qualitative and quantitative research methods in education

Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

Learn How to Put Education Research into Action

Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education Online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .

What’s the Difference Between Educational Equity and Equality?

EdD vs. PhD in Education: Requirements, Career Outlook, and Salary

Top Education Technology Jobs for Doctorate in Education Graduates

American University, EdD in Education Policy and Leadership

Edutopia, “2019 Education Research Highlights”

Formplus, “Qualitative vs. Quantitative Data: 15 Key Differences and Similarities”

iMotion, “Qualitative vs. Quantitative Research: What Is What?”

Scribbr, “Qualitative vs. Quantitative Research”

Simply Psychology, “What’s the Difference Between Quantitative and Qualitative Research?”

Typeform, “A Simple Guide to Qualitative and Quantitative Research”

Request Information

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Streefkerk, R. (2023, June 22). Qualitative vs. Quantitative Research | Differences, Examples & Methods. Scribbr. Retrieved April 2, 2024, from https://www.scribbr.com/methodology/qualitative-quantitative-research/

Is this article helpful?

Raimo Streefkerk

Other students also liked, what is quantitative research | definition, uses & methods, what is qualitative research | methods & examples, mixed methods research | definition, guide & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

• Technical Support
• Find My Rep

You are here

Research and Evaluation in Education and Psychology Integrating Diversity With Quantitative, Qualitative, and Mixed Methods

• Donna M. Mertens - Gallaudet University, USA
• Description

Updated to align with the American Psychological Association and the National Council of Accreditation of Teacher Education accreditation requirements.   Focused on increasing the credibility of research and evaluation, the Fifth Edition of Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods incorporates the viewpoints of various research paradigms into its descriptions of these methods. Students will learn to identify, evaluate, and practice good research, with special emphasis on conducting research in culturally complex communities, based on the perspectives of women, LGBTQ communities, ethnic/racial minorities, and people with disabilities. In each chapter, Dr. Donna M. Mertens carefully explains a step of the research process—from the literature review to analysis and reporting—and includes a sample study and abstract to illustrate the concepts discussed.   The new edition includes over 30 new research studies and contemporary examples to demonstrate research methods including:

• Black girls and school discipline: The complexities of being overrepresented and understudied (Annamma, S.A., Anyon, Y., Joseph, N.M., Farrar, J., Greer, E., Downing, B., & Simmons, J.)
• Learning Cooperatively under Challenging Circumstances: Cooperation among Students in High-Risk Contexts in El Salvador (Christine Schmalenbach)
• Replicated Evidence of Racial and Ethnic Disparities in Disability Identification in U.S. Schools (Morgan, et. al.)
• Relation of white-matter microstructure to reading ability and disability in beginning readers (Christodoulu, et. al.)
• Arts and mixed methods research: an innovative methodological merger (Archibald, M.M. & Gerber, N.)   

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

Supplements

The open-access  Student Study Site  includes the following:

• EXCLUSIVE! Access to certain full-text  SAGE journal articles   have been carefully selected for each chapter. Each article supports and expands on the concepts presented in the chapter. 

study.sagepub.com/mertens5e

Password-protected  Instructor Resources  include the following:

• Editable, chapter-specific Microsoft®  PowerPoint® slides  offer you complete flexibility in easily creating a multimedia presentation for your course. Highlight essential content, features, and artwork from the book.
• Sample course syllabi  for semester and quarter courses provide suggested models for use when creating the syllabi for your courses.
• EXCLUSIVE! Access to certain full-text  SAGE journal articles  that have been carefully selected for each chapter. Each article supports and expands on the concepts presented in the chapter. Combine cutting-edge academic journal scholarship with the topics in your course for a robust classroom experience.
• Chapter-specific exercises and activities offer practical application of the concepts and can be used in-class or as assignments.
• Tables and figures from the book available for download.

This text aligns well with the various Research Methods courses offered in the Department of Educational Policy Studies (EPS). This text is particularly relevant to/for those whose concentration is in Research, Methods, and Statistics (RMS).

NEW TO THIS EDITION: 

• Includes increased coverage of digital research throughout , with examples of research studies that use Twitter, Facebook, and digital data collection strategies.
• Increased coverage of mixed methods studies as examples is used throughout so readers can see how mixed methods can be used with a wide range of research perspectives and methods.
• Intersectionality is added as an essential part of effectively researching with marginalized communities.
• The book aligns with the latest APA and NCATE accreditation standards by increasing the focus on issues related to cultural competency.
• Ethics in research is addressed throughout , emphasizing its importance throughout the process of planning, conducting, and using research.
• Critical issues that have arisen in society are addressed , including violence in schools, sexual abuse, disproportionately severe discipline practices with students of color, and strategies to support inclusion of people with disabilities.
• More about the use of digital resources to disseminate and support the use of research findings is included.
• Reporting and publication of research is aligned with the APA recommendations for quantitative, qualitative, and mixed methods research

KEY FEATURES:  

• Postpositivist, constructivist, transformative, and pragmatic paradigms  discussed, showing how researchers’ views of the world underlie their choice of research approaches.
• Conducting research in culturally complex communities  emphasized throughout.
• A  step-by-step overview of the entire research process.
• Detailed explanation of  how to write a literature review and plan a dissertation.
• Pedagogy that includes  Extending Your Thinking  throughout, providing opportunity for critical application of concepts.

Sample Materials & Chapters

1: An Introduction to Research and Ethical Practice

3: Literature Review and Focusing the Research

For instructors

Select a purchasing option.

• Subject List
• Take a Tour
• For Authors
• Subscriber Services
• Publications
• African American Studies
• African Studies
• American Literature
• Anthropology
• Architecture Planning and Preservation
• Art History
• Atlantic History
• Biblical Studies
• British and Irish Literature
• Childhood Studies
• Chinese Studies
• Cinema and Media Studies
• Communication
• Criminology
• Environmental Science
• Evolutionary Biology
• International Law
• International Relations
• Islamic Studies
• Jewish Studies
• Latin American Studies
• Latino Studies
• Linguistics
• Literary and Critical Theory
• Medieval Studies
• Military History
• Political Science
• Public Health
• Renaissance and Reformation
• Social Work
• Urban Studies
• Victorian Literature
• Browse All Subjects

How to Subscribe

• Free Trials

In This Article Expand or collapse the "in this article" section Quantitative Research Designs in Educational Research

Introduction, general overviews.

• Survey Research Designs
• Correlational Designs
• Other Nonexperimental Designs
• Randomized Experimental Designs
• Quasi-Experimental Designs
• Single-Case Designs
• Single-Case Analyses

Related Articles Expand or collapse the "related articles" section about

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Aliquam ligula odio, euismod ut aliquam et, vestibulum nec risus. Nulla viverra, arcu et iaculis consequat, justo diam ornare tellus, semper ultrices tellus nunc eu tellus.

• Methodologies for Conducting Education Research
• Mixed Methods Research
• Multivariate Research Methodology
• Qualitative Data Analysis Techniques
• Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies
• Researcher Development and Skills Training within the Context of Postgraduate Programs
• Single-Subject Research Design
• Social Network Analysis
• Statistical Assumptions

Other Subject Areas

Forthcoming articles expand or collapse the "forthcoming articles" section.

• Gender, Power, and Politics in the Academy
• Girls' Education in the Developing World
• Non-Formal & Informal Environmental Education
• Find more forthcoming articles...
• Export Citations
• Share This Facebook LinkedIn Twitter

Quantitative Research Designs in Educational Research by James H. McMillan , Richard S. Mohn , Micol V. Hammack LAST REVIEWED: 29 July 2020 LAST MODIFIED: 24 July 2013 DOI: 10.1093/obo/9780199756810-0113

The field of education has embraced quantitative research designs since early in the 20th century. The foundation for these designs was based primarily in the psychological literature, and psychology and the social sciences more generally continued to have a strong influence on quantitative designs until the assimilation of qualitative designs in the 1970s and 1980s. More recently, a renewed emphasis on quasi-experimental and nonexperimental quantitative designs to infer causal conclusions has resulted in many newer sources specifically targeting these approaches to the field of education. This bibliography begins with a discussion of general introductions to all quantitative designs in the educational literature. The sources in this section tend to be textbooks or well-known sources written many years ago, though still very relevant and helpful. It should be noted that there are many other sources in the social sciences more generally that contain principles of quantitative designs that are applicable to education. This article then classifies quantitative designs primarily as either nonexperimental or experimental but also emphasizes the use of nonexperimental designs for making causal inferences. Among experimental designs the article distinguishes between those that include random assignment of subjects, those that are quasi-experimental (with no random assignment), and those that are single-case (single-subject) designs. Quasi-experimental and nonexperimental designs used for making causal inferences are becoming more popular in education given the practical difficulties and expense in conducting well-controlled experiments, particularly with the use of structural equation modeling (SEM). There have also been recent developments in statistical analyses that allow stronger causal inferences. Historically, quantitative designs have been tied closely to sampling, measurement, and statistics. In this bibliography there are important sources for newer statistical procedures that are needed for particular designs, especially single-case designs, but relatively little attention to sampling or measurement. The literature on quantitative designs in education is not well focused or comprehensively addressed in very many sources, except in general overview textbooks. Those sources that do include the range of designs are introductory in nature; more advanced designs and statistical analyses tend to be found in journal articles and other individual documents, with a couple exceptions. Another new trend in educational research designs is the use of mixed-method designs (both quantitative and qualitative), though this article does not emphasize these designs.

For many years there have been textbooks that present the range of quantitative research designs, both in education and the social sciences more broadly. Indeed, most of the quantitative design research principles are much the same for education, psychology, and other social sciences. These sources provide an introduction to basic designs that are used within the broader context of other educational research methodologies such as qualitative and mixed-method. Examples of these textbooks written specifically for education include Johnson and Christensen 2012 ; Mertens 2010 ; Arthur, et al. 2012 ; and Creswell 2012 . An example of a similar text written for the social sciences, including education that is dedicated only to quantitative research, is Gliner, et al. 2009 . In these texts separate chapters are devoted to different types of quantitative designs. For example, Creswell 2012 contains three quantitative design chapters—experimental, which includes both randomized and quasi-experimental designs; correlational (nonexperimental); and survey (also nonexperimental). Johnson and Christensen 2012 also includes three quantitative design chapters, with greater emphasis on quasi-experimental and single-subject research. Mertens 2010 includes a chapter on causal-comparative designs (nonexperimental). Often survey research is addressed as a distinct type of quantitative research with an emphasis on sampling and measurement (how to design surveys). Green, et al. 2006 also presents introductory chapters on different types of quantitative designs, but each of the chapters has different authors. In this book chapters extend basic designs by examining in greater detail nonexperimental methodologies structured for causal inferences and scaled-up experiments. Two additional sources are noted because they represent the types of publications for the social sciences more broadly that discuss many of the same principles of quantitative design among other types of designs. Bickman and Rog 2009 uses different chapter authors to cover topics such as statistical power for designs, sampling, randomized controlled trials, and quasi-experiments, and educational researchers will find this information helpful in designing their studies. Little 2012 provides a comprehensive coverage of topics related to quantitative methods in the social, behavioral, and education fields.

Arthur, James, Michael Waring, Robert Coe, and Larry V. Hedges, eds. 2012. Research methods & methodologies in education . Thousand Oaks, CA: SAGE.

