validity check

• Custom material curves for the most accurate estimates

• Test region analysis

• Four operating modes

• Automatic impact angle compensation

Schmidt rebound hammer and the mobile app (available on iOS & Android)

Another key difference between Schmidt hammers and other rebound hammers is the power of the Schmidt (OS) app . Available for iOS and Android, the Schmidt app delivers rich functionality to make concrete and rock testing a breeze with custom curves for higher precision and report generation in a few taps. The Schmidt Android app has recently been updated for an even better experience, so you can enjoy seamless data transfer and easier collaboration as well as:

• Logbook functionality for capturing additional information for reporting purposes

• Synchronization with the Screening Eagle Workspace platform

• Fast PDF report generation

In general, a Schmidt Hammer is a better investment for professionals who need to perform regular concrete strength tests with high accuracy and reliability on-site. Cheaper rebound hammers are a better option private homeowners and DIYers who need to perform occasional estimations that do not require the highest accuracy.

Ultimately, the best type of hammer for you will depend on your individual needs and budget. If you are a professional who needs to perform regular concrete compressive strength estimations with reliable results fast, then a Schmidt Hammer is a wise investment.

Read more articles and real case studies about concrete compressive strength testing and concrete quality assessment on our Tech Hub .

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Case Study vs. Experiment

What's the difference.

Case studies and experiments are both research methods used in various fields to gather data and draw conclusions. However, they differ in their approach and purpose. A case study involves in-depth analysis of a particular individual, group, or situation, aiming to provide a detailed understanding of a specific phenomenon. On the other hand, an experiment involves manipulating variables and observing the effects on a sample population, aiming to establish cause-and-effect relationships. While case studies provide rich qualitative data, experiments provide quantitative data that can be statistically analyzed. Ultimately, the choice between these methods depends on the research question and the desired outcomes.

Further Detail

Introduction.

When conducting research, there are various methods available to gather data and analyze phenomena. Two commonly used approaches are case study and experiment. While both methods aim to provide insights and answers to research questions, they differ in their design, implementation, and the type of data they generate. In this article, we will explore the attributes of case study and experiment, highlighting their strengths and limitations.

A case study is an in-depth investigation of a particular individual, group, or phenomenon. It involves collecting and analyzing detailed information from multiple sources, such as interviews, observations, documents, and archival records. Case studies are often used in social sciences, psychology, and business research to gain a deep understanding of complex and unique situations.

One of the key attributes of a case study is its ability to provide rich and detailed data. Researchers can gather a wide range of information, allowing for a comprehensive analysis of the case. This depth of data enables researchers to explore complex relationships, identify patterns, and generate new hypotheses.

Furthermore, case studies are particularly useful when studying rare or unique phenomena. Since they focus on specific cases, they can provide valuable insights into situations that are not easily replicated or observed in controlled experiments. This attribute makes case studies highly relevant in fields where generalizability is not the primary goal.

However, it is important to note that case studies have limitations. Due to their qualitative nature, the findings may lack generalizability to broader populations or contexts. The small sample size and the subjective interpretation of data can also introduce bias. Additionally, case studies are time-consuming and resource-intensive, requiring extensive data collection and analysis.

An experiment is a research method that involves manipulating variables and measuring their effects on outcomes. It aims to establish cause-and-effect relationships by controlling and manipulating independent variables while keeping other factors constant. Experiments are commonly used in natural sciences, psychology, and medicine to test hypotheses and determine the impact of specific interventions or treatments.

One of the key attributes of an experiment is its ability to establish causal relationships. By controlling variables and randomly assigning participants to different conditions, researchers can confidently attribute any observed effects to the manipulated variables. This attribute allows for strong internal validity, making experiments a powerful tool for drawing causal conclusions.

Moreover, experiments often provide quantitative data, allowing for statistical analysis and objective comparisons. This attribute enhances the precision and replicability of findings, enabling researchers to draw more robust conclusions. The ability to replicate experiments also contributes to the cumulative nature of scientific knowledge.

However, experiments also have limitations. They are often conducted in controlled laboratory settings, which may limit the generalizability of findings to real-world contexts. Ethical considerations may also restrict the manipulation of certain variables or the use of certain interventions. Additionally, experiments can be time-consuming and costly, especially when involving large sample sizes or long-term follow-ups.

While case studies and experiments have distinct attributes, they can complement each other in research. Case studies provide in-depth insights and a rich understanding of complex phenomena, while experiments offer controlled conditions and the ability to establish causal relationships. By combining these methods, researchers can gain a more comprehensive understanding of the research question at hand.

When deciding between case study and experiment, researchers should consider the nature of their research question, the available resources, and the desired level of control and generalizability. Case studies are particularly suitable when exploring unique or rare phenomena, aiming for depth rather than breadth, and when resources allow for extensive data collection and analysis. On the other hand, experiments are ideal for establishing causal relationships, testing specific hypotheses, and when control over variables is crucial.

In conclusion, case study and experiment are two valuable research methods with their own attributes and limitations. Both approaches contribute to the advancement of knowledge in various fields, and their selection depends on the research question, available resources, and desired outcomes. By understanding the strengths and weaknesses of each method, researchers can make informed decisions and conduct rigorous and impactful research.

