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Temel, Gülhan Orekici; Erdogan, Semra; Selvi, Hüseyin; Kaya, Irem Ersöz – Educational Sciences: Theory and Practice, 2016
Studies based on longitudinal data focus on the change and development of the situation being investigated and allow for examining cases regarding education, individual development, cultural change, and socioeconomic improvement in time. However, as these studies require taking repeated measures in different time periods, they may include various…
Descriptors: Investigations, Sample Size, Longitudinal Studies, Interrater Reliability
Peer reviewedFava, Joseph L.; Velicer, Wayne F. – Multivariate Behavioral Research, 1992
Effects of overextracting factors and components within and between maximum likelihood factor analysis and principal components analysis were examined through computer simulation of a range of factor and component patterns. Results demonstrate similarity of component and factor scores during overextraction. Overall, results indicate that…
Descriptors: Computer Simulation, Correlation, Factor Analysis, Mathematical Models
Peer reviewedReddon, John R.; And Others – Journal of Educational Statistics, 1985
Computer sampling from a multivariate normal spherical population was used to evaluate the type one error rates for a test of sphericity based on the distribution of the determinant of the sample correlation matrix. (Author/LMO)
Descriptors: Computer Simulation, Correlation, Error of Measurement, Matrices
Peer reviewedMendoza, Jorge L.; And Others – Multivariate Behavioral Research, 1991
Using a Monte Carlo simulation, a bootstrap procedure was evaluated for setting a confidence interval on the unrestricted population correlation (rho) assuming various degrees of incomplete truncation on the predictor. Sample size was the most important factor in determining accuracy and stability. Sample size should be at least 50. (SLD)
Descriptors: Computer Simulation, Correlation, Estimation (Mathematics), Mathematical Models
Peer reviewedThompson, Bruce – Journal of Experimental Education, 1991
Monte Carlo methods were used to evaluate the degree to which canonical function and structure coefficients may be differentially sensitive to sampling error. For each of 64 research situations, 1,000 random samples were drawn. Both sets of coefficients were roughly equally influenced; some exceptions are noted. (SLD)
Descriptors: Behavioral Science Research, Computer Simulation, Correlation, Matrices
Peer reviewedZimmerman, Donald W.; Zumbo, Bruno D. – Journal of Experimental Education, 1992
A modified "F" test is derived that includes a correction for nonindependence of between-groups and within-groups sample values in analysis of variance (ANOVA) designs. Computer simulations based on normal and nonnormal distributions illustrate the usefulness of the approach, which was more powerful than conventional within-subjects…
Descriptors: Analysis of Variance, Computer Simulation, Correlation, Mathematical Models
Peer reviewedTracz, 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
Peer reviewedAllen, Nancy L.; Dunbar, Stephen B. – Applied Psychological Measurement, 1990
The standard error (SE) of correlations adjusted for selection with commonly used formulas was investigated. The study provides large-sample approximations of SE using the Pearson-Lawley three-variable correction formula, examines the SE under specific conditions, and compares various estimates of SEs under direct and indirect selection. (TJH)
Descriptors: Computer Simulation, Correlation, Demography, Error of Measurement
De Ayala, R. J. – 1993
Previous work on the effects of dimensionality on parameter estimation was extended from dichotomous models to the polytomous graded response (GR) model. A multidimensional GR model was developed to generate data in one-, two-, and three-dimensions, with two- and three-dimensional conditions varying in their interdimensional associations. Test…
Descriptors: Computer Simulation, Correlation, Difficulty Level, Estimation (Mathematics)
Chang, Yu-Wen; Davison, Mark L. – 1992
Standard errors and bias of unidimensional and multidimensional ability estimates were compared in a factorial, simulation design with two item response theory (IRT) approaches, two levels of test correlation (0.42 and 0.63), two sample sizes (500 and 1,000), and a hierarchical test content structure. Bias and standard errors of subtest scores…
Descriptors: Comparative Testing, Computer Simulation, Correlation, Error of Measurement
Peer reviewedCornwell, John M.; Ladd, Robert T. – Educational and Psychological Measurement, 1993
Simulated data typical of those from meta analyses are used to evaluate the reliability, Type I and Type II errors, bias, and standard error of the meta-analytic procedures of Schmidt and Hunter (1977). Concerns about power, reliability, and Type I errors are presented. (SLD)
Descriptors: Bias, Computer Simulation, Correlation, Effect Size
Chan, Jason C. – 1991
The following seven statistical procedures are compared in terms of the ability to recover a unidimensional latent trait from Likert-type data: (1) factor analysis based on Pearson correlations (FA-PR); (2) factor analysis based on polychoric correlations (FA-PL); (3) the graded response model in item response theory (IRT-GRM); (4) internal…
Descriptors: Comparative Analysis, Computer Simulation, Correlation, Equations (Mathematics)
Carlson, James E. – 1993
In this article some results are presented relating to the dimensionality of instruments containing polytomously scored as well as dichotomously scored items, concentrating on the 1992 National Assessment of Educational Progress' (NAEP) mathematics and reading assessment data and several simulated datasets. The maximum likelihood factor analytic…
Descriptors: Computer Simulation, Correlation, Elementary Secondary Education, Factor Analysis


