Educational Research Overview Research Paper Starter

Educational Research Overview

This article presents an overview of educational research methods and processes. Educational research encompasses the scientific method as related by John Dewey. The educational researcher begins with identifying the research problem or question, forming a hypothesis, gathering data, forming conclusions, and making a reasonable decision to reject or to validate the hypothesis based on the conclusions. Quantitative research methods yield quantifiable results as supporting evidence for the hypothesis. Qualitative research methods provide observational or narrative evidence in support of the hypothesis and previous quantitative studies. Some examples of qualitative studies include case studies, descriptive, ethnographic, and historical research.

Keywords Dewey, John; Educational Research; Hypothesis; Qualitative Research; Quantitative Research; Research Design; Research Problem; Review of Literature; Scientific Method

Research in Education: Educational Research Overview


The goal of educational research is to acquire and arrange educational data that will serve to explain, predict, and control behaviors, events, and educational programs. The scientific method, as developed by John Dewey in the early twentieth century, continues as the primary model for educational research. The scientific method involves identifying the main research problem or question, forming a hypothesis, gathering data, forming conclusions, and making a reasonable decision to reject or to validate the hypothesis based on the conclusions.

John Dewey, an American philosopher and educator, described his concept of the scientific method as it applied to educational research. Dewey believed that the scientific method could be used by educators to provide an objective means for organizing and interpreting educational programs and progress. Dewey identified five steps in the research process that remain the canons of educational research (Charles & Mertler, 2002).

1. Identify the main research problem or question.

  • 2. State the potential answer or solution to the problem or question as the hypothesis statement.
  • 3. Collect, analyze, and interpret data.
  • 4. Form conclusions.
  • 5. Verify and reject hypothesis based on conclusions. (include validity, reliability, measures, correlations, etc.)

The main question or research problem may be a single case or problem (case study) or it may involve evaluating the effectiveness of a program (program evaluation). For example, we might ask the question, Do Johnny's post-test scores improve after he has participated in the school breakfast program? This is an individual case study. We are only concerned with Johnny's scores. If we ask the question, Do the post-test scores for the fourth graders at Riverside Elementary show statistically significant improvement after participating in the school breakfast program for three months? this is a program evaluation. One individual's scores tell us very little about the effectiveness of the program overall. We want to know how this program has benefited the entire fourth grade.

The hypothesis statement is a formal statement of the researcher's prediction of the relationship that exists among the variables in a quantitative study. The hypothesis is stated as the opposite of the intended outcome. This is often confusing to the beginning researcher. The reason that the hypothesis is stated in null or opposite terms is because the researcher should not have any biases or preconceived notions about the outcome of the study. We should refer back to the first example about the school breakfast program: Johnny's post test scores do not show any improvement after participating in the school lunch program for three months. The researcher may have seen previous data that would indicate that eating a good breakfast helps memory retention. However, Johnny's case could be different.

Collecting data may involve any number of methods. The review of literature involves reading scholarly journal articles and books on a topic, synthesizing the results, and reporting on current research and trends in the proposed area of research. A review of literature also involves knowing what key terms to use in that search (Marken & Morrison, 2013). The researcher develops a research problem or question based on the information that they have gathered and considers a method for answering this question. The researcher might develop a pretest to administer to a sample group, might develop a survey questionnaire, or might outline a plan for a descriptive or historical analysis. The reseacher also could design a longitudinal study or correlational study, a case study, or a multiple methods study that includes the use of several methods. The most effective predictors of a successful research study involve multivariate or multiple methods research.

Data analysis for statistical significance, validity, and reliability is an important step for gauging the success of the program and the success of the research method. Data analysis involves providing supporting evidence to prove the hypothesis in quantitative research. The quantitative researcher uses the canons of validity and reliability to make certain that the instrument and the results are accurate. Validity refers to the instrument and the ability of the instrument to measure what it is supposed to measure. Reliability refers to the consistency of the measure. For example, a personality test might be developed to indicate what personality traits are best suited for high school cheerleaders. After a number of high school cheerleading sponsors have used this test and reported amazingly successful results, the company may begin to market their cheerleading inventory to high schools across the country. However, if a motivational expert decides to use the cheerleading inventory with a group of Fortune 500 businesses for selecting CEO's, the results may not be reliable.

Qualitative researchers provide data such as behavioral descriptions, observations, impressions, recordings, photographs, and other documents. Even though qualitative research does not involve statistical analysis, qualitative researchers will often ground their theories by using multiple methods. For example, a teacher involved in an action research project may incorporate a case study, attendance records, disciplinary records, and other data to support theories or conclusions.

Data interpretation can be a complex process. Often surveys and test scores generate another set of questions or problems. Sometimes these ambiguities are caused by a poor instrument and sometimes they simply indicate the need for further research. For example, if 90% of the survey respondents indicate that the classroom is not comfortable, what does that mean? Does it mean that the temperature is not at an acceptable level? Does it mean that the chairs are not comfortable? Even if there is a section for comments, respondents may not have time to write comments. The best strategy is to initially conduct the survey or test with a pilot group to pinpoint any weaknesses in the instrument.

Interpreting the final data should involve a narrative that indicates the implications from the research study as they relate to previous research, specifically to the review of literature as well as implications for the individual or program. The interpretation should indicate levels of statistical significance, and the constructs of reliability and validity.

Conclusions should point to clear solutions that may be substantiated by the data. The concluding comments will indicate whether or not the researcher is able to accept or to reject the initial hypothesis. Conclusions are reasonable assumptions based on the data presented in the research.


Quantitative Research

Quantitative research utilizes the scientific method as Dewey originally described for use in educational research. The interpretation should indicate levels of statistical significance, and the constructs of reliability and validity. Quantitative research may involve some of the following:

• Statistical Analysis

• Sampling

• Scales Of Measurement

• Measures Of Central Tendency

• Measures Of Variability

• Standard Scores

• Correlational Research

• Meta-Analyses

Statistically significant...

(The entire section is 3742 words.)