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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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Writing up a Research Report

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A research report is one big argument about how and why you came up with your conclusions. To make it a convincing argument, a typical guiding structure has developed. In the different chapters, there are distinct issues that need to be addressed to explain to the reader why your conclusions are valid. The governing principle for writing the report is full disclosure: to explain everything and ensure replicability by another researcher.

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Field, A. (2016). An adventure in statistics. The reality enigma . SAGE.

Field, A. (2020). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE.

Früh, M., Keimer, I., & Blankenagel, M. (2019). The impact of Balanced Scorecard excellence on shareholder returns. IFZ Working Paper No. 0003/2019. https://zenodo.org/record/2571603#.YMDUafkzZaQ . Accessed: 9 June 2021.

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Yin, R. K. (2013). Case study research: Design and methods (5th ed.). SAGE.

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Interpretation in qualitative research: What, why, how

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This chapter addresses a wide range of concepts related to interpretation in qualitative research, examines the meaning and importance of interpretation in qualitative inquiry, and explores the ways methodology, data, and the self/researcher as instrument interact and impact interpretive processes. Additionally, the chapter presents a series of strategies for qualitative researchers engaged in the process of interpretation and closes by presenting a framework for qualitative researchers designed to inform their interpretations. The framework includes attention to the key qualitative research concepts transparency, reflexivity, analysis, validity, evidence, and literature. Four questions frame the chapter: What is interpretation, and why are interpretive strategies important in qualitative research? How do methodology, data, and the researcher/self impact interpretation in qualitative research? How do qualitative researchers engage in the process of interpretation? And, in what ways can a framework for interpretation strategies support qualitative researchers across multiple methodologies and paradigms?.

• Data analysis
• Hermeneutics
• Interpretation
• Methodology
• Qualitative research
• Research methods

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Commentary: Writing and Evaluating Qualitative Research Reports

Yelena p. wu.

1 Division of Public Health, Department of Family and Preventive Medicine, University of Utah,

2 Cancer Control and Population Sciences, Huntsman Cancer Institute,

Deborah Thompson

3 Department of Pediatrics-Nutrition, USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine,

Karen J. Aroian

4 College of Nursing, University of Central Florida,

Elizabeth L. McQuaid

5 Department of Psychiatry and Human Behavior, Brown University, and

Janet A. Deatrick

6 School of Nursing, University of Pennsylvania

Objective  To provide an overview of qualitative methods, particularly for reviewers and authors who may be less familiar with qualitative research. Methods  A question and answer format is used to address considerations for writing and evaluating qualitative research. Results and Conclusions  When producing qualitative research, individuals are encouraged to address the qualitative research considerations raised and to explicitly identify the systematic strategies used to ensure rigor in study design and methods, analysis, and presentation of findings. Increasing capacity for review and publication of qualitative research within pediatric psychology will advance the field’s ability to gain a better understanding of the specific needs of pediatric populations, tailor interventions more effectively, and promote optimal health.

The Journal of Pediatric Psychology (JPP) has a long history of emphasizing high-quality, methodologically rigorous research in social and behavioral aspects of children’s health ( Palermo, 2013 , 2014 ). Traditionally, research published in JPP has focused on quantitative methodologies. Qualitative approaches are of interest to pediatric psychologists given the important role of qualitative research in developing new theories ( Kelly & Ganong, 2011 ), illustrating important clinical themes ( Kars, Grypdonck, de Bock, & van Delden, 2015 ), developing new instruments ( Thompson, Bhatt, & Watson, 2013 ), understanding patients’ and families’ perspectives and needs ( Bevans, Gardner, Pajer, Riley, & Forrest, 2013 ; Lyons, Goodwin, McCreanor, & Griffin, 2015 ), and documenting new or rarely examined issues ( Haukeland, Fjermestad, Mossige, & Vatne, 2015 ; Valenzuela et al., 2011 ). Further, these methods are integral to intervention development ( Minges et al., 2015 ; Thompson et al., 2007 ) and understanding intervention outcomes ( de Visser et al., 2015 ; Hess & Straub, 2011 ). For example, when designing an intervention, qualitative research can identify patient and family preferences for and perspectives on desirable intervention characteristics and perceived needs ( Cassidy et al., 2013 ; Hess & Straub, 2011 ; Thompson, 2014 ), which may lead to a more targeted, effective intervention.

Both qualitative and quantitative approaches are concerned with issues such as generalizability of study findings (e.g., to whom the study findings can be applied) and rigor. However, qualitative and quantitative methods have different approaches to these issues. The purpose of qualitative research is to contribute knowledge or understanding by describing phenomenon within certain groups or populations of interest. As such, the purpose of qualitative research is not to provide generalizable findings. Instead, qualitative research has a discovery focus and often uses an iterative approach. Thus, qualitative work is often foundational to future qualitative, quantitative, or mixed-methods studies.

At the time of this writing, three of six current calls for papers for special issues of JPP specifically note that manuscripts incorporating qualitative approaches would be welcomed. Despite apparent openness to broadening JPP’s emphasis beyond its traditional quantitative approach, few published articles have used qualitative methods. For example, of 232 research articles published in JPP from 2012 to 2014 (excluding commentaries and reviews), only five used qualitative methods (2% of articles).

The goal of the current article is to present considerations for writing and evaluating qualitative research within the context of pediatric psychology to provide a framework for writing and reviewing manuscripts reporting qualitative findings. The current article may be especially useful to reviewers and authors who are less familiar with qualitative methods. The tenets presented here are grounded in the well-established literature on reporting and evaluating qualitative research, including guidelines and checklists ( Eakin & Mykhalovskiy, 2003 ; Elo et al., 2014 ; Mays & Pope, 2000 ; Tong, Sainsbury, & Craig, 2007 ). For example, the Consolidated Criteria for Reporting Qualitative Research checklist describes essential elements for reporting qualitative findings ( Tong et al., 2007 ). Although the considerations presented in the current manuscript have broad applicability to many fields, examples were purposively selected for the field of pediatric psychology.

Our goal is that this article will stimulate publication of more qualitative research in pediatric psychology and allied fields. More specifically, the goal is to encourage high-quality qualitative research by addressing key issues involved in conducting qualitative studies, and the process of conducting, reporting, and evaluating qualitative findings. Readers interested in more in-depth information on designing and implementing qualitative studies, relevant theoretical frameworks and approaches, and analytic approaches are referred to the well-developed literature in this area ( Clark, 2003 ; Corbin & Strauss, 2008 ; Creswell, 1994 ; Eakin & Mykhalovskiy, 2003 ; Elo et al., 2014 ; Mays & Pope, 2000 ; Miles, Huberman, & Saldaña, 2013 ; Ritchie & Lewis, 2003 ; Saldaña, 2012 ; Sandelowski, 1995 , 2010 ; Tong et al., 2007 ; Yin, 2015 ). Researchers new to qualitative research are also encouraged to obtain specialized training in qualitative methods and/or to collaborate with a qualitative expert in an effort to ensure rigor (i.e., validity).

We begin the article with a definition of qualitative research and an overview of the concept of rigor. While we recognize that qualitative methods comprise multiple and distinct approaches with unique purposes, we present an overview of considerations for writing and evaluating qualitative research that cut across qualitative methods. Specifically, we present basic principles in three broad areas: (1) study design and methods, (2) analytic considerations, and (3) presentation of findings (see Table 1 for a summary of the principles addressed in each area). Each area is addressed using a “question and answer” format. We present a brief explanation of each question, options for how one could address the issue raised, and a suggested recommendation. We recognize, however, that there are no absolute “right” or “wrong” answers and that the most “right” answer for each situation depends on the specific study and its purpose. In fact, our strongest recommendation is that authors of qualitative research manuscripts be explicit about their rationale for design, analytic choices, and strategies so that readers and reviewers can evaluate the rationale and rigor of the study methods.

Summary of Overarching Principles to Address in Qualitative Research Manuscripts

What Is Qualitative Research?

Qualitative methods are used across many areas of health research, including health psychology ( Gough & Deatrick, 2015 ), to study the meaning of people’s lives in their real-world roles, represent their views and perspectives, identify important contextual conditions, discover new or additional insights about existing social and behavioral concepts, and acknowledge the contribution of multiple perspectives ( Yin, 2015 ). Qualitative research is a family of approaches rather than a single approach. There are multiple and distinct qualitative methodologies or stances (e.g., constructivism, post-positivism, critical theory), each with different underlying ontological and epistemological assumptions ( Lincoln, Lynham, & Guba, 2011 ). However, certain features are common to most qualitative approaches and distinguish qualitative research from quantitative research ( Creswell, 1994 ).

Key to all qualitative methodologies is that multiple perspectives about a phenomenon of interest are essential, and that those perspectives are best inductively derived or discovered from people with personal experience regarding that phenomenon. These perspectives or definitions may differ from “conventional wisdom.” Thus, meanings need to be discovered from the population under study to ensure optimal understanding. For instance, in a recent qualitative study about texting while driving, adolescents said that they did not approve of texting while driving. The investigators, however, discovered that the respondents did not consider themselves driving while a vehicle was stopped at a red light. In other words, the respondents did approve of texting while stopped at a red light. In addition, the adolescents said that they highly valued being constantly connected via texting. Thus, what is meant by “driving” and the value of “being connected” need to be considered when approaching the issue of texting while driving with adolescents ( McDonald & Sommers, 2015 ).

Qualitative methods are also distinct from a mixed-method approach (i.e., integration of qualitative and quantitative approaches; Creswell, 2013b ). A mixed-methods study may include a first phase of quantitative data collection that provides results that inform a second phase of the study that includes qualitative data collection, or vice versa. A mixed-methods study may also include concurrent quantitative and qualitative data collection. The timing, priority, and stage of integration of the two approaches (quantitative and qualitative) are complex and vary depending on the research question; they also dictate how to attend to differing qualitative and quantitative principles ( Creswell et al., 2011 ). Understanding the basic tenets of qualitative research is preliminary to integrating qualitative research with another approach that has different tenets. A full discussion of the integration of qualitative and quantitative research approaches is beyond the scope of this article. Readers interested in the topic are referred to one of the many excellent resources on the topic ( Creswell, 2013b ).

What Are Typical Qualitative Research Questions?

Qualitative research questions are typically open-ended and are framed in the spirit of discovery and exploration and to address existing knowledge gaps. The current manuscript provides exemplar pediatric qualitative studies that illustrate key issues that arise when reporting and evaluating qualitative studies. Example research questions that are contained in the studies cited in the current manuscript are presented in Table 2 .

Example Qualitative Research Questions From the Pediatric Literature

What Are Rigor and Transparency in Qualitative Research?

There are several overarching principles with unique application in qualitative research, including definitions of scientific rigor and the importance of transparency. Quantitative research generally uses the terms reliability and validity to describe the rigor of research, while in qualitative research, rigor refers to the goal of seeking to understand the tacit knowledge of participants’ conception of reality ( Polanyi, 1958 ). For example, Haukeland and colleagues (2015) used qualitative analysis to identify themes describing the emotional experiences of a unique and understudied population—pediatric siblings of children with rare medical conditions such as Turner syndrome and Duchenne muscular dystrophy. Within this context, the authors’ rendering of the diverse and contradictory emotions experienced by siblings of children with these rare conditions represents “rigor” within a qualitative framework.

While debate exists regarding the terminology describing and strategies for strengthening scientific rigor in qualitative studies ( Guba, 1981 ; Morse, 2015a , 2015b ; Sandelowski, 1993a ; Whittemore, Chase, & Mandle, 2001 ), little debate exists regarding the importance of explaining strategies used to strengthen rigor. Such strategies should be appropriate for the specific study; therefore, it is wise to clearly describe what is relevant for each study. For example, in terms of strengthening credibility or the plausibility of data analysis and interpretation, prolonged engagement with participants is appropriate when conducting an observational study (e.g., observations of parent–child mealtime interactions; Hughes et al., 2011 ; Power et al., 2015 ). For an interview-only study, however, it would be more practical to strengthen credibility through other strategies (e.g., keeping detailed field notes about the interviews included in the analysis).

Dependability is the stability of a data analysis protocol. For instance, stepwise development of a coding system from an “a priori” list of codes based on the underlying conceptual framework or existing literature (e.g., creating initial codes for potential barriers to medication adherence based on prior studies) may be essential for analysis of data from semi-structured interviews using multiple coders. But this may not be the ideal strategy if the purpose is to inductively derive all possible coding categories directly from data in an area where little is known. For some research questions, the strategy may be to strengthen confirmability or to verify a specific phenomenon of interest using different sources of data before generating conclusions. This process, which is commonly referred to in the research literature as triangulation, may also include collecting different types of data (e.g., interview data, observational data), using multiple coders to incorporate different ways of interpreting the data, or using multiple theories ( Krefting, 1991 ; Ritchie & Lewis, 2003 ). Alternatively, another investigator may use triangulation to provide complementarity data ( Krefting, 1991 ) to garner additional information to deepen understanding. Because the purpose of qualitative research is to discover multiple perspectives about a phenomenon, it is not necessarily appropriate to attain concordance across studies or investigators when independently analyzing data. Some qualitative experts also believe that it is inappropriate to use triangulation to confirm findings, but this debate has not been resolved within the field ( Ritchie & Lewis, 2003 ; Tobin & Begley, 2004 ). More agreement exists, however, regarding the value of triangulation to complement, deepen, or expand understanding of a particular topic or issue ( Ritchie & Lewis, 2003 ). Finally, instead of basing a study on a sample that allows for generalizing statistical results to other populations, investigators in qualitative research studies are focused on designing a study and conveying the results so that the reader understands the transferability of the results. Strategies for transferability may include explanations of how the sample was selected and descriptive characteristics of study participants, which provides a context for the results and enables readers to decide if other samples share critical attributes. A study is deemed transferable if relevant contextual features are common to both the study sample and the larger population.

Strategies to enhance rigor should be used systematically across each phase of a study. That is, rigor needs to be identified, managed, and documented throughout the research process: during the preparation phase (data collection and sampling), organization phase (analysis and interpretation), and reporting phase (manuscript or final report; Elo et al., 2014 ). From this perspective, the strategies help strengthen the trustworthiness of the overall study (i.e., to what extent the study findings are worth heeding; Eakin & Mykhalovskiy, 2003 ; Lincoln & Guba, 1985 ).

