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Swaminathan, Hariharan; Hambleton, Ronald K.; Sireci, Stephen G.; Xing, Dehui; Rizavi, Saba M. – 2003
The primary objective of this study was to investigate how incorporating prior information improves estimation of item parameters in two small samples. The factors that were investigated were sample size and the type of prior information. To investigate the accuracy with which item parameters in the Law School Admission Test (LSAT) are estimated,…
Descriptors: Estimation (Mathematics), Item Response Theory, Sample Size, Sampling
Lewis, Charla P. – 1999
The sampling distribution is a common source of misuse and misunderstanding in the study of statistics. The sampling distribution, underlying distribution, and the Central Limit Theorem are all interconnected in defining and explaining the proper use of the sampling distribution of various statistics. The sampling distribution of a statistic is…
Descriptors: Estimation (Mathematics), Probability, Sample Size, Sampling
Fan, Xitao; Chen, Michael – 1999
It is erroneous to extend or generalize the inter-rater reliability coefficient estimated from only a (small) proportion of the sample to the rest of the sample data where only one rater is used for scoring, although such generalization is often made implicitly in practice. It is shown that if inter-rater reliability estimate from part of a sample…
Descriptors: Estimation (Mathematics), Generalizability Theory, Interrater Reliability, Sample Size
Peer reviewedLa Du, Terence J.; Tanaka, J. S. – Multivariate Behavioral Research, 1995
After reviewing the multiple fit indices in structural equation models, evidence on their behavior is presented through simulation studies in which sample size, estimation method, and model misspecification varied. Two sampling studies, with and without known populations, are presented, and implications for the use of fit indices are discussed.…
Descriptors: Estimation (Mathematics), Goodness of Fit, Sample Size, Sampling
Rennie, Kimberly M. – 1997
This paper explains the underlying assumptions of the sampling distribution and its role in significance testing. To compute statistical significance, estimates of population parameters must be obtained so that only one sampling distribution is defined. A sampling distribution is the underlying distribution of a statistic. Sampling distributions…
Descriptors: Analysis of Variance, Estimation (Mathematics), Sample Size, Sampling
Fan, Xitao – 1994
This paper empirically and systematically assessed the performance of bootstrap resampling procedure as it was applied to a regression model. Parameter estimates from Monte Carlo experiments (repeated sampling from population) and bootstrap experiments (repeated resampling from one original bootstrap sample) were generated and compared. Sample…
Descriptors: Estimation (Mathematics), Monte Carlo Methods, Regression (Statistics), Sample Size
Groenewald, A. C.; Stoker, D. J. – 1990
In a complex sampling scheme it is desirable to select the primary sampling units (PSUs) without replacement to prevent duplications in the sample. Since the estimation of the sampling variances is more complicated when the PSUs are selected without replacement, L. Kish (1965) recommends that the variance be calculated using the formulas…
Descriptors: Equations (Mathematics), Estimation (Mathematics), Foreign Countries, Mathematical Models
Peer reviewedBentler, 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
Wang, Lin; McNamara, James F. – 1997
This paper shares the findings of an inquiry that evaluated 50 survey articles published in a refereed journal, "Educational Administration Quarterly," by examining the 53 sample designs reported in the articles. The paper presents a typology of the sample designs identified, discusses the problems of sample selection and estimation…
Descriptors: Classification, Editing, Educational Research, Estimation (Mathematics)
Giroir, Mary M.; Davidson, Betty M. – 1989
Replication is important to viable scientific inquiry; results that will not replicate or generalize are of very limited value. Statistical significance enables the researcher to reject or not reject the null hypothesis according to the sample results obtained, but statistical significance does not indicate the probability that results will be…
Descriptors: Estimation (Mathematics), Generalizability Theory, Hypothesis Testing, Probability
Thompson, Bruce – 1992
Three criticisms of overreliance on results from statistical significance tests are noted. It is suggested that: (1) statistical significance tests are often tautological; (2) some uses can involve comparisons that are not completely sensible; and (3) using statistical significance tests to evaluate both methodological assumptions (e.g., the…
Descriptors: Effect Size, Estimation (Mathematics), Evaluation Methods, Regression (Statistics)
Daniel, Thomas Dyson – 1993
Statistical power in music education was examined by taking an in-depth look at quantitative articles published in the "Journal of Research in Music Education" between 1987 and 1991, inclusive. Of the 109 articles of the period, 78 were quantitative, with both parametric and nonparametric procedures considered. Sample sizes were those…
Descriptors: Effect Size, Estimation (Mathematics), Music Education, Nonparametric Statistics
Williams, Janice E. – 1987
A Monte Carlo study was done to determine the adequate sample size for quasi-experimental regression studies, which compare regression lines for two groups and estimate their point of intersection. Populations of 1,000 subjects in each of two groups were constructed (using random normal deviates) to yield equivalent regression lines of opposite…
Descriptors: Computer Simulation, Estimation (Mathematics), Monte Carlo Methods, Quasiexperimental Design
Peer reviewedThompson, Paul A. – Multivariate Behavioral Research, 1991
Application of the bootstrap method to complex psychological analysis is illustrated using a simulation experiment with two populations with small and large samples. The method provides variance estimates, allows testing of nested competing models, and gives a preliminary idea about parameter variability. (SLD)
Descriptors: Computer Simulation, Equations (Mathematics), Error of Measurement, Estimation (Mathematics)
Peer reviewedHarris, Richard J.; Quade, Dana – Journal of Educational Statistics, 1992
A method is proposed for calculating the sample size needed to achieve acceptable statistical power with a given test. The minimally important difference significant (MIDS) criterion for sample size is explained and supported with recommendations for determining sample size. The MIDS criterion is computationally simple and easy to explain. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Experimental Groups, Mathematical Models


