Student modeling is useful in educational research and technology development due to a capability to estimate latent student attributes. Widely used approaches, such as the Additive Factors Model (AFM), have shown satisfactory results, but they can only handle binary outcomes, which may yield potential information loss. In this work, we propose a…

In linear regression models estimated by ordinary least squares, it is often desirable to use hypothesis tests and confidence intervals that remain valid in the presence of heteroskedastic errors. Wald tests based on heteroskedasticity-consistent covariance matrix estimators (HCCMEs, also known as sandwich estimators or simply "robust"…

The transition from study to work is an important one. The jobs that graduates secure after completing their studies may very well shape the trajectory of their future careers, so an understanding of how job search influences employment outcomes has significant implications for theory and higher education practice. This article specifically…

(Purpose) The purpose of this paper is to present an easy-to-understand primer on three important concepts of factor analysis: Factor scores, structure coefficients, and communality coefficients. Given that statistical analyses are a part of a global general linear model (GLM), and utilize weights as an integral part of analyses (Thompson, 2006;…

The frequent use of self-report student surveys in higher education calls into question the possibility of social desirability having an unwanted influence on responses. This research explores the potential presence of social desirability bias with the National Survey of Student Engagement (NSSE), a widely used assessment of student behaviors.…

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

This generic science education study demonstrates the contrast of findings obtained through analyzing all the raw data as opposed to removing various combinations of identified potential outliers provided through the application of six diagnostic procedures. Outliers are defined as extreme data points with large residuals. It is argued that an…

Focusing on linear interpolation, an accurate method of implementing parallel analysis, this article contains tables of 95th percentile eigenvalues from random data than can be used with sample sizes of 50 to 500 subjects and between 5 and 50 variables. An empirical example illustrates how to obtain the eigenvalues. (SLD)