types of descriptive research in education

Using Science to Inform Educational Practices

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments. The main categories of psychological research are descriptive, correlational, and experimental research. Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions.

Research studies that do not test specific relationships between variables are called  descriptive studies . For this method, the research question or hypothesis can be about a single variable (e.g., How accurate are people’s first impressions?) or can be a broad and exploratory question (e.g., What is it like to be a working mother diagnosed with depression?). The variable of the study is measured and reported without any further relationship analysis. A researcher might choose this method if they only needed to report information, such as a tally, an average, or a list of responses. Descriptive research can answer interesting and important questions, but what it cannot do is answer questions about relationships between variables.

Video 2.4.1.  Descriptive Research Design  provides explanation and examples for quantitative descriptive research. A closed-captioned version of this video is available here .

Descriptive research is distinct from  correlational research , in which researchers formally test whether a relationship exists between two or more variables.  Experimental research  goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about causal relationships between variables. We will discuss each of these methods more in-depth later.

Table 2.4.1. Comparison of research design methods

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• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/edpsy/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/adolescent/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Educational Psychology Copyright © 2020 by Nicole Arduini-Van Hoose is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Educational Research Basics by Del Siegle

Types of Research

How do we know something exists? There are a numbers of ways of knowing…

• -Sensory Experience
• -Agreement with others
• -Expert Opinion
• -Scientific Method (we’re using this one)

The Scientific Process (replicable)

• Identify a problem
• Clarify the problem
• Determine what data would help solve the problem
• Organize the data
• Interpret the results

General Types of Educational Research

• Descriptive — survey, historical, content analysis, qualitative (ethnographic, narrative, phenomenological, grounded theory, and case study)
• Associational — correlational, causal-comparative
• Intervention — experimental, quasi-experimental, action research (sort of)

Researchers Sometimes Have a Category Called Group Comparison

• Ex Post Facto (Causal-Comparative): GROUPS ARE ALREADY FORMED
• Experimental: RANDOM ASSIGNMENT OF INDIVIDUALS
• Quasi-Experimental: RANDOM ASSIGNMENT OF GROUPS (oversimplified, but fine for now)

General Format of a Research Publication

• Background of the Problem (ending with a problem statement) — Why is this important to study? What is the problem being investigated?
• Review of Literature — What do we already know about this problem or situation?
• Methodology (participants, instruments, procedures) — How was the study conducted? Who were the participants? What data were collected and how?
• Analysis — What are the results? What did the data indicate?
• Results — What are the implications of these results? How do they agree or disagree with previous research? What do we still need to learn? What are the limitations of this study?

Del Siegle, PhD [email protected]

Last modified 6/18/2019

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• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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Home Market Research

Descriptive Research: Definition, Characteristics, Methods + Examples

Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.

The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.

What is descriptive research?

Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.

The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.

Characteristics of descriptive research

The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Applications of descriptive research with examples

A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:

• Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
• Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
• Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
• Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
• Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

• Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
• Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
• Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
• Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.

Descriptive research methods

There are three distinctive methods to conduct descriptive research. They are:

Observational method

The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.

A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .

Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.

Case study method

Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.

Survey research

In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.

Examples of descriptive research

Some examples of descriptive research are:

• A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
• Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.

Some other research problems and research questions that can lead to descriptive research are:

• Market researchers want to observe the habits of consumers.
• A company wants to evaluate the morale of its staff.
• A school district wants to understand if students will access online lessons rather than textbooks.
• To understand if its wellness questionnaire programs enhance the overall health of the employees.

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Descriptive Research: Methods And Examples

A research project always begins with selecting a topic. The next step is for researchers to identify the specific areas…

A research project always begins with selecting a topic. The next step is for researchers to identify the specific areas of interest. After that, they tackle the key component of any research problem: how to gather enough quality information. If we opt for a descriptive research design we have to ask the correct questions to access the right information. 

For instance, researchers may choose to focus on why people invest in cryptocurrency, knowing how dynamic the market is rather than asking why the market is so shaky. These are completely different questions that require different research approaches. Adopting the descriptive method can help capitalize on trends the information reveals. Descriptive research examples show the thorough research involved in such a study. 

Get to know more about descriptive research design .

Descriptive Research Meaning

Features of descriptive research design, types of descriptive research, descriptive research methods, applications of descriptive research, descriptive research examples.

A descriptive method of research is one that describes the characteristics of a phenomenon, situation or population. It uses quantitative and qualitative approaches to describe problems with little relevant information. Descriptive research accurately describes a research problem without asking why a particular event happened. By researching market patterns, the descriptive method answers how patterns change, what caused the change and when the change occurred, instead of dwelling on why the change happened.

Descriptive research refers to questions, study design and analysis of data conducted on a particular topic. It is a strictly observational research methodology with no influence on variables. Some distinctive features of descriptive research are:

• It’s a research method that collects quantifiable information for statistical analysis of a sample. It’s a quantitative market research tool that can analyze the nature of a demographic
• In a descriptive method of research , the nature of research study variables is determined with observation, without influence from the researcher
• Descriptive research is cross-sectional and different sections of a group can be studied
• The analyzed data is collected and serves as information for other search techniques. In this way, a descriptive research design becomes the basis of further research

To understand the descriptive research meaning , data collection methods, examples and application, we need a deeper understanding of its features.

Different ways of approaching the descriptive method help break it down further. Let’s look at the different types of descriptive research :

Descriptive Survey

Descriptive normative survey, descriptive status.

This type of research quantitatively describes real-life situations. For example, to understand the relation between wages and performance, research on employee salaries and their respective performances can be conducted.

Descriptive Analysis

This technique analyzes a subject further. Once the relation between wages and performance has been established, an organization can further analyze employee performance by researching the output of those who work from an office with those who work from home.

Descriptive Classification

Descriptive classification is mainly used in the field of biological science. It helps researchers classify species once they have studied the data collected from different search stations.

Descriptive Comparative

Comparing two variables can show if one is better than the other. Doing this through tests or surveys can reveal all the advantages and disadvantages associated with the two. For example, this technique can be used to find out if paper ballots are better than electronic voting devices.

Correlative Survey

The researcher has to effectively interpret the area of the problem and then decide the appropriate technique of descriptive research design . 

A researcher can choose one of the following methods to solve research problems and meet research goals:

Observational Method

With this method, a researcher observes the behaviors, mannerisms and characteristics of the participants. It is widely used in psychology and market research and does not require the participants to be involved directly. It’s an effective method and can be both qualitative and quantitative for the sheer volume and variety of data that is generated.

Survey Research

It’s a popular method of data collection in research. It follows the principle of obtaining information quickly and directly from the main source. The idea is to use rigorous qualitative and quantitative research methods and ask crucial questions essential to the business for the short and long term.

Case Study Method

Case studies tend to fall short in situations where researchers are dealing with highly diverse people or conditions. Surveys and observations are carried out effectively but the time of execution significantly differs between the two. 

There are multiple applications of descriptive research design but executives must learn that it’s crucial to clearly define the research goals first. Here’s how organizations use descriptive research to meet their objectives:

• As a tool to analyze participants : It’s important to understand the behaviors, traits and patterns of the participants to draw a conclusion about them. Close-ended questions can reveal their opinions and attitudes. Descriptive research can help understand the participant and assist in making strategic business decisions
• Designed to measure data trends : It’s a statistically capable research design that, over time, allows organizations to measure data trends. A survey can reveal unfavorable scenarios and give an organization the time to fix unprofitable moves
• Scope of comparison: Surveys and research can allow an organization to compare two products across different groups. This can provide a detailed comparison of the products and an opportunity for the organization to capitalize on a large demographic
• Conducting research at any time: An analysis can be conducted at any time and any number of variables can be evaluated. It helps to ascertain differences and similarities

Descriptive research is widely used due to its non-invasive nature. Quantitative observations allow in-depth analysis and a chance to validate any existing condition.

There are several different descriptive research examples that highlight the types, applications and uses of this research method. Let’s look at a few:

• Before launching a new line of gym wear, an organization chose more than one descriptive method to gather vital information. Their objective was to find the kind of gym clothes people like wearing and the ones they would like to see in the market. The organization chose to conduct a survey by recording responses in gyms, sports shops and yoga centers. As a second method, they chose to observe members of different gyms and fitness institutions. They collected volumes of vital data such as color and design preferences and the amount of money they’re willing to spend on it .
• To get a good idea of people’s tastes and expectations, an organization conducted a survey by offering a new flavor of the sauce and recorded people’s responses by gathering data from store owners. This let them understand how people reacted, whether they found the product reasonably priced, whether it served its purpose and their overall general preferences. Based on this, the brand tweaked its core marketing strategies and made the product widely acceptable .

Descriptive research can be used by an organization to understand the spending patterns of customers as well as by a psychologist who has to deal with mentally ill patients. In both these professions, the individuals will require thorough analyses of their subjects and large amounts of crucial data to develop a plan of action.

Every method of descriptive research can provide information that is diverse, thorough and varied. This supports future research and hypotheses. But although they can be quick, cheap and easy to conduct in the participants’ natural environment, descriptive research design can be limited by the kind of information it provides, especially with case studies. Trying to generalize a larger population based on the data gathered from a smaller sample size can be futile. Similarly, a researcher can unknowingly influence the outcome of a research project due to their personal opinions and biases. In any case, a manager has to be prepared to collect important information in substantial quantities and have a balanced approach to prevent influencing the result. 

Harappa’s Thinking Critically program harnesses the power of information to strengthen decision-making skills. It’s a growth-driven course for young professionals and managers who want to be focused on their strategies, outperform targets and step up to assume the role of leader in their organizations. It’s for any professional who wants to lay a foundation for a successful career and business owners who’re looking to take their organizations to new heights.

Explore Harappa Diaries to learn more about topics such as Main Objectives of Research , Examples of Experimental Research , Methods Of Ethnographic Research , and How To Use Blended Learning to upgrade your knowledge and skills.

The Art of Sophisticated Quantitative Description in Higher Education Research

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• First Online: 23 February 2022
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• Daniel Klasik 3 &
• William Zahran 3  

Part of the book series: Higher Education: Handbook of Theory and Research ((HATR,volume 37))

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While the emphasis on causal research in education has become increasingly important in recent years, thoughtful, descriptive analysis remains necessary for providing the conceptual grounding for experimental and quasi-experimental research and understanding our world. Sophisticated quantitative description is an approach to research that does not attempt to determine a causal impact. Instead, its purpose is to critically analyze and present data using purposeful methods to build a theory-driven story about a phenomenon that future research can investigate further. Sophisticated description can offer new ways to look at problems of research and practice, provide context and explanation for causal findings, or open new avenues of research. This chapter defines sophisticated quantitative description and provides an overview of its uses in higher education research. It outlines the numerous goals of sophisticated descriptive research and offers potential methods and approaches for conducting sophisticated description. Exemplars and discussion of published sophisticated descriptive research from the higher education literature are included throughout. The chapter concludes with an application of sophisticated description for analyzing college application behavior in the United States using social network analysis.

Nicholas Hillman was the Associate Editor for this chapter.

