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Gemici, Sinan; Rojewski, Jay W.; Lee, In Heok – Career and Technical Education Research, 2012
Most quantitative analyses in workforce education are affected by missing data. Traditional approaches to remedy missing data problems often result in reduced statistical power and biased parameter estimates due to systematic differences between missing and observed values. This article examines the treatment of missing data in pertinent…
Descriptors: Research Methodology, Statistical Analysis, Data Collection, Pattern Recognition
Wells, Ryan S.; Kolek, Ethan A.; Williams, Elizabeth A.; Saunders, Daniel B. – Journal of Higher Education, 2015
This study replicates and extends a 2004 content analysis of three major higher education journals. The original study examined the methodological characteristics of all published research in these journals from 1996 to 2000, recommending that higher education programs adjust their graduate training to better match the heavily quantitative and…
Descriptors: Research Methodology, Higher Education, Journal Articles, Educational Research
Pohl, Steffi; Steiner, Peter M.; Eisermann, Jens; Soellner, Renate; Cook, Thomas D. – Educational Evaluation and Policy Analysis, 2009
Adjustment methods such as propensity scores and analysis of covariance are often used for estimating treatment effects in nonexperimental data. Shadish, Clark, and Steiner used a within-study comparison to test how well these adjustments work in practice. They randomly assigned participating students to a randomized or nonrandomized experiment.…
Descriptors: Statistical Analysis, Social Science Research, Statistical Bias, Statistical Inference
Feir, Betty J.; Toothaker, Larry E. – 1974
Researchers are often in a dilemma as to whether parametric or nonparametric procedures should be cited when assumptions of the parametric methods are thought to be violated. Therefore, the Kruskal-Wallis test and the ANOVA F-test were empirically compared in terms of probability of a Type I error and power under various patterns of mean…
Descriptors: Analysis of Variance, Comparative Analysis, Nonparametric Statistics, Sampling
Morris, John D.; Huberty, Carl J. – 1986
Formulas for estimating cross-validated hit-rates, the number of correct classifications into an a priori grouping structure, were examined. The following mathematical formulas were compared: McLachlan's formula estimator, two Snappin and Knoke smoothed formula estimators, and the analytic leave-one-out estimator. The R method was included as a…
Descriptors: Classification, Comparative Analysis, Correlation, Estimation (Mathematics)
Peer reviewedHinkle, Dennis E.; And Others – Educational and Psychological Measurement, 1985
The authors previously discussed the importance of effect size and Type II errors as factors in determining sample size. Tables were developed and presented for one-factor designs with k levels. As these tables could not be used for the one sample case, comparable tables were developed and are presented. (Author/DWH)
Descriptors: Comparative Analysis, Effect Size, Research Methodology, Sample Size
Peer reviewedHollenbeck, George P. – Educational and Psychological Measurement, 1972
The Little Jiffy" consists of principal components analysis and varimax rotation of all components with eigenvalues greater than one. (DG)
Descriptors: Cognitive Development, Comparative Analysis, Factor Analysis, Factor Structure
Alvord, Gregory; Macready, George B. – 1982
The Pearson and likelihood ratio statistics are frequently used for assessing the absolute fit of probability models. Researchers are often interested in comparing fits provided by different models which may have a subsuming or non-subsuming relation. A subsuming relation exists when the parameters of the reduced model form a subset of those…
Descriptors: Comparative Analysis, Goodness of Fit, Latent Trait Theory, Mathematical Models
STEWART, E. ELIZABETH; WILLIAMS, RICHARD H. – 1967
THIS DOCUMENT IS SECTION 1 OF A 3-PART REPORT BY THE EDUCATIONAL TESTING SERVICE. THIS SECTION DESCRIBES, IN EXTENSIVE STATISTICAL TERMS, A SAMPLE OF 445 HEAD START CHILDREN IN TERMS OF THEIR SCORES ON (1) THE STANFORD-BINET L-M, (2) THE CALDWELL PRESCHOOL INVENTORY, AND (3) THE PROJECT HEAD START BEHAVIOR INVENTORY. THE SAMPLING PROCEDURES USED…
Descriptors: Comparative Analysis, Enrichment Activities, National Norms, Participant Characteristics
Halperin, Si; Jorgensen, Randall – 1994
The concept of control is fundamental to comparative research. In research designs where randomization of observational units is not possible, control has been exercised statistically from a single covariate by a process of residualization. The alternative, known as subclassification on the propensity score, was developed primarily for…
Descriptors: Comparative Analysis, Control Groups, Psychological Studies, Research Design
Convey, John J. – 1979
The testing of a priori contrasts, post hoc contrasts, and experimental error rates are discussed. Methods for controlling the experimental error rate for a set of a priori contrasts tested simultaneously have been developed by Dunnett, Dunn, Sidak, and Krishnaiah. Each of these methods is discussed and contrasted as to applicability, power, and…
Descriptors: Comparative Analysis, Hypothesis Testing, Literature Reviews, Mathematical Models
Peer reviewedBrandenburg, Dale C.; Forsyth, Robert A. – Journal of Educational and Psychological Measurement, 1974
Descriptors: Achievement Tests, Comparative Analysis, Item Sampling, Mathematical Models
Blumstein, Alfred; Cohen, Jacqueline – Evaluation Quarterly, 1979
Evaluations involving nonrandom assignment to treatment or control groups are vulnerable to an accidental or intentional confounding of a selection effect with the treatment effect. Two techniques, discriminant analysis and base expectancy analysis, permit separate estimation of the selection and treatment effects in the final results. (Author/CTM)
Descriptors: Comparative Analysis, Discriminant Analysis, Hypothesis Testing, Research Design
The Comparability of the Statistical Characteristics of Test Items Generated by Computer Algorithms.
Meisner, Richard; And Others – 1993
This paper presents a study on the generation of mathematics test items using algorithmic methods. The history of this approach is briefly reviewed and is followed by a survey of the research to date on the statistical parallelism of algorithmically generated mathematics items. Results are presented for 8 parallel test forms generated using 16…
Descriptors: Algorithms, Comparative Analysis, Computer Assisted Testing, Item Banks
Chastain, Robert L.; Joe, George W. – 1986
Multivariate methods were used to identify between-set factors relating the criterion set of eleven Wechsler Adult Intelligence Scale Revised subtest variables to the predictor set of demographic variables: age, race, sex, education, occupation, geographic region, and urban versus rural residence. Although factor analysis is usually used to…
Descriptors: Adults, Comparative Analysis, Correlation, Factor Analysis

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