Readers will find this book more of a handbook than a textbook. Different individuals author each of the chapters, representing quantitative, qualitative, and mixed-method designs. The quantitative chapters are on the treatment of advanced statistical applications, including analysis of variance, regression, and multilevel analysis.

Bickman, Leonard, and Debra J. Rog, eds. 2009. The SAGE handbook of applied social research methods . 2d ed. Thousand Oaks, CA: SAGE.

This handbook includes quantitative design chapters that are written for the social sciences broadly. There are relatively advanced treatments of statistical power, randomized controlled trials, and sampling in quantitative designs, though the coverage of additional topics is not as complete as other sources in this section.

Creswell, John W. 2012. Educational research: Planning, conducting, and evaluating quantitative and qualitative research . 4th ed. Boston: Pearson.

Creswell presents an introduction to all major types of research designs. Three chapters cover quantitative designs—experimental, correlational, and survey research. Both the correlational and survey research chapters focus on nonexperimental designs. Overall the introductions are complete and helpful to those beginning their study of quantitative research designs.

Gliner, Jeffrey A., George A. Morgan, and Nancy L. Leech. 2009. Research methods in applied settings: An integrated approach to design and analysis . 2d ed. New York: Routledge.

This text, unlike others in this section, is devoted solely to quantitative research. As such, all aspects of quantitative designs are covered. There are separate chapters on experimental, nonexperimental, and single-subject designs and on internal validity, sampling, and data-collection techniques for quantitative studies. The content of the book is somewhat more advanced than others listed in this section and is unique in its quantitative focus.

Green, Judith L., Gregory Camilli, and Patricia B. Elmore, eds. 2006. Handbook of complementary methods in education research . Mahwah, NJ: Lawrence Erlbaum.

Green, Camilli, and Elmore edited forty-six chapters that represent many contemporary issues and topics related to quantitative designs. Written by noted researchers, the chapters cover design experiments, quasi-experimentation, randomized experiments, and survey methods. Other chapters include statistical topics that have relevance for quantitative designs.

Johnson, Burke, and Larry B. Christensen. 2012. Educational research: Quantitative, qualitative, and mixed approaches . 4th ed. Thousand Oaks, CA: SAGE.

This comprehensive textbook of educational research methods includes extensive coverage of qualitative and mixed-method designs along with quantitative designs. Three of twenty chapters focus on quantitative designs (experimental, quasi-experimental, and single-case) and nonexperimental, including longitudinal and retrospective, designs. The level of material is relatively high, and there are introductory chapters on sampling and quantitative analyses.

Little, Todd D., ed. 2012. The Oxford handbook of quantitative methods . Vol. 1, Foundations . New York: Oxford Univ. Press.

This handbook is a relatively advanced treatment of quantitative design and statistical analyses. Multiple authors are used to address strengths and weaknesses of many different issues and methods, including advanced statistical tools.

Mertens, Donna M. 2010. Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods . 3d ed. Thousand Oaks, CA: SAGE.

This textbook is an introduction to all types of educational designs and includes four chapters devoted to quantitative research—experimental and quasi-experimental, causal comparative and correlational, survey, and single-case research. The author’s treatment of some topics is somewhat more advanced than texts such as Creswell 2012 , with extensive attention to threats to internal validity for some of the designs.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

• About Education »
• Meet the Editorial Board »
• Academic Achievement
• Academic Audit for Universities
• Academic Freedom and Tenure in the United States
• Action Research in Education
• Adjuncts in Higher Education in the United States
• Administrator Preparation
• Adolescence
• Advanced Placement and International Baccalaureate Courses
• Advocacy and Activism in Early Childhood
• African American Racial Identity and Learning
• Alaska Native Education
• Alternative Certification Programs for Educators
• Alternative Schools
• American Indian Education
• Animals in Environmental Education
• Art Education
• Artificial Intelligence and Learning
• Assessing School Leader Effectiveness
• Assessment, Behavioral
• Assessment, Educational
• Assessment in Early Childhood Education
• Assistive Technology
• Augmented Reality in Education
• Beginning-Teacher Induction
• Bilingual Education and Bilingualism
• Black Undergraduate Women: Critical Race and Gender Perspe...
• Blended Learning
• Case Study in Education Research
• Changing Professional and Academic Identities
• Character Education
• Children’s and Young Adult Literature
• Children's Beliefs about Intelligence
• Children's Rights in Early Childhood Education
• Citizenship Education
• Civic and Social Engagement of Higher Education
• Classroom Learning Environments: Assessing and Investigati...
• Classroom Management
• Coherent Instructional Systems at the School and School Sy...
• College Admissions in the United States
• College Athletics in the United States
• Community Relations
• Comparative Education
• Computer-Assisted Language Learning
• Computer-Based Testing
• Conceptualizing, Measuring, and Evaluating Improvement Net...
• Continuous Improvement and "High Leverage" Educational Pro...
• Counseling in Schools
• Critical Approaches to Gender in Higher Education
• Critical Perspectives on Educational Innovation and Improv...
• Critical Race Theory
• Crossborder and Transnational Higher Education
• Cross-National Research on Continuous Improvement
• Cross-Sector Research on Continuous Learning and Improveme...
• Cultural Diversity in Early Childhood Education
• Culturally Responsive Leadership
• Culturally Responsive Pedagogies
• Culturally Responsive Teacher Education in the United Stat...
• Curriculum Design
• Data Collection in Educational Research
• Data-driven Decision Making in the United States
• Deaf Education
• Desegregation and Integration
• Design Thinking and the Learning Sciences: Theoretical, Pr...
• Development, Moral
• Dialogic Pedagogy
• Digital Age Teacher, The
• Digital Citizenship
• Digital Divides
• Disabilities
• Distance Learning
• Distributed Leadership
• Doctoral Education and Training
• Early Childhood Education and Care (ECEC) in Denmark
• Early Childhood Education and Development in Mexico
• Early Childhood Education in Aotearoa New Zealand
• Early Childhood Education in Australia
• Early Childhood Education in China
• Early Childhood Education in Europe
• Early Childhood Education in Sub-Saharan Africa
• Early Childhood Education in Sweden
• Early Childhood Education Pedagogy
• Early Childhood Education Policy
• Early Childhood Education, The Arts in
• Early Childhood Mathematics
• Early Childhood Science
• Early Childhood Teacher Education
• Early Childhood Teachers in Aotearoa New Zealand
• Early Years Professionalism and Professionalization Polici...
• Economics of Education
• Education For Children with Autism
• Education for Sustainable Development
• Education Leadership, Empirical Perspectives in
• Education of Native Hawaiian Students
• Education Reform and School Change
• Educational Statistics for Longitudinal Research
• Educator Partnerships with Parents and Families with a Foc...
• Emotional and Affective Issues in Environmental and Sustai...
• Emotional and Behavioral Disorders
• Environmental and Science Education: Overlaps and Issues
• Environmental Education
• Environmental Education in Brazil
• Epistemic Beliefs
• Equity and Improvement: Engaging Communities in Educationa...
• Equity, Ethnicity, Diversity, and Excellence in Education
• Ethical Research with Young Children
• Ethics and Education
• Ethics of Teaching
• Ethnic Studies
• Evidence-Based Communication Assessment and Intervention
• Family and Community Partnerships in Education
• Family Day Care
• Federal Government Programs and Issues
• Feminization of Labor in Academia
• Finance, Education
• Financial Aid
• Formative Assessment
• Future-Focused Education
• Gender and Achievement
• Gender and Alternative Education
• Gender-Based Violence on University Campuses
• Gifted Education
• Global Mindedness and Global Citizenship Education
• Global University Rankings
• Governance, Education
• Grounded Theory
• Growth of Effective Mental Health Services in Schools in t...
• Higher Education and Globalization
• Higher Education and the Developing World
• Higher Education Faculty Characteristics and Trends in the...
• Higher Education Finance
• Higher Education Governance
• Higher Education Graduate Outcomes and Destinations
• Higher Education in Africa
• Higher Education in China
• Higher Education in Latin America
• Higher Education in the United States, Historical Evolutio...
• Higher Education, International Issues in
• Higher Education Management
• Higher Education Policy
• Higher Education Research
• Higher Education Student Assessment
• High-stakes Testing
• History of Early Childhood Education in the United States
• History of Education in the United States
• History of Technology Integration in Education
• Homeschooling
• Inclusion in Early Childhood: Difference, Disability, and ...
• Inclusive Education
• Indigenous Education in a Global Context
• Indigenous Learning Environments
• Indigenous Students in Higher Education in the United Stat...
• Infant and Toddler Pedagogy
• Inservice Teacher Education
• Integrating Art across the Curriculum
• Intelligence
• Intensive Interventions for Children and Adolescents with ...
• International Perspectives on Academic Freedom
• Intersectionality and Education
• Knowledge Development in Early Childhood
• Leadership Development, Coaching and Feedback for
• Leadership in Early Childhood Education
• Leadership Training with an Emphasis on the United States
• Learning Analytics in Higher Education
• Learning Difficulties
• Learning, Lifelong
• Learning, Multimedia
• Learning Strategies
• Legal Matters and Education Law
• LGBT Youth in Schools
• Linguistic Diversity
• Linguistically Inclusive Pedagogy
• Literacy Development and Language Acquisition
• Literature Reviews
• Mathematics Identity
• Mathematics Instruction and Interventions for Students wit...
• Mathematics Teacher Education
• Measurement for Improvement in Education
• Measurement in Education in the United States
• Meta-Analysis and Research Synthesis in Education
• Methodological Approaches for Impact Evaluation in Educati...
• Mindfulness, Learning, and Education
• Motherscholars
• Multiliteracies in Early Childhood Education
• Multiple Documents Literacy: Theory, Research, and Applica...
• Museums, Education, and Curriculum
• Music Education
• Narrative Research in Education
• Native American Studies
• Note-Taking
• Numeracy Education
• One-to-One Technology in the K-12 Classroom
• Online Education
• Open Education
• Organizing for Continuous Improvement in Education
• Organizing Schools for the Inclusion of Students with Disa...
• Outdoor Play and Learning
• Outdoor Play and Learning in Early Childhood Education
• Pedagogical Leadership
• Pedagogy of Teacher Education, A
• Performance Objectives and Measurement
• Performance-based Research Assessment in Higher Education
• Performance-based Research Funding
• Phenomenology in Educational Research
• Philosophy of Education
• Physical Education
• Podcasts in Education
• Policy Context of United States Educational Innovation and...
• Politics of Education
• Portable Technology Use in Special Education Programs and ...
• Post-humanism and Environmental Education
• Pre-Service Teacher Education
• Problem Solving
• Productivity and Higher Education
• Professional Development
• Professional Learning Communities
• Program Evaluation
• Programs and Services for Students with Emotional or Behav...
• Psychology Learning and Teaching
• Psychometric Issues in the Assessment of English Language ...
• Qualitative, Quantitative, and Mixed Methods Research Samp...
• Qualitative Research Design
• Quantitative Research Designs in Educational Research
• Queering the English Language Arts (ELA) Writing Classroom
• Race and Affirmative Action in Higher Education
• Reading Education
• Refugee and New Immigrant Learners
• Relational and Developmental Trauma and Schools
• Relational Pedagogies in Early Childhood Education
• Reliability in Educational Assessments
• Religion in Elementary and Secondary Education in the Unit...
• Researcher Development and Skills Training within the Cont...
• Research-Practice Partnerships in Education within the Uni...
• Response to Intervention
• Restorative Practices
• Risky Play in Early Childhood Education
• Scale and Sustainability of Education Innovation and Impro...
• Scaling Up Research-based Educational Practices
• School Accreditation
• School Choice
• School Culture
• School District Budgeting and Financial Management in the ...
• School Improvement through Inclusive Education
• School Reform
• Schools, Private and Independent
• School-Wide Positive Behavior Support
• Science Education
• Secondary to Postsecondary Transition Issues
• Self-Regulated Learning
• Self-Study of Teacher Education Practices
• Service-Learning
• Severe Disabilities
• Single Salary Schedule
• Single-sex Education
• Social Context of Education
• Social Justice
• Social Pedagogy
• Social Science and Education Research
• Social Studies Education
• Sociology of Education
• Standards-Based Education
• Student Access, Equity, and Diversity in Higher Education
• Student Assignment Policy
• Student Engagement in Tertiary Education
• Student Learning, Development, Engagement, and Motivation ...
• Student Participation
• Student Voice in Teacher Development
• Sustainability Education in Early Childhood Education
• Sustainability in Early Childhood Education
• Sustainability in Higher Education
• Teacher Beliefs and Epistemologies
• Teacher Collaboration in School Improvement
• Teacher Evaluation and Teacher Effectiveness
• Teacher Preparation
• Teacher Training and Development
• Teacher Unions and Associations
• Teacher-Student Relationships
• Teaching Critical Thinking
• Technologies, Teaching, and Learning in Higher Education
• Technology Education in Early Childhood
• Technology, Educational
• Technology-based Assessment
• The Bologna Process
• The Regulation of Standards in Higher Education
• Theories of Educational Leadership
• Three Conceptions of Literacy: Media, Narrative, and Gamin...
• Tracking and Detracking
• Traditions of Quality Improvement in Education
• Transformative Learning
• Transitions in Early Childhood Education
• Tribally Controlled Colleges and Universities in the Unite...
• Understanding the Psycho-Social Dimensions of Schools and ...
• University Faculty Roles and Responsibilities in the Unite...
• Using Ethnography in Educational Research
• Value of Higher Education for Students and Other Stakehold...
• Virtual Learning Environments
• Vocational and Technical Education
• Wellness and Well-Being in Education
• Women's and Gender Studies
• Young Children and Spirituality
• Young Children's Learning Dispositions
• Young Children's Working Theories
• Privacy Policy
• Cookie Policy
• Legal Notice
• Accessibility

