Here Today, Gone Tomorrow: Understanding Freshman Attrition Using Person-Environment Fit Theory

Here Today, Gone Tomorrow: Understanding Freshman Attrition Using Person-Environment Fit Theory

Jennifer Howard Smith, Ph.D., Fred B. Bryant, Ph.D., David Njus, Ph.D., and Emil J. Posavac, Ph.D.

Applied Research Solutions, Inc., Loyola University Chicago, Luther College, Loyola University Chicago (Emeritus)

Person-Environment (PE) fit theory was used to explore the relationship between student involvement and freshman retention. Incoming freshmen (N=382) were followed longitudinally in a two-wave panel study, the summer before beginning college, and again
during the spring of their freshman year. Involvement levels, a variety of summer and spring preferences (Ps), and spring perceptions (Es) regarding specific aspects of their college environment were assessed. Twelve PE fit indicators were derived and compared with respect to their relationship with student involvement and retention. Results indicated that involvement was linked to some PE fit indicators. Traditional parametric statistical analyses were compared with a new, nonparametric technique, Classification Tree Analysis (CTA), to identify the most accurate classification model for use in designing potential attrition interventions. Discriminant analysis was 14% more accurate than CTA in classifying returners (97% vs. 85%), but CTA was 962% more accurate classifying dropouts (8% vs. 84%). CTA identified nine clusters—five of returners and four of dropouts, revealing that different subgroups of freshmen chose to return (and stay) for different reasons. Students’ end-of-the-year preferences appear to be more important than anticipated preferences, college perceptions, or PE fit levels.

View journal article

Tracing Prospective Profiles of Juvenile Delinquency and Non-Delinquency: An Optimal Classification Tree Analysis

Tracing Prospective Profiles of Juvenile Delinquency and Non-Delinquency: An Optimal Classification Tree Analysis

Hideo Suzuki, Ph.D., Fred B. Bryant, Ph.D., and John D. Edwards, Ph.D.

This study explored multiple variables that influence the development of juvenile delinquency. Two datasets of the National Youth Survey, a longitudinal study of delinquency and drug use among youths from 1976 and 1978, were used: 166 predictors were selected from the 1976 dataset, and later self-reported delinquency was selected from the 1978 dataset. Optimal data analysis was then used to construct a hierarchical classification tree model tracing the causal roots of juvenile delinquency and non-delinquency. Five attributes entered the final model and provided 70.37% overall classification accuracy: prior self-reported delinquency, exposure to peer delinquency, exposure to peer alcohol use, attitudes toward marijuana use, and grade level in school. Prior self-reported delinquency was the strongest predictor of later juvenile delinquency. These results highlight seven distinct profiles of juvenile delinquency and non-delinquency: lay delinquency, unexposed chronic delinquency, exposed chronic delinquency, unexposed non-delinquency, exposed non-delinquency, unexposed reformation, and exposed reformation.

View journal article

How to Save the Binary Class Variable and Predicted Probability of Group Membership from Logistic Regression Analysis to an ASCII Space-Delimited File in SPSS 17 For Windows

How to Save the Binary Class Variable and Predicted Probability of Group Membership from Logistic Regression Analysis to an ASCII Space-Delimited File in SPSS 17 For Windows

Fred B. Bryant, Ph.D.
Loyola University, Chicago

This note explains the steps involved and provides the SPSS syntax needed to run two-group logistic regression analysis using SPSS 17 for Windows, and output to an ASCII space-delimited data file the binary class variable and predicted probability of group membership (i.e., “Y-hat”) from an SPSS logistic regression analysis.

View journal article

An Internet-Based Intervention for Fibromyalgia Self-Management: Initial Design and Alpha Test

An Internet-Based Intervention for Fibromyalgia Self-Management: Initial Design and Alpha Test

William Collinge, Ph.D., Robert C. Soltysik, M.S., and Paul R.Yarnold, Ph.D.

Collinge and Associates and Optimal Data Analysis, LLC

Self-Monitoring And Review Tool, or SMART, is an interactive, internet-based, self-monitoring and feedback system, which helps people discover and monitor links between their own health-related behaviors, management strategies, and symptom levels over time. SMART involves longitudinal collection and optimal analysis of an individual’s self-monitoring data, and delivery of personalized feedback derived from the data. Forty women with fibromyalgia (FM) enrolled in a three-month alpha test of the SMART system. Utilization, satisfaction, and compliance were high across the test period, and higher utilization was predictive of lower anxiety, and improved physical functioning and self-efficacy.

View journal article

Junk Science, Test Validity, and the Uniform Guidelines for Personnel Selection Procedures: The Case of Melendez v. Illinois Bell

Junk Science, Test Validity, and the Uniform Guidelines for Personnel Selection Procedures: The Case of Melendez v. Illinois Bell

Fred B. Bryant, Ph.D. and Elaine K.B. Siegel

Loyola University Chicago and Hager & Siegel, P.C.

This paper stems from a recent federal court case in which a standardized test of cognitive ability developed by AT&T, the Basic Scholastic Aptitude Test (BSAT), was ruled invalid and discriminatory for use in hiring Latinos. Within the context of the BSAT, we discuss spurious statistical arguments advanced by the defense, exploiting certain language in the current Uniform Guidelines for evaluating the fairness and validity of personnel selection tests. These issues include: (a) how to avoid capitalizing on chance; (b) what constitutes “a measure” of job performance; (c) how to judge the meaningfulness of group differences in performance measures; and (d) how to combine data from different sex, race, or ethnic subgroups when computing validity coefficients for the pooled, total sample. Pursuant to the Uniform Guidelines’ standard for unfairness, when one ethnic group scores higher on an employment test, the test is deemed “unfair” if this difference is not reflected in a measure of job performance. Although studies validating selection instruments often survive the unfairness test, such data are vulnerable to bias and manipulation, if appropriate statistical procedures are not used. We consider both the benefits (greater clarity and precision) and the potential costs (loss of legal precedent) of revising the Uniform Guidelines to address these issues. We further discuss legal procedures to limit “junk science” in the courtroom, and the need to reevaluate validity generalization in light of Simpson’s “false correlation” paradox.

View journal article