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| Journal of Educational… | 12 |
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| Journal Articles | 12 |
| Reports - Evaluative | 12 |
| Speeches/Meeting Papers | 1 |
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| National Assessment of… | 3 |
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Peer reviewedHodges, J. L., Jr.; And Others – Journal of Educational Statistics, 1990
An Edgeworth approximation for accurate significance probabilities for the Wilcoxon two-sample test is substantially simplified. A method is developed that allows quick calculations of very accurate probabilities. Exact formulas are given for most of the remaining cases, and tables are presented comparing the new simplification to likely…
Descriptors: Equations (Mathematics), Mathematical Models, Probability, Sampling
Peer reviewedViana, Marlos A. G. – Journal of Educational Statistics, 1993
Use of linear combinations of Fisher's "z" transformations as a combined test for the common correlation parameter based on "k" independent sample correlations has been previously studied. This article considers additional "z" additive properties and methods of combining independent studies when planning the number of…
Descriptors: Bayesian Statistics, Correlation, Equations (Mathematics), Evaluation Criteria
Peer reviewedGibbons, Robert D.; And Others – Journal of Educational Statistics, 1993
A distribution theory is derived for a G. V. Glass-type (1976) estimator of effect size from studies involving paired comparisons. The possibility of combining effect sizes from studies involving a mixture of related and unrelated samples is also explored. Resulting estimates are illustrated using data from previous psychiatric research. (SLD)
Descriptors: Effect Size, Equations (Mathematics), Estimation (Mathematics), Generalization
Peer reviewedWilcox, Rand R. – Journal of Educational Statistics, 1990
Recently, C. E. McCulloch (1987) suggested a modification of the Morgan-Pitman test for comparing the variances of two dependent groups. This paper demonstrates that there are situations where the procedure is not robust. A subsample approach, similar to the Box-Scheffe test, and the Sandvik-Olsson procedure are also assessed. (TJH)
Descriptors: Comparative Analysis, Equations (Mathematics), Error of Measurement, Mathematical Models
Peer reviewedWilson, Mark – Journal of Educational Statistics, 1989
An empirical sampling approach was used to assess the accuracy of a Taylor approximation for the estimation of sampling errors. The sampling errors were in the statistics involved in estimating a path model based on medium-sized samples gathered using five sample designs commonly used in educational research. (TJH)
Descriptors: Educational Research, Error of Measurement, Estimation (Mathematics), Mathematical Models
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
Peer reviewedJohnson, Eugene G.; Rust, Keith F. – Journal of Educational Statistics, 1992
The use of sampling weights in deriving population estimates for the National Assessment of Educational Progress (NAEP) and the effects of nonresponse and undercoverage on those estimates are described. The estimation of sampling variability from complex sample surveys is also reviewed, concentrating on the jackknife repeated replication…
Descriptors: Educational Assessment, Elementary Secondary Education, Estimation (Mathematics), Mathematical Models
Peer reviewedJohnson, Eugene G. – Journal of Educational Statistics, 1989
The effects of certain characteristics (e.g., sample design) of National Assessment of Educational Progress (NAEP) data on statistical analysis techniques are considered. Ignoring special features of NAEP data and proceeding with a standard analysis can produce inferences that underestimate the true variability and overestimate the true degrees of…
Descriptors: Data Collection, Educational Assessment, Elementary Secondary Education, Estimation (Mathematics)
Peer reviewedSpencer, Bruce D.; Foran, Wendene – Journal of Educational Statistics, 1991
Surveys in which one can observe--after sample selection--that each sample member belongs to one or more aggregations are considered. A formula for the probability that a given aggregation contains at least one sample member is applied to eighth grade data from the National Educational Longitudinal Study of 1988 (NELS:88). (TJH)
Descriptors: Educational Assessment, Equations (Mathematics), Estimation (Mathematics), Grade 8
Peer reviewedAlbert, James H. – Journal of Educational Statistics, 1992
Estimating item parameters from a two-parameter normal ogive model is considered using Gibbs sampling to simulate draws from the joint posterior distribution of ability and item parameters. The method gives marginal posterior density estimates for any parameter of interest, as illustrated using data from a 33-item mathematics placement…
Descriptors: Algorithms, Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics)
Peer reviewedRust, Keith F.; Johnson, Eugene G. – Journal of Educational Statistics, 1992
Procedures for obtaining student samples for the National Assessment of Educational Progress (NAEP) and deriving survey weights for analysis of survey data are described. Sample designs are economically and operationally feasible, and weighting procedures result in increased precision of estimates as they account for the probabilities of student…
Descriptors: Data Analysis, Educational Assessment, Elementary Secondary Education, Estimation (Mathematics)
Peer reviewedRaudenbush, Stephen W.; And Others – Journal of Educational Statistics, 1991
A three-level multivariate statistical modeling strategy is presented that resolves the question of whether the unit of analysis should be the teacher or the student. A reanalysis of U.S. high school data (51 Catholic and 59 public schools from the High School and Beyond survey) illustrates the model. (SLD)
Descriptors: Algorithms, Catholic Schools, Educational Environment, Equations (Mathematics)