A good example of managing and documenting rigor and trustworthiness can be found in a study of family treatment decisions for children with cancer ( Kelly & Ganong, 2011 ). The researchers describe how they promoted the rigor of the study and strengthening its credibility by triangulating data sources (e.g., obtaining data from children’s custodial parents, stepparents, etc.), debriefing (e.g., holding detailed conversations with colleagues about the data and interpretations of the data), member checking (i.e., presenting preliminary findings to participants to obtain their feedback and interpretation), and reviewing study procedure decisions and analytic procedures with a second party.

Transparency is another key concept in written reports of qualitative research. In other words, enough detail should be provided for the reader to understand what was done and why ( Ritchie & Lewis, 2003 ). Examples of information that should be included are a clear rationale for selecting a particular population or people with certain characteristics, the research question being investigated, and a meaningful explanation of why this research question was selected (i.e., the gap in knowledge or understanding that is being investigated; Ritchie & Lewis, 2003 ). Clearly describing recruitment, enrollment, data collection, and data analysis or extraction methods are equally important ( Dixon-Woods, Shaw, Agarwal, & Smith, 2004 ). Coherency among methods and transparency about research decisions adds to the robustness of qualitative research ( Tobin & Begley, 2004 ) and provides a context for understanding the findings and their implications.

Study Design and Methods

Is qualitative research hypothesis driven.

In contrast to quantitative research, qualitative research is not typically hypothesis driven ( Creswell, 1994 ; Ritchie & Lewis, 2003 ). A risk associated with using hypotheses in qualitative research is that the findings could be biased by the hypotheses. Alternatively, qualitative research is exploratory and typically guided by a research question or conceptual framework rather than hypotheses ( Creswell, 1994 ; Ritchie & Lewis, 2003 ). As previously stated, the goal of qualitative research is to increase understanding in areas where little is known by developing deeper insight into complex situations or processes. According to Richards and Morse (2013) , “If you know what you are likely to find, …  you should not be working qualitatively” (p. 28). Thus, we do not recommend that a hypothesis be stated in manuscripts presenting qualitative data.

What Is the Role of Theory in Qualitative Research?

Consistent with the exploratory nature of qualitative research, one particular qualitative method, grounded theory, is used specifically for discovering substantive theory (i.e., working theories of action or processes developed for a specific area of concern; Bryant & Charmaz, 2010 ; Glaser & Strauss, 1967 ). This method uses a series of structured steps to break down qualitative data into codes, organize the codes into conceptual categories, and link the categories into a theory that explains the phenomenon under study. For example, Kelly and Ganong (2011) used grounded theory methods to produce a substantive theory about how single and re-partnered parents (e.g., households with a step-parent) made treatment decisions for children with childhood cancer. The theory of decision making developed in this study included “moving to place,” which described the ways in which parents from different family structures (e.g., single and re-partnered parents) were involved in the child’s treatment decision-making. The resulting theory also delineated the causal conditions, context, and intervening factors that contributed to the strategies used for moving to place.

Theories may be used in other types of qualitative research as well, serving as the impetus or organizing framework for the study ( Sandelowski, 1993b ). For example, Izaguirre and Keefer (2014) used Social Cognitive Theory ( Bandura, 1986 ) to investigate self-efficacy among adolescents with inflammatory bowel disease. The impetus for selecting the theory was to inform the development of a self-efficacy measure for adolescent self-management. In another study on health care transition in youth with Type 1 Diabetes ( Pierce, Wysocki, & Aroian, 2016 ), the investigators adapted a social-ecological model—the Socio-ecological Model of Adolescent and Young Adult Transition Readiness (SMART) model ( Schwartz, Tuchman, Hobbie, & Ginsberg, 2011 )—to their study population ( Pierce & Wysocki, 2015 ). Pierce et al. (2016) are currently using the adapted SMART model to focus their data collection and structure the preliminary analysis of their data about diabetes health care transition.

Regardless of whether theory is induced from data or selected in advance to guide the study, consistent with the principle of transparency , its role should be clearly identified and justified in the research publication ( Bradbury-Jones, Taylor, & Herber, 2014 ; Kelly, 2010 ). Methodological congruence is an important guiding principle in this regard ( Richards & Morse, 2013 ). If a theory frames the study at the outset, it should guide and direct all phases. The resulting publication(s) should relate the phenomenon of interest and the research question(s) to the theory and specify how the theory guided data collection and analysis. The publication(s) should also discuss how the theory fits with the finished product. For instance, authors should describe how the theory provided a framework for the presentation of the findings and discuss the findings in context with the relevant theoretical literature.

A study examining parents’ motivations to promote vegetable consumption in their children ( Hingle et al., 2012 ) provides an example of methodological congruence. The investigators adapted the Model of Goal Directed Behavior ( Bagozzi & Pieters, 1998 ) for parenting practices relevant to vegetable consumption (Model of Goal Directed Vegetable Parenting Practices; MGDVPP). Consistent with the adapted theoretical model and in keeping with the congruence principle, interviews were guided by the theoretical constructs contained within the MGDVPP, including parents’ attitudes, subjective norms, and perceived behavioral control related to promoting vegetable consumption in children ( Hingle et al., 2012 ). The study discovered that the adapted model successfully identified parents’ motivations to encourage their children to eat more vegetables.

The use of the theory should be consistent with the basic goal of qualitative research, which is discovery. Alternatively stated, theories should be used as broad orienting frameworks for exploring topical areas without imposing preconceived ideas and biases. The theory should be consistent with the study findings and not be used to force-fit the researcher’s interpretation of the data ( Sandelowski, 1993b ). Divergence from the theory when it does not fit the study findings is illustrated in a qualitative study of hypertension prevention beliefs in Hispanics ( Aroian, Peters, Rudner, & Waser, 2012 ). This study used the Theory of Planned Behavior as a guiding theoretical framework but found that coding separately for normative and control beliefs was not the best organizing schema for presenting the study findings. When divergence from the original theory occurs, the research report should explain and justify how and why the theory was modified ( Bradbury-Jones et al., 2014 ).

What Are Typical Sampling Methods in Qualitative Studies?

Qualitative sampling methods should be “purposeful” ( Coyne, 1997 ; Patton, 2015 ; Tuckett, 2004 ). Purposeful sampling is based on the study purpose and investigator judgments about which people and settings will provide the richest information for the research questions. The logic underlying this type of sampling differs from the logic underlying quantitative sampling ( Patton, 2015 ). Quantitative research strives for empirical generalization. In qualitative studies, generalizability beyond the study sample is typically not the intent; rather, the focus is on deriving depth and context-embedded meaning for the relevant study population.

Purposeful sampling is a broad term. Theoretical sampling is one particular type of purposeful sampling unique to grounded theory methods ( Coyne, 1997 ). In theoretical sampling, study participants are chosen according to theoretical categories that emerge from ongoing data collection and analyses ( Bryant & Charmaz, 2010 ). Data collection and analysis are conducted concurrently to allow generating and testing hypotheses that emerge from analyzing incoming data. The following example from the previously mentioned qualitative interview study about transition from pediatric to adult care in adolescents with type 1 diabetes ( Pierce et al., 2016 ) illustrates the process of theoretical sampling: An adolescent study participant stated that he was “turned off” by the “childish” posters in his pediatrician’s office. He elaborated that he welcomed transitioning to adult care because his diabetes was discovered when he was 18, an age when he reportedly felt more “mature” than most pediatric patients. These data were coded as “developmental misfit” and prompted a tentative hypothesis about developmental stage at entry for pediatric diabetes care and readiness for health care transition. Examining this hypothesis prompted seeking study participants who varied according to age or developmental stage at time of diagnosis to examine the theoretical relevance of an emerging theme about developmental fit.

Not all purposeful sampling, however, is “theoretical.” For example, ethnographic studies typically seek to understand a group’s cultural beliefs and practices ( Creswell, 2013a ). Consistent with this purpose, researchers conducting an ethnographic study might purposefully select study participants according to specific characteristics that reflect the social roles and positions in a given group or society (e.g., socioeconomic status, education; Johnson, 1990 ).

Random sampling is generally not used in qualitative research. Random selection requires a sufficiently large sample to maximize the potential for chance and, as will be discussed below, sample size is intentionally small in qualitative studies. However, random sampling may be used to verify or clarify findings ( Patton, 2015 ). Validating study findings with a randomly selected subsample can be used to address the possibility that a researcher is inadvertently giving greater attention to cases that reinforce his or her preconceived ideas.

Regardless of the sampling method used, qualitative researchers should clearly describe the sampling strategy and justify how it fits the study when reporting study findings (transparency). A common error is to refer to theoretical sampling when the cases were not chosen according to emerging theoretical concepts. Another common error is to apply sampling principles from quantitative research (e.g., cluster sampling) to convince skeptical reviewers about the rigor or validity of qualitative research. Rigor is best achieved by being purposeful, making sound decisions, and articulating the rationale for those decisions. As mentioned earlier in the discussion of transferability , qualitative researchers are encouraged to describe their methods of sample selection and descriptive characteristics about their sample so that readers and reviewers can judge how the current sample may differ from others. Understanding the characteristics of each qualitative study sample is essential for the iterative nature of qualitative research whereby qualitative findings inform the development of future qualitative, quantitative, or mixed-methods studies. Reviewers should evaluate sampling decisions based on how they fit the study purpose and how they influence the quality of the end product.

What Sample Size Is Needed for Qualitative Research?

No definitive rules exist about sample size in qualitative research. However, sample sizes are typically smaller than those in quantitative studies ( Patton, 2015 ). Small samples often generate a large volume of data and information-rich cases, ultimately leading to insight regarding the phenomenon under study ( Patton, 2015 ; Ritchie & Lewis, 2003 ). Sample sizes of 20–30 cases are typical, but a qualitative sample can be even smaller under some circumstances ( Mason, 2010 ).

Sample size adequacy is evaluated based on the quality of the study findings, specifically the full development of categories and inter-relationships or the adequacy of information about the phenomenon under study ( Corbin & Strauss, 2008 ; Ritchie & Lewis, 2003 ). Small sample sizes are of concern if they do not result in these outcomes. Data saturation (i.e., the point at which no new information, categories, or themes emerge) is often used to judge informational adequacy ( Morgan, 1998 ; Ritchie & Lewis, 2003 ). Although enough participants should be included to obtain saturation ( Morgan, 1998 ), informational adequacy pertains to more than sample size. It is also a function of the quality of the data, which is influenced by study participant characteristics (e.g., cognitive ability, knowledge, representativeness) and the researcher’s data-gathering skills and analytical ability to generate meaningful findings ( Morse, 2015b ; Patton, 2015 ).

Sample size is also influenced by type of qualitative research, the study purpose, the sample, the depth and complexity of the topic investigated, and the method of data collection. In general, the more heterogeneous the sample, the larger the sample size, particularly if the goal is to investigate similarities and differences by specific characteristics ( Ritchie & Lewis, 2003 ). For instance, in a study to conduct an initial exploration of factors underlying parents’ motivations to use good parenting practices, theoretical saturation (i.e., the point at which no new information, categories, or themes emerge) was obtained with a small sample ( n  = 15), most likely because the study was limited to parents of young children ( Hingle et al., 2012 ). If the goal of the study had been, for example, to identify racial/ethnic, gender, or age differences in food parenting practices, a larger sample would likely be needed to obtain saturation or informational adequacy.

Studies that seek to understand maximum variation in a phenomenon might also need a larger sample than one that is seeking to understand extreme or atypical cases. For example, a qualitative study of diet and physical activity in young Australian men conducted focus groups to identify perceived motivators and barriers to healthy eating and physical activity and examine the influence of body weight on their perceptions. Examining the influence of body weight status required 10 focus groups to allow for group assignment based on body mass index ( Ashton et al., 2015 ). More specifically, 61 men were assigned to a healthy-weight focus group ( n  = 3), an overweight/obese focus group ( n  = 3), or a mixed-weight focus group ( n  = 4). Had the researcher not been interested in whether facilitators and barriers differed by weight status, its likely theoretical saturation could have been obtained with fewer groups. Depth of inquiry also influences sample size ( Sandelowski, 1995 ). For instance, an in-depth analysis of an intervention for children with cancer and their families included 16 family members from three families. Study data comprised 52 hrs of videotaped intervention sessions and 10 interviews ( West, Bell, Woodgate, & Moules, 2015 ). Depth was obtained through multiple data points and types of data, which justified sampling only a few families.

Authors of publications describing qualitative findings should show evidence that the data were “saturated” by a sample with sufficient variation to permit detailing shared and divergent perspectives, meanings, or experiences about the topic of inquiry. Decisions related to the sample (e.g., targeted recruitment) should be detailed in publications so that peer reviewers have the context for evaluating the sample and determining how the sample influenced the study findings ( Patton, 2015 ).

Qualitative Data Analysis

When conducting qualitative research, voluminous amounts of data are gathered and must be prepared (i.e., transcribed) and managed. During the analytic process, data are systematically transformed through identifying, defining, interpreting, and describing findings that are meant to comprehensively describe the phenomenon or the abstract qualities that they have in common. The process should be systematic ( dependability ) and well-documented in the analysis section of a qualitative manuscript. For example, Kelly and Ganong (2011) , in their study of medical treatment decisions made by families of children with cancer, described their analytic procedure by outlining their approach to coding and use of memoing (e.g., keeping careful notes about emerging ideas about the data throughout the analytic process), comparative analysis (e.g., comparing data against one another and looking for similarities and differences), and diagram drawing (e.g., pictorially representing the data structure, including relationships between codes).

How Should Researchers Document Coding Reliability?

Because the intent of qualitative research is to account for multiple perspectives, the goal of qualitative analysis is to comprehensively incorporate those perspectives into discernible findings. Researchers accustomed to doing quantitative studies may expect authors to quantify interrater reliability (e.g., kappa statistic) but this is not typical in qualitative research. Rather, the emphasis in qualitative research is on (1) training those gathering data to be rigorous and produce high-quality data and on (2) using systematic processes to document key decisions (e.g., code book), clear direction, and open communication among team members during data analysis. The goal is to make the most of the collective insight of the investigative team to triangulate or complement each other’s efforts to process and interpret the data. Instead of evaluating if two independent raters came to the same numeric rating, reviewers of qualitative manuscripts should judge to what extent the overall process of coding, data management, and data interpretation were systematic and rigorous. Authors of qualitative reports should articulate their coding procedures for others to evaluate. Together, these strategies promote trustworthiness of the study findings.