Powered by:

• [66.249.64.20|195.158.225.230]
• 195.158.225.230

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Korean Med Sci
• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

The Combination of Qualitative and Quantitative Research Methods in Mathematics Education: A “Mixed Methods” Study on the Development of the Professional Knowledge of Teachers

• First Online: 01 January 2014

Cite this chapter

• Udo Kelle 6 &
• Nils Buchholtz 7  

Part of the book series: Advances in Mathematics Education ((AME))

8592 Accesses

9 Citations

3 Altmetric

Research about education in mathematics is influenced by the ongoing dispute about qualitative and quantitative research methods. Especially in the domain of professional knowledge of teachers one can find a clear distinction between qualitative, interpretive studies on the one hand and large-scale quantitative assessment studies on the other hand. Thereby the question of how professional knowledge of teachers can be measured and whether the applied constructs are developed on a solid theoretical base is heavily debated. Most studies in this area limit themselves to the use of either qualitative or quantitative methods and data. In this chapter we discuss the limitations of such mono-method studies and we show how a combination of research methods within a “mixed methods design” can overcome these problems. Thereby we lay special emphasis on different possibilities a mixed methods approach offers for a mutual validation of both qualitative and quantitative findings. For this purpose, we draw on data and results coming from an empirical study about a teacher training program in mathematics, where quantitative data measuring the development of professional knowledge of student teachers were related to qualitative in-depth interviews about the training program.

• Mixed methods
• Teacher education

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Beck and Maier distinguish slightly differently between the “normative” and the “interpretive paradigm” going back on Wilson ( 1970 ).

It should be clear from the preceding discussion that this is not so much a problem of quantitative research per se —it may occur if one strictly follows a hypothetico-deductive approach (which is for many reasons advisable if quantitative methods are applied) and if researchers lack empirically contentful hypotheses, workable theories and/or specific knowledge about the domain under study. The latter is often not so much the fault of uninformed researchers but a consequence of the fact that social action is often structured by culture-bound rules and “local knowledge”.

A methodological adjustment of the treatment groups by measures of treatment evaluation (e.g. propensity score matching) has been omitted so far as the use of elaborate statistical methods to determine treatment effects appeared disproportionate due to the small group sizes. Furthermore, the group differences in Abitur grades are not significant and the relationship of school-related pre-cognitions considering the attendance at Advanced or Basic course merely reflects the pre-cognitions of local convenience samples.

It needs to be noted that performance on the level of individual items can vary due to chance and thus should not be over-interpreted.

Anderson, J. R. (1980). Cognitive psychology and its implications . San Francisco: Freeman.

Google Scholar  

Bammé, A. (2004). Science Wars. Von der akademischen zur postakademischen Wissenschaft [Science Wars. From academic to post-academic science]. Frankfurt/New York: Campus Verlag.

Barton, A. H., & Lazarsfeld, P. F. (1969). Some functions of qualitative analysis in social research. In G. C. McCall & J. L. Simmons (Eds.), Issues in participant observation . Reading: Addison-Wesley.

Baumert, J., Bos, W., & Lehmann, R. (Eds.). (2000a). TIMSSS/III. Dritte Internationale Mathematik- und Naturwissenschaftsstudie – Mathematische und naturwissenschaftliche Bildung am Ende der Schullaufbahn. Vol. 1: Mathematische und naturwissenschaftliche Grundbildung am Ende der Pflichtschulzeit [TIMSSS/III. Third international mathematics and science study – Mathematics and science education at the end of schooling. Vol 1: Mathematical and Scientific Literacy at the end of compulsory schooling]. Opladen: Leske + Budrich.

Baumert, J., Bos, W., & Lehmann, R. (Eds.) (2000b). TIMSSS/III. Dritte Internationale Mathematik- und Naturwissenschaftsstudie—Mathematische und physikalische Kompetenzen am Ende der gymnasialen Oberstufe [TIMSSS / III. Third international mathematics and science study—Mathematical and physical competencies at the end of secondary school]. Opladen: Leske + Budrich.

Baumert, J., Klieme, E., Lehrke, M., & Savelsbergh, E. (2000c). Konzeption und Aussagekraft der TIMSS-Leistungstests [Conception and significance of the TIMSS achievement tests]. Die Deutsche Schule , 92 , 103–115 & 196–217.

Baumert, J., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., Schneider, W., Tillmann, K.-J. & Weiß, M. (Eds.) (2001). PISA 2000. Basiskompetenzen von Schülerinnen und Schülern im internationalen Vergleich [PISA 2000. An international comparison of students´ basic competencies of students]. Opladen: Leske + Budrich.

Beck, C., & Maier, H. (1993). Das Interview in der mathematikdidaktischen Forschung [Interviewing in research about mathematics education]. Journal für Mathematik-Didaktik, 14 (2), 147–179.

Article   Google Scholar  

Bender, P. (2004). Die etwas andere Sicht auf den mathematischen Teil der internationalen Vergleichs-Untersuchungen PISA sowie TIMSS und IGLU [A somewhat different view of the mathematical part of the international comparative studies PISA and TIMSS and IGLU]. Mitteilungen der DMV, 12 (2), 101–108.

Bender, P. (2005). PISA, Kompetenzstufen und Mathematik-Didaktik [PISA, levels of competence and mathematics education]. Journal für Mathematik-Didaktik, 26 (3/4), 274–281.

Bender, P. (2006). Was sagen uns PISA & Co, wenn wir uns auf sie einlassen? [What may PISA & Co tell us, if we get involved with them?]. In T. Jahnke & W. Meyerhöfer (Eds.), PISA & Co—Kritik eines Programms . Hildesheim: Franzbecker.

Beutelspacher, A., Danckwerts, R., Nickel, G., Spies, S. & Wickel, G. (2011). Mathematik Neu Denken. Impulse für die Gymnasiallehrerausbildung an Universitäten [Thinking Mathematics in a New Way. Ideas for secondary teacher education at universities]. Wiesbaden: Vieweg +Teubner.

Blömeke, S., Kaiser, G., & Lehmann, R. (Eds.). (2010a). TEDS-M 2008. Professionelle Kompetenz und Lerngelegenheiten angehender Primarstufenlehrkräfte im internationalen Vergleich [An international comparison of professional competence and learning opportunities of prospective primary school teachers]. Münster: Waxmann.

Blömeke, S., Kaiser, G., & Lehmann, R. (Eds.). (2010b). TEDS-M 2008. Professionelle Kompetenz und Lerngelegenheiten angehender Mathematiklehrkräfte für die Sekundarstufe I im internationalen Vergleich [An international comparison of professional competence and learning opportunities of prospective mathematics teachers for secondary school]. Münster: Waxmann.

Blumer, H. (1928). Method in social psychology. Unpublished doctoral dissertation, University of Chicago.

Brannen, J. (Ed.). (1992). Mixing methods: Qualitative and quantitative research . Aldershot: Avebury.

Brewer, J., & Hunter, A. (1989). Multimethod research: A synthesis of styles . Newbury Park: Sage.

Brezinka, W. (1968). Von der Pädagogik zur Erziehungswissenschaft. Vorschläge zur Abgrenzung [From pedagogy to education science. Proposals for a demarcation]. Zeitschrift für Pädagogik, 14 (5), 435–475.

Briedis, K., Egorova, T., Heublein, U., Lörz, M., Middendorff, E., Quast, H., & Spangenberg, H. (2008). Studienaufnahme, Studium und Berufsverbleib von Mathematikern. Einige Grunddaten zum Jahr der Mathematik [Educational and occupational careers of mathematicians. Some basic data for the year of mathematics]. Resource Document. Forum Hochschule 9, Hochschul-Informations-System GmbH. http://www.his.de/pdf/pub_fh/fh-200809.pdf . Accessed 24 Apr 2013.

Bromme, R. (1992). Der Lehrer als Experte: Zur Psychologie des professionellen Wissens [The teacher as an expert: The psychology of professional knowledge]. Bern: Huber.

Bromme, R. (1997). Kompetenzen, Funktionen und unterrichtliches Handeln des Lehrers [The teacher’s competencies, functions and instructional actions of]. In F. E. Weinert (Ed.), Enzyklopädie der Psychologie: Psychologie des Unterrichts und der Schule (Vol. 3, pp. 177–212). Göttingen: Hogrefe.

Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1962). A study of thinking . New York: Science Editions.

Bryman, A. (1988). Quantity and quality in social research . London: Routledge.

Book   Google Scholar  

Bryman, A. (2005). Why do researchers integrate quantitative and qualitative research? Paper presented at the Conference “Mixed Method Research Designs: Advanced Issues and Debates” at the University of Basel, 13 Aug 2005.

