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Hemmert, Giselmar A. J.; Schons, Laura M.; Wieseke, Jan; Schimmelpfennig, Heiko – Sociological Methods & Research, 2018
The literature proposes numerous so-called pseudo-R[superscript 2] measures for evaluating "goodness of fit" in regression models with categorical dependent variables. Unlike ordinary least square-R[superscript 2], log-likelihood-based pseudo-R[superscript 2]s do not represent the proportion of explained variance but rather the…
Descriptors: Regression (Statistics), Sample Size, Predictor Variables, Benchmarking
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
Andrade Brito, Fernanda A. – ProQuest LLC, 2017
Nursing programs across the United States (U.S.) rely upon simulation to complement or substitute for traditional clinical experiences. The purpose of this secondary analysis study is to use de-identified National Nursing Education Network (NNERN) (2015-2016) survey data of nursing students who participated in simulation to examine which selected…
Descriptors: Nursing Education, Sample Size, Multiple Regression Analysis, Clinical Experience
Watkins, Ann E.; Bargagliotti, Anna; Franklin, Christine – Journal of Statistics Education, 2014
Although the use of simulation to teach the sampling distribution of the mean is meant to provide students with sound conceptual understanding, it may lead them astray. We discuss a misunderstanding that can be introduced or reinforced when students who intuitively understand that "bigger samples are better" conduct a simulation to…
Descriptors: Simulation, Sampling, Sample Size, Misconceptions
Finch, Holmes – Journal of Experimental Education, 2010
Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…
Descriptors: Simulation, Predictor Variables, Discriminant Analysis, Comparative Analysis
Kozak, Marcin – Teaching Statistics: An International Journal for Teachers, 2009
This article suggests how to explain a problem of small sample size when considering correlation between two Normal variables. Two techniques are shown: one based on graphs and the other on simulation. (Contains 3 figures and 1 table.)
Descriptors: Sample Size, Correlation, Predictor Variables, Simulation
Rhemtulla, Mijke; Brosseau-Liard, Patricia E.; Savalei, Victoria – Psychological Methods, 2012
A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category…
Descriptors: Factor Analysis, Computation, Simulation, Sample Size
Chan, Wai – Educational and Psychological Measurement, 2009
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…
Descriptors: Multiple Regression Analysis, Intervals, Correlation, Computation
Maydeu-Olivares, Alberto; Coffman, Donna L.; Hartmann, Wolfgang M. – Psychological Methods, 2007
The point estimate of sample coefficient alpha may provide a misleading impression of the reliability of the test score. Because sample coefficient alpha is consistently biased downward, it is more likely to yield a misleading impression of poor reliability. The magnitude of the bias is greatest precisely when the variability of sample alpha is…
Descriptors: Intervals, Scores, Sample Size, Simulation
Peer reviewedMacKinnon, David P.; And Others – Multivariate Behavioral Research, 1995
Analytical solutions for point and variance estimators of the mediated effect, the ratio of mediated to direct effect, and the proportion of the total effect mediated were determined through simulation for different samples. The sample sizes needed for accuracy and stability are discussed with implications for mediated effects estimates. (SLD)
Descriptors: Equations (Mathematics), Error of Measurement, Estimation (Mathematics), Multivariate Analysis
Peer reviewedRaymond, Mark R.; Roberts, Dennis M. – Educational and Psychological Measurement, 1987
Data were simulated to conform to covariance patterns taken from personnel selection literature. Incomplete data matrices were treated by four methods. Treated matrices were subjected to multiple regression analyses. Resulting regression equations were compared to equations from original, complete data. Results supported using covariate…
Descriptors: Data Analysis, Matrices, Multiple Regression Analysis, Personnel Selection
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
Peer reviewedBroodbooks, Wendy J.; Elmore, Patricia B. – Educational and Psychological Measurement, 1987
The effects of sample size, number of variables, and population value of the congruence coefficient on the sampling distribution of the congruence coefficient were examined. Sample data were generated on the basis of the common factor model, and principal axes factor analyses were performed. (Author/LMO)
Descriptors: Factor Analysis, Mathematical Models, Monte Carlo Methods, Predictor Variables
Gierl, Mark J.; Leighton, Jacqueline P.; Tan, Xuan – Journal of Educational Measurement, 2006
DETECT, the acronym for Dimensionality Evaluation To Enumerate Contributing Traits, is an innovative and relatively new nonparametric dimensionality assessment procedure used to identify mutually exclusive, dimensionally homogeneous clusters of items using a genetic algorithm ( Zhang & Stout, 1999). Because the clusters of items are mutually…
Descriptors: Program Evaluation, Cluster Grouping, Evaluation Methods, Multivariate Analysis
Long, Jeffrey D. – Psychological Methods, 2005
Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q[superscript 2]…
Descriptors: Simulation, Social Science Research, Error of Measurement, Least Squares Statistics

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