A staple of many spreadsheet-based management science courses is the use of Excel for activities such as model building, sensitivity analysis, goal seeking, and Monte-Carlo simulation. What might those things look like if carried out using Python? We describe a teaching module in which Python is used to do typical Excel-based modeling and…

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…

Since near the very beginning of electronic computing, Monte Carlo particle transport has been a fundamental approach for solving computational physics problems. Due to the high computational demands and inherently parallel nature of these applications, Monte Carlo transport applications are often performed in the supercomputing environment. That…

Large-scale assessment survey (LSAS) data are collected via complex sampling designs with special features (e.g., clustering and unequal probability of selection). Multilevel models have been utilized to account for clustering effects whereas the probability weighting approach (PWA) has been used to deal with design informativeness derived from…

Likert-type item is the most popular response format for collecting data in social, educational, and psychological studies through scales or questionnaires. However, there is no consensus on whether parametric or non-parametric tests should be preferred when analyzing Likert-type data. This study examined the statistical power of parametric and…

Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…

Previous research includes frequent admonitions regarding the importance of establishing connectivity in data collection designs prior to the application of Rasch models. However, details regarding the influence of characteristics of the linking sets used to establish connections among facets, such as locations on the latent variable, model-data…

Spline growth modelling is a popular tool to model change processes with distinct phases and change points in longitudinal studies. Focusing on linear spline growth models with two phases and a fixed change point (the transition point from one phase to the other), we detail how to find optimal data collection designs that maximize the efficiency…

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…