An alternative approach for introducing instrumental variables in econometrics courses is presented in this article. The method is based on the ordinary least squares omitted variable bias formula. The intuition for the approach capitalizes on students' understanding and intuition of omitted variables. Thus, if students understand omitted variable…

Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, noniterative estimator for latent variable models. Associated with this estimator are equation-specific tests of model misspecification. One issue with equation-specific tests is that they lack specificity, in that they indicate…

Single-case experimental designs (SCEDs) have been used with increasing frequency to identify evidence-based interventions in education. The purpose of this study was to explore how several procedural characteristics, including within-phase variability (i.e., measurement error), number of baseline observations, and number of intervention…

The standard regression technique for modeling binary response variables in education research is logistic regression. The odds ratios from these models are used to quantify and communicate variable effects. These effects are sometimes pooled together as in a meta-analysis. We argue that this process is problematic as odds ratios calculated from…

Logistic regression is one of the most fundamental tools in predictive analytics. Graduate business analytics students are often familiarized with implementation of logistic regression using Python, R, SPSS, or other software packages. However, an understanding of the underlying maximum likelihood model and the mechanics of estimation are often…

A case study of regression analysis based on modeling Gilliland's correlation was described for use in a computational methods course. The case study uses a familiar example to train students in nonlinear least squares regression and to use standardized residual plots for model assessment. Previously published equations for Gilliland's correlation…

The methodology of single-case experimental designs (SCED) has been expanding its efforts toward rigorous design tactics to address a variety of research questions related to intervention effectiveness. Effect size indicators appropriate to quantify the magnitude and the direction of interventions have been recommended and intensively studied for…

Single-Case Experimental Designs (SCEDs) have lately been recognized as a valuable alternative tolarge group studies. SCEDs form a great tool for the evaluation of treatment effectiveness in heterogeneous and low-incidence conditions, which are common in the field of communication disorders. Mediation analysis is indispensable in treatment…

We examined the effect of estimation methods, maximum likelihood (ML), unweighted least squares (ULS), and diagonally weighted least squares (DWLS), on three population SEM (structural equation modeling) fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual…

School district consolidation has continued at a rapid pace in the United States, with one in every nine districts consolidating between 2000 and 2015 (Collins forthcoming). The stated aim of consolidation efforts is usually improved efficiency -- growing larger to lower unit costs or improve student outcomes without spending more money (Callahan…

This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance…

Weighted least squares (WLS), weighted least squares mean-and-variance-adjusted (WLSMV), unweighted least squares mean-and-variance-adjusted (ULSMV), maximum likelihood (ML), robust maximum likelihood (MLR) and Bayesian estimation methods were compared in mixed item response type data via Monte Carlo simulation. The percentage of polytomous items,…

For the least-squares analysis of data having multiple uncertain variables, the generally accepted best solution comes from minimizing the sum of weighted squared residuals over all uncertain variables, with, for example, weights in x[subscript i] taken as inversely proportional to the variance [delta][subscript xi][superscript 2]. A complication…

Diffusion-based item response theory models for responses and response times in tests have attracted increased attention recently in psychometrics. Analyzing response time data, however, is delicate as response times are often contaminated by unusual observations. This can have serious effects on the validity of statistical inference. In this…

This study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-à-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the…