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Acknowledgments

The social network analysis of students’ college application choices described in this chapter was supported by a National Academy of Education/Spencer Foundation postdoctoral fellowship.

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Klasik, D., Zahran, W. (2022). The Art of Sophisticated Quantitative Description in Higher Education Research. In: Perna, L.W. (eds) Higher Education: Handbook of Theory and Research. Higher Education: Handbook of Theory and Research, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-030-76660-3_12

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Scientific Research in Education (2002)

Chapter: 5 designs for the conduct of scientific research in education, 5 designs for the conduct of scientific research in education.

The salient features of education delineated in Chapter 4 and the guiding principles of scientific research laid out in Chapter 3 set boundaries for the design and conduct of scientific education research. Thus, the design of a study (e.g., randomized experiment, ethnography, multiwave survey) does not itself make it scientific. However, if the design directly addresses a question that can be addressed empirically, is linked to prior research and relevant theory, is competently implemented in context, logically links the findings to interpretation ruling out counterinterpretations, and is made accessible to scientific scrutiny, it could then be considered scientific. That is: Is there a clear set of questions underlying the design? Are the methods appropriate to answer the questions and rule out competing answers? Does the study take previous research into account? Is there a conceptual basis? Are data collected in light of local conditions and analyzed systematically? Is the study clearly described and made available for criticism? The more closely aligned it is with these principles, the higher the quality of the scientific study. And the particular features of education require that the research process be explicitly designed to anticipate the implications of these features and to model and plan accordingly.

RESEARCH DESIGN

Our scientific principles include research design—the subject of this chapter—as but one aspect of a larger process of rigorous inquiry. How-

ever, research design (and corresponding scientific methods) is a crucial aspect of science. It is also the subject of much debate in many fields, including education. In this chapter, we describe some of the most frequently used and trusted designs for scientifically addressing broad classes of research questions in education.

In doing so, we develop three related themes. First, as we posit earlier, a variety of legitimate scientific approaches exist in education research. Therefore, the description of methods discussed in this chapter is illustrative of a range of trusted approaches; it should not be taken as an authoritative list of tools to the exclusion of any others. 1 As we stress in earlier chapters, the history of science has shown that research designs evolve, as do the questions they address, the theories they inform, and the overall state of knowledge.

Second, we extend the argument we make in Chapter 3 that designs and methods must be carefully selected and implemented to best address the question at hand. Some methods are better than others for particular purposes, and scientific inferences are constrained by the type of design employed. Methods that may be appropriate for estimating the effect of an educational intervention, for example, would rarely be appropriate for use in estimating dropout rates. While researchers—in education or any other field—may overstate the conclusions from an inquiry, the strength of scientific inference must be judged in terms of the design used to address the question under investigation. A comprehensive explication of a hierarchy of appropriate designs and analytic approaches under various conditions would require a depth of treatment found in research methods textbooks. This is not our objective. Rather, our goal is to illustrate that among available techniques, certain designs are better suited to address particular kinds of questions under particular conditions than others.

Third, in order to generate a rich source of scientific knowledge in education that is refined and revised over time, different types of inquiries and methods are required. At any time, the types of questions and methods depend in large part on an accurate assessment of the overall state of knowl-

edge and professional judgment about how a particular line of inquiry could advance understanding. In areas with little prior knowledge, for example, research will generally need to involve careful description to formulate initial ideas. In such situations, descriptive studies might be undertaken to help bring education problems or trends into sharper relief or to generate plausible theories about the underlying structure of behavior or learning. If the effects of education programs that have been implemented on a large scale are to be understood, however, investigations must be designed to test a set of causal hypotheses. Thus, while we treat the topic of design in this chapter as applying to individual studies, research design has a broader quality as it relates to lines of inquiry that develop over time.

While a full development of these notions goes considerably beyond our charge, we offer this brief overview to place the discussion of methods that follows into perspective. Also, in the concluding section of this chapter, we make a few targeted suggestions for the kinds of work we believe are most needed in education research to make further progress toward robust knowledge.

TYPES OF RESEARCH QUESTIONS

In discussing design, we have to be true to our admonition that the research question drives the design, not vice versa. To simplify matters, the committee recognized that a great number of education research questions fall into three (interrelated) types: description—What is happening? cause—Is there a systematic effect? and process or mechanism—Why or how is it happening?

The first question—What is happening?—invites description of various kinds, so as to properly characterize a population of students, understand the scope and severity of a problem, develop a theory or conjecture, or identify changes over time among different educational indicators—for example, achievement, spending, or teacher qualifications. Description also can include associations among variables, such as the characteristics of schools (e.g., size, location, economic base) that are related to (say) the provision of music and art instruction. The second question is focused on establishing causal effects: Does x cause y ? The search for cause, for example,

can include seeking to understand the effect of teaching strategies on student learning or state policy changes on district resource decisions. The third question confronts the need to understand the mechanism or process by which x causes y . Studies that seek to model how various parts of a complex system—like U.S. education—fit together help explain the conditions that facilitate or impede change in teaching, learning, and schooling. Within each type of question, we separate the discussion into subsections that show the use of different methods given more fine-grained goals and conditions of an inquiry.

Although for ease of discussion we treat these types of questions separately, in practice they are closely related. As our examples show, within particular studies, several kinds of queries can be addressed. Furthermore, various genres of scientific education research often address more than one of these types of questions. Evaluation research—the rigorous and systematic evaluation of an education program or policy—exemplifies the use of multiple questions and corresponding designs. As applied in education, this type of scientific research is distinguished from other scientific research by its purpose: to contribute to program improvement (Weiss, 1998a). Evaluation often entails an assessment of whether the program caused improvements in the outcome or outcomes of interest (Is there a systematic effect?). It also can involve detailed descriptions of the way the program is implemented in practice and in what contexts ( What is happening? ) and the ways that program services influence outcomes (How is it happening?).

Throughout the discussion, we provide several examples of scientific education research, connecting them to scientific principles ( Chapter 3 ) and the features of education ( Chapter 4 ). We have chosen these studies because they align closely with several of the scientific principles. These examples include studies that generate hypotheses or conjectures as well as those that test them. Both tasks are essential to science, but as a general rule they cannot be accomplished simultaneously.

Moreover, just as we argue that the design of a study does not itself make it scientific, an investigation that seeks to address one of these questions is not necessarily scientific either. For example, many descriptive studies—however useful they may be—bear little resemblance to careful scientific study. They might record observations without any clear conceptual viewpoint, without reproducible protocols for recording data, and so

forth. Again, studies may be considered scientific by assessing the rigor with which they meet scientific principles and are designed to account for the context of the study.

Finally, we have tended to speak of research in terms of a simple dichotomy— scientific or not scientific—but the reality is more complicated. Individual research projects may adhere to each of the principles in varying degrees, and the extent to which they meet these goals goes a long way toward defining the scientific quality of a study. For example, while all scientific studies must pose clear questions that can be investigated empirically and be grounded in existing knowledge, more rigorous studies will begin with more precise statements of the underlying theory driving the inquiry and will generally have a well-specified hypothesis before the data collection and testing phase is begun. Studies that do not start with clear conceptual frameworks and hypotheses may still be scientific, although they are obviously at a more rudimentary level and will generally require follow-on study to contribute significantly to scientific knowledge.

Similarly, lines of research encompassing collections of studies may be more or less productive and useful in advancing knowledge. An area of research that, for example, does not advance beyond the descriptive phase toward more precise scientific investigation of causal effects and mechanisms for a long period of time is clearly not contributing as much to knowledge as one that builds on prior work and moves toward more complete understanding of the causal structure. This is not to say that descriptive work cannot generate important breakthroughs. However, the rate of progress should—as we discuss at the end of this chapter—enter into consideration of the support for advanced lines of inquiry. The three classes of questions we discuss in the remainder of this chapter are ordered in a way that reflects the sequence that research studies tend to follow as well as their interconnected nature.

WHAT IS HAPPENING?

Answers to “What is happening?” questions can be found by following Yogi Berra’s counsel in a systematic way: if you want to know what’s going on, you have to go out and look at what is going on. Such inquiries are descriptive. They are intended to provide a range of information from

documenting trends and issues in a range of geopolitical jurisdictions, populations, and institutions to rich descriptions of the complexities of educational practice in a particular locality, to relationships among such elements as socioeconomic status, teacher qualifications, and achievement.

Estimates of Population Characteristics

Descriptive scientific research in education can make generalizable statements about the national scope of a problem, student achievement levels across the states, or the demographics of children, teachers, or schools. Methods that enable the collection of data from a randomly selected sample of the population provide the best way of addressing such questions. Questionnaires and telephone interviews are common survey instruments developed to gather information from a representative sample of some population of interest. Policy makers at the national, state, and sometimes district levels depend on this method to paint a picture of the educational landscape. Aggregate estimates of the academic achievement level of children at the national level (e.g., National Center for Education Statistics [NCES], National Assessment of Educational Progress [NAEP]), the supply, demand, and turnover of teachers (e.g., NCES Schools and Staffing Survey), the nation’s dropout rates (e.g., NCES Common Core of Data), how U.S. children fare on tests of mathematics and science achievement relative to children in other nations (e.g., Third International Mathematics and Science Study) and the distribution of doctorate degrees across the nation (e.g., National Science Foundation’s Science and Engineering Indicators) are all based on surveys from populations of school children, teachers, and schools.

To yield credible results, such data collection usually depends on a random sample (alternatively called a probability sample) of the target population. If every observation (e.g., person, school) has a known chance of being selected into the study, researchers can make estimates of the larger population of interest based on statistical technology and theory. The validity of inferences about population characteristics based on sample data depends heavily on response rates, that is, the percentage of those randomly selected for whom data are collected. The measures used must have known reliability—that is, the extent to which they reproduce results. Finally, the value of a data collection instrument hinges not only on the

sampling method, participation rate, and reliability, but also on their validity: that the questionnaire or survey items measure what they are supposed to measure.

The NAEP survey tracks national trends in student achievement across several subject domains and collects a range of data on school, student, and teacher characteristics (see Box 5-1 ). This rich source of information enables several kinds of descriptive work. For example, researchers can estimate the average score of eighth graders on the mathematics assessment (i.e., measures of central tendency) and compare that performance to prior years. Part of the study we feature (see below) about college women’s career choices featured a similar estimation of population characteristics. In that study, the researchers developed a survey to collect data from a representative sample of women at the two universities to aid them in assessing the generalizability of their findings from the in-depth studies of the 23 women.

Simple Relationships

The NAEP survey also illustrates how researchers can describe patterns of relationships between variables. For example, NCES reports that in 2000, eighth graders whose teachers majored in mathematics or mathematics education scored higher, on average, than did students whose teachers did not major in these fields (U.S. Department of Education, 2000). This finding is the result of descriptive work that explores the correlation between variables: in this case, the relationship between student mathematics performance and their teachers’ undergraduate major.