Bryman, A. (2008). Why do researchers integrate/combine/mesh/blend/mix/merge/fuse/quantitaive and qualitative research? In M. Bergman (Ed.), Advances in mixed methods research. Theories and applications (pp. 87–100). Los Angeles: Sage.

Buchholtz, N., & Blömeke, S. (2012). Mathematik unterrichten lernen. Zur Wirksamkeit hochschuldidaktischer Innovationen in der Mathematik-Lehrerausbildung [Learning to teach mathematics. The effectiveness of innovations in university teaching in mathematics teacher training]. In D. Bosse, L. Criblez, & T. Hascher (Eds.), Reform der Lehrerbildung in Deutschland, Österreich und der Schweiz. Teil 1: Analyse, Perspektiven und Forschung (pp. 255–276). Immenhausen bei Kassel: Prolog-Verlag.

Buchholtz, N., & Kaiser, G. (2013). Improving mathematics teacher education in Germany: Test results from a longitudinal evaluation of innovative programs. International Journal for Science and Mathematics Education, 11 (4), 949–977.

Buchholtz, N., Scheiner, T., Döhrmann, M., Suhl, U., Kaiser, G., & Blömeke, S. (2012). TEDS-shortM. Teacher Education and Development Study—Short Test on Mathematics Content Knowledge (MCK) and Mathematics Pedagogical Content Knowledge (MPCK). Kurzfassung der mathematischen und mathematikdidaktischen Testinstrumente aus TEDS-M, TEDS-LT und TEDS-Telekom . Hamburg: Universität Hamburg.

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 2 , 81–105.

Carpenter, T. P., & Fennema, E. (1992). Cognitively guided instruction: Building on the knowledge of students and teachers. International Journal of Educational Research, 17 (5), 457–470.

Carpenter, T. P., Fennema, E., Peterson, P. L., & Carey, D. (1988). Teachers’ pedagogical content knowledge of students’ problem solving. Journal of Research in Mathematics Education, 19 (5), 385–401.

Carpenter, T. P., Fennema, E., Peterson, P. L., Chiang, C. P., & Loef, M. (1989). Using knowledge of children’s mathematics thinking in classroom teaching: An experimental study. American Educational Research Journal, 26 (4), 499–532.

Cresswell, J. W. (1994). Research design. Qualitative and quantitative approaches . Thousand Oaks: Sage.

Denzin, N. K. (1978). The research act. A theoretical introduction to sociological methods . New York: McGraw Hill.

Deutsches PISA Konsortium (Eds.). (2000). Schülerleistungen im internationalen Vergleich. Eine neue Rahmenkonzeption für die Erfassung von Wissen und Fähigkeiten [An international comparison of students’ performance A new conceptual framework for the assessment of knowledge and skills] . Resource document. Max-Planck-Institut für Bildungsforschung. http://www.mpib-berlin.mpg.de/Pisa/Rahmenkonzeptiondt.pdf . Accessed 24 Apr 2013.

Eilerts, K. (2009). Kompetenzorientierung in der Mathematik-Lehrerausbildung. Empirische Untersuchung zu ihrer Implementierung [Competence orientation in mathematics teacher education. An empirical study about its implementation]. Münster: LIT Verlag.

Erzberger, C. (1998). Zahlen und Wörter. Die Verbindung quantitativer und qualitativer Daten und Methoden im Forschungsprozess [Numbers and words. The combination of quantitative and qualitative data and methods in the research process]. Weinheim: Deutscher Studien Verlag.

Erzberger, C., & Kelle, U. (2003). Making inferences in mixed methods: The rules of integration. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral sciences (pp. 457–488). Thousand Oaks: Sage.

Erzberger, C., & Prein, G. (1997). Triangulation: Validity and empirically based hypothesis construction. Quality & Quantity, 2 , 141–154.

Fennema, E., Carpenter, T. P., Franke, M. L., Levi, L., Jacobs, V. R., & Empson, S. B. (1996). A longitudinal study of learning to use children’s thinking in mathematics instruction. Journal for Research in Mathematics Education, 27 , 403–434.

Fielding, N. G., & Fielding, J. L. (1986). Linking data. Qualitative research methods (Vol. 4). London: Sage.

Flick, U. (1991). Triangulation [Triangulation]. In U. Flick, E. Kardoff, H. Keupp, L. Rosenstiel, & S. Wolff (Eds.), Handbuch qualitative Sozialforschung (pp. 432–434). München: Psychologie Verlags Union.

Flick, U. (1992). Triangulation revisited: Strategy of validation or alternative? Journal of the Theory of Social Behaviour, 2 , 175–197.

Flick, U. (1998). An introduction to qualitative research . Thousand Oaks/London/New Delhi: Sage.

Freudenthal, H. (1983). Didactical phenomenology of mathematical structures . Dordrecht: Reidel.

Gage, N. (1989). The paradigm wars and their aftermath: A “historical” sketch of research and teaching since 1989. Educational Researcher, 18 , 4–10.

Gellert, U. (2006). Mathematik “in der Welt” und mathematische “Grundbildung”. Zur Konsistenz des mathematikdidaktischen Rahmens von PISA [Mathematics “in the world” and mathematical “basic education.” The consistency of the mathematics educational framework of PISA]. In T. Jahnke & W. Meyerhöfer (Eds.), PISA & Co—Kritik eines Programms . Franzbecker: Hildesheim.

Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. Strategies for qualitative research . New York: Aldine.

Gobo, G. (2004). Sampling, representativeness and generalizability. In C. Seale, G. Gobo, J. F. Gubrium, & D. Silverman (Eds.), Qualitative research practice (pp. 435–456). London: Sage.

Grigutsch, S., Raatz, U., & Törner, G. (1998). Einstellung gegenüber Mathematik bei Mathematiklehrern [Beliefs of mathematics teachers concerning mathematics]. Journal für Mathematik-Didaktik, 19 (1), 3–45.

Guba, E. (1981). Criteria for assessing the trustworthiness of naturalistic inquiries. Educational Technology Research and Development, 29 (2), 75–91.

Hagemeister, V. (1999). Was wurde bei TIMSS erhoben? Über die empirische Basis einer aufregenden Studie [What was surveyed with TIMSS? About the empirical basis of an exciting study]. Die Deutsche Schule, 91 , 160–177.

Hammersley, M. (1989). The dilemma of qualitative method. Herbert Blumer and the Chicago tradition . London: Routledge.

Hammersley, M. (1990). What’s wrong with ethnography? The myth of theoretical description. Sociology, 24 (4), 597–615.

Hartig, J., & Kühnbach, O. (2006). Schätzung von Veränderung mit Plausible Values in mehrdimensionalen Rasch-Modellen [Estimation of change with plausible values in multidimensional Rasch models]. In A. Ittel & H. Merkens (Eds.), Veränderungsmessung und Längsschnittstudien in der der Erziehungswissenschaft (pp. 27–44). Wiesbaden: Verlag für Sozialwissenschaften.

Chapter   Google Scholar  

Hopmann, S., Brinek, G., & Retzl, M. (Eds.). (2007). PISA zufolge PISA – PISA According to PISA. Hält PISA, was es verspricht? – [Does PISA keep it’s promises?]. Wien: LIT-Verlag.

Hunter, A., & Brewer, J. (2003). Multimethod research in sociology. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral sciences (pp. 577–594). Thousand Oaks: Sage.

Jablonka, E. (2006). Mathematical Literacy: Die Verflüchtigung eines ambitionierten Testkonstrukts in bedeutungslose PISA-Punkte [Mathematical Literacy: Transforming ambitious test construct into meaningless PISA points]. In T. Jahnke & W. Meyerhöfer (Eds.), PISA & Co—Kritik eines Programms . Hildesheim: Franzbecker.

Jahnke, T. (2009). Kritik empirischer Unvernunft—zur sogenannten Empirischen Bildungsforschung in der Mathematikdidaktik [Critique of empirical irrationality—The so-called empirical education research in mathematics education]. In M. Neubrand (Ed.), Beiträge zum Mathematikunterricht 2009 (pp. 671–674). Münster: WTM-Verlag.

Jahnke, T. (2010). Vom mählichen Verschwinden des Fachs aus der Mathematikdidaktik [The gradual disappearance of the subject from mathematics education]. Mitteilungen der GDM, 89 , 21–24.

Jahnke, T., & Meyerhöfer, W. (Eds.). (2006). PISA & Co—Kritik eines Programms [PISA & Co—Critique of a program]. Hildesheim: Franzbecker.

Jahoda, M., Lazarsfeld, P. F., & Zeisel, H. (1982). Die Arbeitslosen von Marienthal [The unemployed of Marienthal] . Frankfurt am Main: Suhrkamp (Original work published 1933).

Johnson, B., & Turner, L. A. (2003). Data collection strategies in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social & behavioral research (pp. 297–320). Thousand Oaks: Sage.

Kaiser, G. (2000). Internationale Vergleichsuntersuchungen im Mathematikunterricht—Eine Auseinandersetzung mit ihren Möglichkeiten und Grenzen [International comparative studies in mathematics education—A discussion of their possibilities and limitations]. Journal für Mathematik-Didaktik, 21 (3/4), 171–192.

Kaiser, G., & Buchholtz, N. (2014). Overcoming the gap between university and school mathematics. The impact of an innovative programme in mathematics teacher education at the Justus-Liebig-University in Giessen. In S. Rezat, M. Hattermann, & A. Peter-Koop (Eds.), Transformation – A fundamental idea of mathematics education (S. 85–105). Heidelberg: Springer.

Kelle, U. (2001). Sociological explanations between micro and macro and method triangulation. Resource document. Forum Qualitative Social Research , 2 (1), Art. 5. http://www.qualitative-research.net/index.php/fqs/article/view/966/2109 . Accessed 17 April 2013.

Kelle, U. (2006). Combining qualitative and quantitative methods in research practice: Purposes and advantages. Qualitative Research in Psychology, 3 (4), 293–311.

Kelle, U., & Erzberger, C. (2004). Qualitative and quantitative methods—Not in opposition. In U. Flick, E. von Kardorff, & I. Steinke (Eds.), A companion to qualitative research . London: Sage.

Kelle, U., & Lüdemann, C. (1998). Bridge assumptions in rational choice theory: Methodological problems and possible solutions. In H.-P. Blossfeld & G. Prein (Eds.), Rational choice theory and large-scale data analysis (pp. 112–126). Boulder: Westview Press.

Kießwetter, K. (2002). Unzulänglich vermessen und vermessen unzulänglich: PISA & Co [Inadequately measured and overconfidently inadequate: PISA & Co]. Mitteilungen der DMV, 10 (4), 49–58.

Kirsch, A. (1977). Aspekte des Vereinfachens im Mathematikunterricht [Aspects of simplification in mathematics education]. Didaktik der Mathematik, 2 , 87–101.

Klein, F. (1932). Elementary mathematics from an advanced standpoint. Arithmetic, algebra, analysis (translated from the third German edition by Hedrick, E. R., & Noble, C. A.). New York: Macmillan.

Klieme, E., & Bos, W. (2000). Mathematikleistungen und mathematischer Unterricht in Deutschland und Japan. Triangulation qualitativer und quantitativer Analysen am Beispiel der TIMS-Studie [Performance in mathematics and mathematical education in Germany and Japan. Using TIMSS as an example for triangulating qualitative and quantitative findings ]. Zeitschrift für Erziehungswissenschaft, 3 (3), 359–380.

