example of research analysis matrix

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Common Assignments: Literature Review Matrix

Literature review matrix.

As you read and evaluate your literature there are several different ways to organize your research. Courtesy of Dr. Gary Burkholder in the School of Psychology, these sample matrices are one option to help organize your articles. These documents allow you to compile details about your sources, such as the foundational theories, methodologies, and conclusions; begin to note similarities among the authors; and retrieve citation information for easy insertion within a document.

You can review the sample matrixes to see a completed form or download the blank matrix for your own use.

• Literature Review Matrix 1 This PDF file provides a sample literature review matrix.
• Literature Review Matrix 2 This PDF file provides a sample literature review matrix.
• Literature Review Matrix Template (Word)
• Literature Review Matrix Template (Excel)

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The matrix method for literature reviews.

This handout is available for download in DOCX format and PDF format .

What is the Matrix Method, and why should I use it?

Using a review matrix enables you to quickly compare and contrast articles in order to determine the scope of research across time. A review matrix can help you more easily spot differences and similarities between journal articles about a research topic. While they may be helpful in any discipline, review matrices are especially helpful for health sciences literature reviews covering the complete scope of a research topic over time. This guide focuses on the review matrix step in the literature review process and offers tips on how to use it effectively.

Organize your sources

Once you complete your research, organize your source by date in order to make it easier to see changes in research over time.

Begin by creating the blank matrix. The matrices can be easily constructed using table-making software such as Microsoft Excel, Word or OneNote, Google Sheets, or Numbers. Every review matrix should have the same first three column headings: (1) authors, title, and journal, (2) publication year, and (3) purpose.

Be aware that it may be difficult to determine purpose from just a cursory review of the article. In some cases, it may be necessary to first read the paper fully to identify its purpose.

Choose your remaining column topics

Next, carefully read all your articles. Note any important issues you identify. The following broad categories provide some suggestions for determining your own subject headings:

Methodological

Methodology is often an important question. For example, if you are looking at tests of an Ebola vaccine beyond human subjects, it will be important to note what type of animal the test was carried out on, i.e. macaques or mice.

Content-specific

Consider noting what was actually studied. For example, when looking at the effectiveness of traditional Chinese medicine in the treatment of illnesses, it would be important to note what illness was being studied.

Geographical

It may be important to note where the research was completed. For example, if you want to compare the effects of the AIDS epidemic in different countries, you would use country as a column heading.

There are many ways to choose your column headings, and these are just a few suggestions. As you create your own matrix, choose column headings that support your research question and goals.

• Do not include column headings that are explicit in your research question. For example, if you are looking at drug use in adolescents, do not include a column heading for age of study participants. If the answer will be the same for every study, it's generally a bad choice for a column heading.
• Do not try to fully complete a review matrix before reading the articles. Reading the articles is an important way to discern the nuances between studies.

Credit: Adapted from David Nolfi, “Matrix Method for Literature Review: The Review Matrix,” Duquesne University, https://guides.library.duq.edu/matrix , 2020.

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• Synthesis Matrix - A Step-by-Step Guide

The Synthesis Matrix - How to begin

A Synthesis Matrix is a great tool to help you organize and synthesize your research. Essentially, it is a table or chart where you identify your main ideas along the first column and your sources along the top row. Once set up, you can enter your notes and quotes from each source that correspond to each of your main ideas.

• Synthesis Matrix tutorial
• Blank Synthesis Matrix (Google Docs) Use this Google Doc to set up your Synthesis Matrix. Make a copy of the document to enter your information.

Synthesis Matrix - Step 1 - Identify Themes

    What are your main ideas or concepts? 

Think about the assigned reading and the ideas that came up when discussing it in class. What are the ideas or themes that you found most interesting? Or that you are most curious about. Enter these themes or concepts into the first column of the Synthesis Matrix, putting each one in a different row.

These are the themes you will use to search for your secondary sources in the Library's databases.

Synthesis Matrix - Step 2 - Research the Themes

  Look for sources related to your themes

After identifying your main themes or concepts, take a moment to think about them. What are they? Are there other words you could use to describe them? What subject areas or disciplines would address those topics? Before you start searching in a Library database, record this information in your Synthesis Matrix under each theme.

If you are unsure of what words to use you could look up your terms in a dictionary or encyclopedia . You can also look online for ideas, Wikipedia is a good source for this part of your research. You will not use Wikipedia as a source but you can use it to identify keywords and related ideas. 

Search for sources

Use the keywords you identified to search for sources in the Library's databases . Try our SNAP! Search or some of the databases listed below.

Here are a few tips to help you out:

• Begin with a simple search 
• Only enter your concepts - don't enter your thesis statement, research question, or complete sentences
• Use the Advanced Search whenever possible
• Be persistent and flexible - if you're not finding what you need switch your keywords with ones that you identified on your Synthesis Matrix
• If you need help, let us know !

Here is an example of a search.

• SNAP! Search Our SNAP! Search makes finding information and credible sources a breeze. Search almost all of our databases at once with this powerful search. Here, you’ll find journals, books, videos, magazines, and more all in one search.
• Opposing Viewpoints in Context This link opens in a new window Informed viewpoints support learners in developing critical-thinking skills and drawing their own conclusions. Covers current social issues through viewpoints, reference articles, infographics, news, images, video, and audio.
• U.S. History in Context This link opens in a new window Find articles, statistics, images, videos, and other types of sources on the most significant people, events and topics in U.S. History.
• JSTOR This link opens in a new window JSTOR is an excellent source for scholarly, peer-reviewed articles, ebooks and images, covering literature, history, the arts, and more.
• CINAHL Complete This link opens in a new window Nursing and allied health literature. Find evidence based research articles/studies, evidence-based care sheets and practice guidelines.

Synthesis Matrix - Step 3 - Fill in the Matrix

    Read the articles and start filling in the Matrix

Review all the articles you found and choose the ones you would like to use. Read these articles thoroughly, take notes, and highlight passages that relate to your themes.

In this example, I have quotes from the Tom Tiede article that represent the experiences of doctors in the first column. In the next columns, I have quotes from the other articles I chose that represent the same idea - the experiences of medical personnel.

I don't have any quotes from the Horwitz book in this row. I didn't find anything in this source that discussed this aspect of my topic and that's fine. The Horwitz book had good information on PTSD and war that I can use. Not all of my sources will cover all of my topic. You will use your sources and the matrix to create a conversation about your topic, bringing in evidence from an array of sources.

The next rows of the matrix for the topics of War and PTSD are below.

The Synthesis Matrix - Examples and Help

• Blank Synthesis Matrix Use this Google Doc to set up your Synthesis Matrix. Make a copy of the document by clicking on "File" and "Make a Copy" to enter your information. You should be logged in to MyNorthShore to access this document.
• Synthesis Matrix Use this Word document to set up your Synthesis Matrix
• Synthesis Matrix for "Black Men and Public Spaces" This is an example of a Synthesis Matrix based on the article, "Black Men and Public Spaces" by Brent Staples.
• Synthesis Matrix for Story of an Hour
• Tom Tiede - Synthesis Matrix
• Synthesis Matrix for Black Men in Public Spaces Google Doc
• Tom Tiede Synthesis Matrix Example Google Doc
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• Last Updated: Oct 23, 2023 1:44 PM
• URL: https://library.northshore.edu/synthesis-matrix

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• Literature Matrix

What is a literature matrix?

As defined by Judith Garrard in her handbook entitled  Health Sciences Literature Reviews Made Easy: The Matrix Method , a “Review of the literature consists of reading, analyzing, and writing a synthesis of scholarly materials about a specific topic. When reviewing scientific literature, the focus is on the hypotheses, the scientific methods, the strengths and weaknesses of the study, the results, and the authors’ interpretations and conclusions.” When reading materials for a literature review, you should critically evaluate the study’s major aims and results. 

The purpose of completing a literature matrix is to help you identify important aspects of the study. Literature matrixes contain a variety of headings, but frequent headings include: author surname and date, theoretical/ conceptual framework, research question(s)/ hypothesis, methodology, analysis & results, conclusions, implications for future research, and implications for practice. You can add additional columns as needed, and you might consider adding a “notes column” to proactively have important quotations and your thoughts already collected.  As you read journal articles, have your literature matrix ready. It is best to fill in the matrix directly after reading a work, rather than returning to the matrix later.  

Literature Matrix Files

You should use a literature matrix that best helps you to organize your reading and research. Excel workbooks can help to organize your research. Sample basic and complex literature matrixes are provided below: 

• Literature Matrix Basic BLANK
• Literature Matrix Basic SAMPLE
• Literature Matrix Complex BLANK

Synthesize vs. Summarize

When writing your literature review, you will not simply summarize the materials that you found related to your topic. A summary is a recap of the information provided in research articles. Summaries provide basic information about the study, but the details provided in a summary are not enumerative or systematic. 

Synthesizing goes beyond summarizing to explore specific aspects of the research study. When synthesizing the literature, rely on your completed literature matrix to inform your writing. Do you see any tends across publications? Was one type of methodology used repeatedly, why or why not? Did separate teams of researchers come to the same conclusion, differing conclusions, or is the literature inconclusive? Synthesizing requires that you look at the current state of the research overall. 

When preparing to write a synthesis, you will read the literature available, tease apart individual findings and supporting evidence across different articles, and then reorganize this information in a way that presents your understanding of the current state of research in this field.  

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Matrix Method for Literature Review

• The Review Matrix
• Organize Your Sources
• Choose Your Remaining Column Topics
• More Information

Sample Matrix and Templates

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• Review Matrix Example-Ebola Vaccine Clinical Studies This document includes a review matrix of two Ebola vaccine clinical reviews done on humans published by the National Institute of Health.
• Review Matrix Word Template A review matrix template in Microsoft Word.
• Review Matrix Excel Template A review matrix template for Microsoft Excel
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• Last Updated: Feb 20, 2024 10:26 AM
• URL: https://guides.library.duq.edu/matrix

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Literature Review: A Self-Guided Tutorial

Using a synthesis matrix.

• Literature Reviews: A Recap
• Peer Review
• Reading the Literature
• Using Concept Maps
• Developing Research Questions
• Considering Strong Opinions
• 2. Review discipline styles
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• 5. Critically analyze and evaluate
• How to Review the Literature
• 7. Write literature review

A synthesis matrix visually represents your research by organizing your sources by themes:

• Sample Synthesis Matrix Example provided by Ashford University Writing Center .
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• Last Updated: Jul 30, 2024 4:12 PM
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Get Organized

• Lit Review Prep Use this template to help you evaluate your sources, create article summaries for an annotated bibliography, and a synthesis matrix for your lit review outline.

Synthesize your Information

Synthesize: combine separate elements to form a whole.

Synthesis Matrix

A synthesis matrix helps you record the main points of each source and document how sources relate to each other.

After summarizing and evaluating your sources, arrange them in a matrix or use a citation manager to help you see how they relate to each other and apply to each of your themes or variables.  

By arranging your sources by theme or variable, you can see how your sources relate to each other, and can start thinking about how you weave them together to create a narrative.

• Step-by-Step Approach
• Example Matrix from NSCU
• Matrix Template
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Search catalog, soc 303: introduction to research methods(orend): literature matrix.