An example of how these processes are described in the report of a qualitative study is as follows:

The first two authors independently applied the categories to a sample of two interviews and compared their application of the categories to identify lack of clarity and overlap in categories. The investigators created a code book that contained a definition of categories, guidelines for their application, and excerpts of data exemplifying the categories. The first two authors independently coded the data and compared how they applied the categories to the data and resolved any differences during biweekly meetings. ATLAS.ti, version 6.2, was used to document and accommodate ongoing changes and additions to the coding structure ( Palma et al., 2015 , p. 224).

Do I Need to Use a Specialized Qualitative Data Software Program for Analysis?

Multiple computer software packages for qualitative data analysis are currently available ( Silver & Lewins, 2014 ; Yin, 2015 ). These packages allow the researcher to import qualitative data (e.g., interview transcripts) into the software program and organize data segments (e.g., delineate which interview excerpts are relevant to particular themes). Qualitative analysis software can be useful for organizing and sorting through data, including during the analysis phase. Some software programs also offer sophisticated coding and visualization capabilities that facilitate and enhance interpretation and understanding. For example, if data segments are coded by specific characteristics (e.g., gender, race/ethnicity), the data can be sorted and analyzed by these characteristics, which may contribute to an understanding of whether and/or how a particular phenomenon may vary by these characteristics.

The strength of computer software packages for qualitative data analysis is their potential to contribute to methodological rigor by organizing the data for systematic analyses ( John & Johnson, 2000 ; MacMillan & Koenig, 2004 ). However, the programs do not replace the researchers’ analyses. The researcher or research team is ultimately responsible for analyzing the data, identifying the themes and patterns, and placing the findings within the context of the literature. In other words, qualitative data analysis software programs contribute to, but do not ensure scientific rigor or “objectivity” in, the analytic process. In fact, using a software program for analysis is not essential if the researcher demonstrates the use of alternative tools and procedures for rigor.

Presentation of Findings

Should there be overlap between presentation of themes in the results and discussion sections.

Qualitative papers sometimes combine results and discussion into one section to provide a cohesive presentation of the findings along with meaningful linkages to the existing literature ( Burnard, 2004 ; Burnard, Gill, Stewart, Treasure, & Chadwick, 2008 ). Although doing so is an acceptable method for reporting qualitative findings, some journals prefer the two sections to be distinct.

When the journal style is to distinguish the two sections, the results section should describe the findings, that is, the themes, while the discussion section should pull the themes together to make larger-level conclusions and place the findings within the context of the existing literature. For instance, the findings section of a study of how rural African-American adolescents, parents, and community leaders perceived obesity and topics for a proposed obesity prevention program, contained a description of themes about adolescent eating patterns, body shape, and feedback on the proposed weight gain prevention program according to each subset of participants (i.e., adolescents, parents, community leaders). The discussion section then put these themes within the context of findings from prior qualitative and intervention studies in related populations ( Cassidy et al., 2013 ). In the Discussion, when making linkages to the existing literature, it is important to avoid the temptation to extrapolate beyond the findings or to over-interpret them ( Burnard, 2004 ). Linkages between the findings and the existing literature should be supported by ample evidence to avoid spurious or misleading connections ( Burnard, 2004 ).

What Should I Include in the Results Section?

The results section of a qualitative research report is likely to contain more material than customary in quantitative research reports. Findings in a qualitative research paper typically include researcher interpretations of the data as well as data exemplars and the logic that led to researcher interpretations ( Sandelowski & Barroso, 2002 ). Interpretation pertains to the researcher breaking down and recombining the data and creating new meanings (e.g., abstract categories, themes, conceptual models). Select quotes from interviews or other types of data (e.g., participant observation, focus groups) are presented to illustrate or support researcher interpretations. Researchers trained in the quantitative tradition, where interpretation is restricted to the discussion section, may find this surprising; however, in qualitative methods, researcher interpretations represent an important component of the study results. The presentation of the findings, including researcher interpretations (e.g., themes) and data (e.g., quotes) supporting those interpretations, adds to the trustworthiness of the study ( Elo et al., 2014 ).

The Results section should contain a balance between data illustrations (i.e., quotes) and researcher interpretations ( Lofland & Lofland, 2006 ; Sandelowski, 1998 ). Because interpretation arises out of the data, description and interpretation should be combined. Description should be sufficient to support researcher interpretations, and quotes should be used judiciously ( Morrow, 2005 ; Sandelowski, 1994 ). Not every theme needs to be supported by multiple quotes. Rather, quotes should be carefully selected to provide “voice” to the participants and to help the reader understand the phenomenon from the participant’s perspective within the context of the researcher’s interpretation ( Morrow, 2005 ; Ritchie & Lewis, 2003 ). For example, researchers who developed a grounded theory of sexual risk behavior of urban American Indian adolescent girls identified desire for better opportunities as a key deterrent to neighborhood norms for early sexual activity. They illustrated this theme with the following quote: “I don’t want to live in the ‘hood and all that…My sisters are stuck there because they had babies. That isn’t going to happen to me” ( Saftner, Martyn, Momper, Loveland-Cherry, & Low, 2015 , p. 372).

There is no precise formula for the proportion of description to interpretation. Both descriptive and analytic excess should be avoided ( Lofland & Lofland, 2006 ). The former pertains to presentation of unedited field notes or interview transcripts rather than selecting and connecting data to analytic concepts that explain or summarize the data. The latter pertains to focusing on the mechanics of analysis and interpretation without substantiating researcher interpretations with quotes. Reviewer requests for methodological rigor can result in researchers writing qualitative research papers that suffer from analytic excess ( Sandelowski & Barroso, 2002 ). Page limitations of most journals provide a safeguard against descriptive excess, but page limitations should not circumvent researchers from providing the basis for their interpretations.

Additional potential problems with qualitative results sections include under-elaboration, where themes are too few and not clearly defined. The opposite problem, over-elaboration, pertains to too many analytic distinctions that could be collapsed under a higher level of abstraction. Quotes can also be under- or over-interpreted. Care should be taken to ensure the quote(s) selected clearly support the theme to which they are attached. And finally, findings from a qualitative study should be interesting and make clear contributions to the literature ( Lofland & Lofland, 2006 ; Morse, 2015b ).

Should I Quantify My Results? (e.g., Frequency With Which Themes Were Endorsed)

There is controversy over whether to quantify qualitative findings, such as providing counts for the frequency with which particular themes are endorsed by study participants ( Morgan, 1993 ; Sandelowski, 2001 ). Qualitative papers usually report themes and patterns that emerge from the data without quantification ( Dey, 1993 ). However, it is possible to quantify qualitative findings, such as in qualitative content analysis. Qualitative content analysis is a method through which a researcher identifies the frequency with which a phenomenon, such as specific words, phrases, or concepts, is mentioned ( Elo et al., 2014 ; Morgan, 1993 ). Although this method may appeal to quantitative reviewers, it is important to note that this method only fits specific study purposes, such as studies that investigate the language used by a particular group when communicating about a specific topic. In addition, results may be quantified to provide information on whether themes appeared to be common or atypical. Authors should avoid using imprecise language, such as “some participants” or “many participants.” A good example of quantification of results to illustrate more or less typical themes comes from a manuscript describing a qualitative study of school nurses’ perceived barriers to addressing obesity with students and their families. The authors described that all but one nurse reported not having the resources they needed to discuss weight with students and families whereas one-quarter of nurses reported not feeling competent to discuss weight issues ( Steele et al., 2011 ). If quantification of findings is used, authors should provide justification that explains how quantification is consistent with the aims or goals of the study ( Sandelowski, 2001 ).

Conclusions

This article highlighted key theoretical and logistical considerations that arise in designing, conducting, and reporting qualitative research studies (see Table 1 for a summary). This type of research is vital for obtaining patient, family, community, and other stakeholder perspectives about their needs and interests, and will become increasingly critical as our models of health care delivery evolve. For example, qualitative research could contribute to the study of health care providers and systems with the goal of optimizing our health care delivery models. Given the increasing diversity of the populations we serve, qualitative research will also be critical in providing guidance in how to tailor health interventions to key characteristics and increase the likelihood of acceptable, effective treatment approaches. For example, applying qualitative research methods could enhance our understanding of refugee experiences in our health care system, clarify treatment preferences for emerging adults in the midst of health care transitions, examine satisfaction with health care delivery, and evaluate the applicability of our theoretical models of health behavior changes across racial and ethnic groups. Incorporating patient perspectives into treatment is essential to meeting this nation’s priority on patient-centered health care ( Institute of Medicine Committee on Quality of Health Care in America, 2001 ). Authors of qualitative studies who address the methodological choices addressed in this review will make important contributions to the field of pediatric psychology. Qualitative findings will lead to a more informed field that addresses the needs of a wide range of patient populations and produces effective and acceptable population-specific interventions to promote health.

Acknowledgments

The authors thank Bridget Grahmann for her assistance with manuscript preparation.

This work was supported by National Cancer Institute of the National Institutes of Health (K07CA196985 to Y.W.). This work is a publication of the United States Department of Agriculture/Agricultural Research Center (USDA/ARS), Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas. It is also a publication of the USDA/ARS, Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, and funded in part with federal funds from the USDA/ARS under Cooperative Agreement No. 58‐6250‐0‐008 (to D.T.). The contents of this publication do not necessarily reflect the views or policies of the USDA, nor does mention of trade names, commercial products, or organizations imply endorsement from the U.S. government. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflicts of interest : None declared.

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31 Interpretation In Qualitative Research: What, Why, How

Allen Trent, College of Education, University of Wyoming

Jeasik Cho, Department of Educational Studies, University of Wyoming

• Published: 02 September 2020
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This chapter addresses a wide range of concepts related to interpretation in qualitative research, examines the meaning and importance of interpretation in qualitative inquiry, and explores the ways methodology, data, and the self/researcher as instrument interact and impact interpretive processes. Additionally, the chapter presents a series of strategies for qualitative researchers engaged in the process of interpretation and closes by presenting a framework for qualitative researchers designed to inform their interpretations. The framework includes attention to the key qualitative research concepts transparency, reflexivity, analysis, validity, evidence, and literature. Four questions frame the chapter: What is interpretation, and why are interpretive strategies important in qualitative research? How do methodology, data, and the researcher/self impact interpretation in qualitative research? How do qualitative researchers engage in the process of interpretation? And, in what ways can a framework for interpretation strategies support qualitative researchers across multiple methodologies and paradigms?

“ All human knowledge takes the form of interpretation.” In this seemingly simple statement, the late German philosopher Walter Benjamin asserted that all knowledge is mediated and constructed. In doing so, he situates himself as an interpretivist, one who believes that human subjectivity, individuals’ characteristics, feelings, opinions, and experiential backgrounds impact observations, analysis of these observations, and resultant knowledge/truth constructions. Hammersley ( 2013 ) noted,

People—unlike atoms … actively interpret or make sense of their environment and of themselves; the ways in which they do this are shaped by the particular cultures in which they live; and these distinctive cultural orientations will strongly influence not only what they believe but also what they do. (p. 26)

Contrast this perspective with positivist claims that knowledge is based exclusively on external facts, objectively observed and recorded. Interpretivists, then, acknowledge that if positivistic notions of knowledge and truth are inadequate to explain social phenomena, then positivist, hard science approaches to research (i.e., the scientific method and its variants) are also inadequate and can even have a detrimental impact. According to Polyani (1967), “The ideal of exact science would turn out to be fundamentally misleading and possibly a source of devastating fallacies” (as cited in Packer, 2018 , p. 71). So, although the literature often contrasts quantitative and qualitative research as largely a difference in kinds of data employed (numerical vs. linguistic), instead, the primary differentiation is in the foundational, paradigmatic assumptions about truth, knowledge, and objectivity.

This chapter is about interpretation and the strategies that qualitative researchers use to interpret a wide variety of “texts.” Knowledge, we assert, is constructed, both individually (constructivism) and socially (constructionism). We accept this as our starting point. Our aim here is to share our perspective on a broad set of concepts associated with the interpretive, or meaning-making, process. Although it may happen at different times and in different ways, interpretation is part of almost all qualitative research.

Qualitative research is an umbrella term that encompasses a wide array of paradigmatic views, goals, and methods. Still, there are key unifying elements that include a generally constructionist epistemological standpoint, attention to primarily linguistic data, and generally accepted protocols or syntax for conducting research. Typically, qualitative researchers begin with a starting point—a curiosity, a problem in need of solutions, a research question, and/or a desire to better understand a situation from the “native” perspectives of the individuals who inhabit that context. This is what anthropologists call the emic , or insider’s, perspective. Olivier de Sardan ( 2015 ) wrote, “It evokes the meaning that social facts have for the actors concerned. It is opposed to the term etic , which, at times, designates more external or ‘objective’ data, and, at others, the researcher’s interpretive analysis” (p. 65).

From this starting point, researchers determine the appropriate kinds of data to collect, engage in fieldwork as participant observers to gather these data, organize the data, look for patterns, and attempt to understand the emic perspectives while integrating their own emergent interpretations. Researchers construct meaning from data by synthesizing research “findings,” “assertions,” or “theories” that can be shared so that others may also gain insights from the conducted inquiry. This interpretive process has a long history; hermeneutics, the theory of interpretation, blossomed in the 17th century in the form of biblical exegesis (Packer, 2018 ).

Although there are commonalities that cut across most forms of qualitative research, this is not to say that there is an accepted, linear, standardized approach. To be sure, there are an infinite number of variations and nuances in the qualitative research process. For example, some forms of inquiry begin with a firm research question; others start without even a clear focus for study. Grounded theorists begin data analysis and interpretation very early in the research process, whereas some case study researchers, for example, may collect data in the field for a period of time before seriously considering the data and its implications. Some ethnographers may be a part of the context (e.g., observing in classrooms), but they may assume more observer-like roles, as opposed to actively participating in the context. Alternatively, action researchers, in studying issues related to their own practice, are necessarily situated toward the participant end of the participant–observer continuum.

Our focus here is on one integrated part of the qualitative research process, interpretation, the hermeneutic process of collective and individual “meaning making.” Like Willig ( 2017 ), we believe “interpretation is at the heart of qualitative research because qualitative research is concerned with meaning and the process of meaning-making … qualitative data … needs to be given meaning by the researcher” (p. 276). As we discuss throughout this chapter, researchers take a variety of approaches to interpretation in qualitative work. Four general questions guide our explorations:

What is interpretation, and why are interpretive strategies important in qualitative research?