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Investigating racial/ethnic differences in procedure experience in obstetrics & gynecology trainees at a single academic institution: a retrospective cohort study

• Patricia GiglioAyers 1 , 2 ,
• Christine E. Foley 1 , 2 ,
• Beth Cronin 1 , 2 &
• Dayna Burrell 1 , 2  

BMC Medical Education volume  24 , Article number:  561 ( 2024 ) Cite this article

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Discrimination is common in medical education. Resident physicians of races and ethnicities underrepresented in medicine experience daily discrimination which has been proven to negatively impact training. There is limited data on the impact of resident race/ethnicity on OB/GYN surgical training. The objective of this study was to investigate the impact of race/ethnicity on procedural experience in OB/GYN training.

A retrospective analysis of graduated OB/GYN resident case logs from 2009 to 2019 was performed at a single urban academic institution. Self-reported race/ethnicity data was collected. Association between URM and non-URM were analyzed using t-tests. Trainees were categorized by self-reported race/ethnicity into underrepresented in medicine (URM) (Black, Hispanic, Native American) and non-URM (White, Asian).

The cohort consisted of 84 residents: 19% URM ( N  = 16) and 79% non-URM ( n  = 66). Difference between URM and non-URM status and average case volume was analyzed using t-tests. There was no difference between non-URM and URM trainees and reported mean number of Total GYN (349 vs. 334, p  = 0.31) and Total OB (624 vs. 597, P  = 0.11) case logs. However, compared with non-URM, on average URM performed fewer Total procedures (1562 vs. 1469, P  = 0.04). Analyzing individual procedures showed a difference in average number of abortions performed between URM and non-URM (76 vs. 53, P  = 0.02). There were no other statistically significant differences between the two groups.

Conclusions

This single institution study highlights potential differences in trainee experience by race/ethnicity. Larger national studies are warranted to further explore these differences to identify bias and discrimination, and to ensure equitable experience for all trainees.

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Introduction

Discrimination is common in medical education, with nearly 60% of medical trainees experiencing at least one form of harassment or discrimination during their training [ 1 ]. Race/ethnicity has been proven to negatively impact medical student experiences and evaluations [ 2 , 3 ]. Although data remains limited, a rising number of studies explore the impact of race/ethnicity on residency training.

Resident physicians of races & ethnicities underrepresented in medicine endure daily microaggressions and biases [ 4 ]. In general surgery, up to 24% of residents report experiencing discrimination based on race/ethnicity or religion, with highest rates (70%) reported among Black residents [ 5 , 6 , 7 ]. Black surgical residents are 4.2 times more likely to experience high levels of perceived daily discrimination [ 7 ]. Discriminatory acts include being mistaken for another person of the same race, mistaken for nonphysicians, and experiencing different standards of evaluation [ 5 ]. Compared with their White counterparts, non-White residents experience increase feelings of isolation and judgement [ 8 ]. Surgical residents who experience discrimination also reported higher rates of burnout, thoughts of attrition, and suicidal thoughts [ 5 , 6 ]. A recent study investigating the relationship between gender, race/ethnicity and general surgery resident case volume cites a correlation between racial/ethnic categories underrepresented in medicine (URM) (identified as Black, Hispanic or Native American) and lower operative volumes at graduation [ 9 ].

Data regarding the impact of race/ethnicity on training in Obstetrics and Gynecology (OB/GYN) is limited. OB/GYN is reported to have the highest percentage of trainees from racial and ethnic backgrounds underrepresented in medicine at 19% among the surgical subspecialties [ 10 ]. However, recent data from 2022 demonstrated there is a greater proportion of White physicians at the fellowship level compared to residency level [ 11 ]. This trend persists in academic medicine, with a higher proportion of white physicians in leadership positions and with higher academic ranks [ 12 ]. Despite multiple initiatives by national organizations within OB/GYN to address racial and ethnic disparities [ 13 , 14 ], studies exploring racial disparities and discrimination are sparse in OB/GYN literature. To the authors knowledge, there is no published data on the impact of race/ethnicity on resident surgical training in OB/GYN. Specifically, there is no data on the impact of race on the fundamental metric of surgical volume during gynecology residency training. The aim of this study was to begin by exploring the impact of race/ethnicity on OB/GYN procedural experience in residency training at a single institution.

A retrospective analysis of graduated OB/GYN resident procedural case logs per the Accreditation Council for Graduate Medical Education (ACGME) from 2009 to 2019 at a single institution was performed. The research was deemed exempt by the IRB and was determined to be non-human subjects research. Self-reported race/ethnicity as limited by ERAS check boxes was collected. Trainees were categorized into URM (Black, Hispanic, Native American) and non-URM (White, Asian). The institution instructs residents to log a procedure if active participation as the primary surgeon is > 50% of the procedure. The primary outcome was total number of surgical procedures logged by a graduating resident. Secondary outcomes included procedure logs for the following ACGME categories: Normal spontaneous vaginal delivery (NSVD), Cesarean section (CS), Operative delivery (ODEL), Abdominal hysterectomy (AHYST), Vaginal hysterectomy (VHYST), Laparoscopic hysterectomy (LHYST), Minimally Invasive Hysterectomy (MIH), Total Hysterectomy (THYST), Incontinence and pelvic floor (ISPF), Laparoscopy (LAPS), Operative Hysteroscopy (OHYST), Abortion (ABORT), Transvaginal ultrasound (TVUS), Surgery for invasive cancer (SIC). Total numbers of cases, total obstetric (Total OB: CS, NSVD, ODEL), and total gynecologic (Total GYN: THYST, LAPS, OHYST) cases were collected. Residents in OB/GYN who completed the four-year residency training program were included in the analysis. Trainees who transferred training programs during residency or did not complete residency were excluded. Procedures were reported as mean number of procedures per ACGME category per group (URM vs. non-URM). Differences between URM and non-URM status and mean case volumes were analyzed using t-tests.