Such associations cannot be used to infer cause. However, there is a common tendency to make unsubstantiated jumps from establishing a relationship to concluding cause. As committee member Paul Holland quipped during the committee’s deliberations, “Casual comparisons inevitably invite careless causal conclusions.” To illustrate the problem with drawing causal inferences from simple correlations, we use an example from work that compares Catholic schools to public schools. We feature this study later in the chapter as one that competently examines causal mechanisms. Before addressing questions of mechanism, foundational work involved simple correlational results that compared the performance of Catholic high school students on standardized mathematics tests with their

counterparts in public schools. These simple correlations revealed that average mathematics achievement was considerably higher for Catholic school students than for public school students (Bryk, Lee, and Holland, 1993). However, the researchers were careful not to conclude from this analysis that attending a Catholic school causes better student outcomes, because there are a host of potential explanations (other than attending a Catholic school) for this relationship between school type and achievement. For example, since Catholic schools can screen children for aptitude, they may have a more able student population than public schools at the outset. (This is an example of the classic selectivity bias that commonly threatens the validity of causal claims in nonrandomized studies; we return to this issue in the next section.) In short, there are other hypotheses that could explain the observed differences in achievement between students in different sectors that must be considered systematically in assessing the potential causal relationship between Catholic schooling and student outcomes.

Descriptions of Localized Educational Settings

In some cases, scientists are interested in the fine details (rather than the distribution or central tendency) of what is happening in a particular organization, group of people, or setting. This type of work is especially important when good information about the group or setting is non-existent or scant. In this type of research, then, it is important to obtain first-hand, in-depth information from the particular focal group or site. For such purposes, selecting a random sample from the population of interest may not be the proper method of choice; rather, samples may be purposively selected to illuminate phenomena in depth. 2 For example, to better understand a high-achieving school in an urban setting with children of predominantly low socioeconomic status, a researcher might conduct a detailed case study or an ethnographic study (a case study with a focus on culture) of such a school (Yin and White, 1986; Miles and Huberman,

1994). This type of scientific description can provide rich depictions of the policies, procedures, and contexts in which the school operates and generate plausible hypotheses about what might account for its success. Researchers often spend long periods of time in the setting or group in order to understand what decisions are made, what beliefs and attitudes are formed, what relationships are developed, and what forms of success are celebrated. These descriptions, when used in conjunction with causal methods, are often critical to understand such educational outcomes as student achievement because they illuminate key contextual factors.

Box 5-2 provides an example of a study that described in detail (and also modeled several possible mechanisms; see later discussion) a small group of women, half who began their college careers in science and half in what were considered more traditional majors for women. This descriptive part of the inquiry involved an ethnographic study of the lives of 23 first-year women enrolled in two large universities.

Scientific description of this type can generate systematic observations about the focal group or site, and patterns in results may be generalizable to other similar groups or sites or for the future. As with any other method, a scientifically rigorous case study has to be designed to address the research question it addresses. That is, the investigator has to choose sites, occasions, respondents, and times with a clear research purpose in mind and be sensitive to his or her own expectations and biases (Maxwell, 1996; Silverman, 1993). Data should typically be collected from varied sources, by varied methods, and corroborated by other investigators. Furthermore, the account of the case needs to draw on original evidence and provide enough detail so that the reader can make judgments about the validity of the conclusions (Yin, 2000).

Results may also be used as the basis for new theoretical developments, new experiments, or improved measures on surveys that indicate the extent of generalizability. In the work done by Holland and Eisenhart (1990), for example (see Box 5-2 ), a number of theoretical models were developed and tested to explain how women decide to pursue or abandon nontraditional careers in the fields they had studied in college. Their finding that commitment to college life—not fear of competing with men or other hypotheses that had previously been set forth—best explained these decisions was new knowledge. It has been shown in subsequent studies to

generalize somewhat to similar schools, though additional models seem to exist at some schools (Seymour and Hewitt, 1997).

Although such purposively selected samples may not be scientifically generalizable to other locations or people, these vivid descriptions often appeal to practitioners. Scientifically rigorous case studies have strengths and weaknesses for such use. They can, for example, help local decision makers by providing them with ideas and strategies that have promise in their educational setting. They cannot (unless combined with other methods) provide estimates of the likelihood that an educational approach might work under other conditions or that they have identified the right underlying causes. As we argue throughout this volume, research designs can often be strengthened considerably by using multiple methods— integrating the use of both quantitative estimates of population characteristics and qualitative studies of localized context.

Other descriptive designs may involve interviews with respondents or document reviews in a fairly large number of cases, such as 30 school districts or 60 colleges. Cases are often selected to represent a variety of conditions (e.g., urban/rural; east/west; affluent/poor). Such descriptive studies can be longitudinal, returning to the same cases over several years to see how conditions change.

These examples of descriptive work meet the principles of science, and have clearly contributed important insights to the base of scientific knowledge. If research is to be used to answer questions about “what works,” however, it must advance to other levels of scientific investigation such as those considered next.

IS THERE A SYSTEMATIC EFFECT?

Research designs that attempt to identify systematic effects have at their root an intent to establish a cause-and-effect relationship. Causal work is built on both theory and descriptive studies. In other words, the search for causal effects cannot be conducted in a vacuum: ideally, a strong theoretical base as well as extensive descriptive information are in place to provide the intellectual foundation for understanding causal relationships.

The simple question of “does x cause y ?” typically involves several different kinds of studies undertaken sequentially (Holland, 1993). In basic

terms, several conditions must be met to establish cause. Usually, a relationship or correlation between the variables is first identified. 3 Researchers also confirm that x preceded y in time (temporal sequence) and, crucially, that all presently conceivable rival explanations for the observed relationship have been “ruled out.” As alternative explanations are eliminated, confidence increases that it was indeed x that caused y . “Ruling out” competing explanations is a central metaphor in medical research, diagnosis, and other fields, including education, and it is the key element of causal queries (Campbell and Stanley 1963; Cook and Campbell 1979, 1986).

The use of multiple qualitative methods, especially in conjunction with a comparative study of the kind we describe in this section, can be particularly helpful in ruling out alternative explanations for the results observed (Yin, 2000; Weiss, in press). Such investigative tools can enable stronger causal inferences by enhancing the analysis of whether competing explanations can account for patterns in the data (e.g., unreliable measures or contamination of the comparison group). Similarly, qualitative methods can examine possible explanations for observed effects that arise outside of the purview of the study. For example, while an intervention was in progress, another program or policy may have offered participants opportunities similar to, and reinforcing of, those that the intervention provided. Thus, the “effects” that the study observed may have been due to the other program (“history” as the counterinterpretation; see Chapter 3 ). When all plausible rival explanations are identified and various forms of data can be used as evidence to rule them out, the causal claim that the intervention caused the observed effects is strengthened. In education, research that explores students’ and teachers’ in-depth experiences, observes their actions, and documents the constraints that affect their day-to-day activities provides a key source of generating plausible causal hypotheses.

We have organized the remainder of this section into two parts. The first treats randomized field trials, an ideal method when entities being examined can be randomly assigned to groups. Experiments are especially well-suited to situations in which the causal hypothesis is relatively simple. The second describes situations in which randomized field trials are not

feasible or desirable, and showcases a study that employed causal modeling techniques to address a complex causal question. We have distinguished randomized studies from others primarily to signal the difference in the strength with which causal claims can typically be made from them. The key difference between randomized field trials and other methods with respect to making causal claims is the extent to which the assumptions that underlie them are testable. By this simple criterion, nonrandomized studies are weaker in their ability to establish causation than randomized field trials, in large part because the role of other factors in influencing the outcome of interest is more difficult to gauge in nonrandomized studies. Other conditions that affect the choice of method are discussed in the course of the section.

Causal Relationships When Randomization Is Feasible

A fundamental scientific concept in making causal claims—that is, inferring that x caused y —is comparison. Comparing outcomes (e.g., student achievement) between two groups that are similar except for the causal variable (e.g., the educational intervention) helps to isolate the effect of that causal agent on the outcome of interest. 4 As we discuss in Chapter 4 , it is sometimes difficult to retain the sharpness of a comparison in education due to proximity (e.g., a design that features students in one classroom assigned to different interventions is subject to “spillover” effects) or human volition (e.g., teacher, parent, or student decisions to switch to another condition threaten the integrity of the randomly formed groups). Yet, from a scientific perspective, randomized trials (we also use the term “experiment” to refer to causal studies that feature random assignment) are the ideal for establishing whether one or more factors caused change in an outcome because of their strong ability to enable fair comparisons (Campbell and Stanley, 1963; Boruch, 1997; Cook and Payne, in press). Random allocation of students, classrooms, schools—whatever the unit of comparison may be—to different treatment groups assures that these comparison groups are, roughly speaking, equivalent at the time an intervention is introduced (that is, they do not differ systematically on account of hidden

influences) and chance differences between the groups can be taken into account statistically. As a result, the independent effect of the intervention on the outcome of interest can be isolated. In addition, these studies enable legitimate statistical statements of confidence in the results.

The Tennessee STAR experiment (see Chapter 3 ) on class-size reduction is a good example of the use of randomization to assess cause in an education study; in particular, this tool was used to gauge the effectiveness of an intervention. Some policy makers and scientists were unwilling to accept earlier, largely nonexperimental studies on class-size reduction as a basis for major policy decisions in the state. Those studies could not guarantee a fair comparison of children in small versus large classes because the comparisons relied on statistical adjustment rather than on actual construction of statistically equivalent groups. In Tennessee, statistical equivalence was achieved by randomly assigning eligible children and teachers to classrooms of different size. If the trial was properly carried out, 5 this randomization would lead to an unbiased estimate of the relative effect of class-size reduction and a statistical statement of confidence in the results.

Randomized trials are used frequently in the medical sciences and certain areas of the behavioral and social sciences, including prevention studies of mental health disorders (e.g., Beardslee, Wright, Salt, and Drezner, 1997), behavioral approaches to smoking cessation (e.g., Pieterse, Seydel, DeVries, Mudde, and Kok, 2001), and drug abuse prevention (e.g., Cook, Lawrence, Morse, and Roehl, 1984). It would not be ethical to assign individuals randomly to smoke and drink, and thus much of the evidence regarding the harmful effects of nicotine and alcohol comes from descriptive and correlational studies. However, randomized trials that show reductions in health detriments and improved social and behavioral functioning strengthen the causal links that have been established between drug use and adverse health and behavioral outcomes (Moses, 1995; Mosteller, Gilbert, and McPeek, 1980). In medical research, the relative effectiveness of the Salk vaccine (see Lambert and Markel, 2000) and streptomycin (Medical Research Council, 1948) was demonstrated through such trials. We have also learned about which drugs and surgical treatments are useless by depending on randomized controlled experiments (e.g., Schulte et al.,

2001; Gorman et al., 2001; Paradise et al., 1999). Randomized controlled trials are also used in industrial, market, and agricultural research.