How do methodology, data, and the researcher/self impact interpretation in qualitative research?

How do qualitative researchers engage in the process of interpretation?

In what ways can a framework for interpretation strategies support qualitative researchers across multiple methodological and paradigmatic views?

We address each of these guiding questions in our attempt to explicate our interpretation of “interpretation” and, as educational researchers, we include examples from our own work to illustrate some key concepts.

What Is Interpretation, and Why Are Interpretive Strategies Important in Qualitative Research?

Qualitative researchers and those writing about qualitative methods often intertwine the terms analysis and interpretation . For example, Hubbard and Power ( 2003 ) described data analysis as “bringing order, structure, and meaning to the data” (p. 88). To us, this description combines analysis with interpretation. Although there is nothing wrong with this construction, our understanding aligns more closely with Mills’s ( 2018 ) claim that, “put simply, analysis involves summarizing what’s in the data, whereas interpretation involves making sense of—finding meaning in—that data” (p. 176). Hesse-Biber ( 2017 ) also separated out the essential process of interpretation. She described the steps in qualitative analysis and interpretation as data preparation, data exploration, and data reduction (all part of Mills’s “analysis” processes), followed by interpretation (pp. 307–328). Willig ( 2017 ) elaborated: analysis, she claims, is “sober and systematic,” whereas interpretation is associated with “creativity and the imagination … interpretation is seen as stimulating, it is interesting and it can be illuminating” (p. 276). For the purpose of this chapter, we will adhere to Mills’s distinction, understanding analysis as summarizing and organizing and interpretation as meaning making. Unavoidably, these closely related processes overlap and interact, but our focus will be primarily on the more complex of these endeavors, interpretation. Interpretation, in this sense, is in part translation, but translation is not an objective act. Instead, translation necessarily involves selectivity and the ascribing of meaning. Qualitative researchers “aim beneath manifest behavior to the meaning events have for those who experience them” (Eisner, 1991 , p. 35). The presentation of these insider/emic perspectives, coupled with researchers’ own interpretations, is a hallmark of qualitative research.

Qualitative researchers have long borrowed from extant models for fieldwork and interpretation. Approaches from anthropology and the arts have become especially prominent. For example, Eisner’s ( 1991 ) form of qualitative inquiry, educational criticism , draws heavily on accepted models of art criticism. T. Barrett ( 2011 ), an authority on art criticism, described interpretation as a complex set of processes based on a set of principles. We believe many of these principles apply as readily to qualitative research as they do to critique. The following principles, adapted from T. Barrett’s principles of interpretation (2011), inform our examination:

Qualitative phenomena have “aboutness” : All social phenomena have meaning, but meanings in this context can be multiple, even contradictory.

Interpretations are persuasive arguments : All interpretations are arguments, and qualitative researchers, like critics, strive to build strong arguments grounded in the information, or data, available.

  Some interpretations are better than others : Barrett noted that “some interpretations are better argued, better grounded with evidence, and therefore more reasonable, more certain, and more acceptable than others.” This contradicts the argument that “all interpretations are equal,” heard in the common refrain, “Well, that’s just your interpretation.”

There can be different, competing, and contradictory interpretations of the same phenomena : As noted at the beginning of this chapter, we acknowledge that subjectivity matters, and, unavoidably, it impacts one’s interpretations. As Barrett noted, “Interpretations are often based on a worldview.”

Interpretations are not (and cannot be) “right,” but instead, they can be more or less reasonable, convincing, and informative : There is never one “true” interpretation, but some interpretations are more compelling than others.

Interpretations can be judged by coherence, correspondence, and inclusiveness : Does the argument/interpretation make sense (coherence)? Does the interpretation fit the data (correspondence)? Have all data been attended to, including outlier data that do not necessarily support identified themes (inclusiveness)?

Interpretation is ultimately a communal endeavor : Initial interpretations may be incomplete, nearsighted, and/or narrow, but eventually these interpretations become richer, broader, and more inclusive. Feminist revisionist history projects are an exemplary case. Over time, the writing, art, and cultural contributions of countless women, previously ignored, diminished, or distorted, have come to be accepted as prominent contributions given serious consideration.

So, meaning is conferred; interpretations are socially constructed arguments; multiple interpretations are to be expected; and some interpretations are better than others. As we discuss later in this chapter, what makes an interpretation “better” often hinges on the purpose/goals of the research in question. Interpretations designed to generate theory, or generalizable rules, will be better for responding to research questions aligned with the aims of more traditional quantitative/positivist research, whereas interpretations designed to construct meanings through social interaction, to generate multiple perspectives, and to represent the context-specific perspectives of the research participants are better for researchers constructing thick, contextually rich descriptions, stories, or narratives. The former relies on more atomistic interpretive strategies, whereas the latter adheres to a more holistic approach (Willis, 2007 ). Both approaches to analysis/interpretation are addressed in more detail later in this chapter.

At this point, readers might ask, Why does interpretation matter, anyway? Our response to this question involves the distinctive nature of interpretation and the ability of the interpretive process to put unique fingerprints on an otherwise relatively static set of data. Once interview data are collected and transcribed (and we realize that even the process of transcription is, in part, interpretive), documents are collected, and observations are recorded, qualitative researchers could just, in good faith and with fidelity, represent the data in as straightforward ways as possible, allowing readers to “see for themselves” by sharing as much actual data (e.g., the transcribed words of the research participants) as possible. This approach, however, includes analysis, what we have defined as summarizing and organizing data for presentation, but it falls short of what we reference and define as interpretation—attempting to explain the meaning of others’ words and actions. According to Lichtman ( 2013 ),

While early efforts at qualitative research might have stopped at description, it is now more generally accepted that a qualitative researcher goes beyond pure description.… Many believe that it is the role of the researcher to bring understanding, interpretation, and meaning. (p. 17)

Because we are fond of the arts and arts-based approaches to qualitative research, an example from the late jazz drummer, Buddy Rich, seems fitting. Rich explains the importance of having the flexibility to interpret: “I don’t think any arranger should ever write a drum part for a drummer, because if a drummer can’t create his own interpretation of the chart, and he plays everything that’s written, he becomes mechanical; he has no freedom.” The same is true for qualitative researchers: without the freedom to interpret, the researcher merely regurgitates, attempting to share with readers/reviewers exactly what the research subjects shared with him or her. It is only through interpretation that the researcher, as collaborator with unavoidable subjectivities, is able to construct unique, contextualized meaning. Interpretation, then, in this sense, is knowledge construction.

In closing this section, we will illustrate the analysis-versus-interpretation distinction with the following transcript excerpt. In this study, the authors (Trent & Zorko, 2006 ) were studying student teaching from the perspective of K–12 students. This quote comes from a high school student in a focus group interview. She is describing a student teacher she had:

The right-hand column contains codes or labels applied to parts of the transcript text. Coding will be discussed in more depth later in this chapter, but for now, note that the codes are mostly summarizing the main ideas of the text, sometimes using the exact words of the research participant. This type of coding is a part of what we have called analysis—organizing and summarizing the data. It is a way of beginning to say “what is” there. As noted, though, most qualitative researchers go deeper. They want to know more than what is; they also ask, What does it mean? This is a question of interpretation.

Specific to the transcript excerpt, researchers might next begin to cluster the early codes into like groups. For example, the teacher “felt targeted,” “assumed kids were going to behave inappropriately,” and appeared to be “overwhelmed.” A researcher might cluster this group of codes in a category called “teacher feelings and perceptions” and may then cluster the codes “could not control class” and “students off task” into a category called “classroom management.” The researcher then, in taking a fresh look at these categories and the included codes, may begin to conclude that what is going on in this situation is that the student teacher does not have sufficient training in classroom management models and strategies and may also be lacking the skills she needs to build relationships with her students. These then would be interpretations, persuasive arguments connected to the study’s data. In this specific example, the researchers might proceed to write a memo about these emerging interpretations. In this memo, they might more clearly define their early categories and may also look through other data to see if there are other codes or categories that align with or overlap this initial analysis. They may write further about their emergent interpretations and, in doing so, may inform future data collection in ways that will allow them to either support or refute their early interpretations. These researchers will also likely find that the processes of analysis and interpretation are inextricably intertwined. Good interpretations very often depend on thorough and thoughtful analyses.

How Do Methodology, Data, and the Researcher/Self Impact Interpretation in Qualitative Research?

Methodological conventions guide interpretation and the use of interpretive strategies. For example, in grounded theory and in similar methodological traditions, “formal analysis begins early in the study and is nearly completed by the end of data collection” (Bogdan & Biklen, 2007 , p. 73). Alternatively, for researchers from other traditions, for example, case study researchers, “formal analysis and theory development [interpretation] do not occur until after the data collection is near complete” (p. 73).

Researchers subscribing to methodologies that prescribe early data analysis and interpretation may employ methods like analytic induction or the constant comparison method. In using analytic induction, researchers develop a rough definition of the phenomena under study; collect data to compare to this rough definition; modify the definition as needed, based on cases that both fit and do not fit the definition; and, finally, establish a clear, universal definition (theory) of the phenomena (Robinson, 1951, cited in Bogdan & Biklen, 2007 , p. 73). Generally, those using a constant comparison approach begin data collection immediately; identify key issues, events, and activities related to the study that then become categories of focus; collect data that provide incidents of these categories; write about and describe the categories, accounting for specific incidents and seeking others; discover basic processes and relationships; and, finally, code and write about the categories as theory, “grounded” in the data (Glaser, 1965 ). Although processes like analytic induction and constant comparison can be listed as steps to follow, in actuality, these are more typically recursive processes in which the researcher repeatedly goes back and forth between the data and emerging analyses and interpretations.

In addition to methodological conventions that prescribe data analysis early (e.g., grounded theory) or later (e.g., case study) in the inquiry process, methodological approaches also impact the general approach to analysis and interpretation. Ellingson ( 2011 ) situated qualitative research methodologies on a continuum spanning “science”-like approaches on one end juxtaposed with “art”-like approaches on the other.

Researchers pursuing a more science-oriented approach seek valid, reliable, generalizable knowledge; believe in neutral, objective researchers; and ultimately claim single, authoritative interpretations. Researchers adhering to these science-focused, postpositivistic approaches may count frequencies, emphasize the validity of the employed coding system, and point to intercoder reliability and random sampling as criteria that bolster the research credibility. Researchers at or near the science end of the continuum might employ analysis and interpretation strategies that include “paired comparisons,” “pile sorts,” “word counts,” identifying “key words in context,” and “triad tests” (Bernard, Wutich, & Ryan, 2017 , pp. 112, 381, 113, 170). These researchers may ultimately seek to develop taxonomies or other authoritative final products that organize and explain the collected data.

For example, in a study we conducted about preservice teachers’ experiences learning to teach second-language learners, the researchers collected larger data sets and used a statistical analysis package to analyze survey data, and the resultant findings included descriptive statistics. These survey results were supported with open-ended, qualitative data. For example, one of the study’s findings was that “a strong majority of candidates (96%) agreed that an immersion approach alone will not guarantee academic or linguistic success for second language learners.” In narrative explanations, one preservice teacher, representative of many others, remarked, “There has to be extra instructional efforts to help their students learn English … they won’t learn English by merely sitting in the classrooms” (Cho, Rios, Trent, & Mayfield, 2012 , p. 75).

Methodologies on the art side of Ellingson’s ( 2011 ) continuum, alternatively, “value humanistic, openly subjective knowledge, such as that embodied in stories, poetry, photography, and painting” (p. 599). Analysis and interpretation in these (often more contemporary) methodological approaches do not strive for “social scientific truth,” but instead are formulated to “enable us to learn about ourselves, each other, and the world through encountering the unique lens of a person’s (or a group’s) passionate rendering of a reality into a moving, aesthetic expression of meaning” (p. 599). For these “artistic/interpretivists, truths are multiple, fluctuating and ambiguous” (p. 599). Methodologies taking more subjective approaches to analysis and interpretation include autoethnography, testimonio, performance studies, feminist theorists/researchers, and others from related critical methodological forms of qualitative practice. More specifically arts-based approaches include poetic inquiry, fiction-based research, music as method, and dance and movement as inquiry (Leavy, 2017 ). Interpretation in these approaches is inherent. For example, “ interpretive poetry is understood as a method of merging the participant’s words with the researcher’s perspective” (Leavy, 2017 , p. 82).

As an example, one of us engaged in an artistic inquiry with a group of students in an art class for elementary teachers. We called it “Dreams as Data” and, among the project aims, we wanted to gather participants’ “dreams for education in the future” and display these dreams in an accessible, interactive, artistic display (see Trent, 2002 ). The intent was not to statistically analyze the dreams/data; instead, it was more universal. We wanted, as Ellingson ( 2011 , p. 599) noted, to use participant responses in ways that “enable us to learn about ourselves, each other, and the world.” The decision was made to leave responses intact and to share the whole/raw data set in the artistic display in ways that allowed the viewers to holistically analyze and interpret for themselves. Additionally, the researcher (Trent, 2002 ) collaborated with his students to construct their own contextually situated interpretations of the data. The following text is an excerpt from one participant’s response:

Almost a century ago, John Dewey eloquently wrote about the need to imagine and create the education that ALL children deserve, not just the richest, the Whitest, or the easiest to teach. At the dawn of this new century, on some mornings, I wake up fearful that we are further away from this ideal than ever.… Collective action, in a critical, hopeful, joyful, anti-racist and pro-justice spirit, is foremost in my mind as I reflect on and act in my daily work.… Although I realize the constraints on teachers and schools in the current political arena, I do believe in the power of teachers to stand next to, encourage, and believe in the students they teach—in short, to change lives. (Trent, 2002 , p. 49)

In sum, researchers whom Ellingson ( 2011 ) characterized as being on the science end of the continuum typically use more detailed or atomistic strategies to analyze and interpret qualitative data, whereas those toward the artistic end most often employ more holistic strategies. Both general approaches to qualitative data analysis and interpretation, atomistic and holistic, will be addressed later in this chapter.

As noted, qualitative researchers attend to data in a wide variety of ways depending on paradigmatic and epistemological beliefs, methodological conventions, and the purpose/aims of the research. These factors impact the kinds of data collected and the ways these data are ultimately analyzed and interpreted. For example, life history or testimonio researchers conduct extensive individual interviews, ethnographers record detailed observational notes, critical theorists may examine documents from pop culture, and ethnomethodologists may collect videotapes of interaction for analysis and interpretation.

