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Neel, John H. – 1987
Determination of statistical power for analysis of variance procedures requires five elements: (1) significance level; (2) effect size; (3) number of means; (4) error variance; and (5) sample size. Significance levels are traditionally chosen to be 0.5, .01, or .001. Effect size is not discussed in this paper. The number of means is determined by…
Descriptors: Analysis of Variance, Error of Measurement, Mathematical Models, Power (Statistics)
Peer reviewed Peer reviewed
Kristof, Walter – Psychometrika, 1973
Paper is concerned with the hypothesis that two variables have a perfect disattenuated correlation, hence measure the same trait except for errors of measurement. (Author/RK)
Descriptors: Analysis of Variance, Correlation, Error of Measurement, Mathematical Models
Robey, Randall R.; Barcikowski, Robert S. – 1987
The mixed model analysis of variance assumes a mathematical property known as sphericity. Several preliminary tests have been proposed to detect departures from the sphericity assumption. The logic of the preliminary testing procedure is to conduct the mixed model analysis of variance if the preliminary test suggests that the sphericity assumption…
Descriptors: Analysis of Variance, Error of Measurement, Hypothesis Testing, Mathematical Models
Hedges, Larry V. – 1982
Meta-analysis has become an important supplement to traditional methods of research reviewing, although many problems must be addressed by the reviewer who carries out a meta-analysis. These problems include identifying and obtaining appropriate studies, extracting estimates of effect size from the studies, coding or classifying studies, analyzing…
Descriptors: Analysis of Variance, Correlation, Error of Measurement, Mathematical Models
Peer reviewed Peer reviewed
Horn, John L. – Educational and Psychological Measurement, 1971
Descriptors: Analysis of Variance, Error of Measurement, Hypothesis Testing, Mathematical Models
Peer reviewed Peer reviewed
Rentz, R. Robert – Educational and Psychological Measurement, 1980
This paper elaborates on the work of Cardinet, and others, by clarifying some points regarding calculations, specifically with reference to existing computer programs, and by presenting illustrative examples of the calculation and interpretation of several generalizability coefficients from a complex six-facet (factor) design. (Author/RL)
Descriptors: Analysis of Variance, Computation, Computer Programs, Error of Measurement
Peer reviewed Peer reviewed
Novick, Melvin R.; And Others – Psychometrika, 1971
Descriptors: Analysis of Variance, Bayesian Statistics, Error of Measurement, Mathematical Models
Goldstein, Harvey; Ecob, Russell – 1981
Using data from a National Child Development Study (NCDS) in Great Britain, the applications of instrumental variable methods and structural equation models to estimating instrumental variables are presented. A subset of the longitudinal educational and home background data on children born in England, Wales and Scotland in a March week of 1958 is…
Descriptors: Analysis of Variance, Elementary Secondary Education, Error of Measurement, Longitudinal Studies
Edwards, Keith J. – 1971
This paper, a revision of the original document, "Correcting Partial, Multiple, and Canonical Correlations for Attenuation" (see TM 000 535), presents the formula for correcting coefficients of partial correlation for attenuation due to errors of measurement. In addition, the correction for attenuation formulas for multiple and cannonical…
Descriptors: Algebra, Analysis of Variance, Correlation, Data Analysis
Peer reviewed Peer reviewed
Levin, Joel R.; Subkoviak, Michael J. – Applied Psychological Measurement, 1977
Textbook calculations of statistical power or sample size follow from formulas that assume that the variables under consideration are measured without error. However, in the real world of behavioral research, errors of measurement cannot be neglected. The determination of sample size is discussed, and an example illustrates blocking strategy.…
Descriptors: Analysis of Covariance, Analysis of Variance, Error of Measurement, Hypothesis Testing
Newman, Isadore – 1988
The nature and appropriate application of the technique of multivariate analysis are discussed. More specifically, the intent of the paper is to demystify and explain the use of multivariate analysis as well as provide guidelines for selection of the most effective statistics for use in specific situations. For the purpose of this paper, the term…
Descriptors: Analysis of Covariance, Analysis of Variance, Chi Square, Discriminant Analysis
Peer reviewed Peer reviewed
Corder-Bolz, Charles R. – Educational and Psychological Measurement, 1978
Six models for evaluating change are examined via a Monte Carlo study. All six models show a lack of power. A modified analysis of variance procedure is suggested as an alternative. (JKS)
Descriptors: Analysis of Covariance, Analysis of Variance, Educational Change, Error of Measurement
Peer reviewed Peer reviewed
Brennan, Robert L. – Educational Measurement: Issues and Practice, 1992
The framework and procedures of generalizability theory are introduced and illustrated in this instructional module that uses a hypothetical scenario involving writing proficiency. Generalizability analyses are useful for understanding the relative importance of various sources of error and for designing efficient measurement procedures. (SLD)
Descriptors: Analysis of Variance, Data Interpretation, Equations (Mathematics), Error of Measurement
Willson, Victor L. – 1982
The current state of usage of regression models in analysis of variance (ANOVA) designs is empirically examined, and examples of several statistical errors made in usage are presented. The assumptions of the general linear model are that all predictors are known without error of measurement and are fixed with no replication or sample variation; in…
Descriptors: Analysis of Covariance, Analysis of Variance, Error of Measurement, Generalization
Peer reviewed Peer reviewed
Huck, Schuyler W.; And Others – Educational and Psychological Measurement, 1981
Believing that examinee-by-item interaction should be conceptualized as true score variability rather than as a result of errors of measurement, Lu proposed a modification of Hoyt's analysis of variance reliability procedure. Via a computer simulation study, it is shown that Lu's approach does not separate interaction from error. (Author/RL)
Descriptors: Analysis of Variance, Comparative Analysis, Computer Programs, Difficulty Level
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