Such trials are not frequently conducted in education research (Boruch, De Moya, and Snyder, in press). Nonetheless, it is not difficult to identify good examples in a variety of education areas that demonstrate their feasibility (see Boruch, 1997; Orr, 1999; and Cook and Payne, in press). For example, among the education programs whose effectiveness have been evaluated in randomized trials are the Sesame Street television series (Bogatz and Ball, 1972), peer-assisted learning and tutoring for young children with reading problems (Fuchs, Fuchs, and Kazdan, 1999), and Upward Bound (Myers and Schirm, 1999). And many of these trials have been successfully implemented on a large scale, randomizing entire classrooms or schools to intervention conditions. For numerous examples of trials in which schools, work places, and other entities are the units of random allocation and analysis, see Murray (1998), Donner and Klar (2000), Boruch and Foley (2000), and the Campbell Collaboration register of trials at http://campbell.gse.upenn.edu .

Causal Relationships When Randomization Is Not Feasible

In this section we discuss the conditions under which randomization is not feasible nor desirable, highlight alternative methods for addressing causal questions, and provide an illustrative example. Many nonexperimental methods and analytic approaches are commonly classified under the blanket rubric “quasi-experiment” because they attempt to approximate the underlying logic of the experiment without random assignment (Campbell and Stanley, 1963; Caporaso and Roos, 1973). These designs were developed because social science researchers recognized that in some social contexts (e.g., schools), researchers do not have the control afforded in laboratory settings and thus cannot always randomly assign units (e.g., classrooms).

Quasi-experiments (alternatively called observational studies), 6 for example, sometimes compare groups of interest that exist naturally (e.g.,

existing classes varying in size) rather than assigning them randomly to different conditions (e.g., assigning students to small, medium, or large class size). These studies must attempt to ensure fair comparisons through means other than randomization, such as by using statistical techniques to adjust for background variables that may account for differences in the outcome of interest. For example, researchers might come across schools that vary in the size of their classes and compare the achievement of students in large and small classes, adjusting for other differences among schools and children. If the class size conjecture holds after this adjustment is made, the researchers would expect students in smaller classes to have higher achievement scores than students in larger size classes. If indeed this difference is observed, the causal effect is more plausible.

The plausibility of the researchers’ causal interpretation, however, depends on some strong assumptions. They must assume that their attempts to equate schools and children were, indeed, successful. Yet, there is always the possibility that some unmeasured, prior existing difference among schools and children caused the effect, not the reduced class size. Or, there is the possibility that teachers with reduced classes were actively involved in school reform and that their increased effort and motivation (which might wane over time) caused the effect, not the smaller classes themselves. In short, these designs are less effective at eliminating competing plausible hypotheses with the same authority as a true experiment.

The major weakness of nonrandomized designs is selectivity bias—the counter-interpretation that the treatment did not cause the difference in outcomes but, rather, unmeasured prior existing differences (differential selectivity) between the groups did. 7 For example, a comparison of early literacy skills among low-income children who participated in a local preschool program and those who did not may be confounded by selectivity bias. That is, the parents of the children who were enrolled in preschool may be more motivated than other parents to provide reading experiences to their children at home, thus making it difficult to disentangle the several potential causes (e.g., preschool program or home reading experiences) for early reading success.

It is critical in such studies, then, to be aware of potential sources of bias and to measure them so their influence can be accounted for in relation to the outcome of interest. 8 It is when these biases are not known that quasi-experiments may yield misleading results. Thus, the scientific principle of making assumptions explicit and carefully attending to ruling out competing hypotheses about what caused a difference takes on heightened importance.

In some settings, well-controlled quasi-experiments may have greater “external validity”—generalizability to other people, times, and settings— than experiments with completely random assignment (Cronbach et al., 1980; Weiss, 1998a). It may be useful to take advantage of the experience and investment of a school with a particular program and try to design a quasi-experiment that compares the school that has a good implementation of the program to a similar school without the program (or with a different program). In such cases, there is less risk of poor implementation, more investment of the implementers in the program, and potentially greater impact. The findings may be more generalizable than in a randomized experiment because the latter may be externally mandated (i.e., by the researcher) and thus may not be feasible to implement in the “real-life” practice of education settings. The results may also have stronger external validity because if a school or district uses a single program, the possible contamination of different programs because teachers or administrators talk and interact will be reduced. Random assignment within a school at the level of the classroom or child often carries the risk of dilution or blending the programs. If assignment is truly random, such threats to internal validity will not bias the comparison of programs—just the estimation of the strength of the effects.

In the section above ( What Is Happening? ), we note that some kinds of correlational work make important contributions to understanding broad patterns of relationships among educational phenomena; here, we highlight a correlational design that allows causal inferences about the relationship between two or more variables. When correlational methods use what are called “model-fitting” techniques based on a theoretically gener-

ated system of variables, they permit stronger, albeit still tentative, causal inferences.

In Chapter 3 , we offer an example that illustrates the use of model-fitting techniques from the geophysical sciences that tested alternative hypotheses about the causes of glaciation. In Box 5-3 , we provide an example of causal modeling that shows the value of such techniques in education. This work examined the potential causal connection between teacher compensation and student dropout rates. Exploring this relationship is quite relevant to education policy, but it cannot be studied through a randomized field trail: teacher salaries, of course, cannot be randomly assigned nor can students be randomly assigned to those teachers. Because important questions like these often cannot be examined experimentally, statisticians have developed sophisticated model-fitting techniques to statistically rule out potential alternative explanations and deal with the problem of selection bias.

The key difference between simple correlational work and model-fitting is that the latter enhances causal attribution. In the study examining teacher compensation and dropout rates, for example, researchers introduced a conceptual model for the relationship between student outcomes and teacher salary, set forth an explicit hypothesis to test about the nature of that relationship, and assessed competing models of interpretation. By empirically rejecting competing theoretical models, confidence is increased in the explanatory power of the remaining model(s) (although other alternative models may also exist that provide a comparable fit to the data).

The study highlighted in Box 5-3 tested different models in this way. Loeb and Page (2000) took a fresh look at a question that had a good bit of history, addressing what appeared to be converging evidence that there was no causal relationship between teacher salaries and student outcomes. They reasoned that one possible explanation for these results was that the usual “production-function” model for the effects of salary on student outcomes was inadequately specified. Specifically, they hypothesized that nonpecuniary job characteristics and alternative wage opportunities that previous models had not accounted for may be relevant in understanding the relationship between teacher compensation and student outcomes. After incorporating these opportunity costs in their model and finding a sophisticated way to control the fact that wealthier parents are likely to send their

children to schools that pay teachers more, Loeb and Page found that raising teacher wages by 10 percent reduced high school dropout rates by 3 to 4 percent.

WHY OR HOW IS IT HAPPENING?

In many situations, finding that a causal agent ( x ) leads to the outcome ( y ) is not sufficient. Important questions remain about how x causes y . Questions about how things work demand attention to the processes and mechanisms by which the causes produce their effects. However, scientific research can also legitimately proceed in the opposite direction: that is, the search for mechanism can come before an effect has been established. For example, if the process by which an intervention influences student outcomes is established, researchers can often predict its effectiveness with known probability. In either case, the processes and mechanisms should be linked to theories so as to form an explanation for the phenomena of interest.

The search for causal mechanisms, especially once a causal effect has garnered strong empirical support, can use all of the designs we have discussed. In Chapter 2 , we trace a sequence of investigations in molecular biology that investigated how genes are turned on and off. Very different techniques, but ones that share the same basic intellectual approach to casual analysis reflected in these genetic studies, have yielded understandings in education. Consider, for example, the Tennessee class-size experiment (see discussion in Chapter 3 ). In addition to examining whether reduced class size produced achievement benefits, especially for minority students, a research team and others in the field asked (see, e.g., Grissmer, 1999) what might explain the Tennessee and other class-size effects. That is, what was the causal mechanism through which reduced class size affected achievement? To this end, researchers (Bohrnstedt and Stecher, 1999) used classroom observations and interviews to compare teaching in different class sizes. They conducted ethnographic studies in search of mechanism. They correlated measures of teaching behavior with student achievement scores. These questions are important because they enhance understanding of the foundational processes at work when class size is reduced and thus

improve the capacity to implement these reforms effectively in different times, places, and contexts.

Exploring Mechanism When Theory Is Fairly Well Established

A well-known study of Catholic schools provides another example of a rigorous attempt to understand mechanism (see Box 5-4 ). Previous and highly controversial work on Catholic schools (e.g., Coleman, Hoffer, and

Kilgore, 1982) had examined the relative benefits to students of Catholic and public schools. Drawing on these studies, as well as a fairly substantial literature related to effective schools, Bryk and his colleagues (Byrk, Lee, and Holland, 1993) focused on the mechanism by which Catholic schools seemed to achieve success relative to public schools. A series of models were developed (sector effects only, compositional effects, and school effects) and tested to explain the mechanism by which Catholic schools successfully achieve an equitable social distribution of academic achievement. The

researchers’ analyses suggested that aspects of school life that enhance a sense of community within Catholic schools most effectively explained the differences in student outcomes between Catholic and public schools.

Exploring Mechanism When Theory Is Weak

When the theoretical basis for addressing questions related to mechanism is weak, contested, or poorly understood, other types of methods may be more appropriate. These queries often have strong descriptive components and derive their strength from in-depth study that can illuminate unforeseen relationships and generate new insights. We provide two examples in this section of such approaches: the first is the ethnographic study of college women (see Box 5-2 ) and the second is a “design study” that resulted in a theoretical model for how young children learn the mathematical concepts of ratio and proportion.

After generating a rich description of women’s lives in their universities based on extensive analysis of ethnographic and survey data, the researchers turned to the question of why women who majored in nontraditional majors typically did not pursue those fields as careers (see Box 5-2 ). Was it because women were not well prepared before college? Were they discriminated against? Did they not want to compete with men? To address these questions, the researchers developed several theoretical models depicting commitment to schoolwork to describe how the women participated in college life. Extrapolating from the models, the researchers predicted what each woman would do after completing college, and in all cases, the models’ predictions were confirmed.

A second example highlights another analytic approach for examining mechanism that begins with theoretical ideas that are tested through the design, implementation, and systematic study of educational tools (curriculum, teaching methods, computer applets) that embody the initial conjectured mechanism. The studies go by different names; perhaps the two most popular names are “design studies” (Brown, 1992) and “teaching experiments” (Lesh and Kelly, 2000; Schoenfeld, in press).

Box 5-5 illustrates a design study whose aim was to develop and elaborate the theoretical mechanism by which ratio reasoning develops in young children and to build and modify appropriate tasks and assessments that

incorporate the models of learning developed through observation and interaction in the classroom. The work was linked to substantial existing literature in the field about the theoretical nature of ratio and proportion as mathematical ideas and teaching approaches to convey them (e.g., Behr, Lesh, Post, and Silver, 1983; Harel and Confrey, 1994; Mack, 1990, 1995). The initial model was tested and refined as careful distinctions and extensions were noted, explained, and considered as alternative explanations as the work progressed over a 3-year period, studying one classroom intensively. The design experiment methodology was selected because, unlike laboratory or other highly controlled approaches, it involved research within the complex interactions of teachers and students and allowed the everyday demands and opportunities of schooling to affect the investigation.