In addition to the wide range of data types that are collected by qualitative researchers (and most qualitative researchers collect multiple forms of data), qualitative researchers, again influenced by the factors noted earlier, employ a variety of approaches to analyzing and interpreting data. As mentioned earlier in this chapter, some advocate for a detailed/atomistic, fine-grained approach to data (see, e.g., Bernard et al., 2017 ); others prefer a more broad-based, holistic, “eyeballing” of the data. According to Willis ( 2007 ), “Eyeballers reject the more structured approaches to analysis that break down the data into small units and, from the perspective of the eyeballers, destroy the wholeness and some of the meaningfulness of the data” (p. 298).

Regardless, we assert, as illustrated in Figure 31.1 , that as the process evolves, data collection becomes less prominent later in the process, as interpretation and making sense/meaning of the data becomes more prominent. It is through this emphasis on interpretation that qualitative researchers put their individual imprints on the data, allowing for the emergence of multiple, rich perspectives. This space for interpretation allows researchers the freedom Buddy Rich alluded to in his quote about interpreting musical charts. Without this freedom, Rich noted that the process would simply be “mechanical.” Furthermore, allowing space for multiple interpretations nourishes the perspectives of many others in the community. Writer and theorist Meg Wheatley explained, “Everyone in a complex system has a slightly different interpretation. The more interpretations we gather, the easier it becomes to gain a sense of the whole.” In qualitative research, “there is no ‘getting it right’ because there could be many ‘rights’ ” (as cited in Lichtman, 2013 ).

Increasing Role of Interpretation in Data Analysis

In addition to the roles methodology and data play in the interpretive process, perhaps the most important is the role of the self/the researcher in the interpretive process. According to Lichtman ( 2013 ), “Data are collected, information is gathered, settings are viewed, and realities are constructed through his or her eyes and ears … the qualitative researcher interprets and makes sense of the data” (p. 21). Eisner ( 1991 ) supported the notion of the researcher “self as instrument,” noting that expert researchers know not simply what to attend to, but also what to neglect. He describes the researcher’s role in the interpretive process as combining sensibility , the ability to observe and ascertain nuances, with schema , a deep understanding or cognitive framework of the phenomena under study.

J. Barrett ( 2007 ) described self/researcher roles as “transformations” (p. 418) at multiple points throughout the inquiry process: early in the process, researchers create representations through data generation, conducting observations and interviews and collecting documents and artifacts. Then,

transformation occurs when the “raw” data generated in the field are shaped into data records by the researcher. These data records are produced through organizing and reconstructing the researcher’s notes and transcribing audio and video recordings in the form of permanent records that serve as the “evidentiary warrants” of the generated data. The researcher strives to capture aspects of the phenomenal world with fidelity by selecting salient aspects to incorporate into the data record. (J. Barrett, 2007 , p. 418)

Transformation continues when the researcher codes, categorizes, and explores patterns in the data (the process we call analysis).

Transformations also involve interpreting what the data mean and relating these interpretations to other sources of insight about the phenomena, including findings from related research, conceptual literature, and common experience.… Data analysis and interpretation are often intertwined and rely upon the researcher’s logic, artistry, imagination, clarity, and knowledge of the field under study. (J. Barrett, 2007 , p. 418)

We mentioned the often-blended roles of participation and observation earlier in this chapter. The role(s) of the self/researcher are often described as points along a participant–observer continuum (see, e.g., Bogdan & Biklen, 2007 ). On the far observer end of this continuum, the researcher situates as detached, tries to be inconspicuous (so as not to impact/disrupt the phenomena under study), and approaches the studied context as if viewing it from behind a one-way mirror. On the opposite, participant end, the researcher is completely immersed and involved in the context. It would be difficult for an outsider to distinguish between researcher and subjects. For example, “some feminist researchers and postmodernists take a political stance and have an agenda that places the researcher in an activist posture. These researchers often become quite involved with the individuals they study and try to improve their human condition” (Lichtman, 2013 , p. 17).

We assert that most researchers fall somewhere between these poles. We believe that complete detachment is both impossible and misguided. In doing so, we, along with many others, acknowledge (and honor) the role of subjectivity, the researcher’s beliefs, opinions, biases, and predispositions. Positivist researchers seeking objective data and accounts either ignore the impact of subjectivity or attempt to drastically diminish/eliminate its impact. Even qualitative researchers have developed methods to avoid researcher subjectivity affecting research data collection, analysis, and interpretation. For example, foundational phenomenologist Husserl ( 1913/1962 ) developed the concept of bracketing , what Lichtman describes as “trying to identify your views on the topic and then putting them aside” (2013, p. 22). Like Slotnick and Janesick ( 2011 ), we ultimately claim “it is impossible to bracket yourself” (p. 1358). Instead, we take a balanced approach, like Eisner, understanding that subjectivity allows researchers to produce the rich, idiosyncratic, insightful, and yet data-based interpretations and accounts of lived experience that accomplish the primary purposes of qualitative inquiry. Eisner ( 1991 ) wrote, “Rather than regarding uniformity and standardization as the summum bonum, educational criticism [Eisner’s form of qualitative research] views unique insight as the higher good” (p. 35). That said, we also claim that, just because we acknowledge and value the role of researcher subjectivity, researchers are still obligated to ground their findings in reasonable interpretations of the data. Eisner ( 1991 ) explained:

This appreciation for personal insight as a source of meaning does not provide a license for freedom. Educational critics must provide evidence and reasons. But they reject the assumption that unique interpretation is a conceptual liability in understanding, and they see the insights secured from multiple views as more attractive than the comforts provided by a single right one. (p. 35)

Connected to this participant–observer continuum is the way the researcher positions him- or herself in relation to the “subjects” of the study. Traditionally, researchers, including early qualitative researchers, anthropologists, and ethnographers, referenced those studied as subjects . More recently, qualitative researchers better understand that research should be a reciprocal process in which both researcher and the foci of the research should derive meaningful benefit. Researchers aligned with this thinking frequently use the term participants to describe those groups and individuals included in a study. Going a step further, some researchers view research participants as experts on the studied topic and as equal collaborators in the meaning-making process. In these instances, researchers often use the terms co-researchers or co-investigators .

The qualitative researcher, then, plays significant roles throughout the inquiry process. These roles include transforming data, collaborating with research participants or co-researchers, determining appropriate points to situate along the participant–observer continuum, and ascribing personal insights, meanings, and interpretations that are both unique and justified with data exemplars. Performing these roles unavoidably impacts and changes the researcher. Slotnick and Janesick ( 2011 ) noted, “Since, in qualitative research the individual is the research instrument through which all data are passed, interpreted, and reported, the scholar’s role is constantly evolving as self evolves” (p. 1358).

As we note later, key in all this is for researchers to be transparent about the topics discussed in the preceding section: What methodological conventions have been employed and why? How have data been treated throughout the inquiry to arrive at assertions and findings that may or may not be transferable to other idiosyncratic contexts? And, finally, in what ways has the researcher/self been situated in and impacted the inquiry? Unavoidably, we assert, the self lies at the critical intersection of data and theory, and, as such, two legs of this stool, data and researcher, interact to create the third, theory.

How Do Qualitative Researchers Engage in the Process of Interpretation?

Theorists seem to have a propensity to dichotomize concepts, pulling them apart and placing binary opposites on the far ends of conceptual continuums. Qualitative research theorists are no different, and we have already mentioned some of these continua in this chapter. For example, in the previous section, we discussed the participant–observer continuum. Earlier, we referenced both Willis’s ( 2007 ) conceptualization of atomistic versus holistic approaches to qualitative analysis and interpretation and Ellingson’s ( 2011 ) science–art continuum. Each of these latter two conceptualizations inform how qualitative researchers engage in the process of interpretation.

Willis ( 2007 ) shared that the purpose of a qualitative project might be explained as “what we expect to gain from research” (p. 288). The purpose, or what we expect to gain, then guides and informs the approaches researchers might take to interpretation. Some researchers, typically positivist/postpositivist, conduct studies that aim to test theories about how the world works and/or how people behave. These researchers attempt to discover general laws, truths, or relationships that can be generalized. Others, less confident in the ability of research to attain a single, generalizable law or truth, might seek “local theory.” These researchers still seek truths, but “instead of generalizable laws or rules, they search for truths about the local context … to understand what is really happening and then to communicate the essence of this to others” (Willis, 2007 , p. 291). In both these purposes, researchers employ atomistic strategies in an inductive process in which researchers “break the data down into small units and then build broader and broader generalizations as the data analysis proceeds” (p. 317). The earlier mentioned processes of analytic induction, constant comparison, and grounded theory fit within this conceptualization of atomistic approaches to interpretation. For example, a line-by-line coding of a transcript might begin an atomistic approach to data analysis.

Alternatively, other researchers pursue distinctly different aims. Researchers with an objective description purpose focus on accurately describing the people and context under study. These researchers adhere to standards and practices designed to achieve objectivity, and their approach to interpretation falls within the binary atomistic/holistic distinction.

The purpose of hermeneutic approaches to research is to “understand the perspectives of humans. And because understanding is situational, hermeneutic research tends to look at the details of the context in which the study occurred. The result is generally rich data reports that include multiple perspectives” (Willis, 2007 , p. 293).

Still other researchers see their purpose as the creation of stories or narratives that utilize “a social process that constructs meaning through interaction … it is an effort to represent in detail the perspectives of participants … whereas description produces one truth about the topic of study, storytelling may generate multiple perspectives, interpretations, and analyses by the researcher and participants” (Willis, 2007 , p. 295).

In these latter purposes (hermeneutic, storytelling, narrative production), researchers typically employ more holistic strategies. According to Willis ( 2007 ), “Holistic approaches tend to leave the data intact and to emphasize that meaning must be derived for a contextual reading of the data rather than the extraction of data segments for detailed analysis” (p. 297). This was the case with the Dreams as Data project mentioned earlier.

We understand the propensity to dichotomize, situate concepts as binary opposites, and create neat continua between these polar descriptors. These sorts of reduction and deconstruction support our understandings and, hopefully, enable us to eventually reconstruct these ideas in meaningful ways. Still, in reality, we realize most of us will, and should, work in the middle of these conceptualizations in fluid ways that allow us to pursue strategies, processes, and theories most appropriate for the research task at hand. As noted, Ellingson ( 2011 ) set up another conceptual continuum, but, like ours, her advice was to “straddle multiple points across the field of qualitative methods” (p. 595). She explained, “I make the case for qualitative methods to be conceptualized as a continuum anchored by art and science, with vast middle spaces that embody infinite possibilities for blending artistic, expository, and social scientific ways of analysis and representation” (p. 595).

We explained at the beginning of this chapter that we view analysis as organizing and summarizing qualitative data and interpretation as constructing meaning. In this sense, analysis allows us to describe the phenomena under study. It enables us to succinctly answer what and how questions and ensures that our descriptions are grounded in the data collected. Descriptions, however, rarely respond to questions of why . Why questions are the domain of interpretation, and, as noted throughout this text, interpretation is complex. Gubrium and Holstein ( 2000 ) noted, “Traditionally, qualitative inquiry has concerned itself with what and how questions … qualitative researchers typically approach why questions cautiously, explanation is tricky business” (p. 502). Eisner ( 1991 ) described this distinctive nature of interpretation: “It means that inquirers try to account for [interpretation] what they have given account of ” (p. 35).

Our focus here is on interpretation, but interpretation requires analysis, because without clear understandings of the data and its characteristics, derived through systematic examination and organization (e.g., coding, memoing, categorizing), “interpretations” resulting from inquiry will likely be incomplete, uninformed, and inconsistent with the constructed perspectives of the study participants. Fortunately for qualitative researchers, we have many sources that lead us through analytic processes. We earlier mentioned the accepted processes of analytic induction and the constant comparison method. These detailed processes (see, e.g., Bogdan & Biklen, 2007 ) combine the inextricably linked activities of analysis and interpretation, with analysis more typically appearing as earlier steps in the process and meaning construction—interpretation—happening later.

A wide variety of resources support researchers engaged in the processes of analysis and interpretation. Saldaña ( 2011 ), for example, provided a detailed description of coding types and processes. He showed researchers how to use process coding (uses gerunds, “-ing” words to capture action), in vivo coding (uses the actual words of the research participants/ subjects), descriptive coding (uses nouns to summarize the data topics), versus coding (uses “vs” to identify conflicts and power issues), and values coding (identifies participants’ values, attitudes, and/or beliefs). To exemplify some of these coding strategies, we include an excerpt from a transcript of a meeting of a school improvement committee. In this study, the collaborators were focused on building “school community.” This excerpt illustrates the application of a variety of codes described by Saldaña to this text:

To connect and elaborate the ideas developed in coding, Saldaña ( 2011 ) suggested researchers categorize the applied codes, write memos to deepen understandings and illuminate additional questions, and identify emergent themes. To begin the categorization process, Saldaña recommended all codes be “classified into similar clusters … once the codes have been classified, a category label is applied to them” (p. 97). So, in continuing with the study of school community example coded here, the researcher might create a cluster/category called “Value of Collaboration” and in this category might include the codes “relationships,” “building community,” and “effective strategies.”

Having coded and categorized a study’s various data forms, a typical next step for researchers is to write memos or analytic memos . Writing analytic memos allows the researcher(s) to

set in words your interpretation of the data … an analytic memo further articulates your … thinking processes on what things may mean … as the study proceeds, however, initial and substantive analytic memos can be revisited and revised for eventual integration into the report itself. (Saldaña, 2011 , p. 98)

In the study of student teaching from K–12 students’ perspectives (Trent & Zorko, 2006 ), we noticed throughout our analysis a series of focus group interview quotes coded “names.” The following quote from a high school student is representative of many others:

I think that, ah, they [student teachers] should like know your face and your name because, uh, I don’t like it if they don’t and they’ll just like … cause they’ll blow you off a lot easier if they don’t know, like our new principal is here … he is, like, he always, like, tries to make sure to say hi even to the, like, not popular people if you can call it that, you know, and I mean, yah, and the people that don’t usually socialize a lot, I mean he makes an effort to know them and know their name like so they will cooperate better with him.

