This paper studies the performance of the FAT-PET-PEESE (FPP) procedure, a commonly employed approach for addressing publication bias in the economics and business meta-analysis literature. The FPP procedure is generally used for 3 purposes: (1) to test whether a sample of estimates suffers from publication bias, (2) to test whether the estimates…

Person reliability parameters (PRPs) model temporary changes in individuals' attribute level perceptions when responding to self-report items (higher levels of PRPs represent less fluctuation). PRPs could be useful in measuring careless responding and traitedness. However, it is unclear how well current procedures for estimating PRPs can recover…

Questions on voluntary association memberships have been used extensively in social scientific research for decades. Researchers generally assume that these respondent self-reports are accurate, but their measurement has never been assessed. Respondent characteristics are known to influence the accuracy of other self-report variables such as…

The purpose of this research is to examine measurement invariance of collaborative problem solving skills measured by PISA 2015 Xandar subtest for Singapore, Norway, and Turkey. The research was conducted with 2990 participants' data obtained from Turkey (1032), Norway (923), and Singapore (1035) on PISA 2015 collaborative problem solving study.…

Temporality-driven covariate classification had limited impact on: the specification of directed acyclic graphs (DAGs) by 85 novice analysts (medical undergraduates); or the risk of bias in DAG-informed multivariable models designed to generate causal inference from observational data. Only 71 students (83.5%) managed to complete the…

Background/Context: School principals have long played a central role in managing building operations and supervising school employees. Over the past two decades, however, policy developments--including site-based management, external accountability measures, and teacher evaluation systems--have increased expectations that principals also improve…

Effect size is the basis of much evidence-based education policymaking. In particular, it is assumed to measure the educational effectiveness of interventions. Policy is being driven by the influential work of John Hattie, the Education Endowment Foundation, and others, which is grounded in this assumption. This article demonstrates the assumption…

Two approaches to causal inference in the presence of non-random assignment are presented: The Propensity Score approach which pseudo-randomizes by balancing groups on observed propensity to be in treatment, and the Endogenous Treatment Effects approach which utilizes systems of equations to explicitly model selection into treatment. The three…

Longitudinal data analysis has received widespread interest throughout educational, behavioral, and social science research, with latent growth curve modeling currently being one of the most popular methods of analysis. Despite the popularity of latent growth curve modeling, limited attention has been directed toward understanding the issues of…

The article reports three simulation studies conducted to find out whether the effect of a time limit for testing impairs model fit in investigations of structural validity, whether the representation of the assumed source of the effect prevents impairment of model fit and whether it is possible to identify and discriminate this method effect from…

This inquiry is focused on three indicators of the precision of measurement--conditional on fixed values of ?, the latent variable of item response theory (IRT). The indicators that are compared are (1) The traditional, conditional standard errors, s(eX|?) = CSEM; (2) the IRT-based conditional standard errors, s[subscript irt](eX|?)=C[subscript…

One of the important theories in education and psychology is Generalizability (G) Theory and various properties distinguish it from the other measurement theories. To better understand methodological trends of G theory, a thematic content analysis was conducted. This study analyzes the studies using generalizability theory in the field of…

Applying single-level statistical models to multilevel data typically produces underestimated standard errors, which may result in misleading conclusions. This study examined the impact of ignoring multilevel data structure on the estimation of item parameters and their standard errors of the Rasch, two-, and three-parameter logistic models in…

When estimating an average treatment effect with observational data, it's possible to get an unbiased estimate of the causal effect if all confounding variables are observed and reliably measured. In education, confounding variables are often latent constructs. Covariate selection methods used in causal inference applications assume that all…

When latent variables are used as outcomes in regression analysis, a common approach that is used to solve the ignored measurement error issue is to take a multilevel perspective on item response modeling (IRT). Although recent computational advancement allow efficient and accurate estimation of multilevel IRT models, we argue that a two-stage…