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Preece, Peter F. W. – Educational and Psychological Measurement, 1982
The validity of various reliability-corrected procedures for adjusting for initial differences between groups in uncontrolled studies is established for subjects exhibiting linear fan-spread growth. The results are then extended to a nonlinear model of growth. (Author)
Descriptors: Achievement Gains, Analysis of Covariance, Error of Measurement, Hypothesis Testing

Werts, Charles E.; Linn, Robert L. – Educational and Psychological Measurement, 1971
Descriptors: Analysis of Covariance, Correlation, Educational Environment, Error of Measurement
Mandeville, Garrett K. – 1978
The RMC Research Corporation evaluation model C1--the special regression model (SRM)--was evaluated through a series of computer simulations and compared with an alternative model, the norm referenced model (NRM). Using local data and national norm data to determine reasonable values for sample size and pretest posttest correlation parameters, the…
Descriptors: Analysis of Covariance, Error of Measurement, Intermediate Grades, Mathematical Models
Dickinson, Terry L. – 1985
The general linear model was described, and the influence that measurement errors have on model parameters was discussed. In particular, the assumptions of classical true-score theory were used to develop algebraic relationships between the squared multiple correlations coefficient and the regression coefficients in the infallible and fallible…
Descriptors: Analysis of Covariance, Analysis of Variance, Correlation, Error of Measurement
Olejnik, Stephen F.; Porter, Andrew C. – 1978
The statistical properties of two methods of estimating gain scores for groups in quasi-experiments are compared: (1) gains in scores standardized separately for each group; and (2) analysis of covariance with estimated true pretest scores. The fan spread hypothesis is assumed for groups but not necessarily assumed for members of the groups.…
Descriptors: Academic Achievement, Achievement Gains, Analysis of Covariance, Analysis of Variance