International Journal of Quantitative Research in Education
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International Journal of Quantitative Research in Education (5 papers in press)
Leveraging Psychometric Isomorphism in Assessment Development by Katie Kunze, Roy Levy, Vandhana Mehta Abstract: Two studies were conducted to examine ways in which isomorph item families can aid in the creation of exam forms and the assessment of student learning. Methods for selecting isomorph item families for specific uses are described. Study 1 examined the use of isomorphs on high-stakes final exam forms. Study 2 explored using isomorphs for lower-stakes comparisons between pretests and posttests. Results of this work highlight the benefits of using isomorph item families and provide implications for both operational assessments in the Cisco Networking Academy Program, where this work takes place, and for the assessment community at large. Keywords: isomorphs; assessment development; test assembly.
Academic Performance Analysis to Support Proactive Student Advising for an Electrical Engineering Program by Richelle Adams, Cathy Ann Radix Abstract: Using correlation, regression and hierarchical clustering methods, the authors examined three consecutive graduating cohorts of students in an Electrical and Computer Engineering undergraduate programme to determine which courses (or groups of courses) were the best predictors of graduation GPA. The aim was to develop predictive models that support a consistent proactive advising experience. The main impact of this study is the methodology which can be applied to other programmes with similar weighted GPA schemes and with limited data sources. Other impacts were: the model identified which types of courses impacted GPA performance most, bringing clarity as to where cohort-wide intervention may be required; and the model can help us identify earlier at-risk and exceptional students. Keywords: Proactive Advising; Student Performance; Prediction; Engineering Curriculum.
Estimation of Nonlinear Structural Equation Models with Dichotomous Indicator Variables: A Monte Carlo Comparison of Methods by Holmes Finch Abstract: Nonlinear structural equation models (SEMs), which include interactions among latent predictors, as well as quadratic or higher order terms, have been the focus of research over the last three decades, beginning with Kenny and Judd (1984). The great majority of that work has focused on the case where the indicator variables are continuous in nature. However, in practice many nonlinear SEMs will involve the use of responses to items on scales, which are categorical. The focus of the current simulation study was on comparing several methods for modeling nonlinear SEMs when indicator variables were dichotomous. Results of the study showed that a Bayesian approach, as well as a method based on 2-stage least squares, provided the most accurate parameter estimates, the highest power, and the best control over the Type I error rate for the interaction effect. Implications of these findings for practice are discussed. Keywords: Structural equation model; interaction; Bayesian estimation; 2-stage least squares.
Binomial Logistic Modeling for Aggregate Binary Data: Application to Preschoolers' Alphabet Knowledge by Seongah Im, Barbara DeBaryshe Abstract: This study investigated the use of different binomial logistic models as alternatives to the normal model when analysing non-normal aggregate outcomes that are sums of correlated binary responses. The outcome variables provided in the two illustrative examples were preschoolers uppercase and lowercase letter naming knowledge with different shapes of non-normal distributions. The binomial, beta-binomial, and mixed binomial models with logit links were examined and compared to each other and to the normal linear model. Results were consistent in both examples. Among the models compared, the beta-binomial and mixed binomial models with overdispersion parameters captured interdependence among correlated binary responses. In addition, the mixed binomial model further explained remaining overdispersion and best fitted the data. Implications including advocating for the use of the binomial models with overdispersion parameters for clustered data were further discussed. Keywords: Correlated Binary Responses; Non-Normal; Aggregate Data; Overdispersion; Beta-Binomial; Mixed Binomial; Test Scores; Alphabet Knowledge.
State Expenditures to Public Higher Education and Regional Funding Norms: A Panel Data Analysis by Gabriel Serna, Joshua M. Cohen Abstract: Using well understood regional indicators we seek to understand if and how region influences state expenditures to public higher education. We employ an econometric technique that allows for estimation of parameter estimates on time-invariant regressors (Census Divisions) from 199596 through 20072008. Additionally, we are able to provide solid evidence that region matters while also including other well-known drivers of state expenditures to public higher education. We conclude that these relationships are often overlooked in higher education economics research, for better or for worse, and hence warrant further investigation since implications exist for future and prospective policy. Keywords: higher education expenditures; higher education policy; higher education economics; higher education finance; panel data analysis.