This is a North Central University essay about analyzing a statistical research sample. Components include research questions, null and alternative hypotheses, and types of statistical analysis. It is written in APA format, includes references, and has been graded by an instructor (A).

1. NORTHCENTRAL UNIVERSITY
ASSIGNMENT COVER SHEET
Student: Orlanda Haynes Date: 01/14/18
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Instructor: Dr. Shana Corvers
EDR 8201 Week 8—Assignment: Fictitious Statistical Study
Faculty Use Only
Excellent Good Fair Poor Unacceptable

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Assignment
Completion
Understanding
Materials
Expression
Grammar,
Mechanics,
APA,
<Faculty comments here>
Orlanda, you have shown great effort in assignment #8. I really enjoyed having you in this
course and watching your work evolve. I appreciate your SPSS output over the previous
assignments and this assignment is well written and has a strong conceptual rationale. You have
done a great job in the class and I am happy to see that you are understanding the statistical test
concepts and how to apply them to solve real world problems. I have provided feedback in the
margins if it was necessary and I remain impressed at how well you are learning to apply the
various stats methods.You responded to the assignment beautifully in concept. My notes are in
the margin. Great work and best wishes 24/25
EDR 8201 Week 8—Assignment: Fictitious Statistical Study
Scenario (excerpts from the original instructions):
You are the new director of institutional research at a small state university, and you have
been assigned the task of analyzing information for the dean of the School of Education
regarding the performance of their undergraduate students on the often-controversial Graduate
Record Exam (GRE). Many educators believe the GRE is a poor evaluator of undergraduate
performance as well as a poor predictor of graduate school performance. The dean is considering
eliminating the GRE from graduate school admissions requirements. The dean has already

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collected data on four variables: 1) gender, 2) grade point average (GPA), 3) GRE score, and 4)
graduate degree completion frequency. Your job is to develop a proposed analysis to assist the
dean to make an informed decision regarding the future use of the GRE.
. . . You should also discuss the assumptions of each test. No data is required to be
presented. This is similar to a question that you will encounter in your Doctoral Comprehensive
Exams. You should provide information that shows your understanding of the different types of
analyses, as well as possible outcomes of the analyses. In addition, you have to include in your
discussion the possible conclusions based on the possible results; rejecting the null, and not
rejecting the null. Using this information, develop the following foundational components
for a proposed analysis:
1. A relationship research question involving GPA and GRE scores; corresponding
null and alternative hypotheses; the type of statistical analysis to be employed to
determine significance; explanations of fictitious outcomes identifying both non-
significant and significant relationships as related to both null and alternative
hypotheses; and recommendations based on non-significant and significant findings.
Relationship research questions involving GPA and GRE scores:
What is the minimum GRE passing score [s]? What are the minimum admission
GPA scores accepted by most colleges and universities? How do students’ scores
at small state universities compare? What statistical significant relationship exists
between undergraduate students who score the minimum or higher on GRE tests
and have the minimum or higher cumulative GPA? The aim of these questions is

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to determine if there is a statistical significant correlation between the admission
GRE test scores and cumulative graduation GPA scores.
Null and alternative hypotheses:
Ha: A significant relationship will exist between GPA and GRE scores.
Ho: A non-significant relationship will exist between GPA and GRE scores.
The hypothesis was that students who met the minimum or higher in admission
GRE scores would also score higher in cumulative graduation GPA scores.
The types of statistical analysis:
(a) Descriptive statistics (mean, mode, median, standard deviation, maximum and
minimum, kurtosis, skewness, and analysis of score distribution) could be used to
show the distributions and variations of the students’ GRE and GPA test scores.
(b) To evaluate the nature, strength, relationships, and predictive levels between the
GRE and GPA scores, a correlation and regression analysis could be used. The
latter accesses the predictive nature or relationships between variables whereas
correlation analysis identify linear relationship between variables.
(c) A trend analysis could be performed to assessed whether the trend (over time) is
stable, decreasing, or increasing. If the first scenario exists, it would imply a
greater predictability of GRE test scores while the second scenario would suggest
the opposite. The latter, however, would lend credence to the use of GRE test
scores as a stable predictor of cumulative graduation GPAs (Knapp, 2017;
Michaelson, G., & Hardin, 2012; Salkind, 2007; Stone, 2010).
Explanations of fictitious outcomes:

