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Menglin Xu; Jessica A. R. Logan – Educational and Psychological Measurement, 2024
Research designs that include planned missing data are gaining popularity in applied education research. These methods have traditionally relied on introducing missingness into data collections using the missing completely at random (MCAR) mechanism. This study assesses whether planned missingness can also be implemented when data are instead…
Descriptors: Research Design, Research Methodology, Monte Carlo Methods, Statistical Analysis
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Simsek, Ahmet Salih – International Journal of Assessment Tools in Education, 2023
Likert-type item is the most popular response format for collecting data in social, educational, and psychological studies through scales or questionnaires. However, there is no consensus on whether parametric or non-parametric tests should be preferred when analyzing Likert-type data. This study examined the statistical power of parametric and…
Descriptors: Error of Measurement, Likert Scales, Nonparametric Statistics, Statistical Analysis
Jinjin Huang – ProQuest LLC, 2020
Measurement invariance is crucial for an effective and valid measure of a construct. Invariance holds when the latent trait varies consistently across subgroups; in other words, the mean differences among subgroups are only due to true latent ability differences. Differential item functioning (DIF) occurs when measurement invariance is violated.…
Descriptors: Robustness (Statistics), Item Response Theory, Test Items, Item Analysis
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Abdel-Megeed, Samir M. – Journal of Experimental Education, 1984
The minimum number of points required for continuous scaled variables before the Pearson product moment correlation coefficient (PPMCC) ceases to be an accurate estimate if their original correlation coefficient is five. Results also indicated that the PPMCC obtained by using transformed discrete ordinal-level variables tended to underestimate the…
Descriptors: Correlation, Monte Carlo Methods, Research Problems, Scaling
Hutchinson, Susan R. – 1994
The work of R. MacCallum et al. (1992) was extended by examining chance modifications through a Monte Carlo simulation. The stability of post hoc model modifications was examined under varying sample size, model complexity, and severity of misspecification using 2- and 4-factor oblique confirmatory factor analysis (CFA) models with four and eight…
Descriptors: Computer Simulation, Models, Monte Carlo Methods, Reliability
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Thompson, Bruce – 1989
In the present study Monte Carlo methods were employed to evaluate the degree to which canonical function and structure coefficients may be differentially sensitive to sampling error. Sampling error influences were investigated across variations in variable and sample (n) sizes, and across variations in average within-set correlation sizes and in…
Descriptors: Computer Simulation, Correlation, Monte Carlo Methods, Multivariate Analysis
Tryon, Warren W. – 1984
A normally distributed data set of 1,000 values--ranging from 50 to 150, with a mean of 50 and a standard deviation of 20--was created in order to evaluate the bootstrap method of repeated random sampling. Nine bootstrap samples of N=10 and nine more bootstrap samples of N=25 were randomly selected. One thousand random samples were selected from…
Descriptors: Computer Simulation, Estimation (Mathematics), Higher Education, Monte Carlo Methods
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Zimmerman, Donald W. – Journal of Experimental Education, 1987
A program obtained random samples from known populations, some of which violated the homogeneity assumption. Student t tests and Mann-Whitney U Tests were performed on the sample value. Where the t test led to incorrect decisions, the use of Mann-Whitney U test in its place led to poorer results. (JAZ)
Descriptors: Computer Software, Error of Measurement, Monte Carlo Methods, Nonparametric Statistics
Baldwin, Lee; Medley, Donald M. – 1982
The purpose of this study was to compare the results of an analysis of covariance design to a method developed by the authors called within-class regression. Application of this statistical method can be to any quasiexperimental design with a large number of groups. A test of statistical significance to use with the within-class regression…
Descriptors: Analysis of Covariance, Hypothesis Testing, Matched Groups, Monte Carlo Methods
Robey, Randall R.; Barcikowski, Robert S. – 1986
This paper reports the results of a Monte Carlo investigation of Type I errors in the single group repeated measures design where multiple measures are collected from each observational unit at each measurement occasion. The Type I error of three multivariate tests were examined. These were the doubly multivariate F test, the multivariate mixed…
Descriptors: Analysis of Variance, Behavioral Science Research, Comparative Analysis, Hypothesis Testing