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Bowers, John – Educational and Psychological Measurement, 1971
Descriptors: Error of Measurement, Mathematical Models, Test Reliability, True Scores
Liu, Yan; Zumbo, Bruno D. – Educational and Psychological Measurement, 2007
The impact of outliers on Cronbach's coefficient [alpha] has not been documented in the psychometric or statistical literature. This is an important gap because coefficient [alpha] is the most widely used measurement statistic in all of the social, educational, and health sciences. The impact of outliers on coefficient [alpha] is investigated for…
Descriptors: Psychometrics, Computation, Reliability, Monte Carlo Methods

Brennan, Robert L.; Prediger, Dale J. – Educational and Psychological Measurement, 1981
This paper considers some appropriate and inappropriate uses of coefficient kappa and alternative kappa-like statistics. Discussion is restricted to the descriptive characteristics of these statistics for measuring agreement with categorical data in studies of reliability and validity. (Author)
Descriptors: Classification, Error of Measurement, Mathematical Models, Test Reliability

Skinner, Harvey A. – Educational and Psychological Measurement, 1978
The concepts of elevation, scatter, and shape are investigated as parameters in assessing the similarity among profiles of individuals. A computational strategy for differentiating the independent contribution of each parameter to more global indices of resemblance is presented. (Author/JKS)
Descriptors: Data Analysis, Error of Measurement, Mathematical Models, Measurement Techniques

Yarnold, Paul R. – Educational and Psychological Measurement, 1984
Unreliable profiles impose the difficulty that ordinal and interval relations among the individual's scores become uncertain or unstable. A profile reliability coefficient is derived to estimate the relative expected extent of this ordinal and interval "inversion" for any profile of K measures. (Author/DWH)
Descriptors: Error of Measurement, Mathematical Models, Profiles, Test Reliability

Bedeian, Arthur G.; Day, David V.; Kelloway, E. Kevin – Educational and Psychological Measurement, 1997
Methods by which structural models correct for the effects of attenuation due to measurement error are reviewed, and implications of such disattenuation for interpreting the results of structural equation models are considered. Recommendations are made for improving the practice of disattenuation, and caution is urged in drawing inferences based…
Descriptors: Error of Measurement, Estimation (Mathematics), Mathematical Models, Statistical Inference

Huck, Schuyler W. – Educational and Psychological Measurement, 1992
Three factors that increase score variability yet can be associated with an increase, a decrease, or no change in Pearson's correlation coefficient (r) are discussed (restriction of range, errors of measurement, and linear transformations of data). The connection between changes in variability and r depends on how changes occur. (SLD)
Descriptors: Correlation, Equations (Mathematics), Error of Measurement, Groups

Werts, Charles E.; And Others – Educational and Psychological Measurement, 1973
Perspective on article by P. Isaac published in the Psychological Bulletin, 1970, 74, 213-18. (CB)
Descriptors: Analysis of Covariance, Error of Measurement, Mathematical Models, Measurement Techniques

Strauss, David – Educational and Psychological Measurement, 1981
To determine if the observed correlation between two variables can be "explained" by a third variable, a significance test on the partial correlation coefficient is often used. This can be misleading when the third variable is measured with error. This article shows how the problem can be partially overcome. (Author/BW)
Descriptors: Correlation, Error of Measurement, Mathematical Models, Predictive Validity

Kingma, Johannes; Reuvekamp, Johan – Educational and Psychological Measurement, 1987
This paper describes a PASCAL program that computes both different types of transitions and learning statistics suitable for learning experiments in which a two-stage Markov model is used. The frequency counts of the different transitions are used for estimating the parameters of the two-stage Markov model. (Author/LMO)
Descriptors: Computer Software Reviews, Error of Measurement, Goodness of Fit, Input Output
Graham, James M. – Educational and Psychological Measurement, 2006
Coefficient alpha, the most commonly used estimate of internal consistency, is often considered a lower bound estimate of reliability, though the extent of its underestimation is not typically known. Many researchers are unaware that coefficient alpha is based on the essentially tau-equivalent measurement model. It is the violation of the…
Descriptors: Models, Test Theory, Reliability, Structural Equation Models

Feldt, Leonard S. – Educational and Psychological Measurement, 1984
The binomial error model includes form-to-form difficulty differences as error variance and leads to Ruder-Richardson formula 21 as an estimate of reliability. If the form-to-form component is removed from the estimate of error variance, the binomial model leads to KR 20 as the reliability estimate. (Author/BW)
Descriptors: Achievement Tests, Difficulty Level, Error of Measurement, Mathematical Formulas

Brink, Nicholas E. – Educational and Psychological Measurement, 1972
Study compares the Rasch and the Guttman models of measurement and thus adds to the description of the characteristics of Rasch's logistic model. Such knowledge is of importance in making decisions as to which model and which statistics should be used in evaluations of tests. (Author/CB)
Descriptors: Comparative Analysis, Educational Testing, Error of Measurement, Goodness of Fit

Horn, John L. – Educational and Psychological Measurement, 1971
Descriptors: Analysis of Variance, Error of Measurement, Hypothesis Testing, Mathematical Models

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
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