The cohort consisted of 84 residents. Residents who self-selected the ACGME category of “none of the above” ( n  = 2) were excluded from the URM vs. non-URM analyses. There was a total of 82 residents included in the final analysis: 66 non-URM (78.57%). (Table  1 ) There were no differences between non-URM and URM trainees and reported mean number of Total GYN (349 vs. 334, p  = 0.31) and Total OB (624 vs. 597, P  = 0.11) case logs. However, URM trainees had significantly fewer Total procedures (1469 vs. 1562, P  = 0.04) than their non-URM counterparts (Table  2 ). Analyzing specific procedures showed when comparing mean number of abortions, URM trainees experienced significantly less abortions (76 vs. 53, P  = 0.02) than non-URM trainees. No differences were found between non-URM and URM trainees in all other specific individual procedure categories (Table  2 ).

Resident trainees from races and ethnicities underrepresented in medicine experience daily discrimination, however there is limited data on the impact of racial/ethnic discrimination on training and postgraduate experience within OB/GYN. The importance of identifying and addressing racial and ethnic disparities within OB/GYN and medical education is widely accepted. In 2021, the ACGME launched ACGME Equity Matters, an initiative focused on learning and improvement in areas of diversity, equity and incision, and antiracism practices [ 13 ]. In 2020 ACOG, along with leading national and international women’s health organizations, released a joint statement, “Collective Action Addressing Racism.” [ 14 ] This statement specifically cites commitment to education, recognition, and scholarship as ways to eliminate inequalities in women’s health. Despite these initiatives, published research is limited.

This single institution study highlights potential differences in trainee experience by race/ethnicity and calls for further review at training programs across our specialty. This study showed a difference in total procedure experience between URM and non-URM OB/GYN residents during the 10-year time period examined. These differences may suggest discriminatory practices which are limiting procedural experience for URM residents. These findings are similar to recently published data that demonstrated a correlation between general surgery residents underrepresented in medicine or who identified as female, and lower operative volumes at graduation [ 9 ].

Additionally, this study observed a significant difference in the number of abortion procedures logged by URM versus non-URM trainees. In our institution, trainees have the choice to opt out of abortion procedures. This choice is not recorded as a part of the operative log but may confound this particular data point. We are unaware of any correlation between a trainee’s self-identified race and choice to perform abortion procedures. Additional work is needed to evaluate the demonstrated differences on a qualitative level to better identify the root cause(s) of the variation demonstrated, including possible sociocultural influences. Further work must be done to identify unconscious and overt biases and address discrimination to ensure all residents, regardless of race/ethnicity or gender, have an equitable training experience.

This small, single institution study calls for further review of racial and ethnic differences in procedural experience at training programs across our specialty. Although OB/GYN does have the highest percent of URM trainees among the surgical subspecialties, the lower proportion of URM physicians in fellowships and in higher academic rank positions suggests persistent institutional and structural racism. Procedural case logs are an objective and nationally utilized measure which could be further analyzed to identify and ultimately address training differences. If publicly available, these case logs could hold programs accountable for ensuring equitable procedural experience. Addressing any identified differences would not only improve resident experience and skill, but also contribute to the goal of creating a racially and ethnically diverse workforce to improve patient care in OB/GYN.

There are several limitations to this study, including variation in the accuracy and reporting practices of resident procedure logs which may impact data. Although criteria at this institution exist instructing residents to log only procedures which they performed > 50% of as the primary surgeon, residents are individually responsible for tracking and logging procedures. Furthermore, the small sample size of this study at a single institution, coupled with the variation in resident surgical experience and reporting practices between OB/GYN programs nationally, prevent this study from generalizability to all OB/GYN residency programs. This study analyzes total case logs at time of graduation, and therefore does not explore how race/ethnicity may impact procedural experience across the four years of residency and does not account for variation in logging during different times of residency. The authors also recognize that increased procedural numbers do not necessarily translate to procedural competency. Although differences may suggest training inequity among URM vs. non-URM residents, variation in procedural numbers may not reflect trainee competency at time of graduation.

Differences may exist in Obstetrics and Gynecology procedural experience by trainee race/ethnicity. Larger national studies are warranted to further explore these differences to identify bias and discrimination, and to ensure equitable experience for all trainees.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Contributions

PGA and DB were involved in the conception, design, interpretation of data, and manuscript writing. CF was involved in the design of this study, analysis, and editing of the manuscript. BC contributed to the conception, design, and editing of this work. All authors read and approved the final manuscript.

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Ethics, approval, and consent to participate

The ethical approval for the study and informed consent are waived by the Women and Infants Institutional Review Board due to retrospective nature of the study. All methods carried out in the study were performed in accordance with relevant guidelines and regulations.

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The author(s) declare(s) that they have no competing interests. Dr. Dayna Burrell has acted as a BMC Education article review in the past upon request. This data was accepted for oral presentation at the 2023 CREOG and APGO Annual Meeting. The conference took place February 27-March 1, 2023 in National Harbor, Maryland.