• Literature Searching
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Literature Review Matrix: Section One

This section helps you analyze each individual article for its research question(s), method(s), results, and conclusions. It also enables you to evaluate it for its strengths and limitations and identify its themes before you attempt to connect it to other research.

Literature Matrix 2

This part helps you visually connect the themes and identify disparate themes so that you can begin to synthesize established knowledge on your topic and identify alternative points of view on the topic and speak to why those might exist.

Organize Your Articles

A literature review matrix serves to help you visually organize your thoughts on an article.

This is only one option of many that can help you organize your thoughts; you can easily change the first section to reflect your discipline

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The research design matrix: A tool for development planning research studies

2005, Habitat International

This paper introduces the research design matrix as a method of planning research projects. The research design matrix is a system of rows and columns into which the components of a research project fit, including the goal, objectives, definitions, hypotheses, variables, methods of analysis and anticipated conclusions. Thus, the matrix encapsulates the research design, or what the researcher intends to do in the investigation. Given the arrangement of the various concepts involved, a logic is imposed on the project from the beginning of the planning process. The paper has three objectives. The first is to sketch out the background to the thinking underlying the research design matrix. The second is to develop a schematic framework for the research design matrix, defining terms and highlighting the flexibility that can be achieved in its use. The final objective is to present fully developed research design matrices for two research projects that have been successfully completed to give some idea in practice of the approach's viability and robustness.

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For a research to be carried out successfully, it requires suitable research design. This is a plan adopted by a researcher before data collection commences so as to achieve the research objective in a valid way. The essence of research design is to translate a research problem into data for analysis so as to provide relevant answers to research questions at a minimum cost. This paper investigates what research design is, the different kinds of research design and how a researcher can choose the appropriate research design for his/her study. The study reveals that research design choice is guided by a careful analysis of statement of the problem, research questions, conceptual /theoretical framework and analyzing the relevant literature.

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The research design refers to the overall strategy that we choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring we will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that our research problem determines the type of design we should use, not the other way around!

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Utilizing a Matrix Approach to Analyze Qualitative Longitudinal Research: A Case Example During the COVID-19 Pandemic

Lauren d. terzis.

1 School of Social Work, Tulane University, New Orleans, LA, USA

Leia Y. Saltzman

Dana a. logan, joan m. blakey.

2 School of Social Work, University of Minnesota, Minneapolis, MN, USA

Tonya C. Hansel

Qualitative Longitudinal Research (QLR) is an evolving methodology used in understanding the rich and in-depth experiences of individuals over time. QLR is particularly conducive to pandemic or disaster-related studies, where unique and rapidly changing environments warrant fuller descriptions of the human condition. Despite QLR’s usefulness, there are a limited number of articles that detail the methodology and analysis, especially in the social sciences, and specifically social work literature. As researchers adjust their focus to incorporate the impact of the COVID-19 global pandemic, there is a growing need in understanding the progression and adaptation of the pandemic on individuals’ lives. This article provides a process and strategy for implementing QLR and analyzing data in online diary entries. In the provided case example, we explore a phenomenological QLR conducted with graduate level students during the COVID-19 pandemic ( Saltzman et al., 2021 ) , and outline a matrix framework for QLR analysis. This paper provides an innovative way in which to engage in qualitative data collection and analysis for social science research.

Introduction

The COVID-19 global pandemic has altered how social science researchers plan, coordinate, and execute their research studies. As individuals are encouraged to practice social distancing to reduce infection and flatten the curve, this has proved particularly challenging for qualitative researchers who often rely on collecting data via face-to-face interviews ( Jowett, 2020 ). Increases in technology and utilization of web-based platforms (e.g., Qualtrics and Zoom) have allowed for these face-to-face interviews and data collection to continue, while also protecting the safety of both the participants and researchers.

Accompanying the changes in data collection techniques as a result of this crisis is the growing need for social science researchers to understand how the pandemic is impacting their population of interest. This includes adapting their research focus to incorporate the impact the global pandemic will have on our “new normal.” As the pandemic continues, it is critical that social science researchers understand the lived experiences of individuals through the phenomenon of the global pandemic, and how their participants’ experiences have changed throughout the course of the crisis. This understanding can be achieved by using qualitative longitudinal research (QLR) with a phenomenological approach and technology for safe data collection. The goal of this paper is to provide the process and strategies for how social science researchers, and specifically social work researchers can conduct QLR using online diary entries and the analysis of data utilizing a matrix approach.

Qualitative Longitudinal Research

QLR is considered an “evolving methodology” that is rich and helpful in revealing an in-depth understanding of the evolution of people’s lives and changes over time ( Neale, 2016 ). It is unique in that it combines two methodologies, a longitudinal component with a qualitative lens ( Neale, 2016 ). QLR has been especially useful for studies that investigate changes and adaptations to traumatic and historic events, as well as pathways and transitions over time ( Holland et al., 2006 ). Thus, QLR is an appropriate methodology and valuable in investigating adaptation and the impact of the COVID-19 global pandemic.While QLR is considered evolving, it has been used in several disciplines, including anthropology, education, psychology, health studies, sociology, and social policy ( Holland et al., 2006 ). Yates and Mcleod (2007) used QLR to follow 26 Australian secondary school students (12–18 years of age) in an investigation of educational inequalities in different schools. Faculty from the Department of Sociology at the University of Vienna conducted a longitudinal mixed-methods study focusing on young people transitioning to adulthood, following students from grades 5 through 8, with the qualitative interview portion being conducted once a year for 5 years ( Wöhrer et al., 2020 ). Stich and Cipollone (2017) utilized QLR in their urban educational ethnographic study, where they followed 54 students at four low-performing, urban public high schools in Buffalo, New York twice per year for 3 years. Other recent examples of where QLR has been used include, understanding temporal ordering as it relates to social policy ( Patrick et al., 2021 ), medical and health care education ( Balmer, Varpio, Bennet & Teunissen, 2021 ; Ottrey et al., 2021 ), gerontology ( Nevedal et al., 2019 ), children and families ( Warin, 2011 ; Tarrant et al., 2021 ), implementation science ( Van Tiem et al., 2021 ), addictions ( Notley et al., 2020 ), extra care housing ( Cameron et al., 2019 ), and social determinants of HIV ( Barrington et al., 2021 ).

Qualitative Longitudinal Research and Social Work

In social work research specifically, QLR as a methodology is still evolving. To date, only a handful of studies have used QLR. Sansfaçon and Crête (2016) used QLR in exploring professional identity among six social workers who completed semi-structured interviews three times over a 3-year period. The first time point was the social work students’ last year of undergraduate training. The second time point was 6 months after graduation, and the last time point was 18 months into their paid employment as social work professionals ( Sansfaçon & Crête, 2016 ). Social work researchers in England ( Ferguson et al., 2020 ) conducted an ethnographic QLR, where they observed and shadowed two social work departments. They selected a number of cases ( n = 15) to examine the role of social workers engaged in long-term casework with children and families over a period of 12 months. Lam et al. (2017) conducted a longitudinal mixed-methods study on social work students’ learning patterns, where the qualitative portion included the use of four focus group interviews over 3 years (time points at 6 months, 12 months, 24 months, and 48 months). Regarding disaster research, social work researchers conducted a longitudinal mixed-methods study on social work students who were in an MSW program in New York City during the 9/11 terrorist attacks ( Matthieu et al., 2007 ). MSW students completed a questionnaire related to the disaster response, their fieldwork, and their personal and professional needs, where students wrote responses to open-ended questions. Data were collected 1 month after the event and again 6 months later using the same measures ( Matthieu et al., 2007 ).

Despite the growing interest in QLR in various disciplines, there is a dearth of literature regarding the process and strategies used with this methodology across disciplines ( Calman, et al., 2013 ). Several researchers have identified gaps in their discipline as it relates to QLR methodology. A methodological review of QLR in nursing was conducted despite the lack of guidance on how to use QLR in nursing research ( SmithBattle et al., 2018 ). Tuthill and colleagues (2020) also recognized a lack of information to guide researchers on QLR techniques, and provided a methodological article specifically for health behavior and nursing researchers based on their own QLR research. Further, it was also reported that QLR was not used frequently or described in medical education literature, prompting researchers to publish guiding principles based on their experiences and reflection of the method ( Balmer & Richards, 2017 ).

In our review of the literature, we found there is limited guidance on conducting QLR for social sciences and social work specifically, thus the motive for this paper. We will be providing a process in QLR analysis and reflection from our own research study utilizing QLR, with special emphasis on data collection during a global pandemic using Zoom technology. Not only does QLR allow an in-depth understanding of a singular event, but it allows for increased utility to understand how findings may change over time. QLR is useful for identifying patterns in rapidly changing environments; thus efforts to increase utilization in social science and social work research are needed.

Rationale for Qualitative Longitudinal Research

As the world grapples with the COVID-19 global pandemic, researchers are increasingly interested in the impact the phenomenon has on clients and populations with whom they work both in the short- and long-term. Quantitative data can provide useful information regarding prevalence, odds ratios, and other important statistical outcomes of the pandemic, but it does not capture the true story, essence, or experience behind the numbers. A methodological approach that helps understand people’s lived experiences during the pandemic could include utilizing a qualitative phenomenological approach with a longitudinal design.

Longitudinal research is typically associated with quantitative research methodologies and has advantages over cross-sectional designs ( Henwood & Lang, 2003 ; Rajulton, 2001 ). One advantage is that longitudinal designs offer the ability to display the growth, patterns, and true depiction of cause and effect over a period of time, whereas cross-sectional designs only focus on data from a single time point ( Rajulton, 2001 ). The temporal ordering component allows the researchers to track stability and/or changes in participants’ behaviors and responses over the course of a specified time period. Longitudinal designs have previously been helpful in understanding behavior and mental health problems, as well as the effects of interventions. Longitudinal studies have been commonly conducted in other disciplines, and despite the advantages, are not frequently used in social work ( Jenson, 2007 ). Several challenges exist that could contribute to the lack of longitudinal designs in social work research, such as cost, sampling, and expertise needed in the complex data analysis ( Jenson, 2007 ).

Qualitative methods are widely used in social science and social work research as they allow for an in-depth understanding of the phenomena under study in greater context ( Lietz & Zayas, 2010 ). There are five types of qualitative inquiry commonly used: ethnography, phenomenological, case study, narrative, and grounded theory ( Creswell & Poth, 2016 ). Researchers typically adopt one of these approaches to guide the study’s framework and data collection and analysis. The philosophical foundations of these methodologies help increase the quality and rigor of the study ( Lietz & Zayas, 2010 ).