Köller, O., Baumert, J., & Schnabel, K. (1999). Wege zur Hochschulreife. Offenheit des Systems und Sicherung vergleichbarer Standards [Pathways to university entrance qualification. Openness of the system and protection of comparable standards]. Zeitschrift für Erziehungswissenschaft, 2 (3), 385–422.

König, J., & Blömeke, S. (2010). Pädagogisches Unterrichtswissen (PUW). Dokumentation der Kurzfassung des TEDS-M Testinstruments zur Kompetenzmessung in der ersten Phase der Lehrerausbildung [Pedagogical teaching knowledge (PUW). Documentation of the short version of the TEDS-M test instrument for measuring competence in the first phase of teacher education.]. Berlin: Humboldt-Universität.

Krippendorff, K. (2004). Content analysis: An introduction to its methodology (2nd ed.). Thousand Oaks: Sage.

Kunter, M., Baumert, J., Blum, W., Klusmann, U., Krauss, S., & Neubrand, M. (Eds.). (2011). Professionelle Kompetenz von Lehrkräften. Ergebnisse des Forschungsprogramms COACTIV [Professional competence of teachers. Results of the research program COACTIV]. Münster: Waxmann.

Kuntze, S. (2011). Sichtweisen von Studierenden zum Lehren und Lernen im Mathematikunterricht—“Rich pictures” und Multiple-Choice: Gegenüberstellung zweier Erhebungsformate [Perspectives of students on teaching and learning in mathematics education—“rich pictures” and multiple choice: comparison of two survey formats]. In A. Lindmeier & S. Ufer (Eds.), Beiträge zum Mathematikunterricht 2010 (pp. 521–524). Münster: WTM-Verlag.

Lamnek, S. (1995). Qualitative Sozialforschung. Band 1, Methodologie [Qualitative social research. Volume 1, Methodology]. Weinheim: Psychologie Verlags Union.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry . Beverly Hills, CA: Sage.

Lundberg, G. A. (1929/1942). Social research: A study in methods of gathering data. Green: Longmans.

Mayerhöfer, W. (2004a). Zum Kompetenzstufenmodell von PISA [The model of PISA competence levels]. Journal für Mathematik-Didaktik, 25 (3/4), 294–305.

Mayerhöfer, W. (2004b). Zum Problem des Ratens bei PISA [The problem of guessing in PISA]. Journal für Mathematik-Didaktik, 25 (1), 62–69.

Mayerhöfer, W. (2006). PISA & Co als kulturindustrielle Phänomene [PISA & Co as phenomena of culture industry]. In T. Jahnke & W. Meyerhöfer (Eds.), PISA & Co—Kritik eines Programms . Hildesheim: Franzbecker.

Mayring, P. (2000). Qualitative content analysis [28 paragraphs]. Resource document. Forum: Qualitative Social Research, 1 (2), Art. 20, http://nbnresolving.de/urn:nbn:de:0114-fqs0002204 . Accessed 17 Apr 2013.

Mayring, P. (2008). Qualitative Inhaltsanalyse—Grundlagen und Techniken [Qualitative content analysis—Basics and techniques] (10th ed.). Weinheim/Basel: Beltz Verlag.

Mayring, P. (2010). Die Methodenfrage in der fachdidaktischen Forschung – qualitativ, quantitativ, mixed? [The question of method in didactic research—Qualitative, quantitative, mixed?]. Plenary lecture at the 44th annual meeting of the German Society of Didactics of Mathematics (GDM) in Munich, 08–12. March 2010. Abstract in A. Lindmeier & S. Ufer (Eds.), Beiträge zum Mathematikunterricht 2010 . Münster: WTM-Verlag.

Morse, J. M. (2003). Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research . Thousand Oaks: Sage.

Nastasi, B. K., Hitchcock, J. H., & Brown, L. M. (2010). An inclusive framework for conceptualizing mixed methods typologies. In A. Tashakkori & C. Teddlie (Eds.), Mixed methods in social and behavioral research (2nd ed.). Thousand Oaks: Sage.

Neubrand, M. (Ed.). (2004). Mathematische Kompetenzen von Schülerinnen und Schülern in Deutschland—Vertiefende Analysen im Rahmen von PISA 2000 [Mathematical competencies of students in Germany— In-depth investigations in the context of PISA 2000]. Wiesbaden: VS Verlag für Sozialwissenschaften.

Patton, M. Q. (2002). Qualitative research & evaluation methods (3rd ed.). Thousand Oaks: Sage.

Piaget, J., & Szeminska, A. (1965). Die Entwicklung des Zahlenbegriffs beim Kinde [The development of the number concept in the child]. Stuttgart: Klett.

Roethlisberger, F. J., & Dickson, W. J. (1939). Management and the worker . Cambridge: Harvard University Press.

Rossman, G. B., & Wilson, B. L. (1985). Numbers and words. Combining quantitative and qualitative methods in a single-scale evaluation study. Evaluation Review, 9 (5), 627–643.

Rossman, G. B., & Wilson, B. L. (1994). Numbers and words revisited: Being “shamelessly eclectic”. Quality & Quantity, 28 (3), 315–327.

Rost, J. (2004). Lehrbuch Testtheorie—Testkonstruktion. [Textbook test theory—test construction] (4th ed.). Bern: Verlag Hans Huber.

Schulz, A. (2010). Ergebnisorientierung als Chance für den Mathematikunterricht? Innovationsprozesse qualitativ und quantitativ erfassen [Result orientation as an opportunity for teaching mathematics? Capturing innovation processes qualitatively and quantitatively]. Munich: Herbert Utz Verlag.

Schwarz, B. (2013). Strukturen von Lehrerprofessionswissen. Ergebnisse einer empirischen Untersuchung an Mathematiklehramtsstudierenden [Structures of teachers’ professional knowledge. Results of an empirical study of mathematics teachers students.]. Wiesbaden: Vieweg + Teubner.

Seale, C. (1999). The quality of qualitative research . London: Sage.

Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15 (2), 4–14.

Silverman, D. (2000). Doing qualitative research. A practical handbook . London: Sage.

Steinbring, H. (1998). Mathematikdidaktik: Die Erforschung theoretischen Wissens in sozialen Kontexten des Lehrens und Lernens [Mathematics education: Research on theoretical knowledge in social contexts of teaching and learning]. Zentralblatt für Didaktik der Mathematik, 30 (5), 161–167.

Tashakkori, A., & Teddlie, C. (1998). Mixed methods. Combining qualitative and quantitative approaches (Applied social research methods series, Vol. 46). London: Sage.

Tashakkori, A., & Teddlie, C. (2003). Major issues and controversies in the use of mixed methods in the social and behavioral sciences. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral sciences (pp. 3–50). Thousand Oaks: Sage.

Tashakkori, A., & Teddlie, C. (2010). Sage handbook of mixed methods in social and behavioral research (2nd ed.). Los Angeles: Sage.

Warm, T. A. (1989). Weighted likelihood estimation of ability in item response models. Psychometrika, 54 , 427–450.

Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966). Unobstrusive measures: Nonreactive research in the social sciences . Chicago: Rand McNally.

Weinert, F. E. (1999). Konzepte der Kompetenz. Gutachten zum OECD-Projekt “Definition and Selection of Competencies: Theoretical and Conceptual Foundations (DeSeCo)” [Concepts of competence. Report on the OECD project “Definition and Selection of Competencies: Theoretical and Conceptual Foundations (DeSeCo)”.]. Neuchatel: Bundesamt für Statistik.

Wellenreuther, M. (1997). Hypothesenprüfung, Theorieentwicklung und Erkenntnisfortschritt in der Mathematikdidaktik. Ein Plädoyer für Methodenpluralismus [Hypothesis testing, theory development and advances in knowledge in mathematics education. A plea for methodological pluralism]. Journal für Mathematik-Didaktik, 18 (2/3), 186–216.

Wilson, T. P. (1970). Conceptions of interaction and forms of sociological explanation. American Sociological Review, 35 (4), 697–710.

Wittmann, E. C. (1995). Mathematics education as a “design science”. Educational Studies in Mathematics, 29 (4), 355–374.

Witzel, A. (2000). The problem-centered interview [26 paragraphs]. Resource document, Forum: Qualitative Social Research , 1 (1), Art. 22. http://www.qualitative-research.net/index.php/fqs/article/view/1132/2522 . Accessed 17 Apr 2013.

Wuttke, J. (2006). Fehler, Verzerrungen, Unsicherheiten in der PISA-Auswertung [Errors, distortions, uncertainties in PISA assessment]. In T. Jahnke & W. Meyerhöfer (Eds.), PISA & Co—Kritik eines Programms . Hildesheim: Franzbecker.

Wuttke, J. (2009). PISA: Nachträge zu einer nicht geführten Debatte [PISA: supplements to a non-debated debate]. Mitteilungen der GDM, 87 , 22–34.

Znaniecki, F. (1934). The method of sociology . New York: Rinehart.

Download references

Author information

Authors and affiliations.

Helmut-Schmidt-Universität Hamburg, Holstenhoffweg 85, 22043, Hamburg, Germany

Arbeitsbereich Didaktik der Mathematik, Universität Hamburg, Fak. 04, FB ErzWiss 05, Binderstraße 34, 20146, Hamburg, Germany

Nils Buchholtz

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Udo Kelle .

Editor information

Editors and affiliations.

Faculty 3 of Mathematics and Computer Science, University of Bremen, Bremen, Germany

Angelika Bikner-Ahsbahs

Christine Knipping

Mathematics Department, Illinois State University, Normal, Illinois, USA

Norma Presmeg

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media Dordrecht

About this chapter

Kelle, U., Buchholtz, N. (2015). The Combination of Qualitative and Quantitative Research Methods in Mathematics Education: A “Mixed Methods” Study on the Development of the Professional Knowledge of Teachers. In: Bikner-Ahsbahs, A., Knipping, C., Presmeg, N. (eds) Approaches to Qualitative Research in Mathematics Education. Advances in Mathematics Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9181-6_12

Download citation

DOI : https://doi.org/10.1007/978-94-017-9181-6_12

Published : 05 September 2014

Publisher Name : Springer, Dordrecht

Print ISBN : 978-94-017-9180-9

Online ISBN : 978-94-017-9181-6

eBook Packages : Humanities, Social Sciences and Law Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Quantitative research in education : Recent e-books

• Recent e-books

• << Previous: Background information
• Next: Recent print books >>
• Background information
• Recent print books
• Connect to Stanford e-resources
• Last Updated: Jan 23, 2024 12:46 PM
• URL: https://guides.library.stanford.edu/quantitative_research_in_ed

• Frontiers in Psychology
• Quantitative Psychology and Measurement
• Research Topics

Best Practice Approaches for Mixed Methods Research in Psychological Science - Volume II

Total Downloads

Total Views and Downloads

About this Research Topic

Having started as a small movement in the 1980’s, the study of mixed methods research burst onto the scene around the beginning of the second millennium. After decades of intense dispute between supporters of the qualitative perspective and their quantitative counterparts—with both sides having grown deeply ...

