A Comparison of Estimation Methods for Nonlinear Mixed-Effects Models Under Model Misspecification and Data Sparseness: A Simulation Study

A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification

A Comparison of Estimation Methods for Nonlinear Mixed-Effects Models Under Model Misspecification and Data Sparseness: A Simulation Study

A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification

Probabilistic Inferences for the Sample Pearson Product Moment Correlation

Fisher’s correlation transformation is commonly used to draw inferences regarding the reliability of tests comprised of dichotomous or polytomous items. It is illustrated theoretically and empirically that omitting test length and difficulty results in inflated Type I error. An empirically unbiased correction is introduced within the transformation that is applicable under any test conditions

The effect of extreme response and non-extreme response styles on testing measurement invariance

Extreme and non-extreme response styles (RSs) are prevalent in survey research using Likert-type scales. Their effects on measurement invariance (MI) in the context of confirmatory factor analysis are systematically investigated here via a Monte Carlo simulation study. Using the parameter estimates obtained from analyzing a 2007 Trends in International Mathematics and Science Study data set, a population model was constructed. Original and contaminated data with one of two RSs were generated and analyzed via multi-group confirmatory factor analysis with different constraints of MI. The results indicated that the detrimental effects of response style on MI have been underestimated. More specifically, these two RSs had a substantially negative impact on both model fit and parameter recovery, suggesting that the lack of MI between groups may have been caused by the RSs, not the measured factors of focal interest. Practical implications are provided to help practitioners to detect RSs and determine whether RSs are a serious threat to MI

Using Finite Mixture Modeling to Deal with Systematic Measurement Error: A Case Study

Conventional methods and analyses view measurement error as random. A scenario is presented where a variable was measured with systematic error. Mixture models with systematic parameter constraints were used to test hypotheses in the context of general linear models; this accommodated the heterogeneity arising due to systematic measurement error

Logistic Growth Modeling with Markov Chain Monte Carlo Estimation

A new growth modeling approach is proposed to can fit inherently nonlinear (i.e., logistic) function without constraint nor reparameterization. A simulation study is employed to investigate the feasibility and performance of a Markov chain Monte Carlo method within Bayesian estimation framework to estimate a fully random version of a logistic growth curve model under manipulated conditions such as the number and timing of measurement occasions and sample sizes

Investigating the Feasibility of Using Mplus in the Estimation of Growth Mixture Models

Hipp and Bauer (2006) investigated the issues of singularities and local maximum solutions within growth mixture models (GMMs) and made recommendations regarding the use of multiple starting values. Building on their work, this simulation study investigates the feasibility of estimating GMMs within Mplus as measured by convergence to proper, but local solutions