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GiglioAyers, P., Foley, C.E., Cronin, B. et al. Investigating racial/ethnic differences in procedure experience in obstetrics & gynecology trainees at a single academic institution: a retrospective cohort study. BMC Med Educ 24 , 561 (2024). https://doi.org/10.1186/s12909-024-05363-9

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A Comprehensive Perspective on Intracranial Pressure Monitoring and Individualized Management in Neurocritical Care: Results of a Survey with Global Experts

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• Open access
• Published: 29 May 2024

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• Sérgio Brasil   ORCID: orcid.org/0000-0003-2397-9947 1 ,
• Daniel Agustín Godoy 2 ,
• Walter Videtta 3 ,
• Andrés Mariano Rubiano 4 ,
• Davi Solla 1 ,
• Fabio Silvio Taccone 5 ,
• Chiara Robba 6 ,
• Frank Rasulo 7 ,
• Marcel Aries 8 , 9 ,
• Peter Smielewski 10 ,
• Geert Meyfroidt 11 ,
• Denise Battaglini 6 ,
• Mohammad I. Hirzallah 12 ,
• Robson Amorim 1 ,
• Gisele Sampaio 13 ,
• Fabiano Moulin 13 ,
• Cristian Deana 14 ,
• Edoardo Picetti 15 ,
• Angelos Kolias 16 ,
• Peter Hutchinson 16 ,
• Gregory W. Hawryluk 17 , 18 , 19 ,
• Marek Czosnyka 20 ,
• Ronney B. Panerai 21 ,
• Lori A. Shutter 22 ,
• Soojin Park 23 ,
• Carla Rynkowski 24 ,
• Jorge Paranhos 25 ,
• Thiago H. S. Silva 26 ,
• Luiz M. S. Malbouisson 26 &
• Wellingson S. Paiva 1  

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Numerous trials have addressed intracranial pressure (ICP) management in neurocritical care. However, identifying its harmful thresholds and controlling ICP remain challenging in terms of improving outcomes. Evidence suggests that an individualized approach is necessary for establishing tolerance limits for ICP, incorporating factors such as ICP waveform (ICPW) or pulse morphology along with additional data provided by other invasive (e.g., brain oximetry) and noninvasive monitoring (NIM) methods (e.g., transcranial Doppler, optic nerve sheath diameter ultrasound, and pupillometry). This study aims to assess current ICP monitoring practices among experienced clinicians and explore whether guidelines should incorporate ancillary parameters from NIM and ICPW in future updates.

We conducted a survey among experienced professionals involved in researching and managing patients with severe injury across low-middle-income countries (LMICs) and high-income countries (HICs). We sought their insights on ICP monitoring, particularly focusing on the impact of NIM and ICPW in various clinical scenarios.

From October to December 2023, 109 professionals from the Americas and Europe participated in the survey, evenly distributed between LMIC and HIC. When ICP ranged from 22 to 25 mm Hg, 62.3% of respondents were open to considering additional information, such as ICPW and other monitoring techniques, before adjusting therapy intensity levels. Moreover, 77% of respondents were inclined to reassess patients with ICP in the 18–22 mm Hg range, potentially escalating therapy intensity levels with the support of ICPW and NIM. Differences emerged between LMIC and HIC participants, with more LMIC respondents preferring arterial blood pressure transducer leveling at the heart and endorsing the use of NIM techniques and ICPW as ancillary information.

Conclusions

Experienced clinicians tend to personalize ICP management, emphasizing the importance of considering various monitoring techniques. ICPW and noninvasive techniques, particularly in LMIC settings, warrant further exploration and could potentially enhance individualized patient care. The study suggests updating guidelines to include these additional components for a more personalized approach to ICP management.

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Introduction

Our understanding of intracranial pressure (ICP), a critical indicator of brain health, is continuously evolving [ 1 ], with satisfactory evidence supporting its monitoring as a means of outcome improvement [ 2 , 3 ]. However, both the safety limits and therapeutic thresholds for managing and treating ICP are still debatable [ 4 , 5 ], although the current recommendation is based on 22 mm Hg [ 6 ]. Several notable trials investigating traumatic brain injury (TBI), whether targeting ICP alone [ 7 ] or in combination with brain oximetry [ 8 ], employing various therapy strategies, such as decompressive craniectomy [ 9 , 10 ], hypothermia [ 11 ], and others, have highlighted the ongoing need for further advancement in outcome improvement. Possibly, part of this issue may be attributed to attempts to oversimplify ICP as a binary “yes” or “no” phenomenon, neglecting to individualize ICP thresholds based on disease processes or individual variations.

The generation of pressure within the cranium is a dynamic process involving various anatomical structures, such as the brain, cerebrospinal fluid, arterial and venous blood volumes, meninges, and bones. Additionally, pressure in abdominal, thoracic, and cervical cavities also influences intracranial space pressure [ 12 ]. Following an acute brain injury, inflammatory cascades and impairment in cerebral physiological components contribute to brain tissue volume expansion (cerebral edema) and elevation of ICP [ 13 , 14 ]. Moreover, factors such as ventilator-patient desynchrony, improper patient positioning, sedation use, arterial blood pressure (ABP), and volemic management, as well as complications such as ischemia, vasospasm, infection, and secondary hemorrhage, may also lead to intracranial hypertension (IH) development [ 12 ].

