Jacob G. Beard
Florida State University
Lucinda Richards
Florida State University
Thomas Fisher
Florida Department of Education
Citation: Beard, J. G., Richards, L. & Fisher, T. (1983). The estimation of scaled scores and their frequency distributions from item bank difficulty values. Florida Journal of Educational Research, 25(1), 33-44.
Download: Beard.251.pdf (1718 downloads )
(2) Meta-Analysis and the Planning of Future Studies
James K. Brewer
Florida State University
Abstract: A recent meta-analysis on ability grouping by Kulik and Kulik (1982) was used as an example to demonstrate how a researcher might plan for adequate sample sizes and power in future research. Data sufficient to estimate minimum sample sizes and power were gleaned from the meta-analysis study or obtained from its authors. Distributions of harmonic mean sample size and estimated power were displayed and ranges of estimates for each were presented using effect sizes from the meta-analysis with fixed alpha. Although effect sizes were relatively symmetric about .10, power and harmonic mean sample sizes were quite skewed. Recommendations for major professors, re- searchers and journal editors were made to assist in the evaluation and planning of research.
Citation: Brewer, J. K. (1983). Meta-analysis and the planning of future studies. Florida Journal of Educational Research, 25(1), 61-77.
Download: Brewer.251.pdf (986 downloads )
(3) A Decision Theoretic Approach to Student Placement
James J. Higgins
Kansas State University
James R. Schwenke
Bristol-Myers Company
Abstract: A decision theoretic approach is proposed for scheduling a student into a future course when there is more than one possible choice. The statistical aspects of the decision theoretic placement procedure are discussed in detail. A linear model approach is given for utilizing items in a student’s records to estimate the probabilities of attaining various levels of performance or achievement as measured by an appropriate criterion. The probabilities so obtained are combined with subjective judgements via a utility function to arrive at a placement decision. An example is included illustrating the procedure for placing junior high mathematics students into one of two possible math courses.
Citation: Higgins, J. J., & Schwenke, J. R. (1983). A decision theoretic approach to student placement. Florida Journal of Educational Research, 25(1), 45-59.
Download: Higgins.251.pdf (1103 downloads )
(4) Detecting Potentially Biased Test Items
John R. Hills
Florida State University
F.J. King
Florida State University
Citation: Hills, J. R., & King, F. J. (1983). Detecting potentially biased test items. Florida Journal of Educational Research, 25(1), 79-95.
Download: Hills.251.pdf (1005 downloads )
(5) Instructional Strategies as Part of the Content Domain of a Criterion-Referenced Test
Mildred A. Murray
Hillsborough County School System
Citation: Murray, M. A. (1983). Instructional strategies as part of the content domain of a criterion-referenced test. Florida Journal of Educational Research, 25(1), 15-31.
Download: Murray.251.pdf (1855 downloads )
(6) On the Relative Power of Interaction Analysis
Richard L. Tate
Florida State University
Abstract: It is argued that statements in the current literature suggesting that interaction effects are, in general, as easy to detect as main effects are misleading. Different effect definitions which produce different conclusions about the relative power of interaction analysis are considered for both factorial ANOVA and aptitude-treatment-interaction models. Based on what is defined as a reasonable specification of “comparable” effects, it is concluded that the power for simple main and interaction effects is, in general, lower than that for the analysis of main effects.
Citation: Tate, R. L. (1983). On the relative power of interaction analysis. Florida Journal of Educational Research, 25(1), 1-13.
Download: Tate.251.pdf (1074 downloads )