Although we did not ask the focus groups a specific question about whether student teachers knew the K–12 students’ names, the topic came up in every focus group interview. We coded the above excerpt and the others “knowing names,” and these data were grouped with others under the category “relationships.” In an initial analytic memo about this, the researchers wrote,

STUDENT TEACHING STUDY—MEMO #3 “Knowing Names as Relationship Building” Most groups made unsolicited mentions of student teachers knowing, or not knowing, their names. We haven’t asked students about this, but it must be important to them because it always seems to come up. Students expected student teachers to know their names. When they did, students noticed and seemed pleased. When they didn’t, students seemed disappointed, even annoyed. An elementary student told us that early in the semester, “she knew our names … cause when we rose [sic] our hands, she didn’t have to come and look at our name tags … it made me feel very happy.” A high schooler, expressing displeasure that his student teacher didn’t know students’ names, told us, “They should like know your name because it shows they care about you as a person. I mean, we know their names, so they should take the time to learn ours too.” Another high school student said that even after 3 months, she wasn’t sure the student teacher knew her name. Another student echoed, “Same here.” Each of these students asserted that this (knowing students’ names) had impacted their relationship with the student teacher. This high school student focus group stressed that a good relationship, built early, directly impacts classroom interaction and student learning. A student explained it like this: “If you get to know each other, you can have fun with them … they seem to understand you more, you’re more relaxed, and learning seems easier.”

As noted in these brief examples, coding, categorizing, and writing memos about a study’s data are all accepted processes for data analysis and allow researchers to begin constructing new understandings and forming interpretations of the studied phenomena. We find the qualitative research literature to be particularly strong in offering support and guidance for researchers engaged in these analytic practices. In addition to those already noted in this chapter, we have found the following resources provide practical, yet theoretically grounded approaches to qualitative data analysis. For more detailed, procedural, or atomistic approaches to data analysis, we direct researchers to Miles and Huberman’s classic 1994 text, Qualitative Data Analysis , and Bernard et al.’s 2017 book Analyzing Qualitative Data: Systematic Approaches. For analysis and interpretation strategies falling somewhere between the atomistic and holistic poles, we suggest Hesse-Biber and Leavy’s ( 2011 ) chapter, “Analysis and Interpretation of Qualitative Data,” in their book, The Practice of Qualitative Research (second edition); Lichtman’s chapter, “Making Meaning From Your Data,” in her 2013 book Qualitative Research in Education: A User’s Guide (third edition); and “Processing Fieldnotes: Coding and Memoing,” a chapter in Emerson, Fretz, and Shaw’s ( 1995 ) book, Writing Ethnographic Fieldwork . Each of these sources succinctly describes the processes of data preparation, data reduction, coding and categorizing data, and writing memos about emergent ideas and findings. For more holistic approaches, we have found Denzin and Lincoln’s ( 2007 ) Collecting and Interpreting Qualitative Materials and Ellis and Bochner’s ( 2000 ) chapter “Autoethnography, Personal Narrative, Reflexivity” to both be very informative. Finally, Leavy’s 2017 book, Method Meets Art: Arts-Based Research Practice , provides support and guidance to researchers engaged in arts-based research.

Even after reviewing the multiple resources for treating data included here, qualitative researchers might still be wondering, But exactly how do we interpret? In the remainder of this section and in the concluding section of this chapter, we more concretely provide responses to this question and, in closing, we propose a framework for researchers to utilize as they engage in the complex, ambiguous, and yet exciting process of constructing meanings and new understandings from qualitative sources.

These meanings and understandings are often presented as theory, but theories in this sense should be viewed more as “guides to perception” as opposed to “devices that lead to the tight control or precise prediction of events” (Eisner, 1991 , p. 95). Perhaps Erickson’s ( 1986 ) concept of assertions is a more appropriate aim for qualitative researchers. He claimed that assertions are declarative statements; they include a summary of the new understandings, and they are supported by evidence/data. These assertions are open to revision and are revised when disconfirming evidence requires modification. Assertions, theories, or other explanations resulting from interpretation in research are typically presented as “findings” in written research reports. Belgrave and Smith ( 2002 ) emphasized the importance of these interpretations (as opposed to descriptions): “The core of the report is not the events reported by the respondent, but rather the subjective meaning of the reported events for the respondent” (p. 248).

Mills ( 2018 ) viewed interpretation as responding to the question, So what? He provided researchers a series of concrete strategies for both analysis and interpretation. Specific to interpretation, Mills (pp. 204–207) suggested a variety of techniques, including the following:

“ Extend the analysis ”: In doing so, researchers ask additional questions about the research. The data appear to say X , but could it be otherwise? In what ways do the data support emergent finding X ? And, in what ways do they not?

“ Connect findings with personal experience ”: Using this technique, researchers share interpretations based on their intimate knowledge of the context, the observed actions of the individuals in the studied context, and the data points that support emerging interpretations, as well as their awareness of discrepant events or outlier data. In a sense, the researcher is saying, “Based on my experiences in conducting this study, this is what I make of it all.”

“ Seek the advice of ‘critical’ friends ”: In doing so, researchers utilize trusted colleagues, fellow researchers, experts in the field of study, and others to offer insights, alternative interpretations, and the application of their own unique lenses to a researcher’s initial findings. We especially like this strategy because we acknowledge that, too often, qualitative interpretation is a “solo” affair.

“ Contextualize findings in the literature ”: This allows researchers to compare their interpretations to those of others writing about and studying the same/similar phenomena. The results of this contextualization may be that the current study’s findings correspond with the findings of other researchers. The results might, alternatively, differ from the findings of other researchers. In either instance, the researcher can highlight his or her unique contributions to our understanding of the topic under study.

“ Turn to theory ”: Mills defined theory as “an analytical and interpretive framework that helps the researcher make sense of ‘what is going on’ in the social setting being studied.” In turning to theory, researchers search for increasing levels of abstraction and move beyond purely descriptive accounts. Connecting to extant or generating new theory enables researchers to link their work to the broader contemporary issues in the field.

Other theorists offer additional advice for researchers engaged in the act of interpretation. Richardson ( 1995 ) reminded us to account for the power dynamics in the researcher–researched relationship and notes that, in doing so, we can allow for oppressed and marginalized voices to be heard in context. Bogdan and Biklen ( 2007 ) suggested that researchers engaged in interpretation revisit foundational writing about qualitative research, read studies related to the current research, ask evaluative questions (e.g., Is what I’m seeing here good or bad?), ask about implications of particular findings/interpretations, think about the audience for interpretations, look for stories and incidents that illustrate a specific finding/interpretation, and attempt to summarize key interpretations in a succinct paragraph. All these suggestions can be pertinent in certain situations and with particular methodological approaches. In the next and closing section of this chapter, we present a framework for interpretive strategies we believe will support, guide, and be applicable to qualitative researchers across multiple methodologies and paradigms.

In What Ways Can a Framework for Interpretation Strategies Support Qualitative Researchers across Multiple Methodological and Paradigmatic Views?

The process of qualitative research is often compared to a journey, one without a detailed itinerary and ending, but with general direction and aims and yet an open-endedness that adds excitement and thrives on curiosity. Qualitative researchers are travelers. They travel physically to field sites; they travel mentally through various epistemological, theoretical, and methodological grounds; they travel through a series of problem-finding, access, data collection, and data analysis processes; and, finally—the topic of this chapter—they travel through the process of making meaning of all this physical and cognitive travel via interpretation.

Although travel is an appropriate metaphor to describe the journey of qualitative researchers, we will also use “travel” to symbolize a framework for qualitative research interpretation strategies. By design, this framework applies across multiple paradigmatic, epistemological, and methodological traditions. The application of this framework is not formulaic or highly prescriptive; it is also not an anything-goes approach. It falls, and is applicable, between these poles, giving concrete (suggested) direction to qualitative researchers wanting to make the most of the interpretations that result from their research and yet allowing the necessary flexibility for researchers to employ the methods, theories, and approaches they deem most appropriate to the research problem(s) under study.

TRAVEL, a Comprehensive Approach to Qualitative Interpretation

In using the word TRAVEL as a mnemonic device, our aim is to highlight six essential concepts we argue all qualitative researchers should attend to in the interpretive process: transparency, reflexivity, analysis, validity, evidence, and literature. The importance of each is addressed here.

Transparency , as a research concept seems, well, transparent. But, too often, we read qualitative research reports and are left with many questions: How were research participants and the topic of study selected/excluded? How were the data collected, when, and for how long? Who analyzed and interpreted these data? A single researcher? Multiple? What interpretive strategies were employed? Are there data points that substantiate these interpretations/findings? What analytic procedures were used to organize the data prior to making the presented interpretations? In being transparent about data collection, analysis, and interpretation processes, researchers allow reviewers/readers insight into the research endeavor, and this transparency leads to credibility for both researcher and researcher’s claims. Altheide and Johnson ( 2011 ) explained,

There is great diversity of qualitative research.… While these approaches differ, they also share an ethical obligation to make public their claims, to show the reader, audience, or consumer why they should be trusted as faithful accounts of some phenomenon. (p. 584)

This includes, they noted, articulating

what the different sources of data were, how they were interwoven, and … how subsequent interpretations and conclusions are more or less closely tied to the various data … the main concern is that the connection be apparent, and to the extent possible, transparent. (p. 590)

In the Dreams as Data art and research project noted earlier, transparency was addressed in multiple ways. Readers of the project write-up were informed that interpretations resulting from the study, framed as themes , were a result of collaborative analysis that included insights from both students and instructor. Viewers of the art installation/data display had the rare opportunity to see all participant responses. In other words, viewers had access to the entire raw data set (see Trent, 2002 ). More frequently, we encounter only research “findings” already distilled, analyzed, and interpreted in research accounts, often by a single researcher. Allowing research consumers access to the data to interpret for themselves in the Dreams project was an intentional attempt at transparency.

Reflexivity , the second of our concepts for interpretive researcher consideration, has garnered a great deal of attention in qualitative research literature. Some have called this increased attention the reflexive turn (see, e.g., Denzin & Lincoln, 2004 ).

Although you can find many meanings for the term reflexivity, it is usually associated with a critical reflection on the practice and process of research and the role of the researcher. It concerns itself with the impact of the researcher on the system and the system on the researcher. It acknowledges the mutual relationships between the researcher and who and what is studied … by acknowledging the role of the self in qualitative research, the researcher is able to sort through biases and think about how they affect various aspects of the research, especially interpretation of meanings. (Lichtman, 2013 , p. 165)

As with transparency, attending to reflexivity allows researchers to attach credibility to presented findings. Providing a reflexive account of researcher subjectivity and the interactions of this subjectivity within the research process is a way for researchers to communicate openly with their audience. Instead of trying to exhume inherent bias from the process, qualitative researchers share with readers the value of having a specific, idiosyncratic positionality. As a result, situated, contextualized interpretations are viewed as an asset, as opposed to a liability.

LaBanca ( 2011 ), acknowledging the often solitary nature of qualitative research, called for researchers to engage others in the reflexive process. Like many other researchers, LaBanca utilized a researcher journal to chronicle reflexive thoughts, explorations, and understandings, but he took it a step farther. Realizing the value of others’ input, LaBanca posts his reflexive journal entries on a blog (what he calls an online reflexivity blog ) and invites critical friends, other researchers, and interested members of the community to audit his reflexive moves, providing insights, questions, and critique that inform his research and study interpretations.

We agree this is a novel approach worth considering. We, too, understand that multiple interpreters will undoubtedly produce multiple interpretations, a richness of qualitative research. So, we suggest researchers consider bringing others in before the production of the report. This could be fruitful in multiple stages of the inquiry process, but especially in the complex, idiosyncratic processes of reflexivity and interpretation. We are both educators and educational researchers. Historically, each of these roles has tended to be constructed as an isolated endeavor, the solitary teacher, the solo researcher/fieldworker. As noted earlier and in the analysis section that follows, introducing collaborative processes to what has often been a solitary activity offers much promise for generating rich interpretations that benefit from multiple perspectives.

Being consciously reflexive throughout our practice as researchers has benefitted us in many ways. In a study of teacher education curricula designed to prepare preservice teachers to support second-language learners, we realized hard truths that caused us to reflect on and adapt our own practices as teacher educators. Reflexivity can inform a researcher at all parts of the inquiry, even in early stages. For example, one of us was beginning a study of instructional practices in an elementary school. The communicated methods of the study indicated that the researcher would be largely an observer. Early fieldwork revealed that the researcher became much more involved as a participant than anticipated. Deep reflection and writing about the classroom interactions allowed the researcher to realize that the initial purpose of the research was not being accomplished, and the researcher believed he was having a negative impact on the classroom culture. Reflexivity in this instance prompted the researcher to leave the field and abandon the project as it was just beginning. Researchers should plan to openly engage in reflexive activities, including writing about their ongoing reflections and subjectivities. Including excerpts of this writing in research account supports our earlier recommendation of transparency.

Early in this chapter, for the purposes of discussion and examination, we defined analysis as “summarizing and organizing” data in a qualitative study and interpretation as “meaning making.” Although our focus has been on interpretation as the primary topic, the importance of good analysis cannot be underestimated, because without it, resultant interpretations are likely incomplete and potentially uninformed. Comprehensive analysis puts researchers in a position to be deeply familiar with collected data and to organize these data into forms that lead to rich, unique interpretations, and yet interpretations that are clearly connected to data exemplars. Although we find it advantageous to examine analysis and interpretation as different but related practices, in reality, the lines blur as qualitative researchers engage in these recursive processes.

We earlier noted our affinity for a variety of approaches to analysis (see, e.g., Hesse-Biber & Leavy, 2011 ; Lichtman, 2013 ; or Saldaña, 2011 ). Emerson et al. ( 1995 ) presented a grounded approach to qualitative data analysis: In early stages, researchers engage in a close, line-by-line reading of data/collected text and accompany this reading with open coding , a process of categorizing and labeling the inquiry data. Next, researchers write initial memos to describe and organize the data under analysis. These analytic phases allow the researcher(s) to prepare, organize, summarize, and understand the data, in preparation for the more interpretive processes of focused coding and the writing up of interpretations and themes in the form of integrative memos .

Similarly, Mills ( 2018 ) provided guidance on the process of analysis for qualitative action researchers. His suggestions for organizing and summarizing data include coding (labeling data and looking for patterns); identifying themes by considering the big picture while looking for recurrent phrases, descriptions, or topics; asking key questions about the study data (who, what, where, when, why, and how); developing concept maps (graphic organizers that show initial organization and relationships in the data); and stating what’s missing by articulating what data are not present (pp. 179–189).