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In this scenario, two variables were examined. Data showed failure to reject the
null hypothesis (Fail to reject Ho, R = .116, F (1, 39) = .522, p = .474 (p > .05).
Researchers would accept the Ho and reject the Ha.
Recommendations based on non-significant and significant findings:
The data showed weak predictive indices of GRE test scores as stable predictors
of graduation cumulative GPA. Thus, data suggest re-evaluation of GRE test
scores as an overall predictor of academic performance as well as minimizing
reliance on GRE scores at colleges and universities.
2. A relationship research question involving gender, GPA, and GRE scores;
corresponding null and alternative hypotheses; the type of statistical analysis to be
employed to determine significance; explanations of fictitious outcomes identifying
both non-significant and significant relationships as related to both null and
alternative hypotheses; and recommendations based on non-significant and
significant findings.
A relationship research question involving gender, GPA and GRE scores:
What is the predictive power of students’ gender, cumulative GPA, and GRE test
scores for enrollment in college and university STEM majors?
Null and alternative hypotheses:
Ho: A non-significant relationship will exist between gender and academic
achievement (GPA and GRE scores).
Ha: A significant relationship will exist between gender and academic
achievement (GPA and GRE scores).
The type of statistical analysis:

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A binary logistic regression method (Forward LR) is commonly used for
predicting categorical dependent variable when discriminant analysis does not
meet basic assumptions. Dependent variables included GPA and GRE test scores
whereas gender represented the independent variable. In this scenario, GPA and
GRE test scores (predictors) were used to predict an outcome (Knapp, 2017;
Michaelson, G., & Hardin, 2012; Salkind, 2007; Stone, 2010).
Explanations of fictitious outcomes:
Researchers would reject the Ho and accept the Ha. Data would show (R2=.56),
for example, that gender, cumulative GPA, and GRE test scores were
statistically significant predictors of student enrollment in college and university
STEM majors? Test scores for men were higher than those for women (other
factors of equal variances and values).
Recommendations based on non-significant and significant findings:
The data provide an understanding of factors that affect academic enrollment in
STEM related majors. However, limitations of the study include convenience
sampling (small, non-random sample size) which inhibits generalization.
3. An effect research question involving gender and GRE scores; corresponding null
and alternative hypotheses; the type of statistical analysis to be employed to
determine significance; explanations of fictitious outcomes identifying both a non-
significant and a significant effect as related to both null and alternative hypotheses;
and recommendations based on non-significant and significant findings.
An effect research question involving gender and GRE scores:
What effect does gender have on GRE test scores?

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Null and alternative hypotheses:
Ha: A significant relationship will exist between gender and GRE test scores.
Ho: A non-significant relationship will exist between gender and GRE test scores.
The type of statistical analysis:
Logistic and multiple regression, descriptive statistics, t-tests, ANCOVA, and
Chi-square, among others (Knapp, 2017; Michaelson, G., & Hardin, 2012;
Salkind, 2007; Stone, 2010).
Explanations of fictitious outcomes:
Data indicate GRE-Verbal scores average above 500 for men and below 500 for
women. The gender gap in quantitative reasoning measures one-half of a standard
deviation (men scored consistently higher than women.).
Recommendations based on non-significant and significant findings:
Since some findings suggest the GRE testing framework selectively suppress the
admission of women and minorities—particularly in STEM disciplines (Hale,
2010; Orlando, 2005), more recent data (5 years or less) which include underlying
factors are needed.
4. An effect research question involving gender, GRE score, and degree completion
frequency; corresponding null and alternative hypotheses; the type of statistical
analysis to be employed to determine significance; explanations of fictitious
outcomes identifying both a non-significant and a significant effect as related to both
null and alternative hypotheses; and recommendations based on non-significant and
significant findings.

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An effect research question involving gender, GRE scores, and degree completion
frequency:
How GRE scores predict academic success and gender related academic interests
in STEM majors? What is the predictive validity of GRE scores for projecting
educational outcomes for STEM majors?
Null and alternative hypotheses:
Ha: A significant relationship will exist between gender, GRE scores, and
degree completion rate in STEM majors.
Ho: A non-significant relationship will exist between gender, GRE scores,
and degree completion rate in STEM majors.
The type of statistical analysis:
Summary statistics, correlation coefficients, two-level HLM models to estimate
regression models with first-semester and cumulative GPA (separate dependent
variables), 2-level (representing students and institutions), hierarchical linear
model (to estimate regression models with first-semester GPA, R-squared,
Pearson correlations, Hierarchical Linear Modeling (dependent variables [first-
semester GPA and cumulative GPA]), and multiple combination of admission
variables, among others ( (Knapp, 2017; Michaelson, G., & Hardin, 2012;
Salkind, 2007; Stone, 2010).
Explanations of fictitious outcomes:
The GRE variables and GRE test questions had statistically significant
relationships (p≤.01 or .001) to both first-semester and cumulative GPAs.
across all models, and both GRE verbal and quantitative scores and GRE-Q