Among these factors, the intracranial variation of blood volume during each heartbeat is the primary determinant of ICP dynamics, at least on a second-by-second timescale [ 15 ]. Beat by beat, ICP pulse slopes can be observed, showing habitual systolic and diastolic phases in dedicated monitors. This pulse morphology can indicate compromised intracranial compliance (ICC) and may predict IH [ 16 , 17 , 18 ]. Therefore, absolute ICP numbers alone may not be precise indicators because they do not reflect ICC and may not assist in determining therapy strategy following different IH syndromes [ 19 ] and may lead to imprecise cerebral perfusion pressure (CPP) calculation [ 20 ]. This underscores the need for additional parameters for guidance. In this context, multimodality neuromonitoring and ICP waveform (ICPW) or pulse morphology emerge as promising options for better differentiation of patients at risk of developing IH crisis [ 21 , 22 ] (Fig.  1 ).

Three different patients with the same ICP value (20 mm Hg) but distinct ICP pulse morphologies (purple waveforms) representing distinct levels of intracranial compliance. ICP intracranial pressure (Color figure online)

The present study is a survey on perceptions regarding ICP monitoring among neurocritical care professionals. The primary objective was to gather opinions on current ICP monitoring practices from professionals with diverse backgrounds, for a discussion on individualizing ICP management using additional monitoring tools, especially ICPW and noninvasive monitoring (NIM). The study aimed to determine whether, from an expert standpoint, ICP management guidelines can consider the inclusion of ancillary physiological parameters derived from NIM and ICPW in future updates. Differences in opinions among professionals from high-income countries (HICs) and low-middle income countries (LMICs) were explored.

A cross-sectional survey was developed to assess professional practices and knowledge regarding ICP, CPP, and ICPW. The survey was crafted by a steering committee following the checklist for reporting of survey studies (CROSS) recommendations (Supplemental 1) previously published [ 23 ]. A pilot test involving five professionals was conducted, and based on their feedback, the survey was refined before dissemination via email, social media, and medical meetings to eligible participants (details provided in the following section). The survey was hosted on Google Forms for broad accessibility and was available from October 1st to November 30th, 2023. Participation was voluntary and anonymous, with each participant permitted to submit only one response. The online form was designed to automatically conclude once all questions were answered. Additionally, participants were encouraged to share the survey link with other professionals with similar backgrounds who met the participation criteria.

The survey is shown in Supplemental 2. It pertained to patients with acute brain injury undergoing invasive ICP monitoring (regardless of an external drain, parenchymal, or other). Other invasive techniques (e.g., brain oximetry, microdialysis) were not considered as routine, but participants could discuss these techniques in the free-text fields. The survey results, which include an analysis of the divergence in opinions between LMIC and HIC clinicians, are presented following the assessment of each field, which were as follows: (1) agreement with the 22 mm Hg threshold to either start or escalate IH therapy intensity levels (TILs) (based on the Seattle International Brain Injury Consensus Conference recommendations [ 6 ]), (2) general knowledge on ICPW and its practical relevance, (3) agreement on using NIM and ICPW to support the individualization of ICP judgment in selected cases, and (4) agreement with including these additional parameters in future guideline updates.

Participants’ Characteristics

Those surveyed should be active practitioners or researchers with ten or more years of experience in the management and/or research in neurocritical care possessing experience in ICP dynamics. Therefore, participants also should have participated in academic production in the scope of the survey. Participants were not necessarily medical doctors but also nurses and other professionals associated with this practice. The survey was composed of 15 questions in English; therefore, English proficiency was an additional prerequisite. The questions allowed a single response and space for providing any additional comment. Exclusively in the case of ancillary tools available in each participant’s health facility, multiple answers were possible.

Statistical Analysis

A standard descriptive analysis was performed. Variables were presented through absolute and relative frequencies and, when applicable, 95% confidence intervals (CIs) were calculated with the binomial exact method. Inferential exploratory analyses were conducted and, when applicable, the groups were compared using the χ 2 test. LMIC and HIC were defined as the World Bank classification (available at https://datahelpdesk.worldbank.org ). There were no missing data. All tests were two-tailed, and final p values under 0.05 were considered significant. The analyses were conducted with the Statistical Package for Social Sciences software (IBM SPSS Statistics for Windows, version 24.0; IBM Corp., Armonk, NY).

Participants

This survey recruited 109 participants meeting the inclusion criteria, being evenly distributed between HIC and LMIC (55 and 54 participants, respectively). Among the represented institutions, 48 (54%) were from HIC, and 73 (82%) were academic institutions out of a total of 88. The participants consisted of 6 (5%) nurses, 70 (64%) intensivists, 6 (5%) neurologists, 4 (3%) brain physiologists, and 23 (21%) neurosurgeons involved with the care of patients with severe neurological injury.

Guidelines Adherence and CPP Measurement

The survey revealed a common practice of individualizing target ranges for ICP and CPP, with a frequent reliance on adjunctive NIM techniques to support decisions in the scenario of a patient with acute brain injury with invasive ICP monitoring. Only 30 (27.5%) participants followed 22 mm Hg as the threshold for initiating or escalating TIL. A total of 68 (62.4%) participants were more flexible on cutoff choices, and 11 (10.1%) participants surveyed did not follow the recommended cutoff. Regarding CPP monitoring thresholds, 39 (35.7%) participants pursue the 60–70 mm Hg range, whereas 44 (40.3%) rely on this range when it is supported by ancillary means, such as NIM and imaging, and 26 (23.8%) do not rely on this range. Figure  2 shows the most used NIM techniques among participants. The level with which ABP is zeroed and the transducer positioned, which impacts directly with CPP calculation, is also heterogenous; among the respondents, this level was the heart for 60 (55%) participants, the tragus for 36 (33%) participants, and both levels for 13 (11.9%) participants.