Keywords : Symmetry, Quantitizing, Qualitizing, Record Transformation, Qual-Quan Integration

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic Editors

Topic coordinators, recent articles, submission deadlines, participating journals.

Manuscripts can be submitted to this Research Topic via the following journals:

total views

• Demographics

No records found

total views article views downloads topic views

Top countries

Top referring sites, about frontiers research topics.

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

HR Daily Advisor

Practical HR Tips, News & Advice. Updated Daily.

Learning & Development

Quantitative vs. qualitative metrics in training evaluation.

Updated: Sep 19, 2023

Understanding Quantitative Metrics

Where qualitative metrics come into play, striking the right balance, leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

This paper is in the following e-collection/theme issue:

Published on 4.4.2024 in Vol 26 (2024)

Impacts of an Acute Care Telenursing Program on Discharge, Patient Experience, and Nursing Experience: Retrospective Cohort Comparison Study

Authors of this article:

Original Paper

• Courtenay R Bruce, MA, JD   ; 
• Steve Klahn, RN, MBA   ; 
• Lindsay Randle, MBA   ; 
• Xin Li, BS   ; 
• Kelkar Sayali, BS   ; 
• Barbara Johnson, BSN, MBA, DNP   ; 
• Melissa Gomez, MBA   ; 
• Meagan Howard, MHA   ; 
• Roberta Schwartz, PhD   ; 
• Farzan Sasangohar, PhD  

Houston Methodist, Houston, TX, United States

Corresponding Author:

Courtenay R Bruce, MA, JD

Houston Methodist

8100 Greenbriar Drive

Houston, TX, 77030

United States

Phone: 1 281 620 9040

Email: [email protected]

Background: Despite widespread growth of televisits and telemedicine, it is unclear how telenursing could be applied to augment nurse labor and support nursing.

Objective: This study evaluated a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients.

Methods: A retrospective, observational cohort comparison was performed in a large academic hospital system (approximately 2100 beds) in Houston, Texas, comparing patients in our pilot units for the ACTN program (telenursing cohort) between June 15, 2022, and December 31, 2022, with patients who did not participate (nontelenursing cohort) in the same units and timeframe. We used a case mix index analysis to confirm comparable patient cases between groups. The outcomes investigated were patient experience, measured using the Hospital Consumer Assessment of Health Care Providers and Systems (HCAHCPS) survey; nursing experience, measured by a web-based questionnaire with quantitative multiple-choice and qualitative open-ended questions; time of discharge during the day (from electronic health record data); and duration of discharge education processes.

Results: Case mix index analysis found no significant case differences between cohorts ( P =.75). For the first 4 units that rolled out in phase 1, all units experienced improvement in at least 4 and up to 7 HCAHCPS domains. Scores for “communication with doctors” and “would recommend hospital” were improved significantly ( P =.03 and P =.04, respectively) in 1 unit in phase 1. The impact of telenursing in phases 2 and 3 was mixed. However, “communication with doctors” was significantly improved in 2 units ( P =.049 and P =.002), and the overall rating of the hospital and the ”would recommend hospital” scores were significantly improved in 1 unit ( P =.02 and P =04, respectively). Of 289 nurses who were invited to participate in the survey, 106 completed the nursing experience survey (response rate 106/289, 36.7%). Of the 106 nurses, 101 (95.3%) indicated that the ACTN program was very helpful or somewhat helpful to them as bedside nurses. The only noticeable difference between the telenursing and nontelenursing cohorts for the time of day discharge was a shift in the volume of patients discharged before 2 PM compared to those discharged after 2 PM at a hospital-wide level. The ACTN admissions averaged 12 minutes and 6 seconds (SD 7 min and 29 s), and the discharges averaged 14 minutes and 51 seconds (SD 8 min and 10 s). The average duration for ACTN calls was 13 minutes and 17 seconds (SD 7 min and 52 s). Traditional cohort standard practice (nontelenursing cohort) of a bedside nurse engaging in discharge and admission processes was 45 minutes, consistent with our preimplementation time study.

Conclusions: This study shows that ACTN programs are feasible and associated with improved outcomes for patient and nursing experience and reducing time allocated to admission and discharge education.

Introduction

Telemedicine, particularly video televisits, has greatly expanded in the wake of the COVID-19 pandemic [ 1 , 2 ]. Televisits have shown promise as a robust, practical, efficacious, and scalable alternative to in-person office visits that could ameliorate labor supply shortages [ 3 , 4 ]. The published evidence suggests a generally positive attitude toward televisit appointments for chronic care, focused on addressing financial and transportation barriers and improving patients’ access to care [ 5 - 7 ]. Despite the promise shown by televisits, limited attention has been paid to applying this method in the acute care setting and, in particular, on how this promising technology can be leveraged to support nurses.

Estimates suggest that approximately 200,000 open nursing positions will become available each year between 2021 and 2031 [ 8 ]. Telenursing can augment nursing labor supply, decrease nursing workload, maintain patient and nurse safety, and positively impact nursing and patient experiences [ 9 ]. However, the impact of telenursing on outcomes in acute care settings remains a research gap.

To address this gap, this study aimed to evaluate the outcomes associated with a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients compared to patients who did not undergo the ACTN program intervention. Admission and discharge are 2 substantive and time-consuming acute care nursing tasks that involve tedious documentation in the electronic health record (EHR) and extensive interaction with patients and families to gather history and provide patient education [ 10 , 11 ]. We aimed to develop an ACTN program to augment nursing care by conducting admission and discharge processes through telenursing in a large health system. Subsequently, we discuss the impacts on 4 end points: patient experience, nursing experience, time of discharge during the day, and length of time for discharge education processes. We hypothesized that the ACTN program would be associated with higher patient experience scores and improved nursing experience compared to standard admission and discharge practices.

This study was conducted in a large academic hospital system (approximately 2100 beds) in Houston, Texas. The preimplementation methods are reported more extensively in the studies by Hehman et al [ 12 ] and Schwartz et al [ 13 ]. Program implementation was first informed by nursing time and workload surveys and pilot implementation in 4 comparatively understaffed units. The chief innovation officer, along with nursing leaders and ACTN program administrators, met with the bedside nursing staff of these 4 understaffed units to solicit their input on where and how ACTN would add value to their workflow. Bedside nursing staff provided critical input on admission processes that could be delegated to individuals working remotely with no perceived negative impact on patient experience. We conducted participatory workflow design sessions with bedside nursing staff on the ACTN program to cocreate workflow integration points where the remote team could assist [ 13 ].

Pilot Implementation and Procedures

Before implementation, the ACTN administrators trained bedside nurses in pilot units by demonstrating the use of technology during shift huddles. Then, the trainers presented slides on contact information and available support and provided a role demarcation process map, showing what the remote telenurse staff would be doing compared to what the bedside nurses needed to do to launch and conduct discharge education. Furthermore, the trainers invited the nursing staff to observe several discharges to learn how to conduct them. A software with Health Insurance Portability and Accountability Act compliance was uploaded to iPads (Apple Inc) and stored on each unit. Handheld iPads were available, and roaming iPads were made available for patients who could not hold an iPad.

The pilot implementation was staggered in a phased rollout, consisting of 3 sequenced phases, as shown in Figure 1 . Upon admission, the acute care bedside nurse contextualized the ACTN program with patients and families by handing the patient an iPad with a preloaded remote program app (Caregility) and then pressing a soft key to allow the ACTN to enter the patient’s room via the iPad screen. The ACTN introduced themselves, completed the nursing admission profile in the EHR, placed a request for a consultation, and notified the bedside nurse that the admission was completed using secure SMS text messaging [ 13 ]. A similar process was followed for discharge workflow processes, where the ACTN completed patient education on discharge instructions, confirmed the patient’s pharmacy details, confirmed discharge transportation, and arranged for departure.

Bedside nurses used their discretion regarding which patients would be appropriate for the ACTN program. They based this determination principally on whether documentation was needed and whether the patient could benefit from the undivided attention the ACTN program could afford. Furthermore, they excluded patients from the ACTN program if the patients expressed discomfort using an iPad. After the initial rollout, patients’ input was sought on their experience with the ACTN program to identify where and how improvements could be made, and this feedback was incorporated into iterative revisions in subsequent rollouts.

Pilot Outcomes Monitoring

A retrospective, observational cohort comparison was performed, in which all patients in our pilot units for the ACTN program (telenursing cohort) between June 15, 2022, and December 31, 2022, were compared with all patients who did not participate (nontelenursing cohort) in the same units in the same timeframe.

Our primary outcomes were patient experience and nursing experience. Patient experience scope was any process observable by patients [ 14 ]. We compared patient experiences in the telenursing and nontelenursing cohorts by evaluating patients’ responses to the widely used Hospital Consumer Assessment of Health Care Providers and Systems (HCAHPS) survey [ 15 ], which represented 8 aspects (called dimensions) of patient satisfaction. Each dimension was measured using a continuous variable (0 to 100 points).

For the telenursing cohort, we analyzed bedside nurses’ collective responses using a Forms (Microsoft Corp) survey conducted in April 2023. The survey consisted of 5 questions, asking them to indicate whether the ACTN program was helpful using a Likert scale with 5 items (very helpful to very unhelpful). Nurses were asked to provide open-ended comments to explain the reasons for their evaluation. At the end of the survey, we included 2 open-ended fields for nurses to describe opportunities for improvement in future rollouts and provide any additional comments.

Furthermore, we explored the time at which discharge occurred using the EHR admission, discharge, and transfer date and time. We compared the hour of the day the patient was discharged in the telenursing cohort with the hour of the day the patient was discharged in the nontelenursing cohort, hypothesizing a priori that patients might be discharged earlier in the day in the telenursing cohort. Finally, we analyzed the duration of discharge education for both cohorts, measured in minutes.

Data Analysis

The patient demographic data were available for all patients. To confirm that the telenursing cohort had similar patient demographics as the nontelenursing cohort (and therefore to confirm that nurse biases in patient selection for the ACTN program were unlikely), we conducted a case mix index (CMI) evaluation. We first isolated the population of both cohorts into adults (aged ≥18 y). We compared only those patients who were discharged home and excluded those who were on extracorporeal membrane oxygenation or those who underwent a tracheostomy. The remaining population was evaluated to determine whether there was a difference in patient acuity and severity. After confirming that patient acuity and severity were of no significant difference, we included the inpatient and observation populations to evaluate the intervention results.

For the patient experience data, independent sample t tests (2-tailed) were used to compare the telenursing and nontelenursing cohorts across different HCAHPS dimensions and units. Analysis was conducted using R software (R Foundation for Statistical Computing). For the nursing experience survey data, we used Excel (Microsoft Corp) to analyze the responses to multiple-choice, discrete questions and thematic analysis to evaluate the open-text fields. Thematic analysis allows for eliciting key themes that emerge based on recurring statements [ 16 ]. The analysis followed an inductive approach. This approach uses open-ended questions, allowing themes to emerge with a few previously articulated assumptions on responses. Given the limited content, CRB served as the primary coder. Coding labels were used for data contextualizing, allowing for new themes to emerge throughout the coding process, using a codebook [ 16 , 17 ]. We stored emergent patterns and themes in an electronic format.

Ethical Considerations

The hospital’s review board determined that the ACTN pilot would not be considered regulated human subjects research. All data reported in this study were aggregated and deidentified.

