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Polak, Marike; De Rooij, Mark; Heiser, Willem J. – Multivariate Behavioral Research, 2012
In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone & Chave's (1929) "criterion…
Descriptors: Item Response Theory, Measures (Individuals), Test Items, Item Analysis
Leite, Walter L.; Cooper, Lou Ann – Multivariate Behavioral Research, 2010
Based on the conceptualization that social desirable bias (SDB) is a discrete event resulting from an interaction between a scale's items, the testing situation, and the respondent's latent trait on a social desirability factor, we present a method that makes use of factor mixture models to identify which examinees are most likely to provide…
Descriptors: Social Desirability, Measures (Individuals), Item Response Theory, Factor Analysis
Leite, Walter L.; Huang, I-Chan; Marcoulides, George A. – Multivariate Behavioral Research, 2008
This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and…
Descriptors: Mathematics, Measures (Individuals), Diabetes, Simulation
Peer reviewedShye, Samuel – Multivariate Behavioral Research, 1978
Facet technique is used to analyze contents of achievement motive questionnaire items proposed in recent years and to construct an explicit definition for achievement motive. (Author/JKS)
Descriptors: Academic Achievement, Achievement Need, Item Analysis, Motivation
Peer reviewedReynolds, Thomas J. – Multivariate Behavioral Research, 1980
Order analysis, a technique to isolate unidimensional hierarchies representing multidimensional structure of binary data, is reviewed. Several theoretical flaws inherent in the probalistic version are presented. Suggestions of possible directions for future research are offered. (Author)
Descriptors: Factor Analysis, Item Analysis, Matrices, Statistical Analysis
Peer reviewedSilverstein, A. B.; Fisher, Gary – Multivariate Behavioral Research, 1975
Responses of male prisoners to the Personal Orientation Inventory were clustered, using hierarchical linkage analysis. Six second-order clusters accounted for all the items. Reliabilities of these clusters were comparable to those of the first-order clusters. Relative validity of cluster scores and scale scores remains to be determined. (RC)
Descriptors: Cluster Analysis, Correlation, Item Analysis, Personality Measures
Peer reviewedTzeng, Oliver C. S.; Landis, Dan – Multivariate Behavioral Research, 1978
Two popular models for performing multidimensional scaling, Tucker and Messick's points-of-view model, and Tucker's three mode model, are combined into a single analytic procedure, the 3M-POV model. The procedure is described and its strengths are discussed. Carroll and Chang's INDSCAL model is also mentioned. (JKS)
Descriptors: Correlation, Item Analysis, Mathematical Models, Multidimensional Scaling
Peer reviewedSiegert, Richard J.; And Others – Multivariate Behavioral Research, 1988
A study concluding that the Wechsler Adult Intelligence Scale (Revised) (WAIS-R) has three clear factors in its structure is critiqued. An alternative factor comparison technique, FACTOREP, is used with identical data. It is demonstrated that the WAIS-R has only two strong factors--verbal comprehension and perceptual organization. (TJH)
Descriptors: Factor Analysis, Factor Structure, Intelligence Tests, Item Analysis
Peer reviewedCattell, Raymond B.; Burdsal, Charles A. – Multivariate Behavioral Research, 1975
Descriptors: Cluster Analysis, Factor Analysis, Factor Structure, Item Analysis
Peer reviewedSuziedelis, Antanas; And Others – Multivariate Behavioral Research, 1976
A method of typological analysis was applied to computer-generated 96-item questionnaire data for 100 cases, under a variety of conditions to analyze both the item-level and score-level. The results showed a considerable advantage of score-level approach in the number, size, and replicability of clusters recovered. (DEP)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Comparative Analysis
Ferrando, Pere J. – Multivariate Behavioral Research, 2005
This article proposes and describes factor-analytic procedures for assessing and controlling socially desirable responding in binary personality items. The basic procedures are applications of the restricted (confirmatory) item factor analysis model for ordered-categorical variables. Orthogonal and oblique solutions based on marker variables are…
Descriptors: Social Desirability, Factor Analysis, Item Response Theory, Validity
Peer reviewedHunter, Sara; And Others – Multivariate Behavioral Research, 1974
Factor structure underlying responses to the 373-item MMPI was found to be essentially identical to the factor structure previously defined from analysis of intercorrelations among the first 168 items. (Author)
Descriptors: Adults, Clinical Diagnosis, Factor Analysis, Factor Structure
Peer reviewedBramble, William J.; Wiley, David E. – