case study methods observation

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

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

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

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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Case Study Observational Research: A Framework for Conducting Case Study Research Where Observation Data Are the Focus

Affiliation.

• 1 1 University of Otago, Wellington, New Zealand.
• PMID: 27217290
• DOI: 10.1177/1049732316649160

Case study research is a comprehensive method that incorporates multiple sources of data to provide detailed accounts of complex research phenomena in real-life contexts. However, current models of case study research do not particularly distinguish the unique contribution observation data can make. Observation methods have the potential to reach beyond other methods that rely largely or solely on self-report. This article describes the distinctive characteristics of case study observational research, a modified form of Yin's 2014 model of case study research the authors used in a study exploring interprofessional collaboration in primary care. In this approach, observation data are positioned as the central component of the research design. Case study observational research offers a promising approach for researchers in a wide range of health care settings seeking more complete understandings of complex topics, where contextual influences are of primary concern. Future research is needed to refine and evaluate the approach.

Keywords: New Zealand; appreciative inquiry; case studies; case study observational research; health care; interprofessional collaboration; naturalistic inquiry; observation; primary health care; qualitative; research design.

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Understanding Case Study Method in Research: A Comprehensive Guide

Table of Contents

Have you ever wondered how researchers uncover the nuanced layers of individual experiences or the intricate workings of a particular event? One of the keys to unlocking these mysteries lies in the qualitative research focusing on a single subject in its real-life context.">case study method , a research strategy that might seem straightforward at first glance but is rich with complexity and insightful potential. Let’s dive into the world of case studies and discover why they are such a valuable tool in the arsenal of research methods.

What is a Case Study Method?

At its core, the case study method is a form of qualitative research that involves an in-depth, detailed examination of a single subject, such as an individual, group, organization, event, or phenomenon. It’s a method favored when the boundaries between phenomenon and context are not clearly evident, and where multiple sources of data are used to illuminate the case from various perspectives. This method’s strength lies in its ability to provide a comprehensive understanding of the case in its real-life context.

Historical Context and Evolution of Case Studies

Case studies have been around for centuries, with their roots in medical and psychological research. Over time, their application has spread to disciplines like sociology, anthropology, business, and education. The evolution of this method has been marked by a growing appreciation for qualitative data and the rich, contextual insights it can provide, which quantitative methods may overlook.

Characteristics of Case Study Research

What sets the case study method apart are its distinct characteristics:

• Intensive Examination: It provides a deep understanding of the case in question, considering the complexity and uniqueness of each case.
• Contextual Analysis: The researcher studies the case within its real-life context, recognizing that the context can significantly influence the phenomenon.
• Multiple Data Sources: Case studies often utilize various data sources like interviews, observations, documents, and reports, which provide multiple perspectives on the subject.
• Participant’s Perspective: This method often focuses on the perspectives of the participants within the case, giving voice to those directly involved.

Types of Case Studies

There are different types of case studies, each suited for specific research objectives:

• Exploratory: These are conducted before large-scale research projects to help identify questions, select measurement constructs, and develop hypotheses.
• Descriptive: These involve a detailed, in-depth description of the case, without attempting to determine cause and effect.
• Explanatory: These are used to investigate cause-and-effect relationships and understand underlying principles of certain phenomena.
• Intrinsic: This type is focused on the case itself because the case presents an unusual or unique issue.
• Instrumental: Here, the case is secondary to understanding a broader issue or phenomenon.
• Collective: These involve studying a group of cases collectively or comparably to understand a phenomenon, population, or general condition.

The Process of Conducting a Case Study

Conducting a case study involves several well-defined steps:

• Defining Your Case: What or who will you study? Define the case and ensure it aligns with your research objectives.
• Selecting Participants: If studying people, careful selection is crucial to ensure they fit the case criteria and can provide the necessary insights.
• Data Collection: Gather information through various methods like interviews, observations, and reviewing documents.
• Data Analysis: Analyze the collected data to identify patterns, themes, and insights related to your research question.
• Reporting Findings: Present your findings in a way that communicates the complexity and richness of the case study, often through narrative.

Case Studies in Practice: Real-world Examples

Case studies are not just academic exercises; they have practical applications in every field. For instance, in business, they can explore consumer behavior or organizational strategies. In psychology, they can provide detailed insight into individual behaviors or conditions. Education often uses case studies to explore teaching methods or learning difficulties.

Advantages of Case Study Research

While the case study method has its critics, it offers several undeniable advantages:

• Rich, Detailed Data: It captures data too complex for quantitative methods.
• Contextual Insights: It provides a better understanding of the phenomena in its natural setting.
• Contribution to Theory: It can generate and refine theory, offering a foundation for further research.

Limitations and Criticism

However, it’s important to acknowledge the limitations and criticisms:

• Generalizability : Findings from case studies may not be widely generalizable due to the focus on a single case.
• Subjectivity: The researcher’s perspective may influence the study, which requires careful reflection and transparency.
• Time-Consuming: They require a significant amount of time to conduct and analyze properly.

Concluding Thoughts on the Case Study Method

The case study method is a powerful tool that allows researchers to delve into the intricacies of a subject in its real-world environment. While not without its challenges, when executed correctly, the insights garnered can be incredibly valuable, offering depth and context that other methods may miss. Robert K\. Yin ’s advocacy for this method underscores its potential to illuminate and explain contemporary phenomena, making it an indispensable part of the researcher’s toolkit.

Reflecting on the case study method, how do you think its application could change with the advancements in technology and data analytics? Could such a traditional method be enhanced or even replaced in the future?

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Research Methods in Psychology

1 Introduction to Psychological Research – Objectives and Goals, Problems, Hypothesis and Variables

• Nature of Psychological Research
• The Context of Discovery
• Context of Justification
• Characteristics of Psychological Research
• Goals and Objectives of Psychological Research

2 Introduction to Psychological Experiments and Tests

• Independent and Dependent Variables
• Extraneous Variables
• Experimental and Control Groups
• Introduction of Test
• Types of Psychological Test
• Uses of Psychological Tests

3 Steps in Research

• Research Process
• Identification of the Problem
• Review of Literature
• Formulating a Hypothesis
• Identifying Manipulating and Controlling Variables
• Formulating a Research Design
• Constructing Devices for Observation and Measurement
• Sample Selection and Data Collection
• Data Analysis and Interpretation
• Hypothesis Testing
• Drawing Conclusion

4 Types of Research and Methods of Research

• Historical Research
• Descriptive Research
• Correlational Research
• Qualitative Research
• Ex-Post Facto Research
• True Experimental Research
• Quasi-Experimental Research

5 Definition and Description Research Design, Quality of Research Design

• Research Design
• Purpose of Research Design
• Design Selection
• Criteria of Research Design
• Qualities of Research Design

6 Experimental Design (Control Group Design and Two Factor Design)

• Experimental Design
• Control Group Design
• Two Factor Design

7 Survey Design

• Survey Research Designs
• Steps in Survey Design
• Structuring and Designing the Questionnaire
• Interviewing Methodology
• Data Analysis
• Final Report

8 Single Subject Design

• Single Subject Design: Definition and Meaning
• Phases Within Single Subject Design
• Requirements of Single Subject Design
• Characteristics of Single Subject Design
• Types of Single Subject Design
• Advantages of Single Subject Design
• Disadvantages of Single Subject Design

9 Observation Method

• Definition and Meaning of Observation
• Characteristics of Observation
• Types of Observation
• Advantages and Disadvantages of Observation
• Guides for Observation Method

10 Interview and Interviewing

• Definition of Interview
• Types of Interview
• Aspects of Qualitative Research Interviews
• Interview Questions
• Convergent Interviewing as Action Research
• Research Team

11 Questionnaire Method

• Definition and Description of Questionnaires
• Types of Questionnaires
• Purpose of Questionnaire Studies
• Designing Research Questionnaires
• The Methods to Make a Questionnaire Efficient
• The Types of Questionnaire to be Included in the Questionnaire
• Advantages and Disadvantages of Questionnaire
• When to Use a Questionnaire?

12 Case Study

• Definition and Description of Case Study Method
• Historical Account of Case Study Method
• Designing Case Study
• Requirements for Case Studies
• Guideline to Follow in Case Study Method
• Other Important Measures in Case Study Method
• Case Reports

13 Report Writing

• Purpose of a Report
• Writing Style of the Report
• Report Writing – the Do’s and the Don’ts
• Format for Report in Psychology Area
• Major Sections in a Report

14 Review of Literature

• Purposes of Review of Literature
• Sources of Review of Literature
• Types of Literature
• Writing Process of the Review of Literature
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15 Methodology

• Definition and Purpose of Methodology
• Participants (Sample)
• Apparatus and Materials

16 Result, Analysis and Discussion of the Data

• Definition and Description of Results
• Statistical Presentation
• Tables and Figures

17 Summary and Conclusion

• Summary Definition and Description
• Guidelines for Writing a Summary
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• A Process for Paraphrasing and Summarising
• Summary of a Report
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18 References in Research Report

• Reference List (the Format)
• References (Process of Writing)
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Direct observation methods: A practical guide for health researchers

Gemmae m. fix.

a VA Center for Healthcare Organization and Implementation Research, Bedford and Boston, MA, USA

b General Internal Medicine, Boston University School of Medicine, Boston, MA, USA

c Department of Psychiatry, Harvard Medical School, Boston, MA, USA

Mollie A. Ruben

d Department of Psychology, University of Maine, Orono, ME, USA

Megan B. McCullough

e Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA

To provide health research teams with a practical, methodologically rigorous guide on how to conduct direct observation.

Synthesis of authors’ observation-based teaching and research experiences in social sciences and health services research.

This article serves as a guide for making key decisions in studies involving direct observation. Study development begins with determining if observation methods are warranted or feasible. Deciding what and how to observe entails reviewing literature and defining what abstract, theoretically informed concepts look like in practice. Data collection tools help systematically record phenomena of interest. Interdisciplinary teams--that include relevant community members-- increase relevance, rigor and reliability, distribute work, and facilitate scheduling. Piloting systematizes data collection across the team and proactively addresses issues.

Observation can elucidate phenomena germane to healthcare research questions by adding unique insights. Careful selection and sampling are critical to rigor. Phenomena like taboo behaviors or rare events are difficult to capture. A thoughtful protocol can preempt Institutional Review Board concerns.

This novel guide provides a practical adaptation of traditional approaches to observation to meet contemporary healthcare research teams’ needs.

Graphical abstract

• • Health research study designs benefit from observations of behaviors and contexts
• • Direct observation methods have a long history in the social sciences
• • Social science approaches should be adapted for health researchers’ unique needs
• • Health research observations should be feasible, well-defined and piloted
• • Multidisciplinary teams, data collection tools and detailed protocols enhance rigor

1. Introduction

Health research studies increasingly include direct observation methods [ [1] , [2] , [3] , [4] , [5] ]. Observation provides unique information about human behavior related to healthcare processes, events, norms and social context. Behavior is difficult to study; it is often unconscious or susceptible to self-report biases. Interviews or surveys are limited to what participants share. Observation is particularly useful for understanding patients’, providers’ or other key communities’ experiences because it provides an “emic,” insider perspective and lends itself to topics like patient-centered care research [ 1 , 5 , 6 ]. This insider perspective allows researchers to understand end users’ experiences of a problem. For example, patients may be viewed as “non-compliant,” while observations can reveal daily lived experiences that impede adherence to recommended care [ [7] , [8] , [9] , [10] ]. Observation can examine the organization and structure of healthcare delivery in ways that are different from, and complementary to, methods like surveys, interviews, or database reviews. However, there is limited guidance for health researchers on how to use observation.

Observation has a long history in the social sciences, with participant observation as a defining feature of ethnography [ [11] , [12] , [13] ]. Observation in healthcare research differs from the social sciences. Traditional social science research may be conducted by a single individual, while healthcare research is often conducted by multidisciplinary teams. In social science studies, extended time in the field is expected [ 11 ]. In contrast, healthcare research timelines are often compressed and conducted contemporaneous with other work. Compared to social science research questions, healthcare studies are typically targeted with narrowly defined parameters.

These disciplinary differences may pose challenges for healthcare researchers interested in using observation. Given observation’s history in the social sciences there is a need to tailor observation to the healthcare context, with attention to the dynamics and needs of the research team. This paper provides contemporary healthcare research teams a practical, methodologically rigorous guide on when and how to conduct observation.

This article synthesizes the authors’ experiences conducting observation in social science and health services research studies, key literature and experiences teaching observation. The authors have diverse training in anthropology (GF, MM), systems engineering (BK) and psychology (MR). To develop this guide, we reflected on our own experiences, identified literature in our respective fields, found common considerations across these, and had consensus-reaching discussions. We compiled this information into a format initially delivered through courses, workshops, and conferences. In keeping with this pedagogical approach, the format below follows the linear process of study development.

Following the trajectory of a typical health research project, from study development through data collection, analysis and dissemination ( Fig. 1 ), we describe how to design and conduct observation in healthcare related settings. We conclude with data analysis, dissemination of findings, and other key guidance. Importantly, while illustrated as a linear process, many steps inform each other. For example, analysis and dissemination, can inform data collection.

Direct observation across a health research study.

3.1. Study development

3.1.1. study design and research questions.

In developing research using observation, the first step is determining if observation is appropriate. Observation is ideal for studies about naturally occurring behaviors, actions, or events. These include explorations of patient or provider behaviors, interactions, teamwork, clinical processes, or spatial arrangements. The phenomena must be feasible to collect. Sensitive or taboo topics like substance use or sexual practices are better suited to other approaches, like one-on-one interviews or anonymous surveys. Additionally, the phenomena must occur frequently enough to be captured. Trying to observe rare events requires considerable time while yielding little data. Early in the study design process, the scope and resources should be considered. The project budget and the timeline need to account for staffing, designing data collection tools, and pilot testing.

Research questions establish the study goals and inform the methods to accomplish them. In a study examining patients’ experiences of recovery from open heart surgery, the ethnographic study design included medical record data, in-depth interviews, surveys, and observations of patients in their homes, collected over three months following surgery [ 7 ]. By observing patients in their homes GF saw how the household shaped post-surgical diet and exercise. Table 1 provides additional examples of healthcare studies using observation, often as part of a larger, mixed-method design [ 14 , 15 ].

Example studies that use observation.

3.1.2. Data collection procedures

The phenomena to observe should be clearly defined. Research team discussions create a unified understanding of the phenomena, clarify what to observe and record, and ensure data collection consistency. This explication specifies what to look for during observation. For example, a team might operationalize the concept of patient-centered care into specific actions, like how the provider greets the patient. Further, additional nuances within broader domains (e.g., patient-centered care) could be identified while observations are ongoing. The team may identify unanticipated ways that providers enact patient-centered care (e.g., raising non-clinical, but relevant psychosocial topics- like vacations or hobbies- prior to gathering biomedical information). It is also important to look for negative instances, or behaviors that did not happen that should have, or surprising, unexpected findings. A surprise finding during observation was the impetus for further analysis examining how HIV providers think about their patients. While observing HIV care, a provider made an unexpected, judgmental comment about patients who seek pre-exposure prophylaxis (PrEP) to prevent HIV. This statement was documented in the fieldnotes (see 3.1.3 for a further description of fieldnotes) and later discussed with the team, leading to review of other study data and an eventual paper (see Fix et al 2018) [ 1 ]. Leaving room, both literally on the template and conceptually, can provide space for new, unexpected insights.

The sampling strategy outlines the frequency and duration of what is observed and recorded. It requires determining the unit of observation and the observation period. Units of observation are sometimes called “slices” of data. Ambady and Rosenthal [ 20 ] coined the term thin slices, using brief exposures of behavior (6s, 15s, and 30s) to predict teacher effectiveness. While thin slices are predominantly used in psychology, healthcare researchers can apply this concept by recording data for set blocks of time in a larger process, such as recording emergency department activity for the first 15 minutes of each hour.

The unit of observation can be a person (e.g., patient, provider), their behavior (e.g., smiling, eye rolling), an event (e.g., shift change) or interaction (e.g., clinical encounter). Using interactions as the unit of observation requires consideration for repeat observations of some individuals. For example, a fixed number of providers may be repeatedly observed with different patients.

Observation frequency will depend on the frequency of the phenomena. Enough data is needed for variation while also achieving “saturation,” a concept from qualitative methods, which means the point in data collection when no new information is obtained [ 21 ]. For quantitative studies, when examining the relationship between a direct observation measure (e.g., patient smiling) and an outcome (e.g., patient satisfaction), effect sizes from past research should dictate the number of interactions needed to achieve power to detect an effect. The duration of observation (the data slice) can be constrained using parameters as broad as a clinic workday, to distinct events like a clinical encounter.

Observation data can be collected on a continuous, rolling basis, or at predefined intervals. Continuous sampling is analogous to a motion picture—the recorded data mirrors the flow of information captured in a video [ 22 ]. Continuous observation is ideal for understanding what happens throughout an event. It is labor intensive and time-consuming and may result in a small number of observations, although each observation can yield considerable data. For example, a team may want to know about the patient-centeredness of patient-provider interactions. Continuous sampling of a clinical encounter could start when the patient arrives through when they leave, with detailed data collected about both the verbal and nonverbal communication. This could be considered an N of one observation but would yield substantial data. This information could be collected over a continuous day of encounters across several providers and patients, resulting in a considerable amount of data for a small group of people.

In contrast, instantaneous sampling can be conceptualized as snapshots, and is analogous to the thin slice methodology. Psychology research sometimes uses random intervals, while in healthcare research it may be preferable to use predetermined criteria or intervals [ 23 ]. Instantaneous sampling is economical and data collection can happen flexibly across a variety of individuals or times of day or weeks. Disadvantages include losing some of the context that is gained through continuous sampling.

3.1.3. Data collection tools

Data collection tools enable systematic observations, codifying what to observe and record. These tools vary from open-ended to highly structured, depending on the research question(s) and what is known a priori. We describe below three general tool categories—descriptive fieldnotes, semi-structured templates, and structured templates.

3.1.3.1. Descriptive fieldnotes

Descriptive fieldnotes, common in anthropology, are open-ended notes recorded with minimal a priori fields. Descriptive fieldnotes are ideal for research questions where less is known. An almost blank page is used to record the phenomena of interest. Key information such as date, time, location, people present and who recorded the information are useful for later analysis. These notes are jotted sequentially in real-time to maximize data collection, and are filled out and edited later for clarity and details. The flexible and open format facilitates the capture of unanticipated events or interactions.

Descriptive fieldnotes describe in detail what is observed (e.g., who is present, paraphrased statements), while leaving out interpretation. Analytic notes, that interpret what is being observed, can accompany the descriptive notes (e.g., the doctor is frowning and seems skeptical of what the patient is saying), but these analytic notes should be clearly marked as interpretation. One author (GF) demarcates interpretive portions of her fieldnotes using [closed brackets] to identify this portion of the fieldnote as distinct from the descriptive data. Interpretive notes should explain why the observer thinks this might be the case, using supporting data from the observation. Building on the example above, an accompanying interpretive note might say, “[the doctor raised their eyebrows, and does not seem to believe what the patient is saying, similar to what was observed in another encounter- see site 5 fieldnote). This information can be valuable during analysis to contextualize what was recorded and used in a later report or paper. Observation experience builds comfort and expertise with the open-ended, unstructured format.

3.1.3.2. Semi-structured templates

A semi-structured template comprises both open-ended and structured fields ( Fig. 2 ). It includes the same key information described above (i.e., date, time, etc.), then provides prompts for a priori concepts underlying the research questions, often derived from a theoretical model. These literature-based, theoretical concepts should be clearly defined and operationalized. For example, drawing from Street et al’s [ 24 ] framework for patient-centered communication, we can use their six functions (fostering the patient-clinician relationship, exchanging information, responding to emotions, managing uncertainty, making decisions, and enabling self-management) to develop categories for semi-structured coding a template. Like descriptive fieldnotes, the template also provides open-ended space for capturing contextual details about the a priori data recorded in the structured section.

Semi-Structured Observation Template.

3.1.3.3. Structured templates

A structured template in the form of a checklist or recording sheet captures specific, pre-determined phenomena. Structured templates are most useful when the phenomena are known. These templates are commonly used in psychology and engineering. Structured observations are more deductive and based on theoretical models or literature-based concepts. The template prompts the observer to record whether a phenomenon occurred, its frequency, and sometimes its duration or quality. See Keen [ 5 ] or Roter [ 25 ] for example structured templates for recording patient-centered care or patient-provider communication.

All templates should include key elements like the date, time and observer. Descriptive fieldnotes and semi-structured templates should be briefly filled out during the observation, and then written more thoroughly immediately afterwards. Setting aside time during data collection, such as a few hours at the end of each day, facilitates completion of this step. Recording information immediately, rather than weeks or months later, enhances data quality by minimizing recall bias. If written too much later, the recorder might fill in holes in their memory with inaccurate information. Further, small details, written while memories are fresh, may seem unremarkable but later provide critical insights.

For the semi-structured and structured templates, which contain prepopulated fields, there should be an accompanying “codebook” of definitions describing the parameters for each field. For example, building on the previous example using Street et al’s constructs, the code “responding to emotions” could identify instances where patients appear to be sad or worried and the provider responds to these emotions (also termed empathic opportunities and empathic responses) by eliciting, exploring, and validating the patients’ emotions [ 25 , 26 ]. This process operationally defines each concept and facilitates more reliable data capture. If space allows, the codebook can be included in the template and referenced during data collection. Codebooks should be updated through team discussion and as observations are piloted. Definitions from the codebook can be used in later reports and manuscripts.

3.2. Piloting

Given the real-world context within which observation data is collected, pilot-testing helps ensure that ideas work in practice. Piloting provides an opportunity to ensure the research plan works and reduce wasted resources. For example, piloting could reveal issues with the sampling plan (e.g., the phenomena do not happen frequently enough), staffing capacity (e.g., there are too many people to follow) or the codebook (e.g., few of the items specified in the data collection template are observed). Further, piloting gives the team a chance to systematize data collection and address issues before they interfere with the overall study integrity. This process guides what refinements need to be made to the data collection procedures. Piloting should be done at least once in a setting comparable to the intended setting.

3.3. Collecting data, analysis and dissemination

Healthcare studies are commonly conducted by interdisciplinary teams. The observation team should include at minimum two people, including someone with prior observation experience. Having more than one person collecting data increases capacity, distributes the workload and facilitates scheduling flexibility. Multiple observers complement each other’s perspectives and can provide diverse analytic insights. The observers should be engaged early in the research process. Having regular debriefing meetings during data collection ensures data quality and reliability in data collection. Adding key members of relevant communities to the team, such as patients or providers, can further enhance the relevance and help the research team think about the implications of the work.

Observational data collection often takes place in fast-paced clinical settings. For paper-based data collection, consolidating the materials on a clipboard and/or using colored papers or tabs, facilitates access. An electronic tablet to enter information directly bypasses the need for later, manual data entry.

Data analysis should be considered early in the research process. The analytic plan will be informed by both the principles of the epistemological tradition from which the overall study design is drawn and the research questions. Studies using observation are premised on a range of epistemological traditions. Analytical approaches, standards, and terminology differ between anthropologically informed qualitative observations recorded using descriptive fieldnotes versus structured, quantitative checklists premised on psychological or systems engineering principles. A full description of analysis is thus beyond the scope of this guide. Analytic strategies can be found in discipline-specific texts, such as Musante and DeWalt [ 27 ], anthropology; Suen and Ary [ 28 ], psychology; or Lopetegui et al [ 29 ], systems engineering. Regardless of discplinary tradition, analytic decisions should be made based on the study design, research question(s), and objective(s).

Dissemination is a key, final step of the research process. Observation data lends itself to a rich description of the phenomena of interest. In health research, this data is often part of a larger mixed methods study. The observation protocol should be described in a manuscript’s methods section; the results should report on what was observed. Similar to reporting of interview data, the observed data should include key descriptors germane to the research question, like actors, site number, or setting. See Fix et al [ 1 ] and McCullough et al [ 4 ] for examples on how to include semi-structured, qualitative observation data in a manuscript and Waisel et al [ 17 ] and Kuhn et al [ 19 ] for examples of reporting structured, quantitative data in a manuscript.

3.4. Institutional review boards

Healthcare Institutional Review Boards may be unfamiliar with observation. Being explicit about data collection can proactively address concerns. The protocol should detail which individuals will be observed, if and how they will be consented and what will and will not be recorded. Using a reference like the Health Insurance Portability and Accountability Act (HIPAA) identifiers (e.g., name, street address) can guide what identifiable information is collected. The protocol should also describe how the team will protect data, especially while in the field (e.g., “immediately after data collection, written informed consents will be taken to an office and locked in a filing cabinet”).

There are unique risks in studies using observation because data is collected in “the field.” Precautions attentive to these settings protect both participants and research team members. A detailed protocol should describe steps to address potential issues, including rare or distressing events, or what to do if a team member witnesses a clinical emergency or a participant discloses trauma. Additionally, team members may need to debrief after distressing experiences.

4. Discussion & conclusion

4.1. discussion.

The ability to improve healthcare is limited if real-world data are not taken into account. Observation methods can elucidate phenomena germane to healthcare’s most vexing problems. Considerable literature documents the discrepancy between what people report and their behavior [ [30] , [31] , [32] ]. Direct observation can provide important insights into human behavior. In their ethnographic evaluation of an HIV intervention, Evans and Lambert [ 31 ] found, “observation of actual intervention practices can reveal insights that may be hard for [participants] to articulate or difficult to pinpoint, and can highlight important points of divergence and convergence from intervention theory or planning documents.” Further, they saw ethnographic methods as a tool to understand “hidden” information in what they call “private contexts of practice.” While in Rich et al.’s work [ 32 ], asthmatic children were asked about exposure to smoking. Despite not reporting smoking in the home, videos recorded by the children—part of the study design—documented smokers outside their home. The use of observation can help explain research questions as diverse as patients’ health behaviors [ 7 , 10 , 32 ], healthcare delivery [ 3 , 4 ] or the outcomes of a clinical trial [ 9 , 33 ].

A common critique in healthcare research is that observing behavior will change behavior, a concept known as the Hawthorne Effect. Goodwin’s study [ 34 ], using direct observation of physician-patient interactions, explicitly examined this phenomena and found a limited effect. We authors have observed numerous instances of unexpected behavior of healthcare employees such as making disparaging comments about patients, eye rolling, or eating in sterile areas. Thus, those of us who conduct observation often say that if behavior change were as easy as observing people, we could simply place observers in problematic healthcare settings.

The descriptions above on how to use observation are applicable to fields like health services research and implementation and improvement sciences which have similarly adapted other social science approaches.[ [35] , [36] , [37] , [38] , [39] , [40] ] Notably, unlike the social sciences, many health researchers work in teams and thus this guide is written for team-based work. Yet, health researchers sometimes also conduct observations without support from a larger team. While this may be done because of resource constraints, it may raise concerns about the validity of the observations. First, social sciences have a long history of solo researchers collecting and analyzing data, yielding robust, rigorous findings [ 13 , [41] , [42] , [43] ]. Using strategies, such as those outlined above (i.e., writing detailed, descriptive fieldnotes immediately; keeping interpretations separate from the data; looking for negative cases) can enhance rigor. Further, constructs like validity are rooted in quantitative, positivist epistemologies and need to be adapted for naturalistic study designs, like those that include direct observation [ 44 ].

4.2. Innovation

Social science-informed research designs, such as those that include observation, are needed to tackle the dynamic, complex, “wicked problem” that impede high quality healthcare [ 45 ]. Thoughtful, rigorous use of observation tailored to the unique context of healthcare can provide important insights into healthcare delivery problems and ultimately improve healthcare.

Additionally, observation provides several ways to involve key communities, like patients or providers, as participants. Observing patient participants can provide information about healthcare processes or structures, and inform research about patient experiences of care or the extent of patient-centeredness. With the movement towards engaging end users in research, these individuals can contribute more meaningfully [ 46 , 47 ]. As team members, they can define the problem, inform what to observe, how to observe, help interpret data and disseminate findings.

4.3. Conclusion

Observation’s long history in the social sciences provides a robust body of work with strategies that can be inform healthcare research. Yet, traditional social science approaches, such as extended, independent fieldwork may be untenable in healthcare settings. Thus, adapting social science approaches can better meet healthcare researchers’ needs.

This paper provides an innovative, yet practical adaptation of social science approaches to observation that can be feasibly used by health researchers. Team meetings, developing data collection tools and protocols, and piloting, each enhance study quality. During development, teams should determine if observation is an appropriate method. If so, the team should then discuss what and how to collect the data, as described above. Piloting improves data collection procedures. While many aspects of observation can be tailored to health research, analysis is informed by epistemological traditions. Having clear steps for health researchers to follow can increase the rigor or credibility of observation.

Rigorous utilization of observation can enrich healthcare research by adding unique insights into complex problems. This guide provides a practical adaptation of traditional approaches to observation to meet healthcare researchers’ needs and transform healthcare delivery.

This work was supported by the US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development. Dr. Fix is a VA HSR&D Career Development awardee at the Bedford VA (CDA 14-156). Drs. Fix, Kim and McCullough are employed at the Center for Healthcare Organization and Implementation Research, where Dr. Ruben was a postdoctoral fellow. The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Competing Interest

All authors declared no conflict of interests.

Acknowledgements

This work has been previously presented as workshops at the 2015 Veteran Affairs Health Services Research & Development / Quality Enhancement Research Initiative National Meeting (Philadelphia, PA) and the 2016 Academy Health Annual Research Meeting (Boston, MA). We would like to acknowledge Dr. Shihwe Wang for participating in the 2015 workshop; Dr. Adam Rose for encouragement and helpful comments; and the VA Anthropology Group for advancing the utilization of direct observation in the US Department of Veteran Affairs. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

6.5 Observational Research

Learning objectives.

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

What Is Observational Research?

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

Naturalistic Observation

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

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

Participant Observation

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

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

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

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

Structured Observation

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

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

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

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

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

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

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

Case Studies

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

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

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

www.youtube.com/watch?v=KkaXNvzE4pk

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

The Case of “Anna O.”

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

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

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

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

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

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

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

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

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

Archival Research

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

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

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

Key Takeaways

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

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Observation Method in Psychology: Naturalistic, Participant and Controlled

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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:

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

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

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

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

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

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

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

Controlled Observation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Limitations

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

Naturalistic Observation

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

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

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

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

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

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

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

Collecting Naturalistic Behavioral Data

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

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

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

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

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

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

Participant Observation

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

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

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

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

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

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

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

Recording of Data

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

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

The three main sampling methods are:

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

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

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

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

Coding Systems

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

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

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

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

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

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

Macroanalytic coding systems

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

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

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

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

Examples of Macroanalytic Coding Systems:

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

Microanalytic coding systems

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

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

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

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

Examples of Microanalytic Coding Systems:

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

Mesoanalytic coding systems

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

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

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

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

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

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

Examples of Mesoanalytic Coding Systems:

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

Preventing Coder Drift

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

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

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

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

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

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

Reducing Observer Bias

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

Coding Manual

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

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

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

Coder Training

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

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

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

Blind Methods

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

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

Separate teams collecting data versus coding data can reduce bias.

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

Data Analysis Approaches

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

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

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

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

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

simple descriptive statistics

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

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

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

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

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

contingency analyses

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

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

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

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

sequential analyses

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

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

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

observer agreement

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

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

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

advanced statistical approaches

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

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

representation techniques

Representation techniques help organize and visualize data:

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

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

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

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

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

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

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

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

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

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

• Research Methods
• Post last modified: 26 August 2021
• Reading time: 36 mins read
• Post category: Research Methodology

4 Research Methods

4 Major Research Methods are:

Observations

Schedule and questionnaire, case study method.

Table of Content

• 1.1.1 Types of Interview
• 1.1.2 Features of Interviews
• 1.1.3 Essentials for an Effective Interview
• 1.1.4 Advantages of Interviews
• 1.1.5 Disadvantages of Interviews
• 1.1.6 Interview Process
• 1.1.7 Problems Faced in an Interview
• 1.2.1 Characteristics of Observation Method
• 1.2.2 Types of Observation
• 1.2.3 Prerequisites of Observation
• 1.2.4 Advantages of observations
• 1.2.5 Disadvantages of observations
• 1.2.6 Use of observation in business research
• 1.3.1 Importance of questionnaires
• 1.3.2 Types of Questionnaire
• 1.3.3 Advantages of Questionnaires
• 1.3.4 Disadvantages of Questionnaires
• 1.3.5 Preparation of an Effective Questionnaire
• 1.3.6 Difference between schedule and questionnaire
• 1.4.1 Assumptions of case study method
• 1.4.2 Advantages of Case Study Method
• 1.4.3 Disadvantages of Case Study Method
• 1.4.4 Case study as a method of business research

Interviewing is a very effective method of data collection. It is a systematic and objective conversation between an investigator and respondent for collecting relevant data for a specific research study. Along with conversation, learning about the gestures, facial expressions and environmental conditions of a respondent are also very important.

Generally, interview collects a wide range of data from factual demographic data to highly personal and intimate information relating to a person’s opinions, attitudes, values and beliefs, past experience and future intentions.

The interview method is very important in the collection of data from the respondent who is less educated or illiterate. Personal interview is more feasible when the area covered for survey is compact. Probing is a very important part of an interview.

Types of Interview

The following are the various types of interviews:

Structured or directive interview

In this type of interview, the investigator goes to the respondent with a detailed schedule. Some questions in same sequence are asked from all respondents.

Unstructured or non-directive interview

In this type of interview, the respondent is encouraged to give his honest opinion on a given topic without or with minimum help from others.

Focused interview

This is a semi-structured interview where the respondent shares the effect of the experience to the given conditions with the researcher or investigator. It is conducted with those respondents only who have prior experience of conditions given by the investigator.

Analysis of the attitude, emotional feelings for the situations under study is main purpose behind conducting these interviews. A set of fix questions may not be required in this interview but a relevant topic is required which is known to the respondent.

Clinical interview

While a focussed interview is concerned with effects of specific experience, clinical interviews are concerned with broad underlying feelings or motivations or the course of the individual’s life experiences with reference to the research study. It encourages the interviewee to share his experience freely.

Depth interview

To analyse or study the respondent’s emotions, opinions, etc., depth interviews are conducted. This kind of interview aims to collect intensive data about individuals, especially their opinions.

It is a lengthy process to get unbiased data from the respondent. Interviewers should avoid advising or showing this agreement. Instead, the investigator has to motivate the respondent to answer the questions.

Features of Interviews

The following are some of the features of interviews

• The interviewer and the respondent are the participants in any interview. They both are unknown to each other and so it is important for an interviewer to introduce himself first to the respondent.
• An interview has a beginning and a termination point in the relationship between the participants.
• Interview is not a mere casual conversational exchange. It has a specific purpose of collecting data which is relevant to the study.
• Interview is a mode of obtaining a verbal response to questions to put verbally. It is not always face to face.
• Success of interview depends on the interviewer and respondent and how they perceive each other.
• It is not a standardized process.

Essentials for an Effective Interview

The following are the requirements for a successful interview:

• Data availability : The respondent should have complete knowledge of the information required for specific study.
• Role perception : The interviewer and the respondent should be aware of their roles in the interview process. The respondent should be clear about the topic or questions which have to be answered by him. Similarly, it is the responsibility of the interviewer to make the respondent comfortable by introducing himself first. The investigator should not affect the interview situation through subjective attitude and argumentation.
• Respondent’s motivation : The respondent can hesitate to answer the questions. In this case, the approach and skills of the interviewer are very important as he has to motivate the respondent to answer or express himself.

Advantages of Interviews

The following are the advantages of the interview method:

• In-depth and detailed information is collected.
• The interviewer tries to improve the responses and quality of data received.
• He can control the conditions in favour of the research study.
• Interviews help in gathering supplementary information which can be helpful to the study.
• Interviews use special scoring devices, visuals and materials to improve the quality of data or information collected.
• Interviews use observation and probing by the interviewer to see the accuracy and dependability of given data by the respondent.
• Interviews are flexible in nature.

Disadvantages of Interviews

The following are the disadvantages of interviews:

• Interviews consume more time and cost.
• The respondent’s responses can be affected by the way the interviewer asks the questions.
• The respondent may refuse to answer some personal questions which are relevant to the study.
• Recording and coding of data during the interview process may sometimes be difficult for the interviewer.
• The interviewer may not have good communication or interactive skills.

Interview Process

The following are the stages in an interview process:

Preparation

The interviewer needs to make certain preparations to make an interview successful. The interviewer should keep all the copies of the schedule or guide ready. They need to prepare the lists of respondents with their addresses, contact number and meeting time.

They should prepare themselves with all the approaches and skills required to conduct an interview. They should prepare themselves to face all adverse situations during the interview. If the interviewer is not doing such planning, they can fail to collect the right information from respondent.

Introduction

The interviewer is not known to the respondent. Therefore, the interviewer must introduce himself first to every respondent. In the introduction, the interviewer should tell about himself, his organization details and the purpose of his visit.

If the interviewer knows someone who the respondent is familiar with, then he can use that person’s reference to make the respondent more comfortable. The following are some steps which help in motivating the respondent:

• The interviewer should introduce himself with a smiling face and always greet the respondent.
• He should identify and call the respondent by name.
• He must describe how the respondent is selected.
• He should explain the purpose and usefulness of the study.
• He should focus on the value of the respondent’s cooperation.

Developing report

It is important for an interviewer to develop a rapport with the respondent before starting the interview. By doing this, a cordial relationship is established between them. It helps the interviewer understand the inherent nature of the respondent which helps in building a rapport and the discussion can be started with some general topic or with the help of a person who is commonly known to both of them.

Carrying the interview forward

After establishing a rapport, the skills of the interviewer are required to carry the interview forward. The following are some guidelines that should be followed:

• Start the interview in an informal and natural manner.
• Ask all the questions in the same sequence as in the schedule.
• Do not take an answer for granted. It is not necessary that an interviewee will know all answers or will give all answers. The interviewer has to create interest for answering questions.
• The objective of the question should be known to the interviewer to ensure that the correct information is collected for research study.
• Explain the question if it has not been understood properly by the respondent.
• Listen to the respondent carefully with patience.
• Never argue with the respondent.
• Show your concern and interest in the information given by the respondent.
• Do not express your own opinion for answers of any question in the schedule.
• Continue to motivate the respondent.
• If the respondent is unable to frame the right answer, the interviewer should help him by providing alternate questions.
• Ensure that the conversation does not go off track.
• If the respondent is unable to answer a particular question due to some reasons, drop the question at that moment. This question can be asked indirectly later on.

Recording the interview

Responses should be recorded in the same sequence as they are given by the respondent. The response should be recorded at the same time as it is generated. It may be very difficult to remember all the responses later for recording them.

Recording can be done in writing but there may be some problems if the writing skills of an interviewer are not good. Hence, the use of electronic devices like tape recorders can help in this purpose. The interviewer should also record all his probes and other comments on the schedule, but they should be in brackets to ensure that they are set off from response.

Closing the interview

After the interview is over, the interviewer must thank the respondent for his cooperation. He must collect all the papers before leaving the respondent. If the respondent wants to know the result of the survey, the interviewer must ensure that the results are mailed to him when they are ready.

At the end, the interviewer must edit the schedule to check that all the questions have been asked and recorded. Also, abbreviations in recording should be replaced by full words.

Problems Faced in an Interview

The following are some of the main problems faced in an interview:

Inadequate response

Kahn and Cannel laid down five principal symptoms of inadequate response. They are given as follows:

• Partial response in which the respondent gives a relevant but incomplete answer.
• Non-response in which the respondent remains silent or refuses to answer the questions.
• Irrelevant response in which the respondent’s answer is not relevant to the question asked.
• Inaccurate response in which the reply is biased.
• Verbalized response problem which arises because of the respondent’s failure to understand the question.

Interviewer’s biasness, refusal, incapability to understand questions

An interviewer can affect the performance of an interview with his own responses and suggestions. Such biasing factors can never be overcome fully, but their effect can be reduced by training and development techniques.

Non response

Some respondents out of the total respondents fail to respond to the schedule. The reasons for this non response can be non availability, refusal, incapability to understand questions, etc.

Non availability

Some respondents are not available at their places at the time of call. This could be because of odd timings or working hours.

Some respondents refuse to answer the questions. There can be many reasons for this, such as language, odd hours, sickness, no interest in such studies, etc.

Inaccessibility

Some respondents can be inaccessible because of various reasons such as migration, touring job, etc.

Observation can be defined as viewing or seeing. Observation means specific viewing with the purpose of gathering the data for a specific research study. Observation is a classical method of scientific study. It is very important in any research study as it is an effective method for data collection.

Characteristics of Observation Method

The following are the characteristics of the observation method of data collection:

• Physical and mental activity : Eyes observe so many things in our surroundings but our focus or attention is only on data which is relevant to research study.
• Observation is selective : It is very difficult for a researcher to observe everything in his surroundings. He only observes the data which is purposive for his research study and meets with the scope of his study. The researcher ignores all the data which is not relevant to the study.
• Observation is purposive and not casual : Observation is purposive as it is relevant to a particular study. The purpose of observation is to collect data for the research study. It focusses on human behaviour which occurs in a social phenomenon. It analyses the relationship of different variables in a specific context.
• Accuracy and standardization : Observation of pertinent data should be accurate and standardized for its applications.

Types of Observation

Different concepts define the classification of observations.

With respect to an investigator’s role, observation may be:

Participant observation

Non-participant observation

With respect to the method of observation, it can be classified into the following:

Direct observation

Indirect observation

With reference to the control on the system to be observed, observation can be classified into the following:

Controlled observation

Uncontrolled observation

In participant type of observation, the observer is an active participant of the group or process. He participates as well as observes as a part of a phenomenon;

For example, to study the behaviour of management students towards studying and understanding marketing management, the observer or researcher has to participate in the discussion with students without telling them about the observation or purpose. When respondents are unaware of observations, then only their natural interest can be studied.

In non-participant observation, the observer does not participate in the group process. He acknowledges the behaviour of the group without telling the respondents. It requires a lot of skills to record observations in an unnoticeable manner.

In direct observation, the observer and researcher personally observe all the happenings of a process or an event when the event is happening. In this method, the observer records all the relevant aspects of an event which are necessary for study.

He is free to change the locations and focus of the observation. One major limitation of the method is that the observer may not be able to cover all relevant events when they are happening.

Physical presence of an observer is not required and recording is done with the help of mechanical, photographic or electronic devices;

For example, close circuit TV (CCTV) cameras are used in many showrooms to observe the behaviour of customers. It provides a permanent record for an analysis of different aspects of the event.

All observations are done under pre-specified conditions over extrinsic and intrinsic variables by adopting experimental design and systematically recording observations. Controlled observations are carried out either in the laboratory or the field.

There is no control over extrinsic and intrinsic variables. It is mainly used for descriptive research. Participant observation is a typical uncontrolled one.

Prerequisites of Observation

The following are the prerequisites of observation:

• The conditions of observation must provide accurate results. An observer should be in a position to observe the object clearly.
• The right number of respondents should be selected as the sample size for the observation to produce the desired results.
• Accurate and complete recording of an event.
• If it is possible, two separate observers and sets of instruments can be used in all or some observations. Then the result can be compared to measure accuracy and completeness.

Advantages of observations

The following are the advantages of observations:

• It ensures the study of behaviour in accordance with the occurrence of events. The observer does not ask anything from the representatives, he just watches the doing and saying of the sample.
• The data collected by observation defines the observed phenomenon as they occur in their natural settings.
• When an object is not able to define the meaning of its behaviour, observation is best method for analysis; for example, animals, birds and children.
• Observation covers the entire happenings of an event.
• Observation is less biased as compared to questioning.
• It is easier to conduct disguised observation studies as opposed to disguised questioning.
• The use of mechanical devices can generate accurate results.

Disadvantages of observations

The following are the limitations of observation:

• Past studies and events are of no use to observation. For these events and study, one has to go through narrations, people and documents.
• It is difficult to understand attitudes with the help of observation.
• Observations cannot be performed by the choice of the observer. He has to wait for an event to occur.
• It is difficult to predict when and where the event will occur. Thus, it may not be possible for an observer to reach in every event.
• Observation requires more time and money.

Use of observation in business research

Observation is very useful in the following business research purposes:

• Buying behaviour of customer, lifestyles, customs, interpersonal relations, group dynamics, leadership styles, managerial style and actions.
• Physical characteristics of inanimate things like houses, factories, stores, etc.
• Movements in a production plant.
• Flow of traffic, crowd and parking on road.

Primary data can be collected with the help of emails and surveys. The respondents receive the questionnaires from the researcher and are asked to fill them completely and return them to the researcher. It can be performed only when the respondents are educated.

The mail questionnaire should be simple and easy to understand so that the respondents can answer all questions easily. In mail questionnaires, all the answers have to be given and recorded by the respondents and not by the researcher or investigator, as in the case of the personal interview method. There is no face-to-face interaction between the investigator and respondent and so the respondent is free to give answers of his own choice.