The most used noninvasive techniques to support ICP monitoring among the participants (in percentages of incidence). Data are presented in percentages. B4C Brain4Care, ICP intracranial pressure, NIRS near-infrared spectroscopy, ONSD optic nerve sheath diameter, TCCD transcranial color-coded duplex, TCD transcranial Doppler

Relevance of ICPW and NIM

With reference to ICPW monitoring, 71 (65%) participants surveyed considered ICPW fundamental to individualize ICP management, whereas 38 (35%) reported using ICPW in select cases only. The correlation of professionals who consider ICPW fundamental to individualize their patients’ treatment with support to the inclusion of ICPW analysis in future guidelines update was significant ( p  = 0.004). Regarding the use of NIM techniques to support ICP plus CPP monitoring and management individualization, 82 (75.2%) participants agreed to use both NIM and ICP, 23 (21.1%) declared to use NIM occasionally, and only 4 (3.6%) did not see value in using NIM to refine treatment guidance. Likewise, the position of clinicians on behalf of NIM parameters inclusion in future guidelines update was significant ( p  = 0.019).

Role of ICPW in Different ICP Situations

Table 1 and Fig.  3 show the responses for each of the survey’s questions. In controversial situations when ICP is under 22 mm Hg but ICPW presents an abnormal morphology suggestive of poor ICC, 84 (77%) participants would review their patients holistically and consider escalating IH TILs between lower tiers 1 or 2. A total of 84 (77%) participants also agreed that a useful way to clarify these situations might be using NIM. The situation of a patient with borderline ICP values between 18 and 22 mm Hg compels 47 (43%) responders to continue just observing when ICPW keeps its normal shape, whereas 62 (57%) would proceed with reassessing the patient and/or taking additional tests to consider escalating lower TILs. Finally, for patients with ICP between 22 and 25 mm Hg but normal ICPW, 33 (30%) participants escalate TILs, 7 (6%) monitor the ICPW to take actions, and 69 (64%) would still proceed to guide actions after performing further ancillary assessments.

Participants’ answers distributions regarding intracranial pressure waveform (ICPW) relevance according to different intracranial pressure (ICP) levels

Diversity Between LMICs and HICs

Significant differences were observed on ICP management perceptions and practices between LMICs and HICs, as shown in Table  2 . Participants from LMICs are more likely to level ABP at the heart than the tragus and to use multimodal NIM as ancillary information to ICP values. Furthermore, participants from LMICs consider ICPW relevant and are more willing to support its implementation in the guidelines as an ICP refinement. Participants who supported updating guidelines toward a personalized ICP management also agreed with adding NIM in this setting (Table  3 ). There were no differences between LMICs and HICs regarding the use of imaging and transcranial Doppler (TCD) or transcranial color-coded duplex, and these techniques were the most used NIM (70% and 65%, respectively). However, application of optic nerve sheath ultrasound (ONSUS) is significantly higher in LMICs (80%) than in HICs (40.7%, p  < 0.001). Overall, 71 (65%) participants supported considering the inclusion of ICPW analysis in future guidelines update, whereas 38 (35%) think evidence in this regard is still lacking. Support was significantly more prevalent among LMIC participants ( p  = 0.004).

The current study compiled insights from numerous actively engaged academics with more than a decade of experience in treating neurocritical patients and managing ICP. Participants were divided based on regional economic conditions, distinguishing between LMICs and HICs. This differentiation is particularly significant for assessing the impact of resource availability on medical practices. The collected opinions revealed that practitioners are becoming less rigid in adhering to ICP and CPP thresholds, instead using ICPW and NIM as supplementary tools to inform management decisions. There is a growing confidence in customizing thresholds, such as “optimal CPP” or “critical ICP,” based on the observed autoregulatory status [ 24 ].

Novel methods for using ICPW and NIM to increase understanding of cerebrospinal compensatory reserve or brain compliance are emerging [ 25 ]. Furthermore, interest in NIM is also as a means to reduce the financial burden related to ICP monitoring, which has grown exponentially in the last few decades [ 26 ]. Understanding of ICPW and its clinical application receives attention whether professionals are from LMICs or HICs and its interpretation seems to be determinant on decisions. The participants of the present survey agreed with the addition of these components to be considered in future guideline revisions.

