NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 6 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Huang, Francis L. – Journal of Experimental Education, 2022
Experiments in psychology or education often use logistic regression models (LRMs) when analyzing binary outcomes. However, a challenge with LRMs is that results are generally difficult to understand. We present alternatives to LRMs in the analysis of experiments and discuss the linear probability model, the log-binomial model, and the modified…
Descriptors: Regression (Statistics), Monte Carlo Methods, Probability, Error Patterns
Peer reviewed Peer reviewed
Direct linkDirect link
Schoeneberger, Jason A. – Journal of Experimental Education, 2016
The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying…
Descriptors: Sample Size, Models, Computation, Predictor Variables
Peer reviewed Peer reviewed
Direct linkDirect link
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
Peer reviewed Peer reviewed
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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