To further explore and understand the unique experiences of the COVID-19 global pandemic on peoples’ lives, QLR can be framed by utilizing phenomenological inquiry. Phenomenology can be used as an approach to frame qualitative research, where the “essence of the phenomenon” is derived from the unique perspective of individuals who have experienced the phenomenon ( Teherani, et al., 2015 ). The epistemological and ontological asumptions of phenomenology align with QLR and the objective to understand the lived experience of the impact of the pandemic on one’s life over time ( McKoy, 2017 ). Specifically, Husserl’s transcendental phenomenology is a major school of thought and an approach that places empahasis on describing the “essence” of participants’ experiences ( Creswell & Poth, 2016 ; Newbauer et al., 2019 ), and can be considered a guiding approach to QLR. The voice of the participants, instead of the bias of the researcher, are key features of the transcendental approach, in ensuring that the experience of the phenonmenon is accurately portrayed ( Moerer-Urdahl & Cresswell, 2004 ). Transcendental phenomenology is rigorous and systematic, and requires researchers to engage in epoche (also known as bracketing) to ensure that researchers are putting aside their own bias and subjectivity when collecting and analyzing data ( Moustakas, 1994 ; Moerer-Urdahl & Cresswell, 2004 ; Newbauer et al., 2019 ). Bracketing during QLR data collection and analysis is particularly important in the context of the COVID-19 global pandemic, as it is a phenomenon that has impacted everyone around the world.

Qualitative Research and the Global Pandemic

A body of qualitative research on the global pandemic has emerged, examining how it has impacted different populations such as health care workers, first responders, and students. In particular, when we reviewed recently published qualitative studies regarding COVID-19, we identified phenomenology as a commonly used approach to understand the lived experiences of certain populations and groups during the global crisis. Karimi et al., (2020) conducted a qualitative phenomenological study on the lived experiences of nurses in Iran caring for patients with COVID-19. Similarly, in Turkey, researchers used a phenomenological approach to explore the experiences and psychosocial problems of nurses caring for COVID-19 patients ( Kackin et al., 2020 ). In China, researchers used a phenomenological approach to interview nurses who provided care to COVID-19 patients ( Sun et al., 2020 ). Collado-Boira et al. (2020) interviewed final-year nursing and medical students in Spain about their perceptions and psychosocial considerations regarding the pandemic using a phenomenological qualitative approach. Researchers in India also used a phenomenological approach to analyze their qualitative data on the lived experiences of Indian youth during the COVID-19 crisis ( Suhail et al., 2020 ).

Specifically to social work research, to date there have been limited publications using qualitative phenomenology to examine the impact of the global pandemic. In one study in Nigeria, researchers used a qualitative phenomenological research design to interview a small number of social workers to understand the role social workers played during the pandemic ( Ajibo et al., 2020 ). Another phenomenological inquiry study in Nigeria was conducted using focus groups for data collection from social workers on their role and the effect of the “war against COVID-19” ( Ajibo, 2020 , p. 517). Researchers in Spain ( Redondo-Sama et al., 2020 ) conducted a qualitative study of social workers and their responses in the first 15 days of the outbreak in Barcelona using communicative methodology to analyze their data, yet they did not identify one of the five qualitative approaches (such as phenomenology) used to guide their study.

Despite these studies providing important insight on the lived experiences of health care workers, students, and social workers during the global pandemic, the qualitative data appears to have been collected during one single time point measuring the effects and changes related to the pandemic among participants. To address this issue and to gain an in-depth understanding of participants’ experiences of the pandemic and its progression, social work researchers can use a phenomenological approach to qualitative longitudinal research (QLR) to explore adaptations and changes over time.

Diaries as a Type of Qualitative Longitudinal Research

Diary studies are a QLR design methodology that allows for the assessment of change over time ( Bolger et al., 2003 ). This method is often used to assess fluctuating variables. Participants are tasked with reporting everyday life experiences at predetermined time points (e.g., daily, weekly). The determination of time intervals is guided by the frequency or regularity of the phenomena to be studied. Additionally, researchers must consider the burden on participants when scheduling data collection intervals. To mitigate participant burden, it is recommended that researchers employ instruments and collection methods that allow for each diary entry to be completed in just a few minutes ( Bolger et al., 2003 ).

There are several advantages to the use of diary studies over traditional research methods. Diary studies are conducted in a participant’s natural environment ( Bolger et al., 2003 ; Woll, 2013 ). As the time between participants’ experience and the recording of that experience is minimal, diary studies are less likely to be impacted by retrospection. Another advantage of this design is the ability to capture variations within and between research participants ( Bolger et al., 2003 ).

The historical evolution of diary studies is thoroughly illustrated in the seminal work Diary Methods: Capturing Life as it is Lived ( Bolger et al., 2003 ). Diary study technology has become far more advanced than its paper and pencil origins. This methodology was first used in the 1940s. In Bolger et al.’s study, participants were given a questionnaire packet or booklet to fill out and return to the researcher. One downfall of the initial pencil and paper data collection method was that participants often forgot to complete the entry or complete the diary at the predetermined collection interval. To mitigate participants’ forgetfulness, researchers developed an augmented paper diary method. This method involved a signaling device programmed to prompt participant response at the predetermined data collection intervals. Nevertheless, the augmented paper diary method also had drawbacks as it required more resources and could be disruptive to participants ( Bolger et al., 2003 ).

Diary research methodology is frequently employed in nursing studies. The nursing literature has been a thorough investigation of the evolution of illness and experiences of patients, caretakers, and health care professionals ( Woll, 2013 ). Häggström and Nilsson (2009) conducted an 8-year diary case study of a patient living with rheumatoid arthritis. Seibold (2000) researched the experiences of women during menopause, which spanned more than 1 year. Hoeve et al. (2018) explored the lived experiences of 18 novice nurses as they became professional staff nurses. In this study, participants were prompted to share a significant work experience from the previous week with a colleague. Portoghese et al. (2020) utilized a diary method to assess compassion fatigue among 39 hospice workers over a period of eight workdays. Participants completed a diary for each workday regarding the job demands and emotional work display ( Portoghese et al., 2020 ).

Ganeson and Ehrich’s (2009) work was significant for its introduction of diary methods in educational research. Ganeson and Ehrich (2009) conducted a phenomenological diary study with 16 students over 10 weeks as they transitioned from primary into secondary school. This school transition occurred during a pivotal time of numerous emotional, physical, and psychological changes that coincided with other challenges in the adolescents’ life. Previous research had only focused on this time of change from adult professionals’ perspective. However, Ganeson and Ehrich (2009) analyzed the transition from the students’ perspectives and lived experience. Participants were provided “free reign” to record their experiences related to the transition ( Ganeson & Ehrich, 2009 , p. 66). Despite the extensive use of diary methods in the fields of nursing and organizational research, a gap remains in the social work literature. Diary methods can be invaluable in understanding phenomena previously unexplored due to ethical concerns related to interviewing participants ( Woll, 2013 ). Prior research generally confirms writing about lived experiences is easier than talking about them ( Bedwell et al., 2012 ; Corti, 1993 ). Additionally, an advantage of diary methods methodology is the potential to ascertain more data than in an interview, making it highly effective for capturing participant insight over time ( Woll, 2013 ).

Diary studies have become more frequently cited in the field of organizational research ( Ohly et al., 2010 ). Engagement in diary studies has been found beneficial in increasing participant understanding of daily work practices ( Woll, 2013 ). Ohly et al. (2010) provided an overview of organizational studies and determined the research was used to assess work performance, well-being, and affective processes in the workplace. By using this methodological design, researchers confirmed a positive correlation between workers’ happiness and productivity previously unsubstantiated in studies using between-person meta-analysis ( Ohly et al., 2010 ).

Much research attention has been drawn to studying change in response to the COVID-19 pandemic. In regards to publication and review time during the COVID-19 pandemic, Putnam et al. (2020) conducted an analysis of 2427 journals and found that journals are rapidly reviewing COVID-19 articles at a much faster rate than non-COVID-19 articles (11.3 days vs. 106.3 days, p < .001), in order to present new evidence in a timely manner. A Google search of “COVID diary research studies” yields a vast number of calls for papers, postings for the recruitment of research participants, and timely publications on the topic. Michigan State University is conducting a longitudinal diary study regarding language changes during the pandemic ( MSU Today, 2020 ). The University of Texas at Austin is actively recruiting students for a weekly diary study regarding social and student experiences over 3 months during the COVID-19 pandemic ( Texas Today, 2020 ). In a diary study of Dutch adolescents, Van de Groep et al. (2020) explored changes in mood, empathy, and prosocial behavior during the pandemic lockdown over the course of 3 weeks. A study of citizens in Poland included an assessment of emotional intelligence traits and emotional experiences during the COVD-19 pandemic using a daily diary over a 1-week period ( Moroń and Biolik-Moroń, 2021 ). Organizations are utilizing diary studies to better understand the needs of their employees during the pandemic. Microsoft conducted a 10-week daily diary study to learn more about the experiences of software engineers following the work from home directive ( Butler and Jaffe, 2021 ).

Danielsson and Berge (2020) conducted a video diary study of 13 Swedish university-level engineering students with the purpose of exploring identity constitution. The participants were prompted by themed, open-ended prompts to be reflected upon during each diary entry. Additionally, the participants were provided with flexibility for sharing or showing “a place of importance to them” (p. 3). Similar to engineering students, identity as a social worker and the process for developing this identity is critical to pedagogy ( Jones et al., 2020 ). Diary studies with a QLR focus can help researchers better understand the dynamic formation of identity, especially when identity formation is disrupted or altered by current and personal events.

Utilizing Technology in Data Collection

Since the early 1990s, electronic data collection has offered numerous benefits to both participants and researchers. Researchers are able to prompt participant responses and determine when entries are made by viewing the time stamp ( Bolger et al., 2003 ). Randomization of questions can be programmed, thereby reducing repetitiveness. Electronic data collection decreases data entry time and the likelihood of missing values, thereby increasing data accuracy and study compliance. In the context of disaster research, data collecting using technology is advantageous because it allows for remote collection (critical during COVID-19), allows for early and rapid deployment of surveys and interviews; and particularly for qualitative research, reduces the need for transcription which is time consuming and expensive.

Qualitative Longitudinal Research Method: A Case Example

Our case example is an exploration of a phenomenological QLR study conducted with masters-level social work students during the COVID-19 pandemic. Saltzman et al. (2021) published a study protocol article that includes the methodology in greater detail. The goals of the study were to understand the lived experience of Masters of Social Work (MSW) students in real time as they lived through the pandemic, explore risk and protective factors in coping with the stress from COVID-19, and chart changes in coping over time among future social work practitioners. Participants were MSW students and enrolled in either the online or on-ground program at Tulane University, located in New Orleans, Louisiana. Saltzman et al. (2021) began the study in May 2020 as it became clear that the COVID-19 pandemic would be protracted. The study spanned the lockdown through the phase 2 reopening of the City of New Orleans. It was requested that participants submit eight weekly video diary entries over the course of 2 months using the Zoom platform. There were 14 participants in our case example, which garnered 58 diary entries over the course the 2 month collection period.

Analysis Using a Matrix

Data analysis posed two important challenges to us. First, data analysis consisted of identifying similarities and differences across participants at each time point. Second, data analysis also included tracking trajectories of change within and across individuals over 8 weeks. Each piece of diary entry was coded by three members of the research team. The team coded the diary entry line by line. We conducted qualitative data analysis using Dedoose, a qualitative data analysis software. We began data analysis by selecting one participant who had completed all eight diary entries. Each diary session was treated as independent data in order to ensure the codebook encompassed experiences over the 8 week period. Findings from participants who completed all eight entries were used to develop the initial codebook. As we coded data from other participants, we revised the codebook in an iterative fashion. If changes to the codebook were made, entries that had previously been coded were recoded to ensure new codes were applied to data that had already been coded. The process of developing the codebook was memoed in detail as we conducted our analyses. Through this approach, we addressed the first goal of the analysis which was to identify similarities and differences across participants at each time point .