With reference to the most cited NIM techniques by the surveyed, TCD has been acknowledged by its capacity of assessing blood velocities and observe CPP reduction by means of the pulsatility index [ 27 ] or even the ICP/CPP estimation [ 28 , 29 ]. Furthermore, TCD is highly sensitive to cerebrovascular dynamics, allowing clinicians to assess immediate responses at the bedside in the case of changes in ABP and pCO 2 , hydrocephalus, midline shift, and brain death (e.g., see [ 30 ]). The ONSUS has been extensively studied in the last years, with a threshold of ~ 5.8 mm currently adopted as a suitable cutoff for elevated ICP [ 31 ]. Among ONSUS advantages are low costs, short learning curve, and readiness. The pupil-reactivity index (NPi), derived from automated pupillometry, has been the most studied parameter of this technique for its correlation with ICP, understanding NPi decrease as a potential indicator of ICP elevation [ 32 ], while one multicentric trial observed significant correlation between persistent NPi < 3 and poorer outcomes in neurocritical care [ 33 ]. One emerging technique cited by 27.5% of the surveyed (Table  2 ) reproduces ICPW following pulsatile micrometric dilations of the skull at each heartbeat. Its generated waveforms undergo automated analysis and numerical ratios as the quotient between second and first ICP waveform peaks (P2/P1) and time to peak may aid physicians detecting poor ICC [ 34 ]. Figure  4 summarizes this combination of NIM with their respective indicators. Studying such a model and how it can aid in improving ICP judgment warrants prospective validation.

A flowchart proposal (still to be prospectively validated) for ICP management, considering evidence-based ancillary noninvasive neuromonitoring. Techniques may include transcranial Doppler, pupillometry, ICP waveform, and ONSUS. eCPP estimated cerebral perfusion pressure, ICP intracranial pressure, NPI neuropupillary index, ONSUS optic nerve sheath ultrasound, P2/P1 quotient between second and first ICP waveform peaks, TIL therapy intensity level, TTP time to peak

Cerebral perfusion pressure is the force with which blood penetrates brain tissue and its adequacy is fundamental to meet the brain's metabolic demand [ 20 ]. It was described more than 60 years ago by Lassen [ 35 ], who hypothesized that CPP would be the difference between the input (mean ABP [MAP]) and the intracranial resistance provided by the brain tissue and the venous outflow, or ICP. These concepts were proven to be true among healthy individuals, and the formula CPP = MAP − ICP is currently adopted in clinical practice and widely present in neurocritical care literature [ 36 ]. The Brain Trauma Foundation recommend the 60–70 mm Hg range [ 37 ], whereas the Lund recommendations are to keep CPP near 50 mm Hg in severe TBI [ 38 ]. However, once either chronic or acute situations that impair cerebral autoregulation are present [ 39 , 40 ], CPP manipulation strictly by means of changes in ABP just to compensate ICP may be iatrogenic [ 41 ].

The extremely rich branching of cerebral vessels creates a progressive reduction in the actual ABP in intracranial vessels from the circle of Willis to the cerebral small vessels [ 42 ]. This lower ABP, associated with absence of muscular wall in the final arterial line make these vessels much more sensitive to higher ICP and easily collapsible after acute brain injuries, expanding hypoperfusion to its surrounding areas [ 43 ]. Therefore, cerebral blood flow (CBF) stops in the small vessels even with ABP being far from zero, the so-called critical closing pressure which changes in strict adherence with ICP fluctuations [ 44 ]. Furthermore, it was demonstrated that the counterforces between MAP and ICP do not behave as balanced as the formula preconizes, being the cerebrovascular capacity to compensate CPP in face of ICP elevations much more mitigated when compared to cerebrovascular response to ABP changes [ 45 , 46 ].

With all the above, it may be concluded that assessing CPP as the difference between MAP and ICP may not be accurate. Several efforts are being made to provide an accurate estimation and optimization of CPP [ 47 , 48 ]. Unfortunately, optimal CPP is still under development [ 49 , 50 ] and needs dedicated systems that are not widely available especially in LMICs. To estimate CPP properly, it is recommended to monitor ABP with the transducer leveled at the tragus [ 36 , 51 , 52 ], but our result in this regard indicate that this is not always the case, which is consistent with a previous survey among the Brain Trauma Foundation authors [ 53 ]. An ABP leveled at the heart may overestimate CPP between 10 to 20 mm Hg [ 36 , 54 ] (Fig.  5 ). However, even with ABP leveled at the tragus, the resultant CPP may not represent the whole brain CPP. When available, TCD [ 29 ] and brain oximetry [ 55 ] provide adjunctive information.

A representation of how changing ABP level from right atrium to tragus may change CPP calculation. Top, CPP is overestimated in 75 mm Hg, whereas real CPP is 55 mm Hg in the lower picture. It is fundamental to consider patient positioning (bed and head angle) and that these CPP values differences vary among individuals. ABP arterial blood pressure, CPP cerebral perfusion pressure

Parameters extracted and calculated from intracranial pulse slopes. P2/P1 ratio is the ratio between second and first peak amplitudes, whereas TTP is the time from pulse upstroke to the highest amplitude identified, represented in percentage. Rounded intracranial pressure pulse shapes indicating reduced intracranial compliance present with higher P2/P1 ratios and TTP

Adherence to ICP Management Guidelines

The Seattle International Severe Brain Injury Consensus Conference recommended that physicians use their clinical judgment and adapt the guidelines as best possible [ 6 ]. In the present survey, 70% of participants described their practice as flexible with thresholds; this was independent of being based in a LMIC or HIC. LMIC participants were more favorable in considering the inclusion of additional information such as ICPW parameters and NIM in determining IH actions. Probably the most suitable explanation for this finding is the lack of invasive ICP for all patients in need in LMIC areas, compelling these physicians to build up experience using NIM.