Like many such design studies, there were two main products of this work. First, through a theory-driven process of designing—and a data-driven process of refining—instructional strategies for teaching ratio and proportion, researchers produced an elaborated explanatory model of how young children come to understand these core mathematical concepts. Second, the instructional strategies developed in the course of the work itself hold promise because they were crafted based on a number of relevant research literatures. Through comparisons of achievement outcomes between children who received the new instruction and students in other classrooms and schools, the researchers provided preliminary evidence that the intervention designed to embody this theoretical mechanism is effective. The intervention would require further development, testing, and comparisons of the kind we describe in the previous section before it could be reasonably scaled up for widespread curriculum use.

Steffe and Thompson (2000) are careful to point out that design studies and teaching experiments must be conducted scientifically. In their words:

We use experiment in “teaching experiment” in a scientific sense…. What is important is that the teaching experiments are done to test hypotheses as well as to generate them. One does not embark on the intensive work of a teaching experiment without having major research hypotheses to test (p. 277).

This genre of method and approach is a relative newcomer to the field of education research and is not nearly as accepted as many of the other

methods described in this chapter. We highlight it here as an illustrative example of the creative development of new methods to embed the complex instructional settings that typify U.S. education in the research process. We echo Steffe and Thompson’s (2000) call to ensure a careful application of the scientific principles we describe in this report in the conduct of such research. 9

CONCLUDING COMMENTS

This chapter, building on the scientific principles outlined in Chapter 3 and the features of education that influence their application in education presented in Chapter 4 , illustrates that a wide range of methods can legitimately be employed in scientific education research and that some methods are better than others for particular purposes. As John Dewey put it:

We know that some methods of inquiry are better than others in just the same way in which we know that some methods of surgery, arming, road-making, navigating, or what-not are better than others. It does not follow in any of these cases that the “better” methods are ideally perfect…We ascertain how and why certain means and agencies have provided warrantably assertible conclusions, while others have not and cannot do so (Dewey, 1938, p. 104, italics in original).

The chapter also makes clear that knowledge is generated through a sequence of interrelated descriptive and causal studies, through a constant process of refining theory and knowledge. These lines of inquiry typically require a range of methods and approaches to subject theories and conjectures to scrutiny from several perspectives.

We conclude this chapter with several observations and suggestions about the current state of education research that we believe warrant attention if scientific understanding is to advance beyond its current state. We do not provide a comprehensive agenda for the nation. Rather, we

wish to offer constructive guidance by pointing to issues we have identified throughout our deliberations as key to future improvements.

First, there are a number of areas in education practice and policy in which basic theoretical understanding is weak. For example, very little is known about how young children learn ratio and proportion—mathematical concepts that play a key role in developing mathematical proficiency. The study we highlight in this chapter generated an initial theoretical model that must undergo sustained development and testing. In such areas, we believe priority should be given to descriptive and theory-building studies of the sort we highlight in this chapter. Scientific description is an essential part of any scientific endeavor, and education is no different. These studies are often extremely valuable in themselves, and they also provide the critical theoretical grounding needed to conduct causal studies. We believe that attention to the development and systematic testing of theories and conjectures across multiple studies and using multiple methods—a key scientific principle that threads throughout all of the questions and designs we have discussed—is currently undervalued in education relative to other scientific fields. The physical sciences have made progress by continuously developing and testing theories; something of that nature has not been done systematically in education. And while it is not clear that grand, unifying theories exist in the social world, conceptual understanding forms the foundation for scientific understanding and progresses—as we showed in Chapter 2 —through the systematic assessment and refinement of theory.

Second, while large-scale education policies and programs are constantly undertaken, we reiterate our belief that they are typically launched without an adequate evidentiary base to inform their development, implementation, or refinement over time (Campbell, 1969; President’s Committee of Advisors on Science and Technology, 1997). The “demand” for education research in general, and education program evaluation in particular, is very difficult to quantify, but we believe it tends to be low from educators, policy makers, and the public. There are encouraging signs that public attitudes toward the use of objective evidence to guide decisions is improving (e.g., statutory requirements to set aside a percentage of annual appropriations to conduct evaluations of federal programs, the Government Performance and Results Act, and common rhetoric about “evidence-based” and “research-based” policy and practice). However, we believe stronger

scientific knowledge is needed about educational interventions to promote its use in decision making.

In order to generate a rich store of scientific evidence that could enhance effective decision making about education programs, it will be necessary to strengthen a few related strands of work. First, systematic study is needed about the ways that programs are implemented in diverse educational settings. We view implementation research—the genre of research that examines the ways that the structural elements of school settings interact with efforts to improve instruction—as a critical, underfunded, and underappreciated form of education research. We also believe that understanding how to “scale up” (Elmore, 1996) educational interventions that have promise in a small number of cases will depend critically on a deep understanding of how policies and practices are adopted and sustained (Rogers, 1995) in the complex U.S. education system. 10

In all of this work, more knowledge is needed about causal relationships. In estimating the effects of programs, we urge the expanded use of random assignment. Randomized experiments are not perfect. Indeed, the merits of their use in education have been seriously questioned (Cronbach et al., 1980; Cronbach, 1982; Guba and Lincoln, 1981). For instance, they typically cannot test complex causal hypotheses, they may lack generalizability to other settings, and they can be expensive. However, we believe that these and other issues do not generate a compelling rationale against their use in education research and that issues related to ethical concerns, political obstacles, and other potential barriers often can be resolved. We believe that the credible objections to their use that have been raised have clarified the purposes, strengths, limitations, and uses of randomized experiments as well as other research methods in education. Establishing cause is often exceedingly important—for example, in the large-scale deployment of interventions—and the ambiguity of correlational studies or quasi-experiments can be undesirable for practical purposes.

In keeping with our arguments throughout this report, we also urge that randomized field trials be supplemented with other methods, including in-depth qualitative approaches that can illuminate important nuances,

identify potential counterhypotheses, and provide additional sources of evidence for supporting causal claims in complex educational settings.

In sum, theory building and rigorous studies of implementations and interventions are two broad-based areas that we believe deserve attention. Within the framework of a comprehensive research agenda, targeting these aspects of research will build on the successes of the enterprise we highlight throughout this report.

Researchers, historians, and philosophers of science have debated the nature of scientific research in education for more than 100 years. Recent enthusiasm for "evidence-based" policy and practice in education—now codified in the federal law that authorizes the bulk of elementary and secondary education programs—have brought a new sense of urgency to understanding the ways in which the basic tenets of science manifest in the study of teaching, learning, and schooling.

Scientific Research in Education describes the similarities and differences between scientific inquiry in education and scientific inquiry in other fields and disciplines and provides a number of examples to illustrate these ideas. Its main argument is that all scientific endeavors share a common set of principles, and that each field—including education research—develops a specialization that accounts for the particulars of what is being studied. The book also provides suggestions for how the federal government can best support high-quality scientific research in education.

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Research Components

12 Research Design

The story continues….

The next morning, as Harry and Physicus finished the last cold slice of George’s pizza, they had also finished collecting their literature to conduct a review. The friends concluded their theory about the number of mice influencing Pickles’ behavior had considerable support in the research literature. After writing the Literature Review, their research questions will be fully grounded, justified, and worthwhile to be answered.

“So, how do we go about answering our research questions?” asked Harry.

Physicus explained that they will have to analyze their research questions to see what types of answers are required. Knowing this will guide their decisions about how to design the project to answer their questions.

“There are two basic types of answers to research questions, quantitative and qualitative. The types of answers the research questions require tell us what type of research design we need,” said Physicus.

“I guess if I ask how we decide which type of research design we should choose, you will say, ‘It depends?'” uttered Harry.

Physicus’ face brightened as he blurted out, “Absolutely not! Negative!” Physicus continued, “If the research questions are stated well, there will only be two ways in which they can be answered. The research questions are king; they make all the decisions.”

“How come?” Harry appeared confused.

“Well, let us see. Think about our first question. How many mice will Pickles attack at one time? What type of answer does this question require? It requires a numeric answer, correct?” Physicus asked.

“Yes, that is correct,” Harry said.

Physicus continued, “Good. So, does our second question also require a numeric answer?”

“The second question is also answered with a number,” replied Harry

Physicus blurted, “Correct! This means we need to use a quantitative research design!”

Physicus continued, “Now if we had research questions that could not be answered with numbers, we would need to use a qualitative research design to answer our questions with words or phrases instead.”

Harry now appeared relieved, “I get it. So in designing a research project, we simply look for a way to answer the research questions. That’s easy!”

“Well, it depends,” answered Physicus smiling.

Interpreting the Story

There are qualitative, quantitative, mixed methods, and applied research designs. Based on the research questions, the research design will be obvious. Physicus led Harry in determining their study would need a quantitative design, because they only needed numerical data to answer their research questions. If Harry’s questions could only be answered with words or phrases, then a qualitative design would be needed. If the friends had questions needing to be answered with numbers and phrases, then either a mixed methods or an applied research design would have been the choice.

Research Design

The Research Design explains what type of research is being conducted. The writing in this heading also explains why this type of research is needed to obtain the answers to the research or guiding questions for the project. The design provides a blueprint for the methodology. Articulating the nature of the research design is critical for explaining the Methodology (see the next chapter).

There are four categories of research designs used in educational research and a variety of specific research designs in each category. The first step in determining which category to use is to identify what type of data will answer the research questions. As in our story, Harry and Physicus had research questions that required quantitative answers, so the category of their research design is quantitative.

The next step in finding the specific research design is to consider the purpose (goal) of the research project. The research design must support the purpose. In our story, Harry and Physicus need a quantitative research design that supports their goal of determining the effect of the number of mice Pickles encounters at one time on his behavior.  A causal-comparative or quasi-experimental research design is the best choice for the friends because these are specific quantitative designs used to find a cause-and-effect relationship.

Quantitative Research Designs

Quantitative research designs seek results based on statistical analyses of the collected numerical data. The primary quantitative designs used in educational research include descriptive, correlational, causal-comparative, and quasi-experimental designs. Numerical data are collected and analyzed using statistical calculations appropriate for the design. For example, analyses like mean, median, mode, range, etc. are used to describe or explain a phenomenon observed in a descriptive research design. A correlational research design uses statistics, such as correlation coefficient or regression analyses to explain how two phenomena are related. Causal-comparative and quasi-experimental designs use analyses needed to establish causal relationships, such as pre-post testing, or behavior change (like in our story).

The use of numerical data guides both the methodology and the analysis protocols. The design also guides and limits how the results are interpreted. Examples of quantitative data found in educational research include test scores, grade point averages, and dropout rates.

Qualitative Research Designs

Qualitative research designs involve obtaining verbal, perspective, and/or visual results using code-based analyses of collected data. Typical qualitative designs used in educational research include the case study, phenomenological, grounded theory, and ethnography. These designs involve exploring behaviors, perceptions/feelings, and social/cultural phenomena found in educational settings.

Qualitative designs result in a written description of the findings. Data collection strategies include observations, interviews, focus groups, surveys, and documentation reviews. The data are recorded as words, phrases, sentences, and paragraphs. Data are then grouped together to form themes. The process of grouping data to form themes is called coding. The labeled themes become the “code” used to interpret the data. The coding can be determined ahead of time before data are collected, or the coding emerges from the collected data. Data collection strategies often include media such as video and audio recordings. These recordings are transcribed into words to allow for the coding analysis.