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were statistically significant predictors of both first-semester and cumulative
GPA.
Recommendations based on non-significant and significant findings:
Since evidence of validity of test scores interpretation varies (Kane, 2006), large
colleges and universities admission committees should consider kane’s (2006)
interpretative model or similar frameworks. Whereas smaller academic institution
could employ criterion-related frameworks to interpret the degree of association
between GRE test scores and other relevant outcomes data.
5. Finalize your report with a written analysis of your results and recommendations
for the dean based on your findings.
The validity of GRE scores to forecast students’ academic success has been
debated for decades (Cureton, Cureton, & Bishop, 1949; Kuncel, Credé, &
Thomas, 2007; Orlando, 2005). Most colleges and universities admission
committees rely heavily on data to predict students’ overall potential to master
rigorous degree programs, especially STEM disciplines. However, a wealth of
research suggests some GRE foundational constructs (e.g. categorical
construction bias, including questions) inhibit women and minority students’
enrollment in degree programs (Cureton, Cureton, & Bishop, 1949; Hale, 2010;
Orlando, (2005). In fact, Suhayda et al. (2008) findings indicated that GRE
scores are not reliable or stable predictors of students’ academic or career success.
Continuous data in support of this assertion have lead some education
communities to discontinue GRE requirement for admission, choosing instead to
develop customized examinations that address the specific goals and objectives of

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each degree program (Katz, Chow, Motzer, & Woods, 2009; Mupinga &
Mupinga, 2005; Sternberg & Williams, 1997). In view of these findings, it is
advisable that academic institutions re-evaluate their use of GRE scores to predict
students’ academic performances and overall success in graduate degree
programs.
.
References
Cureton, E. E., Cureton, L. W., & Bishop, R. (1949). Prediction of success in graduate study in
psychology at the University of Texas. American Psychologist, 4, 361–362
http://dx.doi.org/10.1037/h0059636
Hale, J. (2010). Does GRE measure anything related to graduate school? Retrieved from
http://psychcentral.com/blog/archives/2010/12/09/does-the-gre-measure-anythingrelated-
to-graduate-school/
Katz, J. R., Chow, C., Motzer, S. A., & Woods, S. L. (2009). The Graduate Record Examination:
Help or hindrance in nursing graduate school admissions? Journal of Professional
Nursing, 25, 369-372. doi: 10.1016/j.profnurs.2009.04.002
Knapp, H. (Academic). (2017). An introduction to the paired t-test [Video file]. London: SAGE

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Publications Ltd
Kuncel, N. R., Credé, M., & Thomas, L. L. (2007). A Meta-analysis of the predictive validity of
the Graduate Management Admission Test (GMAT) and undergraduate grade point
average (UGPA) for graduate student academic performance. Academy of Management
Learning & Education, 6(1), 51-68. doi:10.5465/AMLE.2007.24401702
Michaelson, G., & Hardin, M. (2012). Significance, statistical. In N. J. Salkind (Ed.),
Encyclopedia of research design (p. 1362-1366). Thousand Oak
Mupinga, E. E., & Mupinga, D. M. (2005). Perceptions of International Students Toward
GRE. College Student Journal, 39(2), 402-408. Retrieved from
http://eds.a.ebscohost.com.proxy1.ncu.edu/eds/Citations/FullTextLinkClick?sid=4ff1ed5
2-4bdd-4a88-b4c9-9193dc0a2d3f@sessionmgr4006&vid=2&id=pdfFullText
Orlando, J. (2005). The reliability of GRE scores in predicting graduate school success: A
metaanalytic, cross-functional, regressive, unilateral, post-Kantian, hyper-empirical,
quadruple blind, verbiage-intensive and hemorrhoid-inducing study. Retrieved from
http://ubi quity.acm.org/article.cfm?id=1071921
Salkind, N. J. (2010). Encyclopedia of research design. Thousand Oaks, CA: SAGE Publications
Ltd. doi: http://dx.doi.org.proxy1.ncu.edu/10.4135/9781412961288.n475
Sternberg, R. J., & Williams, W. M. (1997). Does the graduate record examination predict?
meaningful success in the graduate training of psychology? A case study. American
Psychologist, 52(6), 630-641. doi:10.1037/0003-066X.52.6.630
Stone, E. R. (2010). t Test, independent samples. In N. J. Salkind (Ed.), Encyclopedia of research
design (pp. 1552-1556). Thousand Oaks, CA: SAGE Pub
Suhayda, R., Hicks, F., & Fogg, L. (2008). A decision algorithm for admitting students to

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advanced practice programs in nursing. Journal of Professional Nursing, 24, 281-284.
doi: 10.1016/j.profnurs.2007.10.002