Many theorists, like Emerson et al. ( 1995 ) and Mills ( 2018 ) noted here, provide guidance for individual researchers engaged in individual data collection, analysis, and interpretation; others, however, invite us to consider the benefits of collaboratively engaging in these processes through the use of collaborative research and analysis teams. Paulus, Woodside, and Ziegler ( 2008 ) wrote about their experiences in collaborative qualitative research: “Collaborative research often refers to collaboration among the researcher and the participants. Few studies investigate the collaborative process among researchers themselves” (p. 226).

Paulus et al. ( 2008 ) claimed that the collaborative process “challenged and transformed our assumptions about qualitative research” (p. 226). Engaging in reflexivity, analysis, and interpretation as a collaborative enabled these researchers to reframe their views about the research process, finding that the process was much more recursive, as opposed to following a linear progression. They also found that cooperatively analyzing and interpreting data yielded “collaboratively constructed meanings” as opposed to “individual discoveries.” And finally, instead of the traditional “individual products” resulting from solo research, collaborative interpretation allowed researchers to participate in an “ongoing conversation” (p. 226).

These researchers explained that engaging in collaborative analysis and interpretation of qualitative data challenged their previously held assumptions. They noted,

through collaboration, procedures are likely to be transparent to the group and can, therefore, be made public. Data analysis benefits from an iterative, dialogic, and collaborative process because thinking is made explicit in a way that is difficult to replicate as a single researcher. (Paulus et al., 2008 , p. 236)

They shared that, during the collaborative process, “we constantly checked our interpretation against the text, the context, prior interpretations, and each other’s interpretations” (p. 234).

We, too, have engaged in analysis similar to these described processes, including working on research teams. We encourage other researchers to find processes that fit with the methodology and data of a particular study, use the techniques and strategies most appropriate, and then cite the utilized authority to justify the selected path. We urge traditionally solo researchers to consider trying a collaborative approach. Generally, we suggest researchers be familiar with a wide repertoire of practices. In doing so, they will be in better positions to select and use strategies most appropriate for their studies and data. Succinctly preparing, organizing, categorizing, and summarizing data sets the researcher(s) up to construct meaningful interpretations in the forms of assertions, findings, themes, and theories.

Researchers want their findings to be sound, backed by evidence, and justifiable and to accurately represent the phenomena under study. In short, researchers seek validity for their work. We assert that qualitative researchers should attend to validity concepts as a part of their interpretive practices. We have previously written and theorized about validity, and, in doing so, we have highlighted and labeled what we consider two distinctly different approaches, transactional and transformational (Cho & Trent, 2006 ). We define transactional validity in qualitative research as an interactive process occurring among the researcher, the researched, and the collected data, one that is aimed at achieving a relatively higher level of accuracy. Techniques, methods, and/or strategies are employed during the conduct of the inquiry. These techniques, such as member checking and triangulation, are seen as a medium with which to ensure an accurate reflection of reality (or, at least, participants’ constructions of reality). Lincoln and Guba’s ( 1985 ) widely known notion of trustworthiness in “naturalistic inquiry” is grounded in this approach. In seeking trustworthiness, researchers attend to research credibility, transferability, dependability, and confirmability. Validity approaches described by Maxwell ( 1992 ) as “descriptive” and “interpretive” also proceed in the usage of transactional processes.

For example, in the write-up of a study on the facilitation of teacher research, one of us (Trent, 2012 ) wrote about the use of transactional processes:

“Member checking is asking the members of the population being studied for their reaction to the findings” (Sagor, 2000 , p. 136). Interpretations and findings of this research, in draft form, were shared with teachers (for member checking) on multiple occasions throughout the study. Additionally, teachers reviewed and provided feedback on the final draft of this article. (p. 44)

This member checking led to changes in some resultant interpretations (called findings in this particular study) and to adaptations of others that shaped these findings in ways that made them both richer and more contextualized.

Alternatively, in transformational approaches, validity is not so much something that can be achieved solely by employing certain techniques. Transformationalists assert that because traditional or positivist inquiry is no longer seen as an absolute means to truth in the realm of human science, alternative notions of validity should be considered to achieve social justice, deeper understandings, broader visions, and other legitimate aims of qualitative research. In this sense, it is the ameliorative aspects of the research that achieve (or do not achieve) its validity. Validity is determined by the resultant actions prompted by the research endeavor.

Lather ( 1993 ), Richardson ( 1997 ), and others (e.g., Lenzo, 1995 ; Scheurich, 1996 ) proposed a transgressive approach to validity that emphasized a higher degree of self-reflexivity. For example, Lather proposed a “catalytic validity” described as “the degree to which the research empowers and emancipates the research subjects” (Scheurich, 1996 , p. 4). Beverley ( 2000 , p. 556) proposed testimonio as a qualitative research strategy. These first-person narratives find their validity in their ability to raise consciousness and thus provoke political action to remedy problems of oppressed peoples (e.g., poverty, marginality, exploitation).

We, too, have pursued research with transformational aims. In the earlier mentioned study of preservice teachers’ experiences learning to teach second-language learners (Cho et al., 2012 ), our aims were to empower faculty members, evolve the curriculum, and, ultimately, better serve preservice teachers so that they might better serve English-language learners in their classrooms. As program curricula and activities have changed as a result, we claim a degree of transformational validity for this research.

Important, then, for qualitative researchers throughout the inquiry, but especially when engaged in the process of interpretation, is to determine the type(s) of validity applicable to the study. What are the aims of the study? Providing an “accurate” account of studied phenomena? Empowering participants to take action for themselves and others? The determination of this purpose will, in turn, inform researchers’ analysis and interpretation of data. Understanding and attending to the appropriate validity criteria will bolster researcher claims to meaningful findings and assertions.

Regardless of purpose or chosen validity considerations, qualitative research depends on evidence . Researchers in different qualitative methodologies rely on different types of evidence to support their claims. Qualitative researchers typically utilize a variety of forms of evidence including texts (written notes, transcripts, images, etc.), audio and video recordings, cultural artifacts, documents related to the inquiry, journal entries, and field notes taken during observations of social contexts and interactions. Schwandt ( 2001 ) wrote,

Evidence is essential to justification, and justification takes the form of an argument about the merit(s) of a given claim. It is generally accepted that no evidence is conclusive or unassailable (and hence, no argument is foolproof). Thus, evidence must often be judged for its credibility, and that typically means examining its source and the procedures by which it was produced [thus the need for transparency discussed earlier]. (p. 82)

Altheide and Johnson ( 2011 ) drew a distinction between evidence and facts:

Qualitative researchers distinguish evidence from facts. Evidence and facts are similar but not identical. We can often agree on facts, e.g., there is a rock, it is harder than cotton candy. Evidence involves an assertion that some facts are relevant to an argument or claim about a relationship. Since a position in an argument is likely tied to an ideological or even epistemological position, evidence is not completely bound by facts, but it is more problematic and subject to disagreement. (p. 586)

Inquirers should make every attempt to link evidence to claims (or findings, interpretations, assertions, conclusions, etc.). There are many strategies for making these connections. Induction involves accumulating multiple data points to infer a general conclusion. Confirmation entails directly linking evidence to resultant interpretations. Testability/falsifiability means illustrating that evidence does not necessarily contradict the claim/interpretation and so increases the credibility of the claim (Schwandt, 2001 ). In the study about learning to teach second-language learners, for example, a study finding (Cho et al., 2012 ) was that “as a moral claim , candidates increasingly [in higher levels of the teacher education program] feel more responsible and committed to … [English language learners]” (p. 77). We supported this finding with a series of data points that included the following preservice teacher response: “It is as much the responsibility of the teacher to help teach second-language learners the English language as it is our responsibility to teach traditional English speakers to read or correctly perform math functions.” Claims supported by evidence allow readers to see for themselves and to both examine researcher assertions in tandem with evidence and form further interpretations of their own.

Some postmodernists reject the notion that qualitative interpretations are arguments based on evidence. Instead, they argue that qualitative accounts are not intended to faithfully represent that experience, but instead are designed to evoke some feelings or reactions in the reader of the account (Schwandt, 2001 ). We argue that, even in these instances where transformational validity concerns take priority over transactional processes, evidence still matters. Did the assertions accomplish the evocative aims? What evidence/arguments were used to evoke these reactions? Does the presented claim correspond with the study’s evidence? Is the account inclusive? In other words, does it attend to all evidence or selectively compartmentalize some data while capitalizing on other evidentiary forms?

Researchers, we argue, should be both transparent and reflexive about these questions and, regardless of research methodology or purpose, should share with readers of the account their evidentiary moves and aims. Altheide and Johnson ( 2011 ) called this an evidentiary narrative and explain:

Ultimately, evidence is bound up with our identity in a situation.… An “evidentiary narrative” emerges from a reconsideration of how knowledge and belief systems in everyday life are tied to epistemic communities that provide perspectives, scenarios, and scripts that reflect symbolic and social moral orders. An “evidentiary narrative” symbolically joins an actor, an audience, a point of view (definition of a situation), assumptions, and a claim about a relationship between two or more phenomena. If any of these factors are not part of the context of meaning for a claim, it will not be honored, and thus, not seen as evidence. (p. 686)

In sum, readers/consumers of a research account deserve to know how evidence was treated and viewed in an inquiry. They want and should be aware of accounts that aim to evoke versus represent, and then they can apply their own criteria (including the potential transferability to their situated context). Renowned ethnographer and qualitative research theorist Harry Wolcott ( 1990 ) urged researchers to “let readers ‘see’ for themselves” by providing more detail rather than less and by sharing primary data/evidence to support interpretations. In the end, readers do not expect perfection. Writer Eric Liu ( 2010 ) explained, “We don’t expect flawless interpretation. We expect good faith. We demand honesty.”

Last, in this journey through concepts we assert are pertinent to researchers engaged in interpretive processes, we include attention to the literature . In discussing literature, qualitative researchers typically mean publications about the prior research conducted on topics aligned with or related to a study. Most often, this research/literature is reviewed and compiled by researchers in a section of the research report titled “Literature Review.” It is here we find others’ studies, methods, and theories related to our topics of study, and it is here we hope the assertions and theories that result from our studies will someday reside.

We acknowledge the value of being familiar with research related to topics of study. This familiarity can inform multiple phases of the inquiry process. Understanding the extant knowledge base can inform research questions and topic selection, data collection and analysis plans, and the interpretive process. In what ways do the interpretations from this study correspond with other research conducted on this topic? Do findings/interpretations corroborate, expand, or contradict other researchers’ interpretations of similar phenomena? In any of these scenarios (correspondence, expansion, contradiction), new findings and interpretations from a study add to and deepen the knowledge base, or literature, on a topic of investigation.

For example, in our literature review for the study of student teaching, we quickly determined that the knowledge base and extant theories related to the student teaching experience were immense, but also quickly realized that few, if any, studies had examined student teaching from the perspective of the K–12 students who had the student teachers. This focus on the literature related to our topic of student teaching prompted us to embark on a study that would fill a gap in this literature: Most of the knowledge base focused on the experiences and learning of the student teachers themselves. Our study, then, by focusing on the K–12 students’ perspectives, added literature/theories/assertions to a previously untapped area. The “literature” in this area (at least we would like to think) is now more robust as a result.

In another example, a research team (Trent et al., 2003 ) focused on institutional diversity efforts, mined the literature, found an appropriate existing (a priori) set of theories/assertions, and then used the existing theoretical framework from the literature as a framework to analyze data, in this case, a variety of institutional activities related to diversity.

Conducting a literature review to explore extant theories on a topic of study can serve a variety of purposes. As evidenced in these examples, consulting the literature/extant theory can reveal gaps in the literature. A literature review might also lead researchers to existing theoretical frameworks that support analysis and interpretation of their data (as in the use of the a priori framework example). Finally, a review of current theories related to a topic of inquiry might confirm that much theory already exists, but that further study may add to, bolster, and/or elaborate on the current knowledge base.

Guidance for researchers conducting literature reviews is plentiful. Lichtman ( 2013 ) suggested researchers conduct a brief literature review, begin research, and then update and modify the literature review as the inquiry unfolds. She suggested reviewing a wide range of related materials (not just scholarly journals) and additionally suggested that researchers attend to literature on methodology, not just the topic of study. She also encouraged researchers to bracket and write down thoughts on the research topic as they review the literature, and, important for this chapter, that researchers “integrate your literature review throughout your writing rather than using a traditional approach of placing it in a separate chapter” (p. 173).

We agree that the power of a literature review to provide context for a study can be maximized when this information is not compartmentalized apart from a study’s findings. Integrating (or at least revisiting) reviewed literature juxtaposed alongside findings can illustrate how new interpretations add to an evolving story. Eisenhart ( 1998 ) expanded the traditional conception of the literature review and discussed the concept of an interpretive review . By taking this interpretive approach, Eisenhart claimed that reviews, alongside related interpretations/findings on a specific topic, have the potential to allow readers to see the studied phenomena in entirely new ways, through new lenses, revealing heretofore unconsidered perspectives. Reviews that offer surprising and enriching perspectives on meanings and circumstances “shake things up, break down boundaries, and cause things (or thinking) to expand” (p. 394). Coupling reviews of this sort with current interpretations will “give us stories that startle us with what we have failed to notice” (p. 395).

In reviews of research studies, it can certainly be important to evaluate the findings in light of established theories and methods [the sorts of things typically included in literature reviews]. However, it also seems important to ask how well the studies disrupt conventional assumptions and help us to reconfigure new, more inclusive, and more promising perspectives on human views and actions. From an interpretivist perspective, it would be most important to review how well methods and findings permit readers to grasp the sense of unfamiliar perspectives and actions. (Eisenhart, 1998 , p. 397)

Though our interpretation-related journey in this chapter nears an end, we are hopeful it is just the beginning of multiple new conversations among ourselves and in concert with other qualitative researchers. Our aims have been to circumscribe interpretation in qualitative research; emphasize the importance of interpretation in achieving the aims of the qualitative project; discuss the interactions of methodology, data, and the researcher/self as these concepts and theories intertwine with interpretive processes; describe some concrete ways that qualitative inquirers engage the process of interpretation; and, finally, provide a framework of interpretive strategies that may serve as a guide for ourselves and other researchers.