Importance of questionnaires

A questionnaire is a very effective method as well as research tool in any research study. It ensures the collection of a diversified and wide range of scientific data to complete the research objectives. The questionnaire provides all the inputs in the form of relevant data to all statistical methods used in a research study.

Types of Questionnaire

The following are the various categories of questionnaires:

• Structured or standard questionnaire Structured or standard questionnaires contain predefined questions in order to collect the required data for research study. These questions are the same for all the respondents. Questions are in the same language and in the same order for all the respondents.
• Unstructured questionnaire In unstructured questionnaires, the respondent has the freedom

Process of Data Collection

The researcher prepares the mailing list by collecting the addresses of all the respondents with the help of primary and secondary sources of data. A covering letter must accompany every questionnaire, indicating the purpose and importance of the research and importance of cooperation of the respondent for the success of the research study.

Advantages of Questionnaires

The following are the advantages of questionnaires:

• Wide reach and extensive coverage
• Easy to contact the person who is busy
• Respondent’s convenience in completion of questionnaire
• More impersonal, provides more anonymity
• No interviewer’s biasness

Disadvantages of Questionnaires

The following are the disadvantages of questionnaires:

• Low response by respondent
• Low scope in many societies where literary level is low
• More time requirement

Preparation of an Effective Questionnaire

While preparing a questionnaire, the researcher must focus on some key parameters to prepare it. These key parameters are as follows:

• Proper use of open and close probe
• Proper sequence of questions
• Use of simple language
• Asking no personal question in which the respondent is hesitating to answer
• Should not be time consuming
• Use of control questions indicating reliability of the respondent

Collecting Data through Schedule

This method is very similar to the collection of data through questionnaires. The only difference is that in schedule, enumerators are appointed. These enumerators go to the respondents, ask the stated questions in the same sequence as the schedule and record the reply of respondents.

Schedules may be given to the respondents and the enumerators should help them solve the problems faced while answering the question in the given schedule. Thus, enumerator selection is very important in data collection through schedules.

Difference between schedule and questionnaire

Both questionnaire and schedule are popular methods of data collection. The following are the main differences between questionnaire and schedule:

• A questionnaire is generally sent to the respondents through mail, but in case of schedule, it is sent through enumerators.
• Questionnaires are relatively cheaper mediums of data collection as compared to schedules. In the case of questionnaires, the cost is incurred in preparing it and mailing it to respondent, while in schedule, more money is required for hiring enumerators, training them and incurring their field expenses.
• The response rate in questionnaires is low as many people return it without filling. On the other hand, the response rate in schedules is high because they are filled by enumerators.
• In collecting data through questionnaires, the identity of the respondent may not be known, but this is not the case when it comes to schedules.
• Data collection through questionnaires requires a lot of time, which is comparatively very less in case of schedules.
• Generally, there is no personal contact in case of questionnaires, but in schedules, personal contact is always there.
• The literacy level of the respondent is very important while filling questionnaires, but in schedules, the literacy level of the respondent is not a major concern as the responses have to be recorded by enumerators.
• Wider distribution of questionnaires is possible but this is difficult with schedules.
• There is less accuracy and completeness of responses in questionnaires as compared to schedules.
• The success of questionnaires depends on the quality of questions but success of a schedule depends on the enumerators.
• The physical appearance of questionnaire matters a lot, which is less important in case of schedules.
• Observation method cannot be used along with questionnaires but it can be used along with schedule.

We explore and analyse the life of a social chapter or entity, whether it be a family, a person, an institution or a community, with the help of a case study. The purpose of case study method is to identify the factors and reasons that account for particular behaviour patterns of a sample chapter and its association with other social or environmental factors.

Generally social researchers use case study method to understand the complex social phenomenon and to identify the factors related to this phenomenon.

Case study provides the clues and ideas to a researcher for further research study. By adopting case study method, a researcher gets to know about happenings in the past, which could be related to the research studies and analyse the problem with better perspectives.

Assumptions of case study method

The assumptions made in a case study method are as follows:

• Case study depends on the imagination of the investigator who is analysing the case study. The investigator makes up his procedure as he goes along.
• History related to the case is complete and as coherent as it could be.
• It is advisable to supplement the case data by observational, statistical and historical data, since these provide standards for assessing the reliability and consistency of the case material.
• Efforts should be made to ascertain the reliability of life history data by examining the internal consistency of the material.
• A judicious combination of techniques of data collection is a prerequisite for securing data that is culturally meaningful and scientifically significant.

Advantages of Case Study Method

Key advantages of the case study method are as follows:

• Provides the basis for understanding complex social phenomenon and all related factors affecting the social phenomenon.
• Provides clues and ideas for exploratory research. When the researcher is not able to get a fair idea about the research, past happenings mentioned in a case study help the researcher get clues and ideas.
• Case study helps in generating objectives for exploratory research.
• It suggests the new courses of inquiry.
• Case study helps in formulating research hypothesis.

Disadvantages of Case Study Method

Some important disadvantages of case study method are as follows:

• Reliability : Data collected through case study may not be reliable or it can be difficult to verify the reliability of data in the current scenario.
• Adequacy : Data collected through case studies may not be adequate for research work as data is not pertinent to the research conditions.
• Representative : Data presented by case studies represents the happenings with unknown circumstances to a researcher. Hence, it cannot be the true representation of events to a researcher.

Case study as a method of business research

A detailed case study helps the researcher identify the reasons behind business related problems. As it can be possible that that particular incident has happened in past, so the current issues can be sorted out, by referring to the same case.

In depth analysis of selected cases is of particular value to business research when a complex set of variables may be at work in generating observed results and intensive study is needed to unravel the complexities.

The exploratory investigator should have an active curiosity and willingness to deviate from the initial plan, when the finding suggests a new course of enquiry, which might prove more productive. With the help of case study method, the risk can be minimized in any decision-making process.

Business Ethics

( Click on Topic to Read )

• What is Ethics?
• What is Business Ethics?
• Values, Norms, Beliefs and Standards in Business Ethics
• Indian Ethos in Management
• Ethical Issues in Marketing
• Ethical Issues in HRM
• Ethical Issues in IT
• Ethical Issues in Production and Operations Management
• Ethical Issues in Finance and Accounting
• What is Corporate Governance?
• What is Ownership Concentration?
• What is Ownership Composition?
• Types of Companies in India
• Internal Corporate Governance
• External Corporate Governance
• Corporate Governance in India
• What is Enterprise Risk Management (ERM)?
• What is Assessment of Risk?
• What is Risk Register?
• Risk Management Committee

Corporate social responsibility (CSR)

• Theories of CSR
• Arguments Against CSR
• Business Case for CSR
• Importance of CSR in India
• Drivers of Corporate Social Responsibility
• Developing a CSR Strategy
• Implement CSR Commitments
• CSR Marketplace
• CSR at Workplace
• Environmental CSR
• CSR with Communities and in Supply Chain
• Community Interventions
• CSR Monitoring
• CSR Reporting
• Voluntary Codes in CSR
• What is Corporate Ethics?

Lean Six Sigma

• What is Six Sigma?
• What is Lean Six Sigma?
• Value and Waste in Lean Six Sigma
• Six Sigma Team
• MAIC Six Sigma
• Six Sigma in Supply Chains
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Methodology

• Naturalistic Observation | Definition, Guide & Examples

Naturalistic Observation | Definition, Guide, & Examples

Published on February 10, 2022 by Pritha Bhandari . Revised on June 22, 2023.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering with or influencing any variables in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

Table of contents

What is naturalistic observation, types of naturalistic observation methods, how to collect data, data sampling, advantages of naturalistic observation, disadvantages of naturalistic observation, other interesting articles, frequently asked questions about naturalistic observation.

In naturalistic observations, you study your research subjects in their own environments to explore their behaviors without any outside influence or control. It’s a research method used in field studies.

Traditionally, naturalistic observation studies have been used by animal researchers, psychologists, ethnographers, and anthropologists. Naturalistic observations are helpful as a hypothesis -generating approach, because you gather rich information that can inspire further research.

Based on his naturalistic observations, he believed that these birds imprinted on the first potential parent in their surroundings, and they quickly learned to follow them and their actions.

Naturalistic observation is especially valuable for studying behaviors and actions that may not be replicable in controlled lab settings.

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Naturalistic observations can be:

• Covert or overt: You either hide or reveal your identity as an observer to the participants you observe.
• Participant or non-participant: You participate in the activity or behavior yourself, or you observe from the sidelines.

There are four main ways of using naturalistic observations.

Importantly, all of these take place in naturalistic settings rather than experimental laboratory settings. While you may actively participate in some types of observations, you refrain from influencing others or interfering with the activities you are observing too much.

You can use a variety of data collection methods for naturalistic observations.

Audiovisual recordings

Nowadays, it’s common to collect observations through audio and video recordings so you can revisit them at a later stage or share them with other trained observers. It’s best to place these recording devices discreetly so your participants aren’t distracted by them. This can lead to a Hawthorne effect , where participants change their behavior once aware they’re being recorded.

