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Kiers, Henk A. L.; And Others – Psychometrika, 1993
A new procedure is proposed for handling nominal variables in the analysis of variables of mixed measurement levels, and a procedure is developed for handling ordinal variables. Using these procedures, a monotonically convergent algorithm is constructed for the FACTALS method for any mixture of variables. (SLD)
Descriptors: Algorithms, Analysis of Variance, Equations (Mathematics), Least Squares Statistics
Cardinet, Jean; Allal, Linda – New Directions for Testing and Measurement, 1983
A general framework for conducting generalizability analyses is presented. Generalizability theory is extended to situations in which the objects of measurement are not persons but other factors, such as instructional objectives, stages of learning, and treatments. (Author/PN)
Descriptors: Algorithms, Analysis of Variance, Estimation (Mathematics), Mathematical Formulas
Peer reviewed Peer reviewed
Rubin, Donald B.; And Others – Journal of Educational Statistics, 1981
A time-saving and space-saving algorithm is presented for computing the sums of squares and estimated cell means under the additive model in a two-way analysis of variance or covariance with unequal numbers of observations in the cells. The procedure is illustrated. (Author/JKS)
Descriptors: Algorithms, Analysis of Covariance, Analysis of Variance, Computer Programs
Pennell, Roger – 1970
A model and a computer program for performing conjoint measurement is developed. (AG)
Descriptors: Algorithms, Analysis of Variance, Computer Programs, Goodness of Fit
Joreskog, Karl G. – 1970
A general method for estimating the unknown coefficients in a set of linear structural equations is described. In its most general form the method allows for both errors in equations (residuals, disturbances) and errors in variables (errors of measurement, observational errors) and yields estimates of the residual variance-covariance matrix and…
Descriptors: Algorithms, Analysis of Covariance, Analysis of Variance, Computer Programs
Peer reviewed Peer reviewed
Frigon, Jean-Yves; Laurencelle, Louis – Educational and Psychological Measurement, 1993
The statistical power of analysis of covariance (ANCOVA) and its advantages over simple analysis of variance are examined in some experimental situations, and an algorithm is proposed for its proper application. In nonrandomized experiments, an ANCOVA is generally not a good approach. (SLD)
Descriptors: Algorithms, Analysis of Covariance, Analysis of Variance, Educational Research