In addition to coding with Dedoose, we also utilized a coding matrix adapted from the technique outlined in Grossoehme and Lipstein (2016) . We used the coding matrix to graphically represent changes over time, both within a single participant and across participants. Each theme had a row in the matrix that intersected with eight columns representing each time point. Each time a theme was applied coded text, the team member noted the application of the theme within the corresponding cell (e.g., emotional reaction, session 1). This approach differs from the matrix presented in Grossoehme and Lipstein (2016) as it specifically highlights the density of code applications within a cell (i.e., a specific theme at a specific time point). Over the course of 8 weeks, patterns emerged regarding the themes most often applied to data obtained by a participant (e.g., shift from emotional reaction to planning and logistics). Through this graphic representation of change, we were able to highlight the trajectory of experiences as the COVID-19 pandemic unfolded. We could also compare these trajectories across participants. In regard to changes in theme emphasis, some themes become more or less salient for the participant. Moreover, the timing of when these shifts occurred also became more salient. The timing of when shifts occurred was important because it connected the larger societal context to the process of adapting to life during the COVID-19 pandemic.

The example matrix presented here (see Table 1 ) uses an “X” to represent each time a code was applied within a given time point. For example Theme 1 was applied once in Session 1, twice in Session 2, three times in Session 3, not at all in Session 4, and finally once in Sessions 5 and 6. In our matrix example, this demonstrates that Theme 1 became more salient to the participant over time and then decreased in its relevance as other themes became more salient. The reverse pattern can be seen with Theme 4. Theme 4 was not applied at all in Sessions 1- 4 but became relevant to the participant in Session 5–8. A matrix of this kind demonstrates patterns in the endorsement of themes over time (which become more or less important in terms of when they emerge and how long they last). Similarly, the matrix is helpful when looking across themes as it indicated how the experience of the participant changed over time - that is, some themes give way to others in regard to their salience. The matrix is a helpful visual representation of trajectories within qualitative longitudinal analysis.

Example Matrix Participant 1 Sessions 1–8.

Note. The patterns noted in this table do not reflect actual data or responses from participants. This is intended to demonstrate an example of how a completed matrix may appear after data analysis.

Trustworthiness and Rigor

We utilized several different strategies to ensure that we satisfied Lincoln & Guba’s (1985) criteria of trustworthiness in qualitative research. In our QLR case example, we addressed credibility by selecting the diary method as way to reduce research reactivity, as participants were alone during their online diary session. We also engaged in reflexivity and reflection of our own experiences with the global pandemic through journaling and peer debriefing in our regular team meetings ( Lietz & Zayas, 2010 ). Further, observer triangulation was used as at least three research team members were involved in the analysis process ( Lietz & Zayas, 2010 ). To address transferability of the findings, we provided thick descriptions of the phenomenon, so that readers can determine whether the findings apply to similar contexts and populations ( Shenton, 2004 ; Lietz & Zayas, 2010 ). Lastly, we kept an audit trail of all elements of our QLR process so that the research can be replicated in the future ( Shenton, 2004 ; Lietz & Zayas, 2010 ).

The purpose of this study was to highlight QLR as an evolving method used in social science research to provide rich, in-depth revelations about people’s lives and how they cope over time with the COVID-19 global pandemic ( Neale, 2016 ). As QLR grows in popularity, there is a need to document QLR processes and strategies so that as researchers use this methodology, the research can be evaluated for fidelity regardless of the discipline ( Calman et al., 2013 ).

QLR is unique in a variety of ways. QLR combines a longitudinal approach with a qualitative lens ( Neale, 2016 ). Researchers who use QLR have the ability to explore growth and patterns, and provide a true depiction of lived experiences over time ( Rajulton, 2001 ). QLR allows the researchers to track changes in participants’ behaviors and mental health responses over a specified time period. Finally, QLR can help explore the effects of interventions and other issues that are subject to change and adapt depending on the circumstances.

More recently, qualitative methods have been used to examine the impact COVID-19 has on professionals such as nurses, physicians, and students, as well as young people and adults who had COVID-19. Qualitative methods also position researchers to look at the pandemic from the perspective of professionals whose job it is to interact with and help people exposed to COVID-19 ( Ajibo et al., 2020 ). Despite the usefulness of these studies related to the lived experiences of individuals, students, and professionals impacted by the global pandemic, the data focused on one point in time. Given the nature of COVID-19 and its predicted longevity like the flu or other infectious disease, it is essential researchers understand COVID-19, its progression and adaptations needed to combat this disease over time ( Holland et al., 2006 ).

Limitations and Strengths

Zoom diaries are a particular kind of qualitative longitudinal research method ( Bolger et al., 2003 ). Increasingly, this method is being used to assess fluctuating everyday life experiences as well as how people are coping with and handling the COVID-19 pandemic. Nonetheless, the diary method and QLR is not without its limitations and strengths. While this methodological approach provides new and innovative ways in which to engage in qualitative data collection and analysis, there are three important limitations that we wish to highlight. First, the approach is time-consuming not only in the typical amount of time needed to collect longitudinal data, but also in regard to the analysis procedure. This approach generates a large number of data entries; each requiring coding from multiple coders. In addition to traditional coding, the matrix table needs to be filled in for each data entry. Second, while the matrix table provides a useful visual to demonstrate the saturation of various codes and subcode across time, it also lacks nuance regarding the specific circumstances in which the code applied. More specifically, the “X” symbol indicates that the code was applied to a given piece of text. However, it does not provide context or detailed information about what was said. Finally, longitudinal data collection generally is subject to a threat of internal validity commonly referred to as “history”, where extraneous events coincide with the research and may confound the results ( Rubin & Babbie, 2016 ). This is also true in the context of qualitative longitudinal data collection as the experience of each participant is influenced by the environment, which changes over time. In the context of COVID-19 this is both a limitation and a strength as the changing environment demonstrates the dynamic process of coping with the pandemic ( Table 1 ).

Three additional strengths to this approach should also be highlighted. First, given that more data is collected from each participant, fewer participants are needed to reach data saturation. This asset can be a significant strength of this approach, as researchers may not require as many participants to enroll in a study in order to capture the lived experience. Second, the matrix table provides a new data visualization tool for qualitative longitudinal analysis. Options for data visualization in qualitative research have been limited, and so the introduction of a new method for data presentation is noteworthy. Lastly, the matrix gives a graphic representation of thematic salience across participants and over time. In other words, it visually represents which areas of adaptation are most important to a participant in a given moment of time and how that shifts over the course of the experience.

Implications

As social science researchers continue to advance qualitative research, QLR addresses the historic need for qualitative perspectives in research ( Ruckdeschel, 1985 ); in that, we can focus the methodological rigor toward longitudinal processes along with the richness of data that qualitative inquiry provides. QLR is particularly conducive to pandemic or disaster-related studies, where unique and rapidly changing environments warrant fuller descriptions of the human condition. Specifically, QLR can improve researchers’ understanding of resilience despite unexpected events and the mechanisms that facilitate growth or perseverance ( Hansel et al., 2020 ; Staller, 2018 ). The utility of QLR would also be relevant to policy implications and the real time impact on individuals, rather than waiting decades to see results ( Sinha & Piedra, 2020 ). Finally the diary component, as described in the case study, may be useful for us to address reflexivity and emotional responses ( Sanders et al., 2017 ) and personal similarities as expected given the vast reach of the COVID-19 pandemic. QLR is a useful approach for social science researchers to identify patterns in rapidly changing environments, where time is an important factor in understanding the dynamic human response and recovery.

Lessons Learned and Conclusion

QLR offers a flexible tool to expand qualitative methods with a specific utility for examining changes in trends over time. This approach is uniquely suited to understanding the lived experiences of participants during historic moments in society (in our case example, the COVID-19 pandemic). Three main “lessons learned” include the following. Firstly, implementing rigorous data management systems before data collection – these include systems for safety monitoring, linking participant diary entries over time, and storing and analysing data in multiple formats (audio, text, and video). Secondly, pivoting to address external social/historical influences in real time - our data collection took place during the early phases of the COVID-19 pandemic. During this health crisis, policies and closures were continuouly evolving and impacting our particiants. In addition, our data collection spanned the period of time immediately after the murder of George Floyd – again deeply impacting our participants. We accounted for these unanticipated external events by including an open question at the end of our diary prompts asking participants “what else would you like us to know about life this week?”. Finally, as in quantitative longitudinal approaches, attrition in participants over time is a potential challenge in QLR. Strategies to promote retention are similar to those seen in quantitative longitudinal studies (e.g. incentives and reminders). However, QLR, specifically diary studies, may offer particapants an opportunity to experience catharsis and a forum for processing events in real time, a feature that is less prominent in quantitative approaches.

The COVID-19 global pandemic has altered how many researchers, especially qualitative ones, conduct their research and collect data. Using online diary entries with QLR is a way for researchers to safely gather rich, in-depth experiences about people’s lives and discover how they cope over time with the global pandemic. The case example provides a base framework for how to depict and analyze qualitative longitudinal data utilizing a matrix approach. There is increasing interest in understanding how the global pandemic has impacted populations and their lived experiences of the pandemic over time, and QLR is an innovative methodology that enables social science researchers to do so.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Lauren D. Terzis https://orcid.org/0000-0002-1944-2939

