Seeing the Forest and the Trees: Comparison of Two IRTree Models to Investigate the Impact of Full Versus Endpoint-Only Response Option Labeling

In this study, we examined the results and interpretations produced from two different IRTree models—one using paths consisting of only dichotomous decisions, and one using paths consisting of both dichotomous and polytomous decisions. We used data from two versions of an impulsivity measure. In the first version, all the response options had labels; in the second version, only the endpoints were labeled. Based on past research, we hypothesized that the endpoints would be selected more frequently in the endpoint-only labeled condition, and the midpoint response option would be selected more frequently in the fully labeled condition. Results from the two models (dichotomous and polytomous) were similar and indicated that our hypotheses were partially supported—specifically, there was no consistent pattern in terms of which condition saw a higher frequency of midpoint response selection. However, our hypotheses regarding extreme responding in the endpoint-only labeling condition were supported.     

Values-Alignment Messaging Boosts Adolescents’ Motivation to Control Social Media Use

Two preregistered experiments with 2,733 U.S. high school students (age range = 13–19 years) compared the impact of different messages on adolescents’ motivation to control social media use (SMU). A traditional message emphasized the benefits of avoiding SMU, whereas a values-alignment message framed controlling SMU as being consistent with autonomy and social justice. Compared to no message or a traditional message, in both studies, a values-alignment message led to greater motivation to control SMU immediately afterward, and in Study 2, awareness of “addictive” social media designs 3 months later. As hypothesized, values-alignment messaging was more motivating for girls than boys. Results offer preliminary support for leveraging adolescents’ drives for autonomy and social justice to motivate self-regulation of SMU.     

Using Natural Language Processing to Predict Item Response Times and Improve Test Construction

In this article, it is shown how item text can be represented by (a) 113 features quantifying the text's linguistic characteristics, (b) 16 measures of the extent to which an information‐retrieval‐based automatic question‐answering system finds an item challenging, and (c) through dense word representations (word embeddings). Using a random forests algorithm, these data then are used to train a prediction model for item response times and predicted response times then are used to assemble test forms. Using empirical data from the United States Medical Licensing Examination, we show that timing demands are more consistent across these specially assembled forms than across forms comprising randomly‐selected items. Because an exam's timing conditions affect examinee performance, this result has implications for exam fairness whenever examinees are compared with each other or against a common standard.     

Diarrhea Management Training in Early Childhood Settings

This study addresses the health of young children and how to safely and effectively care for children with diarrhea in the home and in early child care settings. A baseline survey showed the prevalence of oral rehydration solution (ORS) usage at last diarrhea bout to be low. An educational workshop was developed to train parents and child care professionals on how to properly manage young children with diarrhea and reduce the risk of diarrhea spread at home and in early childhood settings. Specific intervention activities are discussed and program activities, including specially designed materials for mixing homemade ORS are described. A comparison of pre- and postintervention data indicates substantial gains in knowledge among both urban and rural populations. Implications of research findings are discussed along with suggestions for future research.     

Instructions for Authors

Instructions for Authors

Instructions for Authors     

The stability of kindergarten teachers’ effectiveness: A generalizability study comparing the Framework For Teaching and the Classroom Assessment Scoring System

To meet recent accountability mandates, school districts are implementing assessment frameworks to document teachers’ effectiveness. Observational assessments play a key role in this process, albeit without compelling evidence of their psychometric rigor. Using a sample of kindergarten teachers, we employed Generalizability theory to investigate (across teachers, raters, and lessons) the stability of scores obtained with two different observation measures: The CLASS K-3 and the FFT. We conducted a series of Decision studies to document (for both measures’ constituent domains) the number of lessons per teacher and raters per lesson that would justify the use of observation scores for high stakes decisions. Acceptable, stable scores for individual-level decisions about teachers may generally require more raters and lessons than is typically used in practice (1–2 raters and fewer than 3 lessons). The considerable variability of observation-based scores raises concerns about either measure’s appropriateness for making individual or group decisions about teachers’ effectiveness.