The use of qualitative data guides both the methodology and the analysis protocols. The “squishy” nature of qualitative data (words vs. numbers) and the data coding analysis limits the interpretation and conclusions made from the results. It is important to explain the coding analysis used to provide clear reasoning for the themes and how these relate to the research questions.

Mixed Method Designs

Mixed Methods research designs are used when the research questions must be answered with results that are both quantitative and qualitative. These designs integrate the data results to arrive at conclusions. A mixed method design is used when there are greater benefits to using multiple data types, sources, and analyses. Examples of typical mixed methods design approaches in education include convergent, explanatory, exploratory, and embedded designs. Using mixed methods approaches in educational research allows the researcher to triangulate, complement, or expand understanding using multiple types of data.

The use of mixed methods data guides the methodology, analysis, and interpretation of the results. Using both qualitative (quant) and quantitative (qual) data analyses provides a clearer or more balanced picture of the results. Data are analyzed sequentially or concurrently depending on the design. While the quantitative and qualitative data are analyzed independently, the results are interpreted integratively. The findings are a synthesis of the quantitative and qualitative analyses.

Applied Research Designs

Applied research designs seek both quantitative and qualitative results to address issues of educational practice. Applied research designs include evaluation, design and development, and action research. The purposes of applied research are to identify best practices, to innovate or improve current practices or policies, to test pedagogy, and to evaluate effectiveness. The results of applied research designs provide practical solutions to problems in educational practice.

Applied designs use both theoretical and empirical data. Theoretical data are collected from published theories or other research. Empirical data are obtained by conducting a needs assessment or other data collection methods. Data analyses include both quantitative and qualitative procedures. The findings are interpreted integratively as in mixed methods approaches, and then “applied” to the problem to form a solution.

Telling the research story

The Research Design in a research project tells the story of what direction the plot of the story will take.  The writing in this heading sets the stage for the rising action of the plot in the research story. The Research Design describes the journey that is about to take place. It functions to guide the reader in understanding the type of path the story will follow. The Research Design is the overall direction of the research story and is determined before deciding on the specific steps to take in obtaining and analyzing the data.

The Research Design heading appears in Chapter 3 of the thesis and dissertation projects under the Methodology heading.  A literature review project does not have this heading.

Graduate Research in Education: Learning the Research Story Copyright © 2022 by Kimberly Chappell and Greg I. Voykhansky is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Meaning, Purpose and Types of Descriptive Research 

Back to: Advanced Educational Research

Lesson 1: Meaning, Purpose and Types of Descriptive Research 

Descriptive research refers to the method of  describing a population, situation or phenomenon accurately in a systematic manner. It can answer what, where, when and how questions, but not why questions. It uses several varieties of research methods to investigate one or more variables and the researcher only observes and measures them but does not control or manipulate any of the variables.

1. Glass & Hopkins, 1984: Descriptive research involves gathering data that describe events and then organises, tabulates, depicts, and describes the data collection.

2. Williams, 2007: Descriptive research is research design used to examine the situation involving identification of attributes of a particular phenomenon based on an observational basis.

3. Creswell, 1994: Descriptive method is a research method that tries to describe phenomenon, occurrence, event, that happens in the present. The descriptive method of research is to gather information about present existing conditions. 

4. Best and Kahn, 2006: a research design which aims at describing, recording, analysing and interpreting existing conditions that yield valid and reliable education research results.

5. Wikipedia: Descriptive research is used to describe characteristics of a population or phenomenon being studied. It does not answer questions about how/when/why the characteristics occurred. Rather it addresses the “what” question (what are the characteristics of the population or situation being studied?).

6. Calderon (2012): Descriptive research is a purposive process of gathering, analysing, classifying and tabulating data about prevailing conditions, practices, beliefs, processes, trends and cause effect relationships and the making adequate and accurate interpretation about such data with or without the aid of statistical methods.

7. Manuel & Medel, 1976: Descriptive Research involves the description, recording, analysis, and interpretation of the present nature, composition or processes of phenomena.

8. Nassaji, 2015: Descriptive research is the research design in which data is collected in a qualitative manner and analyzed using quantitative procedures.

9. Dr YP Aggarwal, 2008: Descriptive research is a survey design devoted to the gathering of information about prevailing conditions or situations for the purpose of description and interpretation.

10. Siedlecki, 2020: Descriptive research is used to describe individuals, events and conditions of the subject without manipulation.

Purpose of Descriptive Research:

1. To give an accurate description of the characteristics of a certain group or phenomenon such as adolescents, a specific species of animals, a culture, and the like. 

2. To make predictions pertaining to a certain group or phenomenon on the basis of prevailing conditions like the increase in population rate by 2025 or the growth of pandemic in the coming years. 

3. To study common or specific traits in a particular group of population such as the number of students participating in a charity drive. 

4. To form principles and generalisations in behavioural sciences. 

5. To conduct an in depth study of a phenomenon and provide better and clearer understanding of the same. 

6. To provide a base for decision making.

7. To establish standard norms of behaviour, conduct, or performance. 

8. To reveal abnormal conditions or problems in a certain phenomenon. 

Types of Descriptive Research: 

1. Survey Research: It is the method of collecting data through self-administered or interviewer-administered questionnaires.

2. Observational Research: It is the process of observing and collecting data on a particular population or phenomenon without manipulating variables or controlling conditions. The research can be conducted in naturalistic settings or controlled laboratory settings.

3. Case Study Research: It focuses on a single individual, group, or event. It involves collecting detailed information on the subject through a variety of methods, including interviews, observations, and examination of documents.

4. Focus Group Research: It is the process of focusing on a small group of people to discuss a particular topic or product. Furthermore, the group is usually moderated by a researcher and the discussion is recorded for later analysis.

5. Ethnographic Research: It is the process of conducting detailed observations of a particular culture or community to gain a deep understanding of the beliefs, behaviours, and practices of a particular group.

Descriptive research design can be regarded as a powerful method used by scientists and researchers to gather information about a particular group or phenomenon.

• What is Educational Research? + [Types, Scope & Importance]

Education is an integral aspect of every society and in a bid to expand the frontiers of knowledge, educational research must become a priority. Educational research plays a vital role in the overall development of pedagogy, learning programs, and policy formulation. 

Educational research is a spectrum that bothers on multiple fields of knowledge and this means that it draws from different disciplines. As a result of this, the findings of this research are multi-dimensional and can be restricted by the characteristics of the research participants and the research environment. 

What is Educational Research?

Educational research is a type of systematic investigation that applies empirical methods to solving challenges in education. It adopts rigorous and well-defined scientific processes in order to gather and analyze data for problem-solving and knowledge advancement. 

J. W. Best defines educational research as that activity that is directed towards the development of a science of behavior in educational situations. The ultimate aim of such a science is to provide knowledge that will permit the educator to achieve his goals through the most effective methods.

The primary purpose of educational research is to expand the existing body of knowledge by providing solutions to different problems in pedagogy while improving teaching and learning practices. Educational researchers also seek answers to questions bothering on learner motivation, development, and classroom management. 

Characteristics of Education Research  

While educational research can take numerous forms and approaches, several characteristics define its process and approach. Some of them are listed below:

• It sets out to solve a specific problem.
• Educational research adopts primary and secondary research methods in its data collection process . This means that in educational research, the investigator relies on first-hand sources of information and secondary data to arrive at a suitable conclusion. 
• Educational research relies on empirical evidence . This results from its largely scientific approach.
• Educational research is objective and accurate because it measures verifiable information.
• In educational research, the researcher adopts specific methodologies, detailed procedures, and analysis to arrive at the most objective responses
• Educational research findings are useful in the development of principles and theories that provide better insights into pressing issues.
• This research approach combines structured, semi-structured, and unstructured questions to gather verifiable data from respondents.
• Many educational research findings are documented for peer review before their presentation. 
• Educational research is interdisciplinary in nature because it draws from different fields and studies complex factual relations.

Types of Educational Research 

Educational research can be broadly categorized into 3 which are descriptive research , correlational research , and experimental research . Each of these has distinct and overlapping features. 

Descriptive Educational Research

In this type of educational research, the researcher merely seeks to collect data with regards to the status quo or present situation of things. The core of descriptive research lies in defining the state and characteristics of the research subject being understudied. 

Because of its emphasis on the “what” of the situation, descriptive research can be termed an observational research method . In descriptive educational research, the researcher makes use of quantitative research methods including surveys and questionnaires to gather the required data.

Typically, descriptive educational research is the first step in solving a specific problem. Here are a few examples of descriptive research: 

• A reading program to help you understand student literacy levels.
• A study of students’ classroom performance.
• Research to gather data on students’ interests and preferences. 

From these examples, you would notice that the researcher does not need to create a simulation of the natural environment of the research subjects; rather, he or she observes them as they engage in their routines. Also, the researcher is not concerned with creating a causal relationship between the research variables. 

Correlational Educational Research

This is a type of educational research that seeks insights into the statistical relationship between two research variables. In correlational research, the researcher studies two variables intending to establish a connection between them. 

Correlational research can be positive, negative, or non-existent. Positive correlation occurs when an increase in variable A leads to an increase in variable B, while negative correlation occurs when an increase in variable A results in a decrease in variable B. 

When a change in any of the variables does not trigger a succeeding change in the other, then the correlation is non-existent. Also, in correlational educational research, the research does not need to alter the natural environment of the variables; that is, there is no need for external conditioning. 

Examples of educational correlational research include: 

• Research to discover the relationship between students’ behaviors and classroom performance.
• A study into the relationship between students’ social skills and their learning behaviors. 

Experimental Educational Research

Experimental educational research is a research approach that seeks to establish the causal relationship between two variables in the research environment. It adopts quantitative research methods in order to determine the cause and effect in terms of the research variables being studied. 

Experimental educational research typically involves two groups – the control group and the experimental group. The researcher introduces some changes to the experimental group such as a change in environment or a catalyst, while the control group is left in its natural state. 

The introduction of these catalysts allows the researcher to determine the causative factor(s) in the experiment. At the core of experimental educational research lies the formulation of a hypothesis and so, the overall research design relies on statistical analysis to approve or disprove this hypothesis.

Examples of Experimental Educational Research

• A study to determine the best teaching and learning methods in a school.
• A study to understand how extracurricular activities affect the learning process. 

Based on functionality, educational research can be classified into fundamental research , applied research , and action research. The primary purpose of fundamental research is to provide insights into the research variables; that is, to gain more knowledge. Fundamental research does not solve any specific problems. 

Just as the name suggests, applied research is a research approach that seeks to solve specific problems. Findings from applied research are useful in solving practical challenges in the educational sector such as improving teaching methods, modifying learning curricula, and simplifying pedagogy. 

Action research is tailored to solve immediate problems that are specific to a context such as educational challenges in a local primary school. The goal of action research is to proffer solutions that work in this context and to solve general or universal challenges in the educational sector. 