Leia Y. Saltzman https://orcid.org/0000-0002-2027-6982

• Ajibo H. T. (2020). Effect of COVID-19 on Nigerian socio-economic well-being, health sector pandemic preparedness and the role of Nigerian social workers in the war against COVID-19 . Social Work in Public Health , 35 ( 7 ), 511–522. 10.1080/19371918.2020.180616 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ajibo H. T., Chukwu N. E., Okoye U. O. (2020). COVID-19, lockdown experiences and the role of social workers in cushioning the effect in Nigeria . Journal of Social Work in Developing Societies: Special Issue on COVID-19 Pandemic , 2 ( 2 ), 6–13. https://www.journals.aphriapub.com/index.php/JSWDS/article/view/1141 [ Google Scholar ]
• Balmer D. F., Richards B. F. (2017). Longitudinal qualitative research in medical education . Perspectives on Medical Education , 6 ( 5 ), 306-310. DOI: 10.1007/s40037-017-0374-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Balmer D. F., Varpio L., Bennett D., Teunissen P. W. (2021). Longitudinal qualitative research in medical education: Time to conceptualise time . Medical Education , 55 ( 11 ), 1253–1260. 10.1111/medu.14542 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barrington C., Rosenberg A., Kerrigan D., Blankenship K. M. (2021). Probing the process: Longitudinal qualitative research on social determinants of HIV . AIDS and Behavior , 25 ( Suppl 2 ), S203–S213. 10.1007/s10461-021-03240-w [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bedwell C., McGowan L., Lavender T. (2012). Using diaries to explore midwives’ experiences in intrapartum care: An evaluation of the method in a phenomenological study . Midwifery , 28 ( 2 ), 150–155. 10.1016/j.midw.2010.12.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bolger N., Davis A., Rafaeli E. (2003). Diary methods: Capturing life as it is lived . Annual Review of Psychology , 54 ( 1 ), 579–616. 10.1146/annurev.psych.54.101601.145030 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Butler J., Jaffe S. (2021). Challenges and gratitude: A diary study of software engineers working from home during COVID-19 pandemic . Microsoft ICSE SEIP. https://www.microsoft.com/en-us/research/uploads/prod/2020/07/NFW-Butler-Jaffe.pdf [ Google Scholar ]
• Calman L., Brunton L., Molassiotis A. (2013). Developing longitudinal qualitative designs: Lessons learned and recommendations for health services research . BMC Medical Research Methodology , 13 ( 14 ), 14. 10.1186/1471-2288-13-14 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cameron A., Johnson E. K., Lloyd L., Evans S., Smith R., Porteus J., Darton R., Atkinson T. (2019). Using longitudinal research to explore extra care housing . International Journal of Qualitative Studies on Health and Well-Being , 14 ( 1 ), Article 1593038–1593038. 10.1080/17482631.2019.1593038 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Collado-Boira E. J., Ruiz-Palomino E., Salas-Media P., Folch-Ayora A., Muriach M., Baliño P. (2020). The COVID-19 outbreak” – an empirical phenomenological study on perceptions and psychosocial considerations surrounding the immediate incorporation of final-year Spanish nursing and medical students into the health system . Nursing Education Today , 92 , Article 104504. 10.1016/j.nedt.2020.104504 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Corti L. (1993). Using diaries in social research . Social Research Update , 2 , 1-7. [ Google Scholar ]
• Creswell J. W., Poth C. N. (2016). Qualitative inquiry andnd research design: Choosing among five approaches . Sage publications. [ Google Scholar ]
• Creswell J. W., Poth C. N. (2016). Five Qualitative Approaches to Inquiry. In Qualitative Inquiry & Research Design: Choosing among Five Approaches . Sage Publications. [ Google Scholar ]
• Danielsson A. T., Berge M. (2020). Using video-diaries in educational research exploring identity: Affordances and constraints . International Journal of Qualitative Methods , 19 , 1-9. 10.1177/1609406920973541. [ CrossRef ] [ Google Scholar ]
• Ferguson H., Warwick L., Cooner T. S., Leigh J., Beddoe L., Disney T., Plumridge G. (2020). The nature and culture of social work with children and families in long-term casework: Findings from a qualitative longitudinal study . Child & Family Social Work , 25 ( 3 ), 694–703. 10.1111/cfs.12746 [ CrossRef ] [ Google Scholar ]
• Ganeson K., Ehrich L. C. (2009). Transition into high school: A phenomenological study . Educational Philosophy and Theory , 41 ( 1 ), 60–78. 10.1111/j.1469-5812.2008.00476.x [ CrossRef ] [ Google Scholar ]
• Grossoehme D., Lipstein E. (2016). Analyzing longitudinal qualitative data: The application of trajectory and recurrent cross-sectional approaches . BMC Research Notes , 9 ( 1 ), 1–5. 10.1186/s13104-016-1954-1 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Häggström V. E., Nilsson A. (2009). The meaning of living with rheumatoid arthritis-a single case study based on one woman’s eight-year diary . Tidsskrift for Sygeplejeforskning , 25 ( 3 ), 9–15. [ Google Scholar ]
• Hansel T., Osofsky H., Speier A., Osofsky J. (2020). Postdisaster recovery and resilience: The mediating influences of mental health and environmental quality of life . Traumatology , 26 ( 3 ), 278–284. 10.1037/trm0000213 [ CrossRef ] [ Google Scholar ]
• Henwood L., Lang I. (2003). Qualitative research resources: A consultation exercise with UK social scientists . A report to the ESRC . [ Google Scholar ]
• Hoeve Y. T., Kunnen S., Brouer J., Roodbol P. (2018). The voice of nurses: Novice nurses’ first experiences in a clinical setting. A longitudinal diary study . Journal of Clinical Nursing , 27 ( 7-8 ), 7–8. 10.1111/jocn.14307 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Holland J., Thomson R., Henderson S. (2006). Qualitative longitudinal research: A discussion paper . Families & Social Capital ESRC Research Group Working. Paper No. 21. [ Google Scholar ]
• Jenson J. M. (2007). Longitudinal research in social work: A call to action . Social Work Research , 31 ( 2 ), 67–70. 10.1093/swr/31.2.67 [ CrossRef ] [ Google Scholar ]
• Jones B., Currin-McCulloch J., Petruzzi L., Phillips F., Kaushik S., Smith B. (2020). Transformative teams in health care: Enhancing social work student identity, voice, and leadership in a longitudinal interprofessional education (IPE) course . Advances in Social Work , 20 ( 2 ), 424–439. 10.18060/23671 [ CrossRef ] [ Google Scholar ]
• Jowett A. (2020). Carrying out qualitative research under lockdown – practical and ethical considerations LSE blog . https://blogs.lse.ac.uk/impactofsocialsciences/2020/04/20/carrying-out-qualitative-research-under-lockdown-practical-and-ethical-considerations/
• Kackin O., Ciydem E., Aci O. S., Kutlu F. Y. (2020). Experiences and psychosocial problems of nurses caring for patients diagnosed with COVID-19 in Turkey: A qualitative study . International Journal of Social Psychiatry , 67 ( 2 ), 1–10. 10.1177/0020764020942788 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Karimi Z., Fereidouni Z., Behnammoghadam M., Alimohammadi N., Mousavizadeh A., Salehi T., Mirzaee M. S., Mirzaee S. (2020). The lived experience of nurses caring for patients with COVID-19 in Iran: A phenomenological study . Risk Management and Healthcare Policy , 13 , 1271–1278. 10.2147/RMHP.S258785 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lam C. M., To S. M., Chan W. C. H. (2017). Learning pattern of social work students: A longitudinal study . Social Work Education , 37 ( 1 ), 49–65. 10.1080/02615479.2017.1365831 [ CrossRef ] [ Google Scholar ]
• Lietz C. A., Zayas L. E. (2010). Evaluating qualitative research for social work practitioners . Advances in Social Work , 11 ( 2 ), 188–202. 10.18060/589 [ CrossRef ] [ Google Scholar ]
• Lincoln Y., Guba E. (1985). Naturalistic inquiry . Sage Publications. [ Google Scholar ]
• Matthieu M. M., Conroy K., Lewis S., Ivanoff A., Blackmore E. R. (2007). Student perspectives on the impact of world trade center disaster: A longitudinal qualitative study . Social Work Research , 31 ( 2 ), 121–126. 10.1093/swr/31.2.121 [ CrossRef ] [ Google Scholar ]
• McKoy L. K. (2017). Longitudinal qualitative research and interpretive phenomenological analysis: Philosophical connections and practical considerations . Qualitative Research in Psychology , 14 ( 4 ), 442–458 [ Google Scholar ]
• Moerer-Urdahl T., Cresswell J. W. (2004). Using transcendental phenomenology to explore the “ripple effect” in a leadership mentoring program . International Journal of Qualitative Methods , 3 ( 2 ), 19–35. 10.1177/160940690400300202 [ CrossRef ] [ Google Scholar ]
• Moroń M., Biolik-moroń M. (2020). Trait emotional intelligence and emotional experiences during the COVID- 19 pandemic outbreak in Poland: A daily diary study . Personality and Individual Differences , 168 , Article 110348. 10.1016/j.paid.2020.110348 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moustakas C. (1994). Phenomenological research methods . Sage Publications. [ Google Scholar ]
• MSU Today (2020). Researchers study how COVID pandemic is affecting language change . https://msutoday.msu.edu/news/2020/researchers-study-how-covid-pandemic-is-affecting-language-change/
• Neale B. (2016). What is qualitative longitudinal research? Bloomsbury Academic. 10.5040/9781472532992 [ CrossRef ] [ Google Scholar ]
• Nevedal A. L., Ayalon L., Briller S. H. (2019). A qualitative evidence synthesis review of longitudinal qualitative research in gerontology . The Gerentologist , 59 ( 6 ), e791–e801. 10.1093/geront/gny134 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Newbauer B. E., Witkop C. T., Varpio L. (2019). How phenomenonoligy can help us learn from the experience of others . Perspectives in Medical Education , 8 ( 2 ), 90–97. 10.1007/s40037-019-0509-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Notley C., Ward E., Dawkins L., Holland R. (2020). User pathways of e-cigarette use to support long term tobacco smoking relapse prevention: A qualitative analysis . Addictions , 116 ( 3 ), 596–605. 10.1111/add.15226 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ohly S., Sonnentag S., Nessen C., Zapf D. (2010). Diary studies in organizational research: An introduction and some practical recommendations . Journal of Personnel Psychology , 9 ( 2 ), 79–93. 10.1027/1866-5888/a000009 [ CrossRef ] [ Google Scholar ]
• Ottrey E., Rees C. E., Kemp C., Brock T. P., Leech M., Lyons K., Monrouxe L. V., Morphet J., Palermo C. (2021). Exploring health care graduates' conceptualisations of preparedness for practice: A longitudinal qualitative research study . Medical Education , 55 ( 9 ), 1078–1090. 10.1111/medu.14475 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patrick R., Treanor M., Wenham A. (2021). Qualitative longitudinal research for social policy – where are we now? Social Policy & Society , 20 ( 4 ), 622–628. 10.1017/S1474746421000245 [ CrossRef ] [ Google Scholar ]
• Portoghese I., Galleta M., Larkin P., Sardo S., Campagna M., Finco G., D’Aloha E. (2020). Compassion fatigue, watching patients suffering and emotional display rules among hospice professionals: A daily diary study . BMC Palliative Care , 19 ( 1 ), 1–7. 10.1186/s12904-020-0531-5 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Putnam M. S., Ruderman E. M., Niforatos J. D. (2020). Publication rate and journal review time of COVID-19 research . Mayo Clinical Proceedings , 95 ( 10 ), 2290–2291. 10.1016/j.mayocp.2020.08.017 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rajulton F. (2001). The fundamentals of longitudinal research: An overview . Canadian Studies in Population , 28 ( 2 ), 169–185. 10.25336/P6W897 [ CrossRef ] [ Google Scholar ]
• Redondo-Sama G., Matulic V., Munte-Pascual A., de Vicente I. (2020). Social workers during the COVID-19 crisis: Responding to urgent social needs . Sustainability , 12 ( 20 ), 8596. 10.3390/su12208595 [ CrossRef ] [ Google Scholar ]
• Rubin A., Babbie E. (2016). Essential research methods for social work (4th ed.). Thompson Brooks/Cole. [ Google Scholar ]
• Ruckdeschel R. A. (1985). Qualitative research as a perspective . Social Work Research and Abstracts , 21 ( 2 ), 17–21. 10.1093/swra/21.2.17 [ CrossRef ] [ Google Scholar ]
• Saltzman L. Y., Terzis L. D., Hansel T. C., Blakey J. M., Logan D., Bordnick P. S. (2021). Harnessing technology for research during the COVID-19 pandemic: A mixed methods diary study protocol . International Journal of Qualitative Methods , 20 , 1–8. 10.1177/1609406920986043 [ CrossRef ] [ Google Scholar ]
• Sanders J., Munford R., Liebenberg L. (2017). Positive youth development practices and better outcomes for high risk youth . Child Abuse & Neglect , 69 , 201–212. 10.1016/j.chiabu.2017.04.029 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sansfaçon A. P., Crête J. (2016). Identity development among social workers, from training to practice: Results from a three-year qualitative longitudinal study . Social Work Education , 35 ( 7 ), 767–779. 10.1080/02615479.2016.1211097 [ CrossRef ] [ Google Scholar ]
• Seibold C. (2000). Qualitative research from a feminist perspective in the postmodern era: Methodological, ethical and reflexive concerns . Nursing Inquiry , 7 ( 3 ), 147–155. 10.1046/j.1440-1800.2000.00063.x [ CrossRef ] [ Google Scholar ]
• Shenton A. (2004). Strategies for ensuring trustworthiness in qualitative research projects . Education for information , 22 ( 2 ), 63–75. 10.3233/efi-2004-22201 [ CrossRef ] [ Google Scholar ]
• Sinha G. R., Piedra L. M. (2020). Unbanked in India: A qualitative analysis of 24 years of financial inclusion policies . International Social Work , 64 ( 4 ), 578–595. 10.1177/0020872819881184 [ CrossRef ] [ Google Scholar ]
• SmithBattle L., Lorenz R., Reangsing C., Palmerm J. L., Pitroff G. (2018). A methodological review of qualitative longitudinal research in nursing . Nursing Inquiry , 25 ( 4 ), Article e12248. 10.1111/nin.12248 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Staller K. M. (2018). Faith, hope and courage: Taking a qualitative perspective . Qualitative Social Work , 17 ( 6 ), 735–739. 10.1177/1473325018795507 [ CrossRef ] [ Google Scholar ]
• Stich A. E., Cipollone K. (2017). In and through the urban educational “reform churn”: The illustrative power of qualitative longitudinal research . Urban Education , 56 ( 3 ), 484–510. 10.1177/0042085917690207 [ CrossRef ] [ Google Scholar ]
• Suhail A., Iqbal N., Smith J. A. (2020). Lived experiences of Indian youth amid COVID-19 crisis: An interpretative phenomenological analysis . International Journal of Social Psychiatry , 67 ( 5 ), 1–8. 10.1177/0020764020966021 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sun N., Wei L., Suling S., Jiao D., Song R., Ma L., Wang W., Wang C., Wang Z., You Y., Liu S., Wang W. (2020). A qualitative study on the psychological experience of caregivers of COVID-19 patients . American Journal of Infection Control , 48 ( 6 ), 592–598. 10.1016/j.ajic.2020.03.018. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tarrant A., Way L., Ladlow L. (2021). ‘Oh sorry, I’ve muted you!’: Issues of connection and connectivity in qualitative (longitudinal) research with young fathers and family support professionals . International Journal of Social Research Methodology , 1–14. 10.1080/13645579.2021.198631 [ CrossRef ] [ Google Scholar ]
• Teherani A., Martimianakis T., Stenfors-Hayes T., Wadhwa A., Varpio L. (2015). Choosing a qualitative research approach . Journal of Graduate Medical Education , 7 ( 4 ), 669–670. 10.4300/JGME-D-15-00414.1 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Texas Today (2020). PAID STUDY: COVID-19 student social experiences study (3-month weekly diary study) . https://calendar.utexas.edu/event/paid_study_covid-19_student_social_experiences_study_3-month_weekly_diary_study#.YEEws06Sl9M
• Tuthill E. L., Maltby A. E., DiClemente K., Pellowski J. A. (2020). Longitudinal qualitative methods in health behavior and nursing research: Assumptions, design, analysis and lessons learned . International Journal of Qualitative Methods , 19 , 1-21. 10.1177/1609406920965799. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van de Groep S., Zanolie K., Green K. H., Sweijen S., Crone E. A. (2020). A daily diary study on adolescents’ mood, empathy, and prosocial behavior during the COVID-19 pandemic . Plos One , 15 ( 10 ), 1–20. 10.1371/journal.pone.0240349 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van Tiem J. M., Reisinger H. S., Friberg J. E., Wilson J. R., Fitzwater L., Panos R. J., Moeckil J. (2021). The STS case study: An analysis method for longitudinal qualitative research for implementation science . BMC Medical Research Methodology , 21 ( 1 ), 21–27. 10.1186/s12874-021-01215-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Warin J. (2011). Ethical mindfulness and reflexivity: Managing a research partnership with children and young people in a 14 year qualitative longitudinal research (QLR) study . Qualitative Inquiry , 17 ( 9 ), 805–814. 10.1177/1077800411423196 [ CrossRef ] [ Google Scholar ]
• Wöhrer V., Jesser A., Mataloni B., Schmidt A. (2020). Insights from setting up the first wave of a qualitative longitudinal study . International Journal of Qualitative Methods , 19 , 1–12. 10.1177/1609406920954057 [ CrossRef ] [ Google Scholar ]
• Woll H. (2013). Process diary as methodological approach in longitudinal phenomenological research . Indo-Pacific Journal of Phenomenology , 13 ( 2 ), 1–11. 10.2989/ipjp.2013.13.2.2.1176a [ CrossRef ] [ Google Scholar ]
• Yates L., Mcleod J. (2007). 12 to 18: A qualitative longitudinal study of students, values and difference in Australian schools . Australian Curriculum Studies Association [ Google Scholar ]

