Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

Accurate assessment of the brake torque on a rope-braked cycle ergometer

Sports Engineering - A Monark cycle ergometer is a device globally used in physiological studies to measure the work and energy levels of exercising humans. In this paper a rope-braked cycle...     

The impact of case characteristics on child abuse reporting decisions.

Drawing distinctions on the basis of at least some case characteristics in making reporting decisions makes considerable sense. Using data from a national survey of mandated reporters that included vignettes in which case and personal characteristics were systematically varied, this paper examines the impact of selected characteristics while controlling for others. The data reveal that respondents noticed and responded to case characteristics. Abuse-relevant judgments and reporting intentions varied, often substantially, as a function of case characteristics. Three case characteristics--previous abuse, severity of abuse, and recantation--were powerful predictors of vignette outcomes. A history of previous abuse led to judgments of greater seriousness, a more salutary impact of a report, and greater likelihood of reporting. More severe abuse was more likely to be labelled as abuse, and was more likely to be reported. When the alleged victim retracted her accusation upon questioning by an authority figure, respondents were significantly less likely to intend a report. Child age, perpetrator intent and family socioeconomic status also influenced abuse-relevant judgments and reporting intentions. Respondents were more likely to intend a report when younger children, lazy or angry perpetrators and children from poorer families were portrayed.     

Assessing the Utility of Item Response Models: Computerized Adaptive Testing

How has Item Response Theory helped solve problems in the development and use of computer-adaptive tests? Do we need to balance item content with computer-adaptive tests? Could we use IRT to evaluate unusual responses to computer-delivered tests?