In healthy adults, ICP values remain under 15 mm Hg [ 51 , 56 ]. In face of a sustained range of 22–25 mm Hg, 30% of this survey’s participants opt to escalate IH TILs regardless of any further information. For borderline ICP values like 18–22 mm Hg (and possibly lower values in selected cases) the need for additional data becomes evident, since allowing ICP to remain beyond individual safety thresholds may carry inadvertent consequences [ 56 ]. This is supported by the recent work from Riparbelli et al. [ 4 ], in which a cohort of more than 300 patients found 18 mm Hg as a threshold associated with mortality and unfavorable outcomes.

Intracranial pressure pulse morphology carries useful information [ 15 , 21 ], ancillary to the mean ICP typically shown on bedside monitors (which is measured by averages of time intervals) [ 57 ]. The changes in beat-by-beat ICPW have been extensively studied, especially the correlation between its different peak amplitudes (P1, P2, and P3) following changes in the volume/pressure relationship [ 58 , 59 , 60 ]. Inside the bony box of the skull, the continuously moving interaction between arterial blood flow with the brain and ventricles filled with cerebrospinal fluid, determines the formation of P1 (upstroke peak), P2 (tidal wave), which is associated with ICC, and the buffering reserve. Finally, P3 is associated with the closure of the aortic valve [ 58 ]. Because ICP is correlated with CBF, other variables that influence CBF may also influence ICPW, such as the ventilatory status [ 61 ], blood viscosity, temperature, and the cardiac output [ 62 ].

ICPW parameters including P2 elevation may precede an IH crisis by several minutes [ 17 , 18 ]. This observation led to an increased interest in exploring the parameter P2/P1 ratio, as a descriptor of decreased adaptative capacity [ 63 , 64 ]. Brasil et al. [ 16 ] found the P2/P1 ratio to increase around 10% after promoting an ICP increase of ~ 4 mm Hg from a baseline of ~ 15 mm Hg in a study of patients with TBI without skull damage. Integrated artificial intelligence–based pulse shape index tracing continuously P2 to P1 proportions has recently been proven to associate both with outcome after TBI and CT findings [ 65 , 66 ]. The time to peak (TTP) is a normalized parameter derived from each pulse triggering up to its highest amplitude [ 67 , 68 ]. The pulse slope triggering is time zero and the end of the pulse is time hundred, therefore TTP is the percentage representation of the pulse’s length in which the highest amplitude is identified (Fig.  6 ). These parameters, currently available exclusively in a noninvasive device [ 34 ] soon will be also included in invasive systems to gather ICP values and ICPW automated metrics [ 69 ], what will aid practitioners on interpreting ICPW changes instead of making a simple visual assessment. Given the multiple inherent variables included in this phenomenon [ 57 ], distinguishing the distinct ICPW peaks without the support of a dedicated analytics process may become difficult.

In this survey, it was noted that as ICP exceeds 22 mm Hg, fewer experts rely on ICPW for decision-making. However, ICPW garners more attention in distinguishing patients below this threshold but at risk of experiencing ICC impairment. Therefore, the accurate use of ICPW hinges on the clinician’s confidence in interpreting its patterns. The vast majority of participants expressed confidence in interpreting and applying ICPW in their clinical practices, citing their understanding of the pathophysiology of patients with severe neurological injury.

Limitations

Limitations of the present study include relying on the limited area of survey distribution, which included primarily professionals from Europe and North, Central, and South America, having no participants from Oceania, Asia, and Africa. Survey responses were kept anonymous; therefore, the accuracy of the information provided and fulfillment of prerequisites for participating was based on good faith. The survey did not embrace questioning participants on their perceptions regarding ICP levels more than 25 mm Hg, although it is implicit that over this level, professionals take their actions to treat IH independently of any further information. The survey considered LMICs and HICs exclusively, according to the real situation of their health services, and therefore the results may not represent the actual heterogeneity of resources available especially in LMIC.

Experienced professionals are prone to the individualization of ICP thresholds, by means of a variety of ancillary parameters, which can be obtained using other invasive or noninvasive techniques. Among a majority of these professionals, ICP pulse morphology and NIM techniques (imaging, TCD, pupillometry, and ONSUS) were considered valuable options to be included in future guideline updates. Especially when ICP is below 22 mm Hg but ICC seems to be compromised, the use of supportive invasive or noninvasive techniques to refine bedside judgment increases. Professionals from LMICs are likely more supportive of these ancillary methods to alter IH TILs. Nevertheless, prospective studies to create the ideal ICP monitoring and treatment algorithm are still needed. These implications are fundamental to assess CPP, which also currently lacks standardization in its measurement.

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We thank Antônio Estevam dos Santos Filho for the illustration artwork.

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SB: Original idea, wrote survey and manuscript, elaborated figures. DAG: Piloted survey. WV: Piloted survey. DS: Statistical analysis. All authors: Review, discussion, critical analysis and edition of the manuscript. The final manuscript was approved by all authors.

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Correspondence to Sérgio Brasil .

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Brasil, S., Godoy, D.A., Videtta, W. et al. A Comprehensive Perspective on Intracranial Pressure Monitoring and Individualized Management in Neurocritical Care: Results of a Survey with Global Experts. Neurocrit Care (2024). https://doi.org/10.1007/s12028-024-02008-z

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Received : 23 February 2024

Accepted : 01 May 2024

Published : 29 May 2024

DOI : https://doi.org/10.1007/s12028-024-02008-z

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