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• Synthesizing Sources | Examples & Synthesis Matrix

Synthesizing Sources | Examples & Synthesis Matrix

Published on July 4, 2022 by Eoghan Ryan . Revised on May 31, 2023.

Synthesizing sources involves combining the work of other scholars to provide new insights. It’s a way of integrating sources that helps situate your work in relation to existing research.

Synthesizing sources involves more than just summarizing . You must emphasize how each source contributes to current debates, highlighting points of (dis)agreement and putting the sources in conversation with each other.

You might synthesize sources in your literature review to give an overview of the field or throughout your research paper when you want to position your work in relation to existing research.

Table of contents

Example of synthesizing sources, how to synthesize sources, synthesis matrix, other interesting articles, frequently asked questions about synthesizing sources.

Let’s take a look at an example where sources are not properly synthesized, and then see what can be done to improve it.

This paragraph provides no context for the information and does not explain the relationships between the sources described. It also doesn’t analyze the sources or consider gaps in existing research.

Research on the barriers to second language acquisition has primarily focused on age-related difficulties. Building on Lenneberg’s (1967) theory of a critical period of language acquisition, Johnson and Newport (1988) tested Lenneberg’s idea in the context of second language acquisition. Their research seemed to confirm that young learners acquire a second language more easily than older learners. Recent research has considered other potential barriers to language acquisition. Schepens, van Hout, and van der Slik (2022) have revealed that the difficulties of learning a second language at an older age are compounded by dissimilarity between a learner’s first language and the language they aim to acquire. Further research needs to be carried out to determine whether the difficulty faced by adult monoglot speakers is also faced by adults who acquired a second language during the “critical period.”

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To synthesize sources, group them around a specific theme or point of contention.

As you read sources, ask:

• What questions or ideas recur? Do the sources focus on the same points, or do they look at the issue from different angles?
• How does each source relate to others? Does it confirm or challenge the findings of past research?
• Where do the sources agree or disagree?

Once you have a clear idea of how each source positions itself, put them in conversation with each other. Analyze and interpret their points of agreement and disagreement. This displays the relationships among sources and creates a sense of coherence.

Consider both implicit and explicit (dis)agreements. Whether one source specifically refutes another or just happens to come to different conclusions without specifically engaging with it, you can mention it in your synthesis either way.

Synthesize your sources using:

• Topic sentences to introduce the relationship between the sources
• Signal phrases to attribute ideas to their authors
• Transition words and phrases to link together different ideas

To more easily determine the similarities and dissimilarities among your sources, you can create a visual representation of their main ideas with a synthesis matrix . This is a tool that you can use when researching and writing your paper, not a part of the final text.

In a synthesis matrix, each column represents one source, and each row represents a common theme or idea among the sources. In the relevant rows, fill in a short summary of how the source treats each theme or topic.

This helps you to clearly see the commonalities or points of divergence among your sources. You can then synthesize these sources in your work by explaining their relationship.

If you want to know more about ChatGPT, AI tools , citation , and plagiarism , make sure to check out some of our other articles with explanations and examples.

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Synthesizing sources means comparing and contrasting the work of other scholars to provide new insights.

It involves analyzing and interpreting the points of agreement and disagreement among sources.

You might synthesize sources in your literature review to give an overview of the field of research or throughout your paper when you want to contribute something new to existing research.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

Topic sentences help keep your writing focused and guide the reader through your argument.

In an essay or paper , each paragraph should focus on a single idea. By stating the main idea in the topic sentence, you clarify what the paragraph is about for both yourself and your reader.

At college level, you must properly cite your sources in all essays , research papers , and other academic texts (except exams and in-class exercises).

Add a citation whenever you quote , paraphrase , or summarize information or ideas from a source. You should also give full source details in a bibliography or reference list at the end of your text.

The exact format of your citations depends on which citation style you are instructed to use. The most common styles are APA , MLA , and Chicago .

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Ryan, E. (2023, May 31). Synthesizing Sources | Examples & Synthesis Matrix. Scribbr. Retrieved August 12, 2024, from https://www.scribbr.com/working-with-sources/synthesizing-sources/

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Using a Matrix to Write Your Research Proposal

Is there a way to simplify the preparation of your research or thesis proposal without leaving out the important items to include in its preparation? This article provides four steps that will guide you on how to write your research proposal. Try the matrix approach described and explained in this easy to understand article and reap the benefits.

You may find yourself getting into the trouble of writing and rewriting your thesis proposal because you tend to miss important details pertinent to what you intend to investigate and how you will go about it. Research or thesis proposal preparation is very time-consuming and can cause undue worry, especially if you have set a fixed time frame to finish your thesis. If you desire to have your research proposal approved soonest so you can start gathering the data you need, this is for you.

A systematic way of ensuring that everything is well addressed or covered fully in your research paper is possible using a matrix. This technique is most appropriate when you want to ensure that you have adequate preparation, especially the proper methods to answer the research questions.

What is a matrix?

I would then scrounge for a clean sheet of paper or anything that can serve the purpose to illustrate how a matrix can be used to set one’s mind into focus. A matrix is a table with rows and columns.

Four Steps on How to Write Your Research Proposal

It always pays to be systematic when you do something—in this case, being organized when you write your research proposal. A simple guide always works.

The matrix technique on how to write your research proposal will work great for you, just like the numerous students I mentored in two decades. I enumerate the four steps that you can easily follow.

1. Prepare a table with the following headings for each column:

2. list the research questions.

Under the heading “Research Question,” write the series of research questions that you intend to pursue in their logical order. Logical order means that you arrange research questions chronologically. It is ordered so that answering the first question will facilitate the resolution of the next question.

3. Supply the required methods to answer the research questions

Under the heading “Methods,” look at the left column and think about how you would go about answering the research question. Methods refer to the specific things you will do to collect data.

4. Select the appropriate statistical tool

Under the third column with the heading “Statistical Analysis,” recall your statistics lessons or consult a statistician about the correct  statistical tool  to analyze the facts gathered in the study. Does the research question need simple descriptive statistics such as mean, median, mode, or percentages? Or do you need to apply a correlation analysis, a test of the difference between means, or a multivariate analysis? You can also add the corresponding graphs or tables under this column that you will need for a better discussion of the findings.