Importance of Educational Research

• Educational research plays a crucial role in knowledge advancement across different fields of study. 
• It provides answers to practical educational challenges using scientific methods.
• Findings from educational research; especially applied research, are instrumental in policy reformulation. 
• For the researcher and other parties involved in this research approach, educational research improves learning, knowledge, skills, and understanding.
• Educational research improves teaching and learning methods by empowering you with data to help you teach and lead more strategically and effectively.
• Educational research helps students apply their knowledge to practical situations.

Educational Research Methods 

• Surveys/Questionnaires

A survey is a research method that is used to collect data from a predetermined audience about a specific research context. It usually consists of a set of standardized questions that help you to gain insights into the experiences, thoughts, and behaviors of the audience. 

Surveys can be administered physically using paper forms, face-to-face conversations, telephone conversations, or online forms. Online forms are easier to administer because they help you to collect accurate data and to also reach a larger sample size. Creating your online survey on data-gathering platforms like Formplus allows you to.also analyze survey respondent’s data easily. 

In order to gather accurate data via your survey, you must first identify the research context and the research subjects that would make up your data sample size. Next, you need to choose an online survey tool like Formplus to help you create and administer your survey with little or no hassles. 

An interview is a qualitative data collection method that helps you to gather information from respondents by asking questions in a conversation. It is typically a face-to-face conversation with the research subjects in order to gather insights that will prove useful to the specific research context. 

Interviews can be structured, semi-structured , or unstructured . A structured interview is a type of interview that follows a premeditated sequence; that is, it makes use of a set of standardized questions to gather information from the research subjects. 

An unstructured interview is a type of interview that is fluid; that is, it is non-directive. During a structured interview, the researcher does not make use of a set of predetermined questions rather, he or she spontaneously asks questions to gather relevant data from the respondents. 

A semi-structured interview is the mid-point between structured and unstructured interviews. Here, the researcher makes use of a set of standardized questions yet, he or she still makes inquiries outside these premeditated questions as dedicated by the flow of the conversations in the research context. 

Data from Interviews can be collected using audio recorders, digital cameras, surveys, and questionnaires. 

• Observation

Observation is a method of data collection that entails systematically selecting, watching, listening, reading, touching, and recording behaviors and characteristics of living beings, objects, or phenomena. In the classroom, teachers can adopt this method to understand students’ behaviors in different contexts. 

Observation can be qualitative or quantitative in approach . In quantitative observation, the researcher aims at collecting statistical information from respondents and in qualitative information, the researcher aims at collecting qualitative data from respondents. 

Qualitative observation can further be classified into participant or non-participant observation. In participant observation, the researcher becomes a part of the research environment and interacts with the research subjects to gather info about their behaviors. In non-participant observation, the researcher does not actively take part in the research environment; that is, he or she is a passive observer. 

How to Create Surveys and Questionnaires with Formplus

• On your dashboard, choose the “create new form” button to access the form builder. You can also choose from the available survey templates and modify them to suit your need.
• Save your online survey to access the form customization section. Here, you can change the physical appearance of your form by adding preferred background images and inserting your organization’s logo.
• Formplus has a form analytics dashboard that allows you to view insights from your data collection process such as the total number of form views and form submissions. You can also use the reports summary tool to generate custom graphs and charts from your survey data. 

Steps in Educational Research

Like other types of research, educational research involves several steps. Following these steps allows the researcher to gather objective information and arrive at valid findings that are useful to the research context. 

• Define the research problem clearly. 
• Formulate your hypothesis. A hypothesis is the researcher’s reasonable guess based on the available evidence, which he or she seeks to prove in the course of the research.
• Determine the methodology to be adopted. Educational research methods include interviews, surveys, and questionnaires.
• Collect data from the research subjects using one or more educational research methods. You can collect research data using Formplus forms.
• Analyze and interpret your data to arrive at valid findings. In the Formplus analytics dashboard, you can view important data collection insights and you can also create custom visual reports with the reports summary tool. 
• Create your research report. A research report details the entire process of the systematic investigation plus the research findings. 

Conclusion 

Educational research is crucial to the overall advancement of different fields of study and learning, as a whole. Data in educational research can be gathered via surveys and questionnaires, observation methods, or interviews – structured, unstructured, and semi-structured. 

You can create a survey/questionnaire for educational research with Formplu s. As a top-tier data tool, Formplus makes it easy for you to create your educational research survey in the drag-and-drop form builder, and share this with survey respondents using one or more of the form sharing options. 

Connect to Formplus, Get Started Now - It's Free!

• education research
• educational research types
• examples of educational research
• importance of educational research
• purpose of educational research
• busayo.longe

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Exploring Various Types of Descriptive Survey Studies in Educational Research

Table of Contents

Have you ever considered how researchers understand what people think, feel, or do in a given educational setting? Descriptive survey studies are a primary tool in the belt of educational researchers, providing a snapshot of the current state or changes over time in educational scenarios. These surveys are as diverse as they are informative, and in this blog, we’ll dive into the different types and what makes each unique.

Understanding the breadth of population coverage

Let’s begin by exploring the population coverage in descriptive survey studies. This aspect determines the scope and generalizability of the study’s findings.

Census surveys

Census surveys are ambitious, aiming to include every individual within a population. They are comprehensive, capturing data that reflects the entire group without sampling errors. Think of them as a national census, but for educational research.

Sample surveys

On the other hand, sample surveys seek representation through a carefully selected subset of the larger population. These samples can be random, stratified, or clustered, each with its own method to ensure as accurate a reflection of the whole as possible. Sample surveys are practical, cost-effective, and widely used in education research where studying an entire population might be impossible or impractical.

Peering through the lens of time

The timeframe over which a survey is conducted can greatly influence the type of data collected and the insights derived from it.

Cross\-sectional surveys

Like a snapshot, cross-sectional surveys capture data at a single point in time. They offer a ‘here and now’ perspective, allowing researchers to analyze current conditions or opinions within an educational environment.

Longitudinal surveys

Contrastingly, longitudinal surveys are the ‘time-lapse’ of educational research. They track changes over time, providing insights into trends, patterns, and long-term effects in educational settings. These types of surveys can be resource-intensive but are invaluable in understanding the dynamics of education over time.

Delving into the nature of data

The type of data collected in these surveys can vary, but they typically fall into two categories:

Quantitative data

Quantitative data is all about numbers. These surveys produce measurable, countable data that can be statistically analyzed. They’re excellent for answering how many, how much, or how often questions.

Qualitative data

Qualitative data, in contrast, is narrative and descriptive. It’s the stories, opinions, and experiences of individuals. This data type is richer in detail but harder to quantify, often requiring content analysis or coding to discern patterns and themes.

Classifying surveys by purpose

The intent behind a survey study can shape its design and the nature of the questions asked.

Status surveys

Status surveys aim to describe what exists in the present. They answer questions like: What is the current state of teacher satisfaction? How many students have access to digital learning resources?

Comparative surveys

When researchers want to look at differences or similarities between groups, comparative surveys come into play. These might explore variations in educational outcomes based on different teaching methods or compare student engagement across various schools.

Evaluation surveys

Lastly, evaluation surveys are used to assess the effectiveness of a program, policy, or intervention. They might be utilized to determine the impact of a new curriculum or the success of a professional development initiative for teachers.

Descriptive survey studies are a cornerstone of educational research, providing rich, varied insights into the complex world of education. Whether taking the comprehensive route with a census survey or capturing changes over time with a longitudinal study, this research methodology is indispensable for understanding and improving educational systems.

What do you think? How might the findings from these different types of survey studies impact educational policy and practice? Can you think of a recent change in education that might benefit from a longitudinal survey study?

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Educational Research

1 Introduction to Educational Research

• Knowledge: Nature and Types
• Sources of Knowledge
• Nature and Conceptions of Social Reality
• Purposes of Research
• Types of Studies in Educational Research

2 Knowledge Generation – Historical Perspective-I

• Scientific Method

3 Knowledge Generation – Historical Perspective-II

• Positivistic Paradigm
• Emergence of Field Methods
• Review (Rethinking) of Concepts and Constructs
• Varied Studies in Education

4 Approaches to Educational Research – Assumptions, Scope and Limitations

• Nature of Educational Phenomena
• Conceptions of Viewing Reality
• Limitations of the Approaches

5 Descriptive Research

• Meaning and Nature of Descriptive Survey Research
• Types of Descriptive Survey Studies
• Steps of Conducting Descriptive Research
• Context and Relevance of Descriptive Studies in Educational Research

6 Experimental Research-I

• Characteristics of Experimental Research
• Experimental Design
• Validity of Experimental Design
• Controls in an Experiment

7 Experimental Research-II

• Types of Experimental Design
• Pre-experimental Designs
• True Experimental Designs
• Quasi Experimental Designs

8 Qualitative Research

• Definition of Qualitative Research
• Characteristics of Qualitative Research
• Types of Qualitative Methods
• Common Steps of Conducting Qualitative Studies
• Verification of Trustworthiness of Qualitative Research

9 Philosophical and Historical Studies

• Philosophical Studies
• Historical Research
• New Trends in Historical Approaches to Education
• Enhancing the Importance of Historical Research

10 Identification of Problem and Formulation of Research Questions

• Nature of a Problem
• Identification of a Research Problem
• Sources for Selecting a Research Problem
• Definition and Statement of the Problem
• Research Questions

11 Hypothesis – Nature of Formulation

• Meaning of the Hypothesis
• Sources of Hypothesis
• Types of Hypothesis
• Testing of the Hypothesis
• Characteristics of a Good Hypothesis
• Significance and Importance of a Hypothesis

12 Sampling

• Meaning of Population and Sample
• Methods/Designs of Sampling
• Probability Sampling
• Non-probability Sampling
• Characteristics of a Good Sample

13 Tools and Techniques of Data Collection

• Tools of Data Collection
• Techniques of Data Collection
• Characteristics and Criteria for Selection of a Good Tool

14 Analysis of Quantitative Data (Descriptive Statistical Measures – Selection and Application)

• Types of Data
• Graphic Representation of Quantitative Data
• Descriptive Statistical Measures
• Normal Probability Curve

15 Analysis of Quantitative Data – Inferential Statistics Based on Parametric Tests

• Inferential Statistics
• Parametric Tests: Uses and Assumptions
• Statistical Inference Based on Parametric Tests
• Testing the Statistical Significance of the Difference Between Means
• Statistical Inference Regarding Pearson’s Co-efficient of Correlation

16 Analysis of Quantitative Data – Inferential Statistics Based on Non-Parametric Tests

• Non-parametric Tests
• Statistical Inference Based on Non-parametric Tests: Unrelated Samples
• Statistical Inference Based on Non-parametric Tests: Related Samples
• Statistical Inference Regarding Correlations Using Non-parametric Data

17 Data Analysis Techniques in Qualitative Research

• Codification
• Categorization and Classification
• Content Analysis
• Triangulation

18 Computer Data Analysis

• What is SPSS?
• Basic Steps in Data Analysis
• Defining, Editing, and Entering Data
• Data File Management Functions
• Running a Preliminary Analysis

19 Writing Proposal or Synopsis

• Purpose of Writing a Research Proposal
• Format of a Research Proposal/Synopsis

20 Methods of Literature Search or Review

• Need and Purpose of Literature Search
• Types of Literature Search
• Steps Involved in Literature Search
• Methods of Literature Search
• Methods of Review and their Implications

21 Research Report – Various Components and Structure

• Significance of a Research Report
• Types of Research Reports
• Format of a Research Report

22 Scheme of Chapterisation and Referencing

• Need for Chapterisation and its Functions
• Diversity in Chapterisation
• Referencing and Footnotes -Need and Importance
• Various Styles of Referencing

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Educational Research its Meaning, Characteristics, Types and Importance

Back to: Research Methodology or Methodology of educational research

Meaning of Educational Research:

Educational research describes a systematic effort to comprehend the educational process properly, usually to increase its effectiveness. It is the study of educational issues using a scientific approach.