However, make sure you receive informed consent (in a written format ) from each participant prior to recording them.

Note-taking

You can take notes while conducting naturalistic observations. Note down anything that seems relevant or important to you based on your research topic and interests in an unstructured way.

Tally counts

If you’re studying specific behaviors or events, it’s often helpful to make frequency counts of the number of times these occur during a certain time period. You can use a tally count to easily note down each instance that you observe in the moment.

There’s a lot of information you can collect when you conduct research in natural, uncontrolled environments. To simplify your data collection , you’ll often use data sampling.

Data sampling allows you to narrow down the focus of your data recording to specific times or events.

Time sampling

You record observations only at specific times. These time intervals can be randomly selected (e.g., at 8:03, 10:34, 12:51) or systematic (e.g., every 2 hours). You record whether your behaviors of interest occur during these time periods.

Event sampling

You record observations only when specific events occur. You may use a tally count to note the frequency of the event or take notes each time you see the event occurring.

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Naturalistic observation is a valuable tool because of its flexibility, external validity, and suitability for research topics that can’t be studied in a lab.

Flexibility

Because naturalistic observation is a non-experimental method, you’re not bound to strict procedures. You can avoid using rigid protocols and also change your methods midway if you need to.

Ecological validity

Naturalistic observations are particularly high in ecological validity , because you use real life environments instead of lab settings. People don’t always act in the same ways in and outside the lab. Your participants behave in more authentic ways when they are unaware they’re being observed, mitigating the risk of a Hawthorne effect .

Naturalistic observations help you study topics that you can’t in the lab for ethical reasons. You can also use technology to record conversations, behaviors, or other noise, provided you have consent or it’s otherwise ethically permissible.

The downsides of naturalistic observation include its lack of scientific control, ethical considerations , and potential for bias from observers and subjects.

Lack of control

Since you perform research in natural environments, you can’t control the setting or any variables . Without this control, you won’t be able to draw conclusions about causal relationships . You also may not be able to replicate your findings in other contexts, with other people, or at other times.

Ethical considerations

Most people don’t want to be observed as they’re going about their day without their explicit consent or awareness. It’s important to always respect privacy and try to be unobtrusive. It’s also best to use naturalistic observations only in public situations where people expect they won’t be alone.

Observer bias

Because you indirectly collect data, there’s always a risk of observer bias in naturalistic observations. Your perceptions and interpretations of behavior may be influenced by your own experiences, and inaccurately represent the truth. This type of bias is particularly likely to occur in participant observation methods.

Subject bias

When you observe subjects in their natural environment, they may sometimes be aware they’re being observed. As a result, they may change their behaviors to act in more socially desirable ways to confirm your expectations, or the perception of high or low expectations may cause a Pygmalion effect .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

You can use several tactics to minimize observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure interrater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardize your observation procedures to make sure they are structured and clear.

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

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Back to Journals » International Journal of General Medicine » Volume 17

Association Between Periodontal Diseases and Hypothyroidism: A Case–Control Study

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Authors AlAhmari FM   , Albahouth HS , Almalky HA   , Almutairi ES , Alatyan MH , Alotaibi LA  

Received 19 June 2024

Accepted for publication 13 August 2024

Published 20 August 2024 Volume 2024:17 Pages 3613—3619

DOI https://doi.org/10.2147/IJGM.S476430

Checked for plagiarism Yes

Review by Single anonymous peer review

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Editor who approved publication: Dr Jacopo Manso

Fatemah Mohammed AlAhmari, 1 Hind Saleh Albahouth, 2 Hadeel Ali Almalky, 2 Ebtihal Saad Almutairi, 2 Muzun Hamoud Alatyan, 2 Lama Ali Alotaibi 2 1 Department of Periodontics and Community Dentistry, College of Dentistry, King Saud University, Riyadh, Saudi Arabia; 2 College of Dentistry, King Saud University, Riyadh, Saudi Arabia Correspondence: Fatemah Mohammed AlAhmari, King Saud University, Riyadh, Saudi Arabia, Email [email protected] Objective: Periodontal diseases are chronic inflammatory disorders influenced by systemic health of the individual. This study aimed to investigate the association between hypothyroidism and periodontal disease in a cohort of adult Saudi population. Methods: This case–control study included 201 adults with hypothyroidism on hormone replacement therapy and 188 healthy controls. The medical files of patients were reviewed to check thyroid stimulation hormone (TSH) and free thyroxine (FT4) levels. Participants completed a questionnaire on demographic and health information, followed by a comprehensive periodontal examination. Pearson chi-square and binary logistic regression analyses determined associations, with a significance set at p ≤ 0.05. Results: Gingivitis was found in 20.9% of cases and 58% of controls. Periodontitis stages I, II, III and IV were in general higher in cases compared to controls (23.4%, 27.9%, 21.9%, 6% in cases versus 13.8%, 17%, 9.6%, 1.6% in controls, respectively). Mean PPD and CAL values were higher in cases (5.54 ± 2.5 and 3.88 ± 3.1) than in controls (4.03 ± 1.6 and 1.72 ± 2.4). Significant associations between periodontal status and hypothyroidism were found (p Conclusion: The findings of the current study showed that, in a cohort of adult Saudi subjects, patients with hypothyroidism have higher prevalence and more severe periodontal disease symptoms compared to controls, suggesting significant association. Keywords: hypothyroidism, periodontal diseases, hormone replacement therapy, association, case–control study

Introduction

Periodontitis is one of the most common infectious diseases in humans. It is a chronic bacterial infection characterised by persistent inflammation, connective tissue breakdown and alveolar bone destruction. The chronic inflammation associated with the disease is attributed to the subgingival bacteria-induced immune response dysregulation. Severe periodontitis affects 7.4% of the world’s population, making it a serious global public health challenge. 1 , 2 Clinically, the disease can cause impaired function and aesthetics, adversely affects the overall quality of life of affected individuals, and if not properly treated, it will irreversibly progress and result in tooth loss. There is a growing body of evidence that indicates that periodontitis is independently associated with several systemic conditions, including cardiovascular disease, type 2 diabetes, respiratory diseases, premature birth, osteoporosis, Alzheimer’s disease, rheumatoid arthritis, and other autoimmune diseases. 2 , 3

Hypothyroidism is a common endocrine disorder identified as failure of the thyroid gland to produce adequate thyroid hormone to meet the metabolic demands of the body. If left untreated, it can lead to other significant comorbidities, such as hypertension, dyslipidemia, infertility, cognitive impairment, and neuromuscular dysfunction. 4 The most common cause of hypothyroidism is primary gland failure, either due to congenital causes, autoimmune thyroiditis, or infiltrative diseases. The disease can also occur due to insufficient thyroid gland stimulation by the hypothalamus or pituitary gland. Iodine deficiency, surgical thyroidectomy, and some medications can also induce hypothyroidism in some patients. Autoimmune thyroid disease is the most common aetiology of hypothyroidism in the United States. In some other countries, iodine deficiency is highly prevalent, causing hypothyroidism affecting children and infants too. 5 , 6

Periodontitis shares risk factors with other chronic noncommunicable diseases and has bidirectional associations with general health and other systemic diseases. There is a growing global consensus that improving oral and periodontal health positively impact the systemic health and well-being. Previous investigations had suggested a reciprocal relationship between endocrine disorders, including hypothyroidism, and periodontal diseases and that this relationship is mediated through the immune system. Hypothyroidism may be associated with an increased risk of periodontal disease. 7–9 There is debate whether their concomitance reflects a causal link is coincidence, or the result of one unmasking the other and the data from the Saudi Arabian population are limited. The aim of the current study was to investigate the relationship between hypothyroidism and periodontal status in a cohort of Saudi Arabian adult population.

Materials and Methods

The test group consisted of 201 cases selected according to predefined criteria of being adults (age ≥ 18 years), diagnosed with primary hypothyroidism and treated with hormone replacement therapy. Cases were excluded if their medical records indicated previous diagnosis of other chronic diseases such as diabetes mellitus or other systemic conditions such as cardiovascular diseases, renal diseases, cancer, and hepatic disorders; received other pharmaceutical agents such as antibiotics, steroids, anti-inflammatory medications and/or bisphosphonates within the past 60 days; and/or received any periodontal treatment in the past 60 days. Female patients were excluded if they were pregnant and/or nursing. The control group consisted of 188 healthy age and gender matching adults. All participants responded to a pre-examination questionnaire to collect information about the age, sex, education level, smoking habits, medical history, oral hygiene behaviours, brushing frequency, duration since hypothyroidism was first diagnosed, and the dose of medication used. The periodontal status of all participants was then assessed via comprehensive full oral exam recording the probing pocket depth (PPD; distance between the marginal gingiva and the bottom of the periodontal pocket, in millimeters), plaque index (PI; The presence or absence of dental plaque at four points mesial, buccal, lingual, and distal on each tooth, determined after the application of a disclosing agent), bleeding on probing (BOP; the occurrence of bleeding within 15 seconds after probing, indicating a positive result) and clinical attachment loss (CAL; distance between the cemento-enamel junction and the bottom of the pocket, in millimeters). A Williams’ periodontal probe (Hu-Friedy ® PW6) was utilized for measuring the clinical parameters including PPD and CAL. Six sites per tooth were assessed, and a diagnosis of periodontitis was established when subjects had at least two sites with a PPD ≥ 4 mm and a CAL ≥ 1 mm (one on each tooth). A case with 30% or more of teeth involved was classified as generalized periodontitis. Individuals with mean PPD <3mm and less than 10% bleeding sites with absence of clinically detectable signs of inflammation were categorized as having a healthy periodontium. Classification of periodontitis was determined based on the criteria proposed by the 2017 World Workshop on the Classifications of Periodontal and Peri-implant Diseases and Conditions. 3 , 10

Statistical Analysis

Data were analyzed using IBM SPSS Statistical software for Windows version 26.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics (mean, standard deviation, frequencies, and percentages) were used to describe the quantitative and categorical variables. Pearson chi-square test and odds ratios were used to assess and measure the association between categorical variables and outcome (cases and controls). Student’s t -test for independent samples was used to compare the mean values of quantitative variables between cases and controls. The binary logistic regression was used to identify the independent variables associated with hypothyroidism diagnosis. A p-value of ≤0.05 and 95% confidence intervals were used to report the statistical significance and precision of results.

