Examining differential item functioning due to item difficulty and alternative attractiveness

A method for analyzing test item responses is proposed to examine differential item functioning (DIF) in multiple-choice items through a combination of the usual notion of DIF, for correct/incorrect responses and information about DIF contained in each of the alternatives. The proposed method uses incomplete latent class models to examine whether DIF is caused by the attractiveness of the alternatives, difficulty of the item, or both. DIF with respect to either known or unknown subgroups can be tested by a likelihood ratio test that is asymptotically distributed as a chi-square random variable.     

Subjects' inferential performance and the interaction of data and hypotheses in probabilistic inference tasks

In a recent paper, Knez (1991) showed an interaction of data and hypotheses in probabilistic inference tasks. The results illustrated two, earlier not obtained, significant main effects on subjects' hypothesis sampling , viz. the effect of different forms of data presentation and subjects' execution of cognitive control over their hypothesis pool throughout the series of trials. The present paper followed up these results in that the subjects' hypothesis testing , in Knez (1991) was subjected to an analysis. Hence, to see if the effects mentioned above significantly influenced the subjects' hypothesis testing, as they did for subjects' hypothesis sampling. The results showed a consistency with Knez (1991), i.e. the results emphasize the interaction of data and hypothesis in probabilistic inference tasks, as well as the subjects' execution of cognitive control over their hypothesis pool concerning both the subjects' hypothesis sampling and testing.     

Criterion-related construct validity

Established results on latent variable models are applied to the study of the validity of a psychological test. When the test predicts a criterion by measuring a unidimensional latent construct, not only must the total score predict the criterion, but the joint distribution of criterion scores and item responses must exhibit a certain pattern. The presence of this population pattern may be tested with sample data using the stratified Wilcoxon rank sum test. Often, criterion information is available only for selected examinees, for instance, those who are admitted or hired. Three cases are discussed: (i) selection at random, (ii) selection based on the current test, and (iii) selection based on other measures of the latent construct. Discriminant validity is also discussed.This work was supported in part by Grant SES-87-01890 from the Measurement Methods and Data Improvement Program of the U.S. National Science Foundation.     

The social phobia and anxiety inventory: Construct validity

The Social Phobia and Anxiety Inventory (SPAI) is a new instrument composed of social phobia and agoraphobia subscales. The latter scale is used to detect social anxiety that may result from agoraphobia. The SPAI's construct validity was assessed through several procedures. First, confirmatory factor analyses were conducted to validate the existence of the two subscales. Second, exploratory factor analyses examined the underlying structure of the social phobia subscale. Third, a Q factor procedure determined if different anxiety diagnostic groups could be differentiated by their SPAI response pattern. The results confirmed the utility of the two SPAI subscales and identified a number of dimensions contained within the social phobia subscale which differed depending upon the specific subject sample. In addition, the complaints of social phobies appeared more homogeneous than those of an agoraphobic comparison group. The results are discussed in terms of construct validity and the sensitivity of the SPAI to various dimensions of social phobia fears.This study was supported in part by NIMH Grants 41852, 30915, 18269, and 16884.     

Statistical consistency and hypothesis testing for nonmetric multidimensional scaling

The properties of nonmetric multidimensional scaling (NMDS) are explored by specifying statistical models, proving statistical consistency, and developing hypothesis testing procedures. Statistical models with errors in the dependent and independent variables are described for quantitative and qualitative data. For these models, statistical consistency often depends crucially upon how error enters the model and how data are collected and summarized (e.g., by means, medians, or rank statistics). A maximum likelihood estimator for NMDS is developed, and its relationship to the standard Shepard-Kruskal estimation method is described. This maximum likelihood framework is used to develop a method for testing the overall fit of the model.     

Multifactor measurement of bodily feelings: Conceptual development,scale construction,and empirical validation

A series of studies is described demonstrating a coherent exploratory and confirmatory factor-analytic approach to the scaling of self-reported bodily feelings. This approach attempts to optimize the orthogonality, parsimony, and meaningfulness of a multifactor inventory. Psychometric properties of the resulting 35-item Bodily Feeling Scale (BFS) are presented and its comparability with other inventories is discussed. A validity study using item-content-based scales of the Minnesota Multiphasic Personality Inventory showed both concurrent and discriminative validity of the BFS. Implications for clinical and research utility are discussed.Portions of this research were conducted while the first author was supported by a University Graduate Fellowship awarded by the Graduate School of Arts and Sciences at Vanderbilt University.Professor Nunnally passed away in August 1982. His major contributions to the several stages of this research program are recognized through coauthorship.     

Millon Clinical Multiaxial Inventory (MCMI) computer-generated diagnoses: How do they compare to clinician judgment?

During the past several decades, computers have achieved increasing prominence in psychological assessment procedures. This is particularly true for computer-based test interpretation and diagnosis. This study reports on a study designed to compare the accuracy of computer-based diagnoses with clinician-generated diagnoses. The Millon Clinical Multiaxial Inventory (MCMI) was administered to 151 consecutively admitted inpatients at a large private psychiatric hospital. The computer-generated diagnoses were compared with those generated by admitting psychiatrists. The results indicated that the MCMI diagnostic impressions underestimated the severity of depressive disorders when compared with clinician diagnoses on Axis I. Specifically, clinicians diagnosed major depression much more frequently than did the MCMI. In addition, clinicians diagnosed anxiety disorders much less frequently than did the MCMI.