Hunter College, City University of New York, Department of Curriculum & Teaching

ADSUP 705 - RESEARCH SEMINAR in

EDUCATION ADMINISTRATION and SUPERVISION

Instructor: Dr. Anthony G. Picciano, e-mail address: antho13926@aol.com

Week 10

Topic(s): Correlational Research

Readings:

Key Questions:

Summary:

Summary of LIST Discussion for Week 10

This week we examined correlational research.

Correlational research is used to explore co-varying relationships between two or more variables. A simple definition of a co-varying relationship is when one variable changes so does the other variable(s). Please see my notes below for a further description of correlational research.

Our discussion then proceeded to critique the Graduate Record Exam study by Hebert and Holmes. Those of you who responded to my initial questions had little problem identifying the purpose, methodology, and stating an opinion on whether or not the researchers accomplished their purpose. The authors here have provided a succinct, straight-forward study. With regard to the findings, Brenda, Michelle V. and others correctly stated them as follows:

  1. Statistically significant correlation between GRE-V scores and GGPA (.348);
  2. Statistically significant correlation between GRE-T scores and GGPA (.342);
  3. No correlation between GRE-Q scores and GGPA (.175).

Note that the GRE-T is dependent on the GRE-V score.

Several of you (Lauren, Sean, Mimi, Michelle V. and Maureen) raised the question whether or not the findings in this study are applicable to other schools of education. This was a critical question. Debra questioned the applicability of this study to other institutions. I would like to think that this study is applicable to other schools, however, we do not have enough information about the students, the academic program, or the University of New Hampshire. Student demographic data such as gender, ethnicity, race, age, part-time or full-time status, working professional, academic performance indicators, etc. as well as data about the academic program such as grade analysis, mean GPA, mean GRE, etc. would have been helpful and easily collected from student transcripts. In this respect, I believe the authors could have provided their readers with a more useful study had they provided some student and program information. Our acceptance of the applicability of the findings in this study would likely depend upon the similarities of the University of New Hampshire (students, curriculum, grading pattern) to other (or our own) environment

With regard to the time period of the study, while Sean provides a provocative theory regarding Vietnam, Steve C. points out that in the 1970s, major questions began to be asked on college campuses regarding bias against minorities on standardized tests. In fact , a number of colleges and universities such as CUNY changed their policies in the 1970s and stopped requiring standardized tests such as the SATs and GREs as criteria for admission.

The CUNY Board of Trustees in 1999 changed its policy (again) for undergraduate admissions by requiring applicants to take and submit SAT scores. The purpose of this change was to use the SATs as one of several criteria for admission to a senior college. Most of you questioned whether admission to CUNY or any other college should be based on the results of one test.

We concluded our discussion with a quote from Alfie Kozol that appears in the current issue of Education Week. Referring to the standards movement: "the fatal flaws of the standards and accountability movement...over the past decade is that...it makes damnable standardized tests the ultimate arbiter--and engine--for learning."

Instructor Notes

Our textbook (Charles, Chapter 12) covers the topic of correlational research well. Students should make sure they have read this material.

I. Comments on Correlational Research

Correlational research is used to explore co-varying relationships between two or more variables. A simple definition of a co-varying relationship is when one variable changes so does the other variable(s). The purpose of correlational research is to:

  1. to identify variables that relate to one each other (i.e. is there a relationship between family income and grade point average; is there a relationship between part time employment and grade point average);
  2. to make predictions of one variable from another variable (i.e. can I.Q. test scores be used to predict student achievement; can SAT scores be used to predict college grade point averages);
  3. to examine possible cause and effect relationships between one variable and another.

A caution has to be advised when considering correlational research and cause and effect. Major researchers such as B.F. Skinner posit that while we can make many conclusions identifying a relationship between one or more variables, establishing cause and effect is very difficult and maybe impossible due to the myriad interactions of many variables in social science research.

In education-based correlational studies, data is frequently collected using standardized measures such as test scores. Report presentations almost always use hypotheses in the form of "No relationship exists between variable X and variable Y." Data analysis using correlation coefficients is generally quantitative. Rather than rich descriptive narrative as we saw in the Caswell County and Teachers in Bars studies, correlation presentations tend to be succinct relying on statistical analyses of correlation coefficients and regression. Of the various quantitative methodologies, correlational research is among the easiest to design and apply. For this reason, it is popular and frequently used in conjunction with other research methodologies.

II. Statistical Analysis in Correlational Research

  1. Correlation the relationship between two or more variables or sets of data. It is expressed in the form of a coefficient with +1.00 indicating a perfect positive correlation; -1.00 indicating a perfect inverse correlation; 0.00 indicating a complete lack of a relationship.

    Note: Magnitude of Relationship

  2. Pearson's Product Moment Coefficient (r) is the most often used and most precise coefficient; and generally used with continuous variables.
  3. Spearman Rank Order Coefficient (p) is a form of the Pearson's Product Moment Coefficient which can be used with ordinal or ranked data.
  4. Phi Correlation Coefficient is a form of the Pearson's Product Moment Coefficient which can be used with dichotomous variables (i.e. pass/fail, male/female).
  5. Regression the use of correlation to plot a line illustrating the linear relationship of two variables X and Y. It is based on the slope of the line which is represented by the formula : Y = a + bX where

    Regression is used extensively in making predictions based on finding unknown Y values from known X values. (i.e. predicting college GPA from known high school grade point averages.

  6. Multiple Regression is the same as regression except that it attempts to predict Y from two or more independent X variables. The formula for multiple regression is an extension of the linear regression formula: Y = a + b1 X1 + b2 X2 + .... (i.e. predicting college GPA from known high school grade point averages and SAT scores.

III. Case Study - "Graduate record examinations aptitude test scores as a predictors of graduate grade point average" by David Hebert and Alan Holmes (1979). Educational and Psychological Measurement, 39, pp. 415-419.

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