Now, guided by your matrix, you will be able to answer your research questions with confidence. You make sure that everything is covered by setting a one-to-one correspondence in the research proposal’s crucial elements, i.e., the research questions, the methodology, and the corresponding statistical analysis.

An example is given below to show the matrix on how to write your research proposal. The research topic is about  illegal fishing , a practice rampant in many places where effective policy is needed to regulate or minimize unlawful activities.

But before you go, please read the final note in the next section.

Check Correspondence of the Matrix with the Study’s Conceptual Framework

The next step is to check whether the variables that you included in your research proposal are compatible with your research’s conceptual framework. If not, you must revise your research proposal based on what you mean with the conceptual framework as your guide.

© 2013 December 4 P. A. Regoniel Updated: 18 November 2020

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Aug 15, 2024 • 13 minutes read

Mixed methods research explained: Combine data like a pro

From heatmaps to interviews, here’s how to blend qualitative and quantitative data for holistic user insights.

Ella Webber

Mixed methods research is one of the most popular and powerful UX research approaches—blending numbers with narrative to garner a holistic understanding of your product or research question.

Whether you’re in UX research and design, education, healthcare, or social sciences, mixed methods research can help you find insights and make better decisions.

Read on for a breakdown of what mixed methods are, their strengths and weaknesses, when to use them, and how to analyze the data.

UX research made easy

Explore the power of combining quantitative and qualitative research to discover new insights and test final solutions.

What is mixed methods research and when should you use it?

Mixed methods research involves collecting, analyzing, and integrating both quantitative and qualitative UX research methods within a single study. It is unique to other UX research techniques in that it combines data types, encouraging product teams to use qualitative feedback to explain the story behind quantitative numbers.

• Quantitative data can come from UX surveys , product analytics , usability testing , experiments, or statistical databases and provide broad numerical insights
• Qualitative data is gathered through user interviews , focus groups, or contextual inquiries and offers a deep, contextual understanding of the subject matter

Why use a mixed methods approach?

The power of mixed methods research is simple: it allows you to combine the best parts of both types of data—quantitative research methods, like surveys, give you broad trends, while qualitative methods, such as interviews, dig deep into personal experiences.

Anthony J. Onwuegbuzie and R. Burke Johnson, in Mixed Methods Research: A Research Paradigm Whose Time Has Come , highlight how blending these methods allows researchers to leverage the strengths of both approaches. They identify mixed methods research as one of the “three core research paradigms: qualitative, quantitative, and mixed methods.”

Like any technique, however, mixed methods research has both strengths and weaknesses to consider.

When should you use mixed methods research?

Mixed UX research methods are useful when neither qualitative nor quantitative data alone can fully answer your research question . Evaluative research further helps to assess the effectiveness of your mixed method research findings and ensure they meet user needs.

For example, use mixed methods research when:

• You need to go beyond numbers (generalizability): Quantitative methods, like surveys, provide broad trends and patterns that are relevant to a wider population. For example, a survey might show that most users enjoy a new app feature, but it won’t capture why some users might dislike it.
• The why matters (contextualization): Mixed methods allow you to put numerical findings in context, adding rich detail to your conclusions. For example, if analytics show that users are spending less time on your app (quantitative), interviews can help you understand the reasons behind this behavior, such as frustration with a recent update or a lack of engaging content (qualitative).
• Credibility is important (credibility and triangulation): When both data types converge on the same conclusion, it strengthens your findings. For example, the combined evidence is more credible if survey data indicates that most users prefer a particular software interface and focus groups echo this preference.
• You need to track changes (developmental purposes): Mixed methods are invaluable when one type of data informs the other. For example, initial qualitative research with a small group of beta testers can uncover key issues and user needs, which can then be explored quantitatively with a larger user base to see how widespread these issues are.
• Understand complex issues (complementary insights): Different data types can offer complementary insights. For example, in a study on software usability, quantitative data might show a drop in task completion rates, while qualitative data reveals specific pain points and user frustrations. This combined approach can guide more effective design improvements.

What are the types of mixed methods research design?

The type of mixed methods research design you choose depends on your research goals, the timing of data collection, and each data type. Here are some key factors to consider:

• Your research approach: Are you trying to understand existing findings (explanatory) or dig deeper into a topic (exploratory)?
• Your research questions: Do your questions need big-picture answers (like how many users are happy) and detailed explanations (like why some users are unhappy)?
• Existing data availability: Is there any existing information you can use from previous studies or a research repository (like user demographics)?
• Data you can collect yourself: What kind of in-depth information do you need to gather from users (through interviews, testing, etc.)?

Whether you're a data diver or a narrative novelist, understanding these research methods can make your studies more dynamic and insightful.

📚 A UX research repository is crucial for keeping track of research findings. You need a centralized database to store and manage all your qualitative and quantitative data. This ensures that your research is organized, accessible, and reusable for future studies.

Let’s look at the most common types of mixed methods research design:

Convergent parallel

Convergent parallel design involves collecting qualitative and quantitative data simultaneously but analyzing them separately. The primary goal is to merge the two datasets to provide a complete understanding of the research problem.

For example, let’s say you want to study user satisfaction with a new mobile app. Here’s how you might use the convergent parallel design:

• Qualitative results: Conduct in-depth user interviews with 30 participants to gather detailed insights into their experiences and perceptions of the app. Plus, analyze 200 user reviews from app stores. You might use prompts like, "What features do you find most valuable?" and "Please describe any difficulties you've experienced while using the app."
• Quantitative study: Use analytics data to measure user engagement metrics like session duration and feature usage, then distribute UX surveys to gather quantitative satisfaction scores.

Concurrent embedded design

Embedded design is a mixed methods research approach where qualitative and quantitative data are collected simultaneously, but one type of data is supplementary to the other.

The secondary data provides additional context and can help explain or clarify the primary findings. This approach is particularly beneficial when time or resources are limited, as it allows for a more comprehensive analysis without doubling the workload.

Explanatory sequential design

Explanatory sequential design is a popular mixed methods research approach introduced by John W. Creswell and Vicki L. Plano Clark. This research design involves collecting and analyzing quantitative data first, followed by qualitative data collection and analysis.

According to Creswell, this approach is particularly useful when researchers need to explain relationships found in quantitative data.

The process typically involves two phases:

• Quantitative phase: This involves collecting numerical data through methods like surveys or experiments. The goal here is to identify patterns, trends, or relationships.
• Qualitative phase: Qualitative phase: After analyzing the quantitative data, researchers collect qualitative data with qualitative approaches, like interviews or focus groups, to provide deeper insights. This phase helps explain the ‘why’ or ‘how’ behind the quantitative findings.

Creswell emphasizes that one of the strengths of this design is its simple structure, making it easy for researchers to manage and for audiences to understand the research process and findings.

Exploratory sequential design

Exploratory sequential design begins with qualitative data collection and analysis, followed by quantitative data collection. This immersive approach helps generate rich, detailed data that lays a strong foundation for the subsequent quantitative analysis.

For example, let’s say a researcher wants to understand why people don't meditate regularly. They could start with generative research techniques , like conducting workshops where participants discuss their daily routines and barriers to meditation. These qualitative insights reveal underlying themes and patterns, like time constraints and lack of motivation.

Next, the researcher analyzes these qualitative data to identify key factors impacting wellbeing habits. Based on these insights, they develop a survey to quantitatively measure how widespread these barriers are among a larger population.

So, that’s how you collect data. But how do you analyze it? Unsurprisingly, there are multiple analysis and interpretation methods commonly used in mixed methods research. Let’s look at some.

How to analyze mixed methods research data: 3 Ways to combine qualitative and quantitative data

Combining different types of research data can add credibility to your research findings. Let’s look at how to conduct mixed methods research:

Triangulation protocol

Following a thread, mixed methods matrix.

The triangulation protocol in mixed methods research is a systematic way to use multiple data sources, techniques, or perspectives to get a clear understanding of a research problem. The goal is to capitalize on the strengths of both types of data while minimizing their individual weaknesses.

Let's say you want to conduct a study aiming to evaluate the effectiveness of a new educational program on student performance, and you arrive at the following datasets:

• Quantitative finding: 80% of students improved their math scores after the program
• Qualitative finding: Students reported that interactive activities helped them understand math concepts better

When you merge these findings, the research concludes that the interactive activities (identified qualitatively) are likely a significant factor contributing to the improved scores (quantitatively).

The following a thread method allows researchers to trace a specific theme or concept across both qualitative and quantitative data sets.

Here’s how it works:

• Identify key themes: Begin by identifying key themes or variables that are central to your research questions. These themes will serve as the ‘threads’ you’ll follow through your data.
• Extracting data: Extract relevant data segments related to each theme from qualitative (e.g. interviews, focus groups) and quantitative (e.g. surveys, statistical data) sources. This involves coding qualitative data and identifying relevant quantitative measures.
• Mapping data: Create a map or matrix that links data segments from different sources according to the identified themes. This matrix helps visualize how different data points converge or diverge on the same theme.
• Comparative analysis: Compare the data segments within each theme to identify patterns, consistencies, and discrepancies. Look for how qualitative narratives support or contradict quantitative findings.
• Synthesis and interpretation: Synthesize the findings to develop an understanding of each theme. Interpret the data by integrating the qualitative insights with the quantitative results, explaining how they complement or contrast with each other.

A mixed methods matrix is a visual tool used to integrate and compare qualitative and quantitative data in mixed methods research. It helps researchers organize data according to key themes or variables, facilitating a comprehensive analysis and interpretation.

The matrix consists of several rows and columns:

• Rows represent key themes or research questions
• Columns represent different data sources or methods (e.g. interviews, surveys, observations)

By populating each cell with relevant data segments, researchers can easily identify areas of convergence, divergence, and complementarity. Let’s say you want to answer this research question: How does a new health intervention impact patient satisfaction and health outcomes?

You would populate the matrix as follows:

How to conduct mixed methods research: A mixed method research example

Let’s say you own a project management app and want to understand user satisfaction and identify areas for improvement. Here are eight steps to apply mixed methods research—using the convergent parallel technique—to discover user pain points and create a better user experience.

Step 1: Define your research objectives

In UX research , asking the right questions is crucial for identifying user needs and pain points effectively. But in order to write the right user research questions , you need to define a clear objective. What are you looking to understand?

Defining a clear UX research objective helps guide all other research decisions and acts as a lighthouse that guides your research project.

In our example , our research objective could be ‘to explore user experience and identify areas for improvement within our project management app’.

Step 2: Design your study and recruit participants

Ensure your study is designed to allow integration of both quantitative and qualitative data. There are various mixed method research designs to choose from—the right one for you depends on your research objectives and preferences.

At this stage, you should also establish a clear strategy for data integration and decide how you’ll combine the qualitative and quantitative data during the UX reporting and analysis phase. This might involve merging data sets for comparative analysis , or embedding one data set within the other to provide additional context.

The integration plan should reflect your research goals and ensure that the combined data offers a clear understanding. For our study, we’ll design a convergent parallel mixed methods study and triangulate our data during the analysis phase. This enables us to find our what and our why.

