ABSTRACT
A comprehensive approach to the interpretation of difference scores is presented. Formulas for the test of statistical significance between two test scores, computed by a confidence interval, and for the calculation of the probabilities for the power of the statistical test, underinterpretation, overinterpretation, and misinterpretation are provided. Definitions and examples of their use in score interpretation are provided.
Subject(s)
Confidence Intervals , Data Interpretation, Statistical , Female , Humans , Male , Models, Statistical , Neuropsychological Tests , ProbabilityABSTRACT
Suppose one has a battery of K subtests and a composite for the battery is defined as the mean of the K standardized subtest scores. An individual's single-subtest deviation score is the difference between the individual's score on any single subtest and his composite score. A cluster deviation score is the difference between an examinee's average for a small set (cluster) of subtests and his composite. Formulas are given for the test of statistical significance of the individual's subtest or cluster deviation score and the internal consistency reliability of such deviation scores.
Subject(s)
Models, Psychological , Neuropsychological Tests , Psychology/methods , Psychology/statistics & numerical data , Humans , Reproducibility of ResultsABSTRACT
Formulas requiring the computation of only three standard deviations are presented for computing the interjudge reliability coefficient for any number of judges. These formulas yield coefficients identical to those obtained from a one-way repeated-measures analysis of variance. Even researchers with small handheld calculators can use this simple approach.
Subject(s)
Algorithms , Analysis of Variance , Humans , Models, Statistical , Observer Variation , Psychometrics , Reproducibility of ResultsABSTRACT
When the reliability of test scores must be estimated by an internal consistency method, partition of the test into just 2 parts may be the only way to maintain content equivalence of the parts. If the parts are classically parallel, the Spearman-Brown formula may be validly used to estimate the reliability of total scores. If the parts differ in their standard deviations but are tau equivalent, Cronbach's alpha is appropriate. However, if the 2 parts are congeneric, that is, they are unequal in functional length or they comprise heterogeneous item types, a less well-known estimate, the Angoff-Feldt coefficient, is appropriate. Guidelines in terms of the ratio of standard deviations are proposed for choosing among Spearman-Brown, alpha, and Angoff-Feldt coefficients.