The demographics of the telenursing and nontelenursing cohorts were relatively similar. Both cohorts had an average age of 60 years with an SD of 16.91; had a similar distribution in race and ethnicity (approximately 92/2319, 3.96% Asian; 525/2319, 22.64% Black; 425/2319, 18.33% Hispanic; 70/2319, 3.02% Native American, declined to identify, or other categories; and 1202/2319, 51.83% White); and had a similar distribution in female participants versus male participants (1249/2319, 53.86% vs 1070/2319, 46.14%). To further understand the population, the CMI analysis for acuity and severity showed that the CMI was slightly higher in the telenursing cohort than in the nontelenursing cohort, but the difference was not statistically significant ( P =.75).

Patient Experience

Among the first 4 units that rolled out in phase 1, all units experienced improvement in at least 4 and up to 7 HCAHPS domains (Table S1 in Multimedia Appendix 1 ). On average, 6 out of 8 HCAHPS domains were improved for patients in the telenursing cohort. All 4 units experienced improvements in the “overall rating” domain, and 3 of the 4 units experienced improvements in “likelihood to recommend” domain for patients in the telenursing cohort compared to those in the nontelenursing cohort within the same units. The improvement scores ranged from 1.4% for the neurosurgery unit (36 beds) to 11.6% for the medical unit (37 beds). Furthermore, all 4 units in the first phase of roll out experienced improved scores in the “responsiveness” domain by >4 points (ranging from 5% to 10.1%). A total of 2 out of the 4 units also experienced improvements in the “communication with nurses” (ranging from 1.7% to 3%) and “communication about medicines” (ranging from 3.3% to 11.7%) domains. The 2 units that did not experience improvement in the communication domains were the combined medical and surgery neurology and neurosurgical units (36 beds). Only the neurosurgical unit showed statistically significant improvements in 2 dimensions: “communication with doctors” ( P =.03) and “would recommend hospital” ( P =.04).

For the 7 units that rolled out during phase 2, only 1 orthopedic surgery unit (28 beds) experienced improvements in every domain (ranging from 0.9% to 12.5%). Medical observation unit 1 also improved in 5 areas. However, only improvements in “communication with doctors” ( P =.002), “overall rating of hospital” ( P =.02), and “would recommend hospital” ( P =.04) were statistically significant . The remaining units experienced improvements in some domains for the telenursing cohort compared to the nontelenursing cohort, with no improvement in other domains. However, the scores for “communication with nurses” and “communication with doctors” domains were improved for most of the units that rolled out in phase 2 (Table S2 in Multimedia Appendix 1 ).

For the 2 units that rolled out in phase 3, both of which were surgical cardiac units with 36 beds, 1 unit experienced improvement in every domain except “responsiveness” (ranging from 1% to 12%). The other unit only experienced improvement in the “communication with doctors” (4.9%) and “care transitions” domains (1.1%). However, none of these improvements were statistically significant (Table S3 in Multimedia Appendix 1 ).

Nursing Experience

Of the 289 nurses who were invited to participate in the survey, 106 completed the survey (36.7% response rate). Of the 106 nurses, 101 (95.3%) indicated that the ACTN program was “very helpful” or “somewhat helpful” to them as bedside nurses.

Quantitative Findings

The main reasons nurses gave for the program’s helpfulness included that it saved them time (94/106, 88.7%), allowed them to focus on more urgent clinical needs (90/106, 84.9%), allowed them to focus on activities they felt were more in line with their skill level (55/106, 51.9%), and allowed patients to have undivided attention for their discharge education (52/106, 49.1%). Among the 5 nurses who indicated that the ACTN program was somewhat unhelpful or very unhelpful, 3 (60%) indicated that workflows were not clear or needed further refinement or clarification. Furthermore, the nurse respondents shared several barriers and provided opportunities for improvement, with 91 (85.8%) out of 106 nurses offering suggestions.

Qualitative Findings

For the free-text explanation fields, all but 3 nurses (103/106, 97.2%) provided additional comments on the ACTN program helpfulness. Three themes emerged from the qualitative analysis of the free-text comments: (1) most of the nurses’ comments reflected that telenurses help bedside nurses save time, (2) respondents indicated that extra hands provided emotional and physical support in providing patient care, and (3) respondents perceived an improvement in patient safety by having a telenurse who could “catch missed” issues.

Time Saving

One of the perceived benefits of the telenursing program was saving time. One nurse said the following:

... Just putting in home medications alone takes up so much time. This new telenurse service helps [save time]

Several nurses highlighted that admission and discharge processes are so complex and time-consuming that shifting this work to the ACTN program freed nurses to perform other activities, as reflected by this nurse:

The tele RN is able to spend as much time possible sufficiently educating an admission or discharge while allowing me time to respond to the needs of my other patients saving me time on one patient especially charting.

Emotional and Physical Support

For the second theme, several responses focused less on time management and perceived efficiencies and instead centered more on the emotional appeal and support in having an extra hand, as one nurse mentioned:

Being in such a fast-paced unit, it can be a bit stressful with so many discharges and admissions. Having a helpful hand is beneficial.

Improved Patient Safety

Finally, the third theme was perceived improvement in patient safety by having a telenurse who could “catch missed” issues (eg, an incorrectly identified pharmacy details), simultaneously allowing the primary bedside nurse to focus more intensely on other needs, essentially creating a 2-fold safety promotion. Some nurses noted that they could begin carrying out orders while the telenurses began completing the admission, facilitating quicker treatment and resolution of care needs, thereby improving the safety and quality of care. One nurse mentioned the following:

Allows [telenurses] to take on thorough and accurate admissions, while also preventing any rushing the patient might experience from the primary RN.

When asked for areas of improvement, the most recurring theme was having 24 hours of support during the weekend and during the week. The second theme for improvement was the reduced time to connect to a telenurse. The third theme was the availability of iPads. Nurses mentioned that iPads could sometimes be unavailable in patients’ rooms or they may not be fully charged.

Time of Discharge

The time of day distribution is presented in Figure 2 . The only noticeable difference between the telenursing and nontelenursing cohorts was a shift in the volume of patients discharged before 2 PM compared with those discharged after 2 PM at a hospital-wide level ( Table 1 ). At an individual unit level, these results were not consistent and could be further explored by patient population and their needs to discharge. The variation was further illustrated when reviewing the length of stay of patients in the telenursing and nontelenursing cohorts. Only 5 out of the 12 units showed a decrease in the average inpatient length of stay.

Discharge Length

The ACTN admissions averaged 12 minutes and 6 seconds (SD 7 min and 29 s), and the discharges averaged 14 minutes and 51 seconds (SD 8 min and 10 s). The average duration for ACTN calls was 13 minutes and 17 seconds (SD 7 min and 52 s). Traditional cohort standard practice of a bedside nurse engaging in discharge and admission processes was 45 minutes, consistent with our preimplementation nursing time study.

Principal Findings

Our results suggest that the ACTN program was associated with positive nursing experiences because it saved time. Furthermore, the ACTN program was associated with higher HCAHPS scores in several domains but only in the first series of units that piloted the intervention. In phase 1, the improvement in “communication with doctors” and “would recommend hospital” scores in 1 unit was statistically significant. In phase 2, the improvement in “communication with doctors” score was significant in 2 units and that in “overall rating of hospital” and “would recommend hospital” scores were significant in 1 unit. The time of day discharge was nearly the same in both the telenursing and nontelenursing cohorts. The duration for discharge processes was less than half in the ACTN cohort compared to the nonintervention cohort.

At the time of writing this paper, the United States was experiencing a critical nursing shortage that will likely reach an epidemic level in the next few decades [ 8 ]. Despite the promise shown by telenursing, to our knowledge, only 1 existing paper documents the impact of ACTN programs on HCAHPS-measured patient satisfaction using a small cohort of patients in a single, time-limited pre- and posttelenursing analysis [ 18 ]. A study by Schuelke et al [ 18 ] revealed a 6.2% increase in “communication with meds” and 12.7% increase in “communication with nursing” domain scores; other HCAHPS domains were not evaluated. This research builds upon the promising work of Schuelke et al [ 18 ], evaluating the impact of an ACTN program on several units with a much larger cohort of patients using a staggered rollout and comparing all HCAHPS domains between telenursing and nontelenursing cohorts within the same time frame and in the same units.

By conducting granular HCAHPS analyses, we identified what we believed to be a time sequence variability in that units that rolled out in phase 1 performed considerably stronger in HCAHPS impacts than units that rolled out in later phases. An explanation for this sequence effect might be that some later adopters had less potential for high effect size, given that the first 4 units of the rollout were specifically chosen for their staffing problems compared to later units. ACTN support might have augmented the staffing support to such a degree that allowed the impacts of the program to be more salient. An alternative explanation is that the early adopters and promoters tend to have greater diffusion uptake, greater saturation and adoptability, and greater impacts compared to late adopters or those resistant to adoption [ 19 , 20 ]. Our anecdotal evidence suggests that early adopters might have wanted the telenursing program to succeed; therefore, they applied consistent implementation practices to ensure success. Adopters in later stages were more aware of barriers and potential downsides and might have been more ambivalent about telenursing and, therefore, less likely to modify their behaviors to promote the telenursing program’s success.

Another interesting finding was that the ACTN program seemed to be effective for both medical and surgical units of all specialties. Phase 1 was a mix of medical and surgical units; however, all units experienced increases in scores. Phases 2 and 3 experienced mixed results, without a clear lead for one specialty over the other. This may suggest that ACTN programs are broadly applicable across acute settings and that success depends most crucially on the need and desire of unit leaders.

Our time of day discharge findings showed only a few quantitative positive efficiencies. However, our discharge duration analysis and nursing experience survey results showed that ACTN has major time-saving benefits for nurses, suggesting a discrepancy between perceived and actual time savings versus time-of-day discharge savings. One explanation for this discrepancy may be that many factors beyond nursing impact the time of the day a patient is discharged; therefore, while the bedside nurses’ time is saved, the remaining discharge processes beyond nurses remain unaffected. Specifically, there are 3 segments of time during discharge processes: (1) the time for the discharge order and medication reconciliation [ 21 ] to the time the after-visit summary (AVS) is populated and printed [ 22 ]; (2) the time the AVS is completed and printed to the time the discharge instructions are provided; and (3) the time from providing the discharge instructions to the actual discharge ( Figure 3 ). Notably, telenurses’ involvement is currently limited to only the second segment of time. Specifically, telenurses’ involvement is not initiated until the AVS is printed by the nurse, which means that telenurses cannot positively impact any discharge activity that occurs between the time the discharge order is written and the time the AVS is printed. However, there are inefficiencies and bottlenecks in discharge processes that occur well before the AVS is printed [ 23 , 24 ]. For instance, the discharging physician may write a conditional discharge order early in the morning, listing conditions that cannot be fulfilled within a few hours or it may take bedside nursing longer than anticipated time to print the AVS.

