Browsing by Author "Overall, John E."
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Item Calculation of adjusted response frequencies using least squares regression methods(1980) Overall, John E.The use of general linear regression methods for the analysis of categorical data is recommended. The general linear model analysis of a 0,1 coded response variable produces estimates of the same response probabilities that might otherwise be estimated from frequencies in a multiway contingency table. When factors in the design are correlated, the regression analysis estimates the same response probabilities that would be estimated from the simple marginal frequencies in a balanced orthogonal design. The independent effects that are estimated by the regression analysis are the unweighted means of the response probabilities in various cells of a cross-classification design; however, it is not necessary that all cells in a complex design be filled in order for the estimates to have that interpretation. The advantages of the general linear model analysis include familiarity of most psychologists with the methods, availability of computer programs, and ease of application to problems that are too complex for development of complete multiway contingency tables.Item Contradictions can never a paradox resolve(1989) Overall, John E.The fact that difference scores tend to be less reliable than the original measurements from which they are calculated should not be a matter of concern in testing the significance of treatment-induced change. The reliabilities of the original measurements are important because unreliability attenuates correlation, and substantial correlation between prescores and postscores is required for difference scores to be of value in controlling for individual differences. Reliability notwithstanding, difference scores provide superior control over true baseline differences in quasi-experimental research, whereas the analysis of covariance (ANCOVA) is generally preferable for baseline control in randomized experimental designs. Index terms: analysis of covariance, baseline correction, difference scores, measurement of change, reliability.Item Determinants of alcohol abuse in a psychiatric population: A two-dimensional model(1982) Overall, John E.A method for multidimensional scaling of group differences in categorical data patterns was used to investigate configural relationships among alcohol use and abuse groups. The analysis resulted in a model from which two primary etiologic concepts, plus a moderator, were derived. Exposure is the concept that summarizes demographic factors related to level of alcohol use. However, problem drinking differs from frequent drinking along a dimension in the demographic domain that is independent of the exposure dimension. Duration of frequent alcohol use is a concept that relates to age, duration, and chronicity variables. The relationship between resources and responsibilities appears to be a moderator in the dimension separating frequent and problem drinking. Low income and family responsibilities interact to make frequent alcohol use more likely to be perceived as a problem.Item Discriminant Analysis with Categorical Data(1977) Overall, John E.; Woodward, J. ArthurA method for studying relationships among groups in terms of categorical data patterns is described. The procedure yields a dimensional representation of configural relationships among multiple groups and a quantitative scaling of categorical data patterns for use in subsequent assignment of new individuals to the groups. Two examples are used to illustrate potential of the method. In the first, profile data that were previously analyzed by metric multiple discriminant function analysis are reanalyzed by the nonmetric categorical data pattern technique with highly similar results. The second example examines relationships among psychiatric syndrome groups in terms of similarities in patterns of categorical background variables. Results appear consistent with other available information concerning the epidemiology of psychiatric disorders.Item Distinguishing between measurements and dependent variables(1989) Overall, John E.Humphreys and Drasgow (1989b) recognize two types of dependent variables: the original measurements collected in an experiment and mathematical variables that are subjected to statistical analysis. Overall and Woodward (1975) were explicitly concerned with the latter, whereas Humphreys and Drasgow contend that they were concerned with reliability of the original measurements from which difference scores may be computed. These are quite different matters. Criticisms should focus on points of disagreement, and there has never been any disagreement concerning the importance of reliability of the original measurements. The notion that treatment effects should be considered a part of the true variance for calculation of reliability estimates is rejected as stemming from their failure to understand the basic difference between reliability and validity. Index terms: control of individual differences, difference scores, measurement of change, reliability of the marginal distribution, statistical power, within-group reliabilities.Item Estimating individual rater reliabilities(1992) Overall, John E.; Magee, Kevin N.Rating scales have no inherent reliability that is independent of the observers who use them. The often reported interrater reliability is an average of perhaps quite different individual rater reliabilities. It is possible to separate out the individual rater reliabilities given a number of independent raters who observe the same sample of ratees. Under certain assumptions, an external measure can replace one of the raters, and individual reliabilities of two independent raters can be estimated. In a somewhat similar fashion, estimates of treatment effects present in ratings by two independent raters can provide the external frame of reference against which differences in their individual reliabilities can be evaluated. Models for estimating individual rater reliabilities are provided for use in selecting, evaluating, and training participants in clinical research. Index terms: attenuation, correlation, individual raters, interrater reliability, multiple raters, rater reliability, rating scales, reliability of ratings, significance.Item Replication as a rule for determining the number of clusters in hierarchical cluster analysis(1992) Overall, John E.; Magee, Kevin N.A single higher-order cluster analysis can be used to group cluster mean profiles derived from several preliminary analyses. Replication is confirmed when each higher-order cluster contains one cluster mean profile from each of the several preliminary analyses. This study evaluated the utility of replication as a stopping rule in hierarchical cluster analysis. Replication defined by higher-order clustering identifies the correct number of underlying populations that have distinct density regions in the multivariate measurement space. When increased within-population variance obliterates population distinctions, the replication criterion provides an underestimation of the actual number of latent populations. In the case of no true cluster structure or in the case of only two latent populations, chance replication can occur. Thus, replication suggested by higher-order cluster analysis is not a conservative test for the absence of a cluster structure, but it does provide valid evidence concerning the number of latent populations when several are present. Index terms: cluster analysis, cluster means, hierarchical clustering, replication in cluster analysis, stopping rule in cluster analysis, validity of cluster analysis.