Marginal maximum likelihood, a common estimation method for item response theory models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for marginal…

Item response tree (IRTree) models are a flexible framework to control self-reported trait measurements for response styles. To this end, IRTree models decompose the responses to rating items into sub-decisions, which are assumed to be made on the basis of either the trait being measured or a response style, whereby the effects of such person…

Multiple regression is often used to compare the importance of two or more predictors. When the predictors being compared are measured with error, the estimated coefficients can be biased and Type I error rates can be inflated. This study explores the impact of measurement error on comparing predictors when one is measured with error, followed by…

The present simulation study indicates that a method where the regression effect of a predictor (X) on an outcome at follow-up (Y1) is calculated while adjusting for the outcome at baseline (Y0) can give spurious findings, especially when there is a strong correlation between X and Y0 and when the test-retest correlation between Y0 and Y1 is…

This article serves as a practical guide to mediation design and analysis by evaluating the ability of mediation models to detect a significant mediation effect using limited data. The cross-sectional mediation model, which has been shown to be biased when the mediation is happening over time, is compared with longitudinal mediation models:…

The standardized mean difference (SMD) is perhaps the most important meta-analytic effect size. It is typically used to represent the difference between treatment and control population means in treatment efficacy research. It is also used to represent differences between populations with different characteristics, such as persons who are…

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

A typical question in multiple regression analysis is to determine if a set of predictors gives the same degree of predictor power in two different populations. Olkin and Finn (1995) proposed two asymptotic-based methods for testing the equality of two population squared multiple correlations, [rho][superscript 2][subscript 1] and…

Corrections for restriction in range due to explicit selection assume linearity of regression and homoscedastic array variances. A Monte Carlo study was conducted to examine the effects of some common forms of violation of these assumptions. (Author/CP)

To determine if the observed correlation between two variables can be "explained" by a third variable, a significance test on the partial correlation coefficient is often used. This can be misleading when the third variable is measured with error. This article shows how the problem can be partially overcome. (Author/BW)

The general problem of using group status to estimate true scores given multiple measures is considered in this paper. (Authors)

One of the most important effect sizes used in meta-analysis is the standardized mean difference (SMD). In this article, the conditions under which SMD effect sizes based on different measures of the same construct are directly comparable are investigated. The results show that SMD effect sizes from different measures of the same construct are…

A microcomputer program that computes statistical power for analyses performed by multiple regression/correlation is described. The program features a spreadsheet-like interface, outputting the effect size and value of power corresponding to the input parameters, including predictor variables, sample size, alpha, and error type. (TJH)