To create a wider cascade effect for positively impacting the discharge processes for all segments of time, we are currently trying to obtain greater transparency through EHR reporting in what occurs for segments 1 and 3. For instance, at present, we know that at least 2 hospitals in our 8-hospital system have high incidence rates of conditional discharge orders that should be reduced. One hospital anecdotally reports that the discharging physician identifies incorrect pharmacies, which requires a nurse to send the scripts back to the discharging pharmacist to reconcile before discharge education can occur [ 25 ]; however, the prevalence and location of these issues remain speculative. Segment 3 is a black box of time [ 26 ]—the time it takes for hospital transport or an ambulance to arrive and move the patient to their destination and the time it takes for the family to pick up the patient. All these factors impact the discharge processes and need to be fully elucidated, explored, and streamlined. Furthermore, we hope to facilitate processes that enable telenurses to print the AVS, to remove the dependency on bedside nurses to begin the discharge education process.

Limitations

This study has several noteworthy limitations. First, the study was conducted in 1 health system and the results may not be generalizable to other settings with different patient populations, processes, and implementation strategies [ 27 ]. Second, in this study, we did not control for other factors that could impact patient and provider satisfaction as well as discharge times; telenursing can only improve upon one component in a complex set of factors limiting discharge efficiency and satisfaction outcomes. Finally, participating nurses were aware of the ongoing study, and this knowledge might have affected their behavior [ 28 ].

Future Directions

After the completion of this pilot study, the ACTN admission and discharge program has been rolled out to pilot medical units and all surgical and observation units. Our rationale for expansion rested on the premise that nursing experience is important to maintain and strengthen, particularly at a time when turnover is high in the health care industry in general. It is important to reduce staff inefficiencies in workload as a means of preserving or strengthening organizational morale and cost saving. Because our nursing experience findings for the ACTN program heavily supported the program, this served as the primary motivation for expansion. The nursing experience findings, coupled with the findings related to time-savings in discharge education and modest improvement, though not negative, in the HCAHPS findings for the ACTN program compared to the nontelenursing cohort, further supported expansion.

The initial scope for expansion included a complete system-wide implementation for all admissions and discharges. Furthermore, we are planning to expand the ACTN program beyond admissions and discharges. Responsive to qualitative feedback reported earlier, the next phase of the ACTN program will add safeguards on high-risk medications by having telenurses conduct double-checks, skin assessments, hourly rounding assistance, and auditing of safety functions and educational activities. These activities were chosen because they are time-intensive for nursing staff on the patient floors. Additional support in these areas would be a staff morale booster in addition to improved efficiencies for bedside nursing. Conducting hourly rounding using the ACTN program will require more time and resources; however, conducting high-quality, uninterrupted hourly rounds is known to be effective at improving patient safety and patient experience outcomes [ 29 ]. Therefore, we suspect that the ACTN program will have some positive impacts if rounds are consistently conducted, even if conducted virtually.

In addition, the ACTNs have been motivating other specialties to adopt or consider a similar program as the ACTN program to support stretched staffing. These specialties include respiratory care, in which virtual support can quickly identify patients in need of intensive on-site support; pharmacy, in which direct communication with staff on medications and patient training can happen through virtual means; infection control, in which room environments can be reviewed through virtual audits, moving quickly from floor to floor; and guest relations and spiritual care, in which patients can be visited virtually upon patient request. Furthermore, physicians who wish to either virtually enter inpatient rooms during their clinic days or from home can quickly drop in to see patients using the virtual program. For these groups to further develop advanced inpatient telemedicine programs, additional technology will be required, including cameras that can zoom into various portions of the room and advanced sound capabilities. Future work could expand programs similar to ACTN to specialties such as respiratory therapy, pharmacy, infection prevention, and spiritual care.

Conclusions

This study provides preliminary evidence suggesting that telenursing may effectively address nursing shortages in acute care settings and positively impact patient and provider satisfaction as well as admission and discharge times. More work is needed to validate the findings in other settings, use other satisfaction metrics, and investigate the impact of telenursing on the quality of care and cost.

Acknowledgments

The authors would like to thank Jacob M Kolman, MA, ISMPP CMPP, senior scientific writer, Houston Methodist Academic Institute, for the critical review and for providing formatting feedback on this manuscript. The authors would also like to thank Amir Hossein Javid for his help with statistical analysis.

Data Availability

Data sharing is not applicable as no data sets were generated during this study.

Authors' Contributions

All authors were involved in the conceptualization, review and approval, and writing of the manuscript. LR, BJ, MG, RS, SK, and MH were extensively involved in the implementation of the project. BJ, MH, SK, and MG conducted the training. SK and XL conducted the analyses. CRB wrote and edited the manuscript, inserted and refined the citations, and provided critical feedback during implementation and analyses. CRB and FS were involved in all stages of writing and publication. All authors meaningfully contributed to the drafting, writing, brainstorming, executing, finalizing, and approving of the manuscript.

Conflicts of Interest

None declared.

Additional outcome information for Hospital Consumer Assessment of Health Care Providers and Systems, time of day discharges, and discharge education processes.

• Greiwe J. Telemedicine lessons learned during the COVID-19 pandemic. Curr Allergy Asthma Rep. Jan 21, 2022;22(1):1-5. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Doraiswamy S, Abraham A, Mamtani R, Cheema S. Use of telehealth during the COVID-19 pandemic: scoping review. J Med Internet Res. Dec 01, 2020;22(12):e24087. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ekeland AG, Bowes A, Flottorp S. Effectiveness of telemedicine: a systematic review of reviews. Int J Med Inform. Nov 2010;79(11):736-771. [ CrossRef ] [ Medline ]
• Nanda M, Sharma R. A review of patient satisfaction and experience with telemedicine: a virtual solution during and beyond COVID-19 pandemic. Telemed J E Health. Dec 01, 2021;27(12):1325-1331. [ CrossRef ] [ Medline ]
• Yu X, Bayram A. Managing capacity for virtual and office appointments in chronic care. Health Care Manag Sci. Dec 2021;24(4):742-767. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Laskowski ER, Johnson SE, Shelerud RA, Lee JA, Rabatin AE, Driscoll SW, et al. The telemedicine musculoskeletal examination. Mayo Clin Proc. Aug 2020;95(8):1715-1731. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hughes L, Petrella A, Phillips N, Taylor RM. Virtual care and the impact of COVID-19 on nursing: a single centre evaluation. J Adv Nurs. Feb 2022;78(2):498-509. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pulse on the nation’s nurses survey series: COVID-19 two-year impact assessment survey. American Nurses Foundation. Mar 1, 2022. URL: https:/​/www.​nursingworld.org/​~4a2260/​contentassets/​872ebb13c63f44f6b11a1bd0c74907c9/​covid-19-two-year-impact-assessment-written-report-final.​pdf [accessed 2023-04-14]
• Abraham C, Jensen C, Rossiter L, Dittman Hale D. Telenursing and remote patient monitoring in cardiovascular health. Telemed J E Health. Sep 08, 2023. (forthcoming). [ CrossRef ] [ Medline ]
• Rouleau G, Gagnon M, Côté J, Payne-Gagnon J, Hudson E, Dubois CA. Impact of information and communication technologies on nursing care: results of an overview of systematic reviews. J Med Internet Res. Apr 25, 2017;19(4):e122. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Moy AJ, Schwartz JM, Chen R, Sadri S, Lucas E, Cato KD, et al. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc. Apr 23, 2021;28(5):998-1008. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hehman MC, Fontenot NM, Drake GK, Musgrove RS. Leveraging digital technology in nursing. Health Emerg Disaster Nurs. 2023;10:41-45. [ CrossRef ]
• Schwartz RL, Hamlin SK, Vozzella GM, Randle LN, Klahn S, Maris GJ, et al. Utilizing telenursing to supplement acute care nursing in an era of workforce shortages: a feasibility pilot. Comput Inform Nurs. Feb 01, 2024;42(2):151-157. [ CrossRef ] [ Medline ]
• Cleary PD. Evolving concepts of patient-centered care and the assessment of patient care experiences: optimism and opposition. J Health Polit Policy Law. Aug 28, 2016;41(4):675-696. [ CrossRef ] [ Medline ]
• CAHPS hospital survey. Agency for Healthcare Research and Quality. URL: https://www.ahrq.gov/cahps/surveys-guidance/hospital/index.html [accessed 2023-10-12]
• Strauss A, Corbin JM. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Thousand Oaks, CA. Sage Publications; 1990.
• Hennink MM, Kaiser BN, Marconi VC. Code saturation versus meaning saturation: how many interviews are enough? Qual Health Res. Mar 2017;27(4):591-608. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schuelke S, Aurit S, Connot N, Denney S. Virtual nursing: the new reality in quality care. Nurs Adm Q. 2019;43(4):322-328. [ CrossRef ] [ Medline ]
• Rogers E. Diffusion of Innovations. New York, NY. Free Press of Glenco; 1962.
• Mohammadi MM, Poursaberi R, Salahshoor MR. Evaluating the adoption of evidence-based practice using Rogers's diffusion of innovation theory: a model testing study. Health Promot Perspect. 2018;8(1):25-32. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fernandes BD, Almeida PH, Foppa AA, Sousa CT, Ayres LR, Chemello C. Pharmacist-led medication reconciliation at patient discharge: a scoping review. Res Social Adm Pharm. May 2020;16(5):605-613. [ CrossRef ] [ Medline ]
• Federman A, Sarzynski E, Brach C, Francaviglia P, Jacques J, Jandorf L, et al. Challenges optimizing the after visit summary. Int J Med Inform. Dec 2018;120:14-19. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bristol AA, Elmore CE, Weiss ME, Barry LA, Iacob E, Johnson EP, et al. Mixed-methods study examining family carers' perceptions of the relationship between intrahospital transitions and patient readiness for discharge. BMJ Qual Saf. Aug 2023;32(8):447-456. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Piniella NR, Fuller TE, Smith L, Salmasian H, Yoon CS, Lipsitz SR, et al. Early expected discharge date accuracy during hospitalization: a multivariable analysis. J Med Syst. May 12, 2023;47(1):63. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schnipper JL, Reyes Nieva H, Yoon C, Mallouk M, Mixon AS, Rennke S, et al. What works in medication reconciliation: an on-treatment and site analysis of the MARQUIS2 study. BMJ Qual Saf. Aug 2023;32(8):457-469. [ CrossRef ] [ Medline ]
• Abu HO, Anatchkova MD, Erskine NA, Lewis J, McManus DD, Kiefe CI, et al. Are we "missing the big picture" in transitions of care? Perspectives of healthcare providers managing patients with unplanned hospitalization. Appl Nurs Res. Dec 2018;44:60-66. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Leung L. Validity, reliability, and generalizability in qualitative research. J Family Med Prim Care. 2015;4(3):324-327. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Merrett F. Reflections on the Hawthorne effect. Educ Psychol. Jan 2006;26(1):143-146. [ CrossRef ]
• Mitchell M, Lavenberg JG, Trotta RL, Umscheid CA. Hourly rounding to improve nursing responsiveness: a systematic review. J Nurs Adm. Sep 2014;44(9):462-472. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by T de Azevedo Cardoso, G Eysenbach; submitted 06.11.23; peer-reviewed by C Jensen; comments to author 08.12.23; revised version received 16.01.24; accepted 17.02.24; published 04.04.24.

©Courtenay R Bruce, Steve Klahn, Lindsay Randle, Xin Li, Kelkar Sayali, Barbara Johnson, Melissa Gomez, Meagan Howard, Roberta Schwartz, Farzan Sasangohar. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.