According to J.W. Best, “educational research  is that activity which is directed towards the development of a science of behavior in educational situations.” The ultimate aim of such a science is to provide knowledge that will permit the educator to achieve his goals by the most effective methods.”

According to Lazarsfeld and Sieber, “educational research  here means the whole of the efforts carried out by the public or private bodies in order to improve educational methods and educational activity in general, whether involving scientific research at a high level or more modest experiments concerning the school system and educational methods.”

According to Monroe, “The final purpose of educational research is to ascertain principles and develop procedures in the field of education.”

It may be roughly described as any deliberate effort to comprehend anything after recognizing a complicated educational issue that goes beyond immediate self-interest and is expressed in an unhelpful way. To understand, anticipate, and manage occurrences in educational contexts, it seeks to identify basic rules or interpretations of behaviour.

Characteristics of Educational Research

While there are many different formats and methods for educational research, its process and strategy are defined by a number of factors. Among them are:

• It aims to address a particular issue.
• Primary and secondary research approaches are used in educational research to gather data. To draw a valid conclusion from educational research, the researcher must rely on both primary sources of information and secondary data.
• Empirical data is used in educational research. This is a consequence of its most scientific methodology.
• Since it analyzes reliable facts, educational research is completely objective.
• To produce the most accurate findings possible in educational research, the researcher uses a certain methodology, intricate steps, and evaluation.
• Insights from educational research help create ideas and concepts that offer a deeper understanding of current problems.
• This research method mixes organized, semi-structured, and unstructured questions to get data from respondents that can be verified.

Types of Educational Research

Descriptive research, correlational research, and experimental research are the three basic categories into which educational research can be divided. These each have unique and intersecting characteristics.

• Descriptive research: In this kind of educational study, the researcher only aims to gather information regarding the current state of affairs. Determining the situation and quality of the study topic that is being underexplored forms the basis of descriptive research.

Descriptive research may be referred to as an observational research approach since it places a heavy focus on the “what” of the circumstance. To acquire the necessary information for descriptive educational research, the investigator uses quantitative research techniques such as surveys and questionnaires.

• You may learn more about students’ literacy skills by using a reading programme.
• a review of pupils’ performance in class.
• a study to compile information on pupils’ tastes and topics of concern.
• Correlational research: Knowledge of the statistical link between two research variables is sought in this sort of educational research. When doing correlational research, the researcher examines two variables to find a link between them.

Research on correlations may be neutral, harmful, or meaningless. A rise in factor A causes a rise in factor B, while a rise in factor A causes a drop in variable B is known as a negative correlation.

The correlation is negligible if a change in one of the variables does not result in a subsequent change in the other. Additionally, there is no requirement for external conditioning in correlational educational research since the variables’ natural environments do not need to be changed.

• study to ascertain the link between student conduct and academic achievement.
• An investigation into the connection between pupils’ social abilities and their academic behavior.
• Experimental research: A research strategy known as experimental educational research aims to determine the cause-and-effect association between the variables in the study setting. It uses quantitative research techniques to establish the links between the variables under study and their causes and effects.

Two groups are commonly used in experimental educational research: the randomized controlled trial and the experimental class. While the control group is left in its natural condition, the researcher makes certain modifications to the experimental group, such as altering the environment or adding a stimulant.

The addition of these accelerators permits the researcher to identify the study’s causal factor(s). The creation of a premise is at the heart of experimental educational research. Therefore, the total research concept was based on statistical analysis to support or refute this hypothesis.

• research to identify the most effective learning strategies in a classroom.
• research to determine the impact of leisure activities on schooling.

Importance of educational research

• Educational research is essential to increase learning in a variety of academic subjects.
• It offers solutions to real-world educational problems by applying scientific principles.
• Educational reformulation depends on the results of educational research, particularly applied research.
• Educational research enhances learning, expertise, abilities, and comprehension for the researcher as well as other parties participating in this study technique.
• Educational research enhances instructional methodologies by arming you with the knowledge to help you educate and manage more effectively and successfully.
• Learners who use their information in practical contexts benefit from educational research.

Descriptive Research

Topics covered in session 6.

• Definition and Characteristics of Historical Research
• Appropriateness/Limitations

Basic Characteristics of Descriptive Research

• It provides a descriptive analysis of a given population or sample. Any inferences are left to the readers.
• Qualitative, quantitative or a combination of both types of data can be presented.
• Hypotheses or broad research questions are used.

Data Sources

• Persons such as teachers, students, parents, administrators, etc.
• Documents such as policy statements, curricular guidelines.
• Records such as student transcripts.

Research Tools

• Structured interviews.
• Structured questionnaires and surveys
• Standardized tests.

Procedural Considerations

• Hypotheses generally not used
• Research questions/Sub-questions generally stated.
• Statistics used tend to be descriptive and show measures of central tendency or measures of spread (dispersion) such as: frequency distributions; mean, median, mode; standard deviation; Chi-square

Report Presentation

• Reports can use on both qualitative and quantitative presentations.
• Statistical data, if used, is usually in simple descriptive form (i.e. frequencies, mean, standard deviations, etc.)

FOR MORE INFORMATION ON THE TOPICS COVERED IN THIS SESSION, PLEASE REFER TO CHAPTER 1 OF A.G. PICCIANO " EDUCATIONAL RESEARCH PRIMER ".

A Review of Dissertations from an Online Asynchronous Learning Design and Technologies Educational Doctoral Program

Practitioner-focused educational doctoral programs have grown substantially in recent years. Dissertations in Practice (DiPs), which are the culminating research report and evaluation method in these programs, differ from traditional PhD dissertations in their focus on addressing a problem of practice and on connecting theories with practice. As part of our ongoing program evaluation, we reviewed DiPs from doctoral students who graduated from an online asynchronous Educational Doctoral program in Learning Design and Technologies at the University of South Carolina. Findings revealed that most students chose a pragmatic philosophical paradigm, adopted a mixed methods research design, reported an action research intervention implemented with populations in K-12 schools, used surveys and interviews as data sources, and analyzed data with descriptive/inferential statistics and thematic analysis. Implications for the program curriculum are discussed.

Akojie, P., Entrekin, F., Bacon, D., & Kanai, T. (2019). Qualitative meta-data analysis: Perceptions and experiences of online doctoral students. American Journal of Qualitative Research, 3(1), 117–135. https://doi.org/10.29333/ajqr/5814

Amrein-Beardsley, A., Zambo, D., Moore, D. W., Buss, R. R., Perry, N. J., Painter, S. R., ... & Puckett, K. S. (2012). Graduates respond to an innovative educational doctorate program. Journal of Research on Leadership Education, 7(1), 98–122.

Anguera, M. T., Blanco-Villaseñor, A., Losada, J. L., Sánchez-Algarra, P., & Onwuegbuzie, A. J. (2018). Revisiting the difference between mixed methods and multimethods: Is it all in the name?. Quality & Quantity, 52, 2757–2770. https://doi.org/10.1007/s11135-018-0700-2

Archer, L. A., & Hsiao, Y. H. (2023). Examining the frequency and implementation of validation techniques: A content analysis of EdD dissertations in educational leadership. Journal of Global Education and Research, 7(2), 166–182. https://www.doi.org/10.5038/2577-509X.7.2.1261

Ari, F., Vasconcelos, L., Tang, H., Grant, M., Arslan-Ari, I., & Moore, A. (2022). Program evaluation of an online EdD in Learning Design and Technologies: Recent graduates’ perspectives. Tech Trends, 66, 699–709. https://doi.org/10.1007/s11528-022-00744-7

Arslan-Ari, I., Ari, F., Grant, M. M., & Morris, W. S. (2018). Action research experiences for scholarly practitioners in an online education doctorate program: Design, reality, and lessons learned. Tech Trends, 62, 441–449. https://doi.org/10.1007/s11528-018-0308-3

Arslan-Ari, I., Ari, F., Grant, M. M., Vasconcelos, L., Tang, H., & Morris, W. S. (2020). Becoming action researchers: Crafting the curriculum and learning experiences for scholarly practitioners in educational technology. In E. Romero-Hall (Ed.), Research Methods in Learning Design and Technology (pp. 78-93). Routledge.

Bargal, D. (2008). Action research: A paradigm for achieving social change. Small Group Research, 39(1), 17–27. https://doi.org/10.1177/1046496407313407

Belzer, A., & Ryan, S. (2013). Defining the problem of practice dissertation: where’s the practice, what’s the Problem? Planning and Changing, 44(3/4), 195–207.

Bender, S., Rubel, D. J., & Dykeman, C. (2018). An interpretive phenomenological analysis of doctoral counselor education students’ experience of receiving cybersupervision. Journal of Counselor Preparation & Supervision, 11(1), Article 7. https://digitalcommons.sacredheart.edu/jcps/vol11/iss1/7/

Bolliger, D. U., & Halupa, C. (2012) Student perceptions of satisfaction and anxiety in an online doctoral program. Distance Education, 33(1), 81–98. https://doi.org/10.1080/01587919.2012.667961

Buss, R. (2018). Using action research as a signature pedagogy to develop EdD students’ inquiry as practice abilities. Impacting Education: Journal on Transforming Professional Practice, 3(1). https://doi.org/10.5195/ie.2018.46

Buss, R. R., & Zambo, D. (2016). A practical guide for students and faculty in CPED-influenced programs working on an action research dissertation in practice. Carnegie Project on the Education Doctorate.

Byrnes, D., Uribe-Flórez, L. J., Trespalacios, J., & Chilson, J. (2019). Doctoral e-mentoring: Current practices and effective strategies. Online Learning, 23(1), 236–248. https://doi.org/10.24059/olj.v23i1.1446

Carnegie Project for the Educational Doctorate (2009). Working principles for the professional practice doctorate in education. https://cped.memberclicks.net/the-framework

Chan, E., Heaton, R. M., Swidler, S. A., & Wunder, S. (2013). Examining CPED cohort dissertations: A window into the Learning of EdD students. Planning and Changing, 44(3/4), 266–285.

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.

Czerniawski, G. (2022). Power, positionality and practitioner research: Schoolteachers’ experiences of professional doctorates in education. British Educational Research Journal, 49, 1372–1386. https://doi.org/10.1002/berj.3902

Dawson, K., & Kumar, S. (2014). An analysis of professional practice EdD dissertations in educational technology. TechTrends, 58(4), 62–72. https://doi.org/10.1007/s11528-014-0770-5

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