In closing, we note that the TRAVEL framework, construed as a journey to be undertaken by researchers engaged in interpretive processes, is not designed to be rigid or prescriptive, but instead is designed to be a flexible set of concepts that will inform researchers across multiple epistemological, methodological, and theoretical paradigms. We chose the concepts of transparency, reflexivity, analysis, validity, evidence, and literature (TRAVEL) because they are applicable to the infinite journeys undertaken by qualitative researchers who have come before and to those who will come after us. As we journeyed through our interpretations of interpretation, we have discovered new things about ourselves and our work. We hope readers also garner insights that enrich their interpretive excursions. Happy travels!

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48 Report Writing

Pa . Raajeswari

INTRODUCTION

Research report is a condensed form or a brief description of the research work done by the researcher. It involves several steps to present the report in the form of thesis or dissertation. The research report is the final stage to be completed in the process of the research. It is an important part of the research study. In research report, a researcher will have to make a complete account of the research activity conducted.A report is the exposition of a research project and It is the end product of a research activity.

In research report, the problem of research methodology used and conclusion drawn will be described. Writing of a research report is different type of wok in the research process. It requires to have different skill to complete the task, therefore due care should be taken in this stage of report writing.The report serves the following purpose to convey to the interested persons the whole result of the study in detail and to enable the reader to comprehend the data and to determine the validity of the conclusions.

Generally, a research report, whether it is called a dissertation or thesis, consists of three parts:

I. The preliminary, i.e., Prefactory pages

II.  The text, i.e., main body of the report III. The reference material.

I.   Preliminary section

The preliminaries consist of the following components:

• The title page,
• Preface including acknowledgements iii) Table of contents,
• iList of tables,
• List of figures (and illustrations),
• List of abbreviations.

II. Text or Context

The text of a dissertation/thesis consists of the following sections:

i) Introduction (Introductory chapter)

ii) Main body of the report (usually divided into two or three chapters and sections), and

iii)Conclusion (Summary, Recommendations/suggestions).

III. Reference material

The reference material is generally divided as follows:

i)  Bibliography

ii)   Appendices

iii)   Glossary of Terms (if any), and iv)Index (if any).

The first page of the report is the title page. The title page should carry a concise and adequately descriptive title of the research study. Its principal use is to tell a prospective reader whether or not to refer to the report. The title should not claim more for the study than it actually delivers. It should not be generalized, either from the data gathered or from the methodology employed. Although title page format differs from one institution to another, it usually includes:

• The name of the topic
• The relationship of the report to a course or the academic degree for which the degree is conferred
• The name of the author
• The name of the institution where the report is to be submitted, and
• The date of presentation of the report

PHYTOCHEMICAL, ANTIOXIDANT, ANTI INFLAMMATORY AND HEPATOPROTECTIVE ACTIVITY OF LOTUS SEED AND MICROENCAPSULATION OF SEED EXTRACT By C.  Meenakshi (14PFN007) A THESIS SUBMITTED TO

AVINASHILINGAM INSTITUTE FOR HOME SCIENCE AND HIGHER EDUCATION FOR WOMEN COIMBATORE-641043

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE IN  FOOD SCIENCE AND NUTRITION  APRIL, 2016

Acknowledgement and Preface

A ‘preface’ is not a synonym to either ‘Acknowledgement’ or ‘Foreword’. A preface may include reasons why, in the first place, the topic was selected by the researcher. It may explain about history, scope, methodology or the writer’s opinion of the place of study in the field of knowledge and how it will provide a basis for the dissertation. This is an appropriate place for him to make acknowledgements. If the researcher has opted to discuss the significance and nature of his research in a subsequent chapter, usually introductory chapter, then he may not write a ‘preface’. But he may use the page for only ‘acknowledgements’.

Table of contents

The purpose of table of contents is to provide an outline of the content of the report. If logical and appropriate topical headings are used in the report, the table of contents presenting them becomes helpful to a reader. The table of contents appears after the preface/acknowledgements.

It may contain only a list of titles of chapters and heir appropriate Roman numerals, followed by page number on which each chapter begins or it may be more analytical, containing besides the chapter titles, subheads or section headings or words or phrases indicating the subject-matter of the chapters. Subtitles, however, should be indented under each chapter title and be followed by the specific page or section references. If reference to specific pages for subtitles is not desired, the subtitles may be run together and separated by semi-colons or dashes when so many subheadings exist, it may give an overcrowded look if all these are included in the table of contents. Hence, these may be placed at the beginning of that particular chapter below the title and the number of the chapter.

The table of contents should also include the acknowledgements/ preface, list of tables, figures etc., and the first page is not referred in the table of contents because it is always the tile page. Similarly, the table of contents page is also not referred in the table of contents.

While typing a dissertation/thesis, the table of contents is typed last. The heading ‘TABLE OF CONTENTS’ should be the centre at the top of the page and in capital letters.

List of Tables and Figures

If tables and figures are included in this report, separate pages for them should follow the table of contents. Both tables and figures sometimes are given together in the same page itself. Figures and illustrations are synonymous terms and refer to maps, drawings, graphs, charts, diagrams and photographs. The term “plates”is used for photographs. The full titles of tables and figures, worded exactly as they appear in the text are presented with corresponding/connective numbers and page location.

Arabic numerals are usually used for identifying tables,figures,etc.In the list of tables and figures,the titles should be typed with initial capitals,rather than all capital letters .Where a  separate list is given for “plates”it is usually numbered in capitalized Roman numerals. However, some institutions prescribe all capital letters for all titles and captions in tables and figures. Whatever the form used it should be used consistently throughout a research report .

Abbreviation

In the reference, whether in the form of footnotes or literature cited or bibliographical notes, it is possible to use abbreviations to avoid writing and typing long names in full. Only such names should be abbreviated which are likely to appear too often in the report. Names of persons are never abbreviated. Most of the forms of abbreviations are universally acceptable because of the international readership of the scholarly dissertations, and thesis.T6he list of abbreviations should appear before the beginning of the main text

THE TEXT(CONTEXT)CHAPTERS

In a thesis or a dissertation,the text or context may comprise chapters.In a short term paper or report they May consist of sections appropriately set off by centered headings.The division of the text into chapters/sections/sub-divisions should reflect the organization of the parts with one another and with the whole,i.e., the division should be logical to make the contents meaningful. The text usually consists of:

(i)Introduction

(ii)Main body of the report devoted for analysis, interpretation and presentation of data,

(iii)Summary and conclusion.

Introduction

The introductory chapter normally includes the following:

Introduce the subject by highlighting its special features in about two to four paragraphs/pages; the introduction should interest the reader in the subject –Matter of research. It must not be dull, confused, aimless and lacking in precision .

1.  Statement of the problem :Aclear statement of the nature and importance of the problem with specific questions to be answered or hypothesis to be tested; a consideration of significance of the problem and its historical background is also appropriate the key questions and the location of problem in theoretical context of the concerned discipline/subject should be specified.

2. A breakdown of the problem into constituent elements, major subdivisions, of questions.

3. The objective /purposes of the research study.

4.  Review of Literature: That is summarizing the current status of research works already done.A review of the pertinent past work and contradictions, pitfalls and other failings of the earlier work,mainly to substantiate the need for another research study.

5.  Significance of and justification for the present study: The significance of the problem, the contribution which the study is expected to make to theory, its practical importance and the national relevance should be specifically indicated.

6. The scope of the study pointing out the exact coverage reported upon, and position of the research within its larger context.

7.  Conceptual Framework: That is, various concepts or domains proposed to be used in a research require to be stated. Definitions or special meanings of all important terms so as to enable the reader to understand the concepts underlying the development of the investigation.

8. Methodology Adopted: The methodology describing the research design used, the data collection method employed, sampling design, how the field work was carried out, the variables and controls employed, the reliability of instruments selected or constructed and the statistical tools and procedures used in the analysis.

9.  Limitations of the study: A statement as to the limitations of the research project under study. All research designs have limitations and so do all research implementations. The sincere investigator faces these problems and reports hem carefully and honestly in the introduction itself.

10. Chapterisation: Preview of the scheme of chapters in the main body of the thesis and their interrelationship.

Main Body of the Report

This is the heart of the research report and probably the largest section of the report. It should be an organized presentation of results and each major division of the problem should be presented in a separate chapter. This may take a few chapters, an optimum number being five to present all the arguments, documentation, ideas, concepts, interpretations and findings. The chapter should include a discussion of the issue or part of the problem investigated and the evidence used in its solution. If this becomes lengthy, a summary of the evidence may be made at the end of the chapter. Through textual situation and tabular and graphic devices, the data are critically analyzed and interpreted, complex tables should be placed in the appendix.

The data themselves should be described fully, they should be analyzed in detail, and all the evidence resulting from the analysis should be presented. These chapters are primarily for the use of the reader who wishes to make a detailed study of the problem; therefore, every bit of relevant evidence should be supported by logical reasoning and empirical facts. Materials should be organized systematically and presented under appropriate headings, and subheadings.

Conclusions

This is the last part of the text (or the context) of the report. It consists of the summary, conclusions/generalizations, suggestions and recommendations. The summary may be more or less a re-statement of the topical sentences of the various findings. Summaries of findings may be subordinated under the conclusion statements. All these statements may be numbered or coded in some way so that they refer to pages or tables in the findings section, upon which they are based.

REFERENCE MATERIAL

A. Bibliography

A bibliography means booklists,i.e., a list of written sources,either published or unpublished,consulted in the preparation of the report during the course of research ,books,periodicals,articles,government documents, unpublished materials, pamphlets, films, radio or television broadcasts, records, lectures, interviews, etc. Bibliography may refer to all the documents which have bearing on the dissertation/thesis, irrespective of their being actually referred to or not, in the text. The aim is to permit the reader to find the exact item you consulted. Consequently, there is a standard form.

Bibliographical may be arranged according to the alphabetical order,chronological order,divisions of the subject,kinds of works listed,etc.,Generally the simplest and best arrangement for a short bibliography is the alphabetical order of the author’s last names,i.e., the titles are arranged alphabetically by surnames.Some list of books is most convenient if arranged in the chronological order, divisions of the subject, kinds of works listed, etc. Generally the simplest and best arrangement for a short bibliography is the alphabetical order of the author’s last names, i.e., the titles are arranged in the chronological order of the publications. The chronological order is especially appropriate whenever a historical or development plan prevails. Works on history, for example, might be classified according to countries. Works on education might be divided according to the various levels such as elementary, secondary, higher secondary, and higher education. Writers sometimes desire to make separate divisions for primary sources and secondary sources, for books and periodicals, for signed and unsigned works. Current practice favours one comprehensive listing- not divisions into primary sources and secondary sources or books, journals, newspapers, documents and official papers and manuscripts; although in an historical study such an ordering may be required.

B.APPENDICES

An appendix or appendices is used for additional or supplementary materials which have not found places in the main text. Here should be included complex or master tables, original data schedule questionnaires and interview forms, copies of cover letters used in the study,  documents and lon explanatory notes to the text, instructions to field workers, statistical tests and any other materials evidence of considerable reference value.

C.SYNOPSIS ABSTRACTS

In several institutions an abstract or synopsis of the thesis may be required. A synopsis should not be too long and in some institutions they limit either in the number of words or pages as stipulated. It is also attached to thesis. In such cases, it should be drafted keeping in mind the following points:

• A short statements of the problems:
• A brief descriptions explaining the methodology and procedures used in collecting the data: and
• A condensed summary of the findings of the study

Index may be either subject index or author index. An index of either type is not included in graduate/ post graduate students, research reports. However, if the report is being prepared for publication and is a technical manuscript or is intended as a work of reference, an index is desirable.

The index, if prepared should give an alphabetically arranged, detailed reference to all important matters discussed in the report, such as names of persons [ if separate author index is not prepared], places, events, definitions, concepts and vital statements. The researcher, therefore, should study the indexes of other research reports published in his field and acquaint himself with the basics of the technique of index- making.

Footnotes are called footnotes because they are traditionally placed at the foot of the page. Modern writers/ printers for their own convenience have introduced the practices of collecting them at the end of a book or chapter. Thus, footnotes may appear at the end of the thesis [but before the appendix] or chapter or at bottom of each page. Placing footnotes at the end of each chapter is found more convenient, particularly if they are very numerous, this method of documentation combines with bibliography notes.

A research report should not be stuffed with quotations. They will be construed as page filling devices and poor arguments. Where quotations are used, they should be brief, not exceeding one or two lines sub staining the views already expressed in the analysis.

Tables are numbered consecutively throughout the entire report including those tables that may be placed in the appendix. The tables should be referred to in the body of the text by referring to their serial number and page. The researcher should digest the table and present the burden of it in the summary form in the text. Tables should contain the following components: a)the table number, b)the caption or title)the box-heads, i.e., the captions identifying the vertical columns, d)the stud, the first column in the table, identifying the row entries, and e)the field, the columns containing data.

The commonest forms of figures used in reports are line graphs, bar graphs,pie charts,area or volume charts, pictorial charts,maps,diagrams of apparatus,and photographs. It should be borne in mind that figures are not intended as substitutes for textual description but included to emphasize certain significant relationships.

When geographical location or identification is important,maps may be used.Identification may be by the use of dots,circles or other symbols and density or characteristics of areas represented by shading or crosshatching.

Well students in this session we have discussed about the various aspects in report writing which includes three main components. First to start with preliminary section namely, title page, acknowledgement, table of contents, lists of tables, lists of figures, and list of abbreviations. Second part is introduction, main body of the report and conclusion. The last aspects is the reference material in which bibliography, appendices, glossary of terms and index are given.

Hence, report writing is an art, which should be given at most importance because the hard work, planning and execution of the research work carried out by researcher is depicted or expressed in black and white by the means of report. So all the work carried out should be written carefully in a simple and understandable language for a third person to easily understand the content of a report, which is the real success of a researcher. To conclude students all of you should develop interest and learn minute tricks in writing a good research report. All the best.

• http://grammar.yourdictionary.com/style-and-usage/report-writing-format.html
• http://www.careeranna.com/articles/report-writing-format-sample-report/
• https://www.slideshare.net/tulikapaul524/report-writingtypes-format-structure-and-relevance
• https://student.unsw.edu.au/writing-report
• https://www.skillsyouneed.com/write/report-writing.html

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14. Interpretation and Report Writing

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Computer Science > Computer Vision and Pattern Recognition

Title: mm1: methods, analysis & insights from multimodal llm pre-training.

Abstract: In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, including both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.

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