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Peer reviewedDe Ayala, R. J. – Journal of Applied Measurement, 2003
Studied four different approaches for handling missing data for their capacity to mitigate against the effect of omitted responses on person location estimation. Results from a Monte Carlo study show that the hot-decking procedure performed best of the methods examined. (SLD)
Descriptors: Data Analysis, Monte Carlo Methods, Rating Scales, Simulation
Peer reviewedSeltzer, Michael; Novak, John; Choi, Kilchan; Lim, Nelson – Journal of Educational and Behavioral Statistics, 2002
Examines the ways in which level-1 outliers can impact the estimation of fixed effects and random effects in hierarchical models (HMs). Also outlines and illustrates the use of Markov Chain Monte Carlo algorithms for conducting sensitivity analyses under "t" level-1 assumptions, including algorithms for settings in which the degrees of…
Descriptors: Algorithms, Estimation (Mathematics), Markov Processes, Monte Carlo Methods
Peer reviewedReinartz, Werner J.; Echambadi, Raj; Cin, Wynne W. – Multivariate Behavioral Research, 2002
Tested empirically the applicability of a method developed by S. Mattson for generating data on latent variables with controlled skewness and kurtosis of the observed variables. Monte Carlo simulation results suggest that Mattson's method appears to be a good approach to generate data with defined levels of skewness and kurtosis. (SLD)
Descriptors: Computer Simulation, Monte Carlo Methods, Structural Equation Models
Peer reviewedDonoghue, John R. – Multivariate Behavioral Research, 1995
This article examines using moment-based statistics to screen variables that are then used in clustering. A Monte Carlo study found that screening variables was a viable alternative to both ultrametric weighting and forward selection of variables. Advantages and disadvantages of screening are discussed. (SLD)
Descriptors: Cluster Analysis, Monte Carlo Methods, Research Methodology, Selection
Peer reviewedBarnette, J. Jackson – Educational and Psychological Measurement, 1999
Investigated the effects of types and prevalence of response patterns that might be provided by nonattending respondents on Cronbach's alpha (L. Cronbach, 1970) using three simulated data sets. Effects were greater as a function of increased prevalence in the respondent group, but as few as 5% of some types of nonattending patterns had inflating…
Descriptors: Attention, Monte Carlo Methods, Reliability, Responses
Peer reviewedTataryn, Douglas J.; Wood, James M.; Gorsuch, Richard L. – Educational and Psychological Measurement, 1999
Examined the optimal value of "k" for promax factor rotations through a Monte Carlo study involving 10,080 factor analyses. Results show that in factor-analytic studies using promax, the value of "k" may be set appropriately at 2, 3, or 4. (Author/SLD)
Descriptors: Factor Analysis, Factor Structure, Monte Carlo Methods, Simulation
Peer reviewedSeraphine, Anne E. – Applied Psychological Measurement, 2000
Examined the performance of DIMTEST, through simulation, for unidimensional and two-dimensional data that exhibited ceiling effects generated through changes in location and scale of the theta distribution. Results indicate that the power of DIMTEST is reduced as the location shifts upward and the scale shifts downward. Considers the selection…
Descriptors: Difficulty Level, Item Response Theory, Monte Carlo Methods
Peer reviewedBijmolt, Tammo H. A.; DeSarbo, Wayne S.; Wedel, Michel – Multivariate Behavioral Research, 1998
A multidimensional scaling procedure is introduced that attempts to derive a spatial representation of stimuli unconfounded by the effect of subjects' degrees of familiarity with these stimuli. A Monte Carlo study investigating the extent to which the procedure recovers known parameters shows that the procedure succeeds in adjusting for…
Descriptors: Familiarity, Models, Monte Carlo Methods, Multidimensional Scaling
Peer reviewedPaxton, Pamela; Curran, Patrick J.; Bollen, Kenneth A.; Kirby, Jim; Chen, Feinian – Structural Equation Modeling, 2001
Illustrates the design and planning of Monte Carlo simulations, presenting nine steps in planning and performing a Monte Carlo analysis from developing a theoretically derived question of interest through summarizing the results. Uses a Monte Carlo simulation to illustrate many of the relevant points. (SLD)
Descriptors: Monte Carlo Methods, Research Design, Simulation, Statistical Analysis
Peer reviewedTomas, Jose M.; Hontangas, Pedro M.; Oliver, Amparo – Multivariate Behavioral Research, 2000
Assessed two models for confirmatory factor analysis of multitrait-multimethod data through Monte Carlo simulation. The correlated traits-correlated methods (CTCM) and the correlated traits-correlated uniqueness (CTCU) models were compared. Results suggest that CTCU is a good alternative to CTCM in the typical multitrait-multimethod matrix, but…
Descriptors: Matrices, Monte Carlo Methods, Multitrait Multimethod Techniques, Simulation
Peer reviewedRupinski, Melvin T.; Dunlap, William P. – Educational and Psychological Measurement, 1996
The use of Monte Carlo methods demonstrated that a formula presented by M. G. Kendall for estimating Pearson's rho from tau is somewhat more accurate than a formula presented by K. Pearson for estimating Pearson's rho from a Spearman's rho coefficient. Implications for meta-analysis of correlations are discussed. (SLD)
Descriptors: Correlation, Estimation (Mathematics), Meta Analysis, Monte Carlo Methods
Peer reviewedDumenci, Levent; Windle, Michael – Multivariate Behavioral Research, 2001
Used Monte Carlo methods to evaluate the adequacy of cluster analysis to recover group membership based on simulated latent growth curve (LCG) models. Cluster analysis failed to recover growth subtypes adequately when the difference between growth curves was shape only. Discusses circumstances under which it was more successful. (SLD)
Descriptors: Cluster Analysis, Group Membership, Monte Carlo Methods, Simulation
Peer reviewedBrusco, Michael J.; Cradit, J. Dennis – Psychometrika, 2001
Presents a variable selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. Subjected the heuristic to Monte Carlo testing across more than 2,200 datasets. Results indicate that the heuristic is extremely effective at eliminating masking variables. (SLD)
Descriptors: Cluster Analysis, Heuristics, Monte Carlo Methods, Selection
Thornton, Thomas L.; Gilden, David L. – Psychological Review, 2007
A long-standing issue in the study of how people acquire visual information centers around the scheduling and deployment of attentional resources: Is the process serial, or is it parallel? A substantial empirical effort has been dedicated to resolving this issue. However, the results remain largely inconclusive because the methodologies that have…
Descriptors: Data Interpretation, Monte Carlo Methods, Cognitive Processes, Research Methodology
Fan, Xitao; Wang, Lin – 1995
The jackknife and bootstrap methods are becoming more popular in research. Although the two approaches have similar goals and employ similar strategies, information is lacking with regard to the comparability of their results. This study systematically investigated the issue for a canonical correlation analysis, using data from four random samples…
Descriptors: Comparative Analysis, Correlation, Monte Carlo Methods, Sample Size

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