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Cohen, Allan S.; And Others – Applied Psychological Measurement, 1996
Type I error rates for the likelihood ratio test for detecting differential item functioning (DIF) were investigated using Monte Carlo simulations. Type I error rates for the two-parameter model were within theoretically expected values at each alpha level, but those for the three-parameter model were not. (SLD)
Descriptors: Identification, Item Bias, Item Response Theory, Maximum Likelihood Statistics

Segall, Daniel O. – Journal of Educational and Behavioral Statistics, 2002
Developed an item response model for characterizing test-compromise that enables the estimation of item preview and score-gain distributions. In the approach, models parameters and posterior distributions are estimated by Markov Chain Monte Carlo procedures. Simulation study results suggest that when at least some test items are known to be…
Descriptors: Estimation (Mathematics), Item Response Theory, Markov Processes, Models

Holland, Paul W. – Psychometrika, 1990
The Dutch Identity is presented as a useful tool for expressing the basic equations of item response models that relate the manifest probabilities to the item response functions and the latent trait distribution. Ways in which the identity may be exploited are suggested and illustrated. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Item Response Theory, Mathematical Models

Molenaar, Ivo W.; Hoijtink, Herbert – Psychometrika, 1990
Statistical properties of person fit indices are reviewed as indicators of the extent to which a person's score pattern is in agreement with a measurement model. Distribution of a fit index and ability-free fit evaluation are discussed. The null distribution was simulated for a test of 20 items. (SLD)
Descriptors: Item Banks, Item Response Theory, Mathematical Models, Monte Carlo Methods

Lautenschlager, Gary J.; And Others – Educational and Psychological Measurement, 1989
A method for estimating the first eigenvalue of random data correlation matrices is reported, and its precision is demonstrated via comparison to the method of S. J. Allen and R. Hubbard (1986). Data generated in Monte Carlo simulations with 10 sample sizes reaching up to 1,000 were used. (SLD)
Descriptors: Computer Simulation, Correlation, Equations (Mathematics), Estimation (Mathematics)

Law, Kenneth S. – Journal of Educational and Behavioral Statistics, 1995
Two new methods of estimating the mean population correlation (M) and the standard deviation of population correlations (SD) were suggested and tested by Monte Carlo simulations. Results show no consistent advantage to using the Pearson correlation or Fisher's Z in estimating M or SD; estimates from all methods are similar. (SLD)
Descriptors: Computer Simulation, Correlation, Effect Size, Estimation (Mathematics)

Keselman, H. J.; And Others – Psychometrika, 1995
This paper explains how to obtain generally robust and powerful analyses with any of the recommended nonorthogonal solutions by adapting a modification of the Welch-James procedure for comparing means when population variances are heterogeneous. Results from a Monte Carlo study support use of the procedure. (SLD)
Descriptors: Comparative Analysis, Equations (Mathematics), Monte Carlo Methods, Power (Statistics)

DeSarbo, Wayne S.; And Others – Psychometrika, 1992
TSCALE, a multidimensional scaling procedure based on the contrast model of A. Tversky for asymmetric three-way, two-mode proximity data, is presented. TSCALE conceptualizes a latent dimensional structure to describe the judgmental stimuli. A Monte Carlo analysis and two consumer psychology applications illustrate the procedure. (SLD)
Descriptors: Consumer Economics, Data Analysis, Equations (Mathematics), Mathematical Models

Keselman, H. J. – Journal of Educational Statistics, 1994
Six stepwise multiple-comparison procedures for repeated-measures means were compared for their overall familywise rates of Type I error when multisample sphericity and multivariate normality were not satisfied. Robust stepwise procedures were identified by Keselman, Keselman, and Shaffer (1991) with respect to three definitions of power. (SLD)
Descriptors: Comparative Analysis, Equations (Mathematics), Monte Carlo Methods, Multivariate Analysis

Thompson, 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

Kano, Yutaka – Psychometrika, 1990
Based on the usual factor analysis model, this paper investigates the relationship between improper solutions and the number of factors. The properties of the noniterative estimation method of M. Ihara and Y. Kano in exploratory factor analysis are also discussed. The estimators were compared in a Monte Carlo experiment. (TJH)
Descriptors: Comparative Analysis, Estimation (Mathematics), Factor Analysis, Mathematical Models

Stone, Clement A.; Sobel, Michael E. – Psychometrika, 1990
Using Monte Carlo methods, the applicability of large sample theory to maximum likelihood estimates of total indirect effects in sample sizes of 50, 100, 200, 400, and 800 was studied. Samples of at least 200 and 400 are required for the recursive and nonrecursive models, respectively, that were assessed. (TJH)
Descriptors: Estimation (Mathematics), Mathematical Models, Maximum Likelihood Statistics, Monte Carlo Methods

Price, Lydia J. – Multivariate Behavioral Research, 1993
The ability of the NORMIX algorithm to recover overlapping population structures was compared to the OVERCLUS procedure and another clustering procedure in a Monte Carlo study. NORMIX is found to be more accurate than other procedures in recovering overlapping population structure when appropriate implementation options are specified. (SLD)
Descriptors: Algorithms, Classification, Cluster Analysis, Comparative Analysis

Bentler, Peter M.; Yuan, Ke-Hai – Multivariate Behavioral Research, 1999
Studied the small sample behavior of several test statistics based on the maximum-likelihood estimator but designed to perform better with nonnormal data. Monte Carlo results indicate the satisfactory performance of the "F" statistic recently proposed by K. Yuan and P. Bentler (1997). (SLD)
Descriptors: Estimation (Mathematics), Maximum Likelihood Statistics, Monte Carlo Methods, Sample Size
Monte Carlo Based Null Distribution for an Alternative Goodness-of-Fit Test Statistic in IRT Models.

Stone, Clement A. – Journal of Educational Measurement, 2000
Describes a goodness-of-fit statistic that considers the imprecision with which ability is estimated and involves constructing item fit tables based on each examinee's posterior distribution of ability, given the likelihood of the response pattern and an assumed marginal ability distribution. Also describes a Monte Carlo resampling procedure to…
Descriptors: Goodness of Fit, Item Response Theory, Mathematical Models, Monte Carlo Methods