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Shear, Benjamin R.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2013
Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…
Descriptors: Error of Measurement, Multiple Regression Analysis, Data Analysis, Computer Simulation
DeMars, Christine E. – Educational and Psychological Measurement, 2005
Type I error rates for PARSCALE's fit statistic were examined. Data were generated to fit the partial credit or graded response model, with test lengths of 10 or 20 items. The ability distribution was simulated to be either normal or uniform. Type I error rates were inflated for the shorter test length and, for the graded-response model, also for…
Descriptors: Test Length, Item Response Theory, Psychometrics, Error of Measurement

Zimmerman, Donald W. – Educational and Psychological Measurement, 1985
A computer program simulated guessing on multiple-choice test items and calculated deviation IQ's from observed scores which contained a guessing component. Extensive variability in deviation IQ's due entirely to chance was found. (Author/LMO)
Descriptors: Computer Simulation, Error of Measurement, Guessing (Tests), Intelligence Quotient

Smith, Richard M. – Educational and Psychological Measurement, 1988
This study investigated the distributional properties of the standardized residual that is commonly used in the Rasch model calibration program to develop various indices of item and person fit. The power of the standardized residual to detect measurement disturbances is also addressed. (TJH)
Descriptors: Computer Simulation, Error of Measurement, Goodness of Fit, Guessing (Tests)

Jamieson, John – Educational and Psychological Measurement, 1995
Computer simulations indicate that the correlation between baseline and change, by itself, does not invalidate the use of gain scores to measure change, but when the negative correlation is accompanied by decrease in variance from pretest to posttest, covariance is a superior measure of change. (SLD)
Descriptors: Analysis of Covariance, Change, Computer Simulation, Correlation

Cornwell, 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

De Ayala, R. J. – Educational and Psychological Measurement, 1992
Effects of dimensionality on ability estimation of an adaptive test were examined using generated data in Bayesian computerized adaptive testing (CAT) simulations. Generally, increasing interdimensional difficulty association produced a slight decrease in test length and an increase in accuracy of ability estimation as assessed by root mean square…
Descriptors: Adaptive Testing, Bayesian Statistics, Computer Assisted Testing, Computer Simulation