The sample was classified into 201 hypothyroidism patient (Cases, mean age 40.55 ± 9.7 years, 48.3% males) and 188 healthy subjects (Control, mean age 39.37 ± 11 years, 45.2% males). The characteristics of the two groups are presented in Table 1 . A brushing frequency of one time a day was reported for 34.8% and 56.9% and two times a day for 38.3 and 30.9% of the cases and control groups, respectively. The distribution of periodontal disease was generalized in 91.9% of cases and 76.1% among controls. The periodontal status was classified as gingivitis in 20.9% of cases and 58% of controls. Periodontitis classification of stages I, II, III and IV was in general higher in cases compared to controls (23.4%, 27.9%, 21.9%, 6% in cases versus 13.8%, 17%, 9.6%, 1.6% in controls, respectively). The mean values of PI and BOP in cases were 49.22 ± 19.6 and 56.06 ± 22.2 versus 47.85 ± 23.3 and 57.79 ± 26.6 among controls, respectively. The mean values of PPD and CAL in cases were 5.54 ± 2.5 and 3.88 ± 3.1 versus 4.03 ± 1.6 and 1.72 ± 2.4 among controls, respectively ( Table 1 ).

Periodontitis is the most common form of periodontal diseases, which includes a group of inflammatory diseases that affect the periodontal supporting tissues of the teeth. It is commonly regarded a “silent disease” since patients present with no or few symptoms until the disease progresses to destroy the periodontal soft tissues and alveolar bone. 11 Periodontitis is considered the main cause of tooth loss after the third decade of life. Current evidence indicates that periodontitis is a complex disease with multiple potential contributing factors including genetics and epigenetics, environmental, and behavioural factors. Low socioeconomic status, poor oral hygiene, psychological stress, advanced age, use of certain medications, and some systemic conditions are well-recognised risk factors that contribute to the initiation and progression of periodontal diseases. 11 , 12

Hypothyroidism is one of the most common hormone deficiency disorders. According to the time of onset, it could be classified as congenital or acquired. Symptoms of hypothyroidism include fatigue, weight gain, alteration in cognition, infertility, menstrual abnormalities, irregular heart rate, and depression. Monotherapy with levothyroxine at doses to normalize the serum thyroid-stimulating hormone (TSH) is the standard of care for treating hypothyroidism. 13 , 14 Studies had previously suggested that hypothyroidism may be associated with an increased risk of periodontal diseases. The present study was conducted to investigate the association between hypothyroidism and periodontal status in a cohort of Saudi Arabian adult population.

We have demonstrated a significantly increased prevalence and severity of periodontitis in subjects with treated hypothyroidism compared to controls. This increase seen in adult Saudi patients diagnosed with hyperthyroidism was similar to reports on other ethnic populations. 15–18 In the current study, subjects with hypothyroidism had significantly higher PPD and CAL when compared with matched controls. Attard and Zarb, 19 in their study, demonstrated an association between hypothyroidism and peri-implant radiographic bone loss, compared with normal controls. 19 Rahangdale and Galgali 20 reported statistically significant higher PPD and CAL in hypothyroidism patients in comparison to the controls. They concluded that, since all other variables that might affect the periodontal status of the patients were controlled, the history of hypothyroidism and replacement therapy probably had the main effect on PPD and CAL, the most reliable measure of periodontitis. Our data, together with previous observations, support the generally accepted view that chronic inflammatory periodontal diseases are associated with endocrinal morbidity.

In adults, the integrity of the skeletal structures, including the alveolar bone, is maintained by bone remodelling, a process controlled by thyroid hormones and TSH. 21 Animal models of hypothyroidism have demonstrated alterations in bone metabolism, through a mechanism by which thyroid hormone has direct or indirect effects on bone cells. 22–24 Feitosa et al 22 used an experimental periodontitis model in rats to evaluate, histologically, the influence of thyroid hormones on the rate of periodontal disease progression. The results indicated that hypothyroidism significantly increased the bone loss resulting from ligature-induced periodontitis and the number of TRAP-positive cells on the linear surface of bone crest. They concluded that decreased serum levels of thyroid hormones may enhance periodontitis-related bone loss, as a function of an increased number of resorbing cells. It is possible to speculate that the significant association between higher distribution and severity of periodontal disease in hypothyroidism cases in the current study was related to the negative effect of hypothyroidism on bone remodelling sequence.

The present study is the first to indicate that the periodontal status of the hypothyroidism cases was significantly associated with the hormone replacement therapy dose and duration. Patients on higher doses of medication and for longer duration suffered more severe periodontal tissue destruction. This might imply that the duration of the disease onset and the degree of hormonal deficiency are critical in determining the periodontal tissue response. It has been proposed that the cytokines produced due to thyroid dysfunction might act as initiators for an amplified inflammatory cascade systemically. 25 This, in combination with the existing inflammatory reaction in the periodontium due to the endotoxins produced by microbial plaque, might lead to higher local inflammatory mediator concentration in the periodontal tissues, including matrix metalloproteinases, leading to excessive periodontal tissue breakdown. Furthermore, it has been reported that in patients with hypothyroidism using a large dose of thyroxine replacement therapy, the risk of bone fracture increased compared to small doses, which could be attributed to lower bone density and poor bone quality reported with high-versus-low-dose thyroxine replacement. 26 , 27

In summary, we have shown that, in a cohort of adult Saudi subjects, patients with hypothyroidism have higher prevalence and more severe periodontal disease compared to controls, suggesting association. However, it is important to interpret the data carefully, since case–control study is not the best approach to show a cause-and-effect relationship. Furthermore, the result of the current study was based on data collected from a single hospital, and its conclusions might not be entirely generalizable. Despite the limitations, this study offers guidance for future research and presents evidence of correlation from a group that has not been previously explored. The results of this study thus support routine periodontal evaluation for patients with hypothyroidism. Further studies are required to investigate the pathophysiology of periodontal tissue diseases and its relationship to the underlying endocrinal disorder.

The findings of the current study showed that, in a cohort of adult Saudi subjects, patients with hypothyroidism have higher prevalence and more severe periodontal disease symptoms compared to controls, suggesting significant association. However, the study was not sufficiently powered to estimate the association in the general population. Further studies are required to investigate the pathophysiology of the periodontal tissue reaction and its relationship to the underlying endocrinal disorder.

The authors report no conflicts of interest in this work.

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JSmol Viewer

Forecasting in-flight icing over greece: insights from a low-pressure system case study.

1. Introduction

2. data and algorithm, 2.1. the in-flight icing potential algorithm, 2.2. the nwp model, 2.3. observations, 2.3.1. satellite data, 2.3.2. radar data, 2.3.3. metars, 3.1. case study, 3.2. cloud presence estimation with ipa, 3.2.1. cloud mask, 3.2.2. cloud base and top heights, 3.3. icing potential forecast scenarios, 4. qualitative evaluation of the algorithm over greece, 4.1. comparison with satellite observations, 4.2. comparison with radar observations, 4.3. comparison with metars, 4.4. the dynamics of ipa in investigating in-flight icing, 5. conclusions—suggestions, author contributions, institutional review board statement, data availability statement, conflicts of interest.

METAR LGBL 121220Z 09009KT 9999 VCTS FEW018CB FEW020TCU SCT030 BKN080 14/09 Q1004=

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Share and Cite

Louka, P.; Samos, I.; Gofa, F. Forecasting In-Flight Icing over Greece: Insights from a Low-Pressure System Case Study. Atmosphere 2024 , 15 , 990. https://doi.org/10.3390/atmos15080990

Louka P, Samos I, Gofa F. Forecasting In-Flight Icing over Greece: Insights from a Low-Pressure System Case Study. Atmosphere . 2024; 15(8):990. https://doi.org/10.3390/atmos15080990

Louka, Petroula, Ioannis Samos, and Flora Gofa. 2024. "Forecasting In-Flight Icing over Greece: Insights from a Low-Pressure System Case Study" Atmosphere 15, no. 8: 990. https://doi.org/10.3390/atmos15080990

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