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Hsin-Yun Lee; You-Lin Chen; Li-Jen Weng – Journal of Experimental Education, 2024
The second version of Kaiser's Measure of Sampling Adequacy (MSA[subscript 2]) has been widely applied to assess the factorability of data in psychological research. The MSA[subscript 2] is developed in the population and little is known about its behavior in finite samples. If estimated MSA[subscript 2]s are biased due to sampling errors,…
Descriptors: Error of Measurement, Reliability, Sampling, Statistical Bias
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
Skidmore, Susan Troncoso; Thompson, Bruce – Journal of Experimental Education, 2011
In the present Monte Carlo simulation study, the authors compared bias and precision of 7 sampling error corrections to the Pearson r[superscript 2] under 6 x 3 x 6 conditions (i.e., population ρ values of 0.0, 0.1, 0.3, 0.5, 0.7, and 0.9, respectively; population shapes normal, skewness = kurtosis = 1, and skewness = -1.5 with kurtosis =…
Descriptors: Monte Carlo Methods, Sampling, Error Correction, Statistical Bias
Harwell, Michael; Maeda, Yukiko – Journal of Experimental Education, 2008
There is general agreement that meta-analysis is an important tool for synthesizing study results in quantitative educational research. Yet, a shared feature of many meta-analyses is a failure to report sufficient information for readers to fully judge the reported findings, such as the populations to which generalizations are to be made,…
Descriptors: Educational Research, Meta Analysis, Research Methodology, Statistical Analysis

Zimmerman, Donald W. – Journal of Experimental Education, 1986
A computer program randomly sampled ordered pairs of scores from known populations that departed from bivariate normal form and calculated correlation coefficients from sample values. Hypotheses were tested (1) that population correlations are zero using the t statistic; and (2) that population correlations have non-zero values using the r to z…
Descriptors: Correlation, Hypothesis Testing, Sampling, Statistical Distributions

Hsu, Tse-Chi; Sebatane, E. Molapi – Journal of Experimental Education, 1979
A Monte Carlo technique was used to investigate the effect of the differences in covariate means among treatment groups on the significance level and the power of the F-test of the analysis of covariance. (Author/GDC)
Descriptors: Analysis of Covariance, Correlation, Research Design, Research Problems