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Ming-Chi Tseng – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The primary objective of this investigation is the formulation of random intercept latent profile transition analysis (RI-LPTA). Our simulation investigation suggests that the election between LPTA and RI-LPTA for examination has negligible impact on the estimation of transition probability parameters when the population parameters are generated…
Descriptors: Monte Carlo Methods, Predictor Variables, Research Methodology, Test Bias
Ruscio, John; Walters, Glenn D. – Psychological Assessment, 2011
Taxometric analyses have proven helpful for distinguishing categorical and dimensional data. Many taxometric procedures require at least 3 variables for analysis. What if a construct is defined by only 2 conceptually nonredundant characteristics or a data set contains only 2 empirically nonredundant variables? In Study 1, we performed extensive…
Descriptors: Foreign Countries, Classification, Child Behavior, Elementary School Students
Knofczynski, Gregory T.; Mundfrom, Daniel – Educational and Psychological Measurement, 2008
When using multiple regression for prediction purposes, the issue of minimum required sample size often needs to be addressed. Using a Monte Carlo simulation, models with varying numbers of independent variables were examined and minimum sample sizes were determined for multiple scenarios at each number of independent variables. The scenarios…
Descriptors: Sample Size, Monte Carlo Methods, Predictor Variables, Prediction
Cheung, Mike W. L. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
Mediators are variables that explain the association between an independent variable and a dependent variable. Structural equation modeling (SEM) is widely used to test models with mediating effects. This article illustrates how to construct confidence intervals (CIs) of the mediating effects for a variety of models in SEM. Specifically, mediating…
Descriptors: Structural Equation Models, Probability, Intervals, Sample Size

Chan, Wai; Yung, Yiu-Fai; Bentler, Peter M.; Tang, Man-Lai – Educational and Psychological Measurement, 1998
Two bootstrap tests are proposed to test the independence hypothesis in a two-way cross table. Monte Carlo studies are used to compare the traditional asymptotic test with these bootstrap methods, and the bootstrap methods are found superior in two ways: control of Type I error and statistical power. (SLD)
Descriptors: Hypothesis Testing, Monte Carlo Methods, Power (Statistics), Predictor Variables
Donoghue, John R.; Jenkins, Frank – 1992
Monte Carlo methods were used to investigate the effect of misspecification of the second level in a two-level hierarchical linear model (HLM). Sample composition, heterogeneity of the group size, level of intraclass correlation, and correlation between second-level predictors were manipulated. Each of 20 generated data sets was analyzed nine…
Descriptors: Correlation, Estimation (Mathematics), Models, Monte Carlo Methods

Paunonen, Sampo V. – Educational and Psychological Measurement, 1997
A Monte Carlo simulation evaluated conditions that contribute to excessively high coefficients of congruence when fitting one factor pattern matrix into the space of a targeted pattern. Results support the conclusion that orthogonal Procrustes methods of factor rotation do produce spurious coefficients between predictor and criterion factor…
Descriptors: Factor Structure, Matrices, Monte Carlo Methods, Orthogonal Rotation
Takane, Yoshio; Hwang, Heungsun – Psychometrika, 2005
Lazraq and Cleroux (Psychometrika, 2002, 411-419) proposed a test for identifying the number of significant components in redundancy analysis. This test, however, is ill-conceived. A major problem is that it regards each redundancy component as if it were a single observed predictor variable, which cannot be justified except for the rare…
Descriptors: Redundancy, Monte Carlo Methods, Predictor Variables, Psychometrics

May, Kim; Hittner, James B. – Journal of Experimental Education, 1997
A Monte Carlo evaluation of four test statistics for comparing dependent zero-order correlations was conducted with four sample sizes and three population distributions. Results indicate that choice of optimal test statistic depends on sample size and distribution, and predictor intercorrelation and effect size or magnitude of the…
Descriptors: Correlation, Effect Size, Monte Carlo Methods, Predictor Variables
Fidalgo, Angel M.; Ferreres, Doris; Muniz, Jose – Journal of Experimental Education, 2004
The aim of this work was to determine, in terms of Type I and Type II error rates, the risks of applying various statistical procedures for evaluating differential item functioning. To this end, the authors carried out a simulation study in which the Mantel-Haenszel and SIBTEST procedures were applied in conjunction. The variables manipulated were…
Descriptors: Test Bias, Sample Size, Statistical Analysis, Predictor Variables
Finch, W. Holmes; Schneider, Mercedes K. – Educational and Psychological Measurement, 2006
This study compares the classification accuracy of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR), and classification and regression trees (CART) under a variety of data conditions. Past research has generally found comparable performance of LDA and LR, with relatively less research on QDA and…
Descriptors: Classification, Sample Size, Effect Size, Discriminant Analysis

Tracz, Susan M.; And Others – Educational and Psychological Measurement, 1992
Effects of violating the independence assumption when combining correlation coefficients in a meta-analysis were studied. This Monte-Carlo simulation varied sample size, predictor number, population intercorrelation among predictors, and population correlation between predictors and criterion. Combining statistics from nonindependent data in a…
Descriptors: Computer Simulation, Correlation, Equations (Mathematics), Mathematical Models
Silver, N. Clayton; Hittner, James B.; May, Kim – Journal of Experimental Education, 2004
The authors conducted a Monte Carlo simulation of 4 test statistics or comparing dependent correlations with no variables in common. Empirical Type 1 error rates and power estimates were determined for K. Pearson and L. N. G. Filon's (1898) z, O. J. Dunn and V. A. Clark's (1969) z, J. H. Steiger's (1980) original modification of Dunn and Clark's…
Descriptors: Monte Carlo Methods, Simulation, Effect Size, Sample Size
Ogasawara, Haruhiko – Psychometrika, 2004
Formulas for the asymptotic biases of the parameter estimates in structural equation models are provided in the case of the Wishart maximum likelihood estimation for normally and nonnormally distributed variables. When multivariate normality is satisfied, considerable simplification is obtained for the models of unstandardized variables. Formulas…
Descriptors: Evaluation Methods, Bias, Factor Analysis, Structural Equation Models
Lambert, Richard G.; Curlette, William L. – 1995
Validity generalization meta-analysis (VG) examines the extent to which the validity of an instrument can be transported across settings. VG offers correction and summarization procedures designed in part to remove the effects of statistical artifacts on estimates of association between criterion and predictor. By employing a random effects model,…
Descriptors: Correlation, Error of Measurement, Estimation (Mathematics), Meta Analysis