This is also when you need to recruit research participants for your study. Consider what you’re studying and identify your target test audience. You then need to create a call-out for your research study—either on socials, via email, or with In-Product Prompts .

Alternatively, you can find and filter research participants using Maze Panel , then manage your participant relationships using Maze Reach .

Step 3: Collect quantitative data

Next up, you want to start gathering your quantitative data. A good way to do this is with a survey to collect numerical data that can be statistically analyzed. For example, a user satisfaction survey that includes rating scales (1–10) for various aspects of the software.

For our research into app user satisfaction, we asked:

• Please rate your overall satisfaction with the app (1–10)
• How often do you use the app per week?
• How easy is the app to use on a scale of 1 to 10?
• How likely are you to recommend the app to a friend or colleague (1–10)?

❓ Need a quick and easy way to create and manage surveys? Maze Feedback Surveys simplify your feedback collection process so you can focus on making the changes your customers want to see. You can quickly create surveys tailored to your needs with Maze's survey templates .

Step 4: Collect qualitative data

Once you’ve got your quantitative data, it’s time to collect your qualitative data. Consider conducting user interviews or focus groups to obtain detailed, descriptive data that provides context and deep understanding.

For our study, we selected 20 users from the survey who gave varied ratings and conducted 30-minute interviews, asking:

• What do you like most about the app?
• What features do you find difficult to use?
• Can you describe a recent experience using the app?
• What improvements would you suggest?

💬 User interviews are resource-intensive and time-consuming. Speed them up with Maze’s end-to-end user interview solution: Interview Studies .

Step 5: Quantitative data analysis

Now you’ve got all your data—it’s time to dig in. For your quantitative data, this involves using statistical methodology to identify trends and patterns.

When we looked at our example data, we calculated:

• CSAT score: 75%
• Frequency of use: 70% use the app daily
• Ease of use average score: 6.8/10
• Net Promoter Score (NPS): 65

Step 6: Qualitative data analysis

Analyzing qualitative data involves coding and categorizing qualitative responses to uncover themes and patterns. Identify recurring themes in user feedback, such as ease of use, functionality, and improvement areas. If you’re using Maze Interview Studies to analyze your findings, you can automatically extract key themes and summaries to speed this process up.

When reviewing qualitative data, we found a number of interesting nuggets in our qualitative data:

• Users express dissatisfaction with the app’s usability, specifically the navigation between different functionalities
• Users wish they could access their billing details via the app, instead of solely via the web
• User find the core functionality—the project management features—to be highly valuable to their day-to-day, but also report finding the interface to be clunky and unintuitive

Step 7: Integrate data and interpret findings

Following your analysis, combine the findings from both data sets and draw conclusions. Look for correlations and insights that span both types of data.

Example integration:

• High satisfaction scores (75%) but lower ease of use (6.8/10) prove a strong product market fit but call for a more intuitive experience
• Further qualitative research agreed with this conclusion and identified specific areas for improvement, such as adding additional functionalities and improving the interface

Step 8: Report findings to stakeholders for buy-in

Present the integrated results to highlight how qualitative insights support or explain quantitative trends.

The format of your report will depend on your audience:

• Internal stakeholders (project managers, designers): Consider a concise report with clear visuals like charts, graphs, and user quotes to highlight key findings and actionable recommendations
• External stakeholders (clients, investors): Create a formal report with a clear introduction, methodology section, and comprehensive results presentation, summarizing key findings and highlighting the impact on user satisfaction and app usage

Always strive to go beyond what the data says and explain why it matters.

For example, once we’d conducted our research and drawn conclusions, we compiled this into a report that shared:

• Research methods: We used mixed methods research (surveys and interviews) to explore existing user pain points and satisfaction levels.
• Overall findings: User satisfaction is moderately high (7.5/10), indicating a generally positive reception. However, the ease of use score (6.8/10) and qualitative feedback highlight significant usability issues for new users.
• Actionable next steps based on findings: Simplify the user interface to improve the experience for new users, potentially increasing overall satisfaction and ease of use scores.

Conducting mixed methods research with Maze

Mixed methods research is one of the most effective ways to boost your UX insights, and gather a more rounded understanding of your users’ problems and perspectives. Combining research methods and types of data can uncover insights you may otherwise miss. And while there are ideal times to conduct qualitative, quantitative, or mixed methods research, ultimately it really is as simple as more research = more insights .

If you’re looking for the ideal research companion to help conduct mixed methods research, consider Maze. Maze is the user research platform that empowers all teams with the research methods they need to get game-changing insights. Whether it’s a mixed methods study or a one-off test—Maze helps you gather accurate insights, faster, for more informed decision-making.

Frequently asked questions about mixed methods research

What is the purpose of mixed methods research?

The purpose of mixed methods research is to combine quantitative and qualitative data to provide a more complete understanding of a research problem. This approach helps validate findings, explore complex issues from multiple perspectives, and produce more reliable and actionable results.

What’s the difference between qualitative and quantitative research?

• Qualitative research explores non-numerical data to understand concepts, opinions, or experiences. It uses methods like interviews, focus groups, and observations to gather in-depth insights.
• Quantitative research focuses on numerical data to quantify variables and uncover patterns. It uses methods like surveys, experiments, and statistical analysis to measure and analyze data.

What is the difference between mixed methods and multiple methods?

Mixed methods research integrates qualitative (e.g. interviews) and quantitative (e.g. surveys) data within a single study. Multiple methods research uses various research approaches, but they can be either qualitative or quantitative. For example, it might use surveys and experiments (quantitative) or interviews and focus groups (qualitative) in different parts of a study without combining the data.

Sample Tables

These sample tables illustrate how to set up tables in APA Style . When possible, use a canonical, or standard, format for a table rather than inventing your own format. The use of standard formats helps readers know where to look for information.

There are many ways to make a table, and the samples shown on this page represent only some of the possibilities. The samples show the following options:

• The sample factor analysis table shows how to include a copyright attribution in a table note when you have reprinted or adapted a copyrighted table from a scholarly work such as a journal article (the format of the copyright attribution will vary depending on the source of the table).
• The sample regression table shows how to include confidence intervals in separate columns; it is also possible to place confidence intervals in square brackets in a single column (an example of this is provided in the Publication Manual ).
• The sample qualitative table and the sample mixed methods table demonstrate how to use left alignment within the table body to improve readability when the table contains lots of text.

Use these links to go directly to the sample tables:

Sample demographic characteristics table

Sample results of several t tests table, sample correlation table, sample analysis of variance (anova) table, sample factor analysis table, sample regression table, sample qualitative table with variable descriptions, sample mixed methods table.

These sample tables are also available as a downloadable Word file (DOCX, 37KB) . For more sample tables, see the Publication Manual (7th ed.) as well as published articles in your field.

Sample tables are covered in the seventh edition APA Style manuals in the Publication Manual Section 7.21 and the Concise Guide Section 7.21

Related handout

• Student Paper Setup Guide (PDF, 3MB)

Sociodemographic Characteristics of Participants at Baseline

Note. N = 150 ( n = 50 for each condition). Participants were on average 39.5 years old ( SD = 10.1), and participant age did not differ by condition.

a Reflects the number and percentage of participants answering “yes” to this question.

Results of Curve-Fitting Analysis Examining the Time Course of Fixations to the Target

Note. For each subject, the logistic function was fit to target fixations separately. The maximum asymptote is the asymptotic degree of looking at the end of the time course of fixations. The crossover point is the point in time the function crosses the midway point between peak and baseline. The slope represents the rate of change in the function measured at the crossover. Mean parameter values for each of the analyses are shown for the 9-year-olds ( n = 24) and 16-year-olds ( n = 18), as well as the results of t tests (assuming unequal variance) comparing the parameter estimates between the two ages.

Descriptive Statistics and Correlations for Study Variables

Means, Standard Deviations, and One-Way Analyses of Variance in Psychological and Social Resources and Cognitive Appraisals

*** p < .001.

Results From a Factor Analysis of the Parental Care and Tenderness (PCAT) Questionnaire

Note. N = 307. The extraction method was principal axis factoring with an oblique (Promax with Kaiser Normalization) rotation. Factor loadings above .30 are in bold. Reverse-scored items are denoted with an (R). Adapted from “Individual Differences in Activation of the Parental Care Motivational System: Assessment, Prediction, and Implications,” by E. E. Buckels, A. T. Beall, M. K. Hofer, E. Y. Lin, Z. Zhou, and M. Schaller, 2015, Journal of Personality and Social Psychology , 108 (3), p. 501 ( https://doi.org/10.1037/pspp0000023 ). Copyright 2015 by the American Psychological Association.

Moderator Analysis: Types of Measurement and Study Year

Note . Number of studies = 120, number of effects = 782, total N = 52,578. CI = confidence interval; LL = lower limit; UL = upper limit.

Master Narrative Voices: Struggle and Success and Emancipation

Integrated Results Matrix for the Effect of Topic Familiarity on Reliance on Author Expertise

Note . We integrated quantitative data (whether students selected a card about nuclear power or about climate change) and qualitative data (interviews with students) to provide a more comprehensive description of students’ card selections between the two topics.

9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

Published: August 08, 2024

One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.

From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.

And let me tell you: having the right marketing research methods in your toolbox is a must.

Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.

How to Choose a Marketing Research Method

Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.

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1. Identify your objective.

What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.

Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.

For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).

Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.

2. Determine what type of data and research you need.

Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)

• Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
• Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.

Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.

For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.

However, truth be told, the two really work together.

Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.

For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.

(As they should.)

Primary Research vs Secondary Research

You’ll also want to understand the difference between primary and secondary research.

Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.

Some examples include conducting experiments, surveys, interviews, observations, or focus groups.

Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.

So, which is better?

Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.

You are in full control and best equipped to get the reliable information you need.

3. Put it all together.

Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.

For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.

You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.

This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.

Best Market Research Methods for 2024

Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.

Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.

Primary Research

1. interviews.

Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.

I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.

What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.

What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.

Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.

2. Focus Groups

Focus groups are similar to conducting interviews but on a larger scale.

In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.

They are ideal for collecting long-form, open-ended feedback, and subjective opinions.

One well-known focus group you may remember was run by Domino’s Pizza in 2009 .

After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.

It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.

The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”

The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.

The brand continues to conduct focus groups and share real footage from them in its promotion:

What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.

What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.

Best for: Product research or development

Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.

3. Surveys or Polls

Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.

They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.

A great example of a close-ended survey is HubSpot’s annual State of Marketing .

In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.

The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.

It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.

Apple is no stranger to surveys, either.

In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”

"For example, we did a large voluntary survey of email subscribers and top readers a few years back."

While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”  

Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.

The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.

People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.

What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.

What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.

Best for: Product testing and competitive analysis

Pro tip:  " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."

Secondary Research

8. public domain or third-party research.

While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.

Some reputable resources you can use include:

• Pew Research Center
• McKinley Global Institute
• Relevant Global or Government Organizations (i.e United Nations or NASA)

It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).

If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)

What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.

What I dislike: You may not find data specific to your needs.

Best for: Companies under a time or resource crunch, adding factual support to content

Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”

9. Buy Research

If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .

What I like: Same as public domain research

What I dislike: You may not find data specific to your needs. It also adds to your expenses.

Best for: Companies under a time or resource crunch or adding factual support to content

Which marketing research method should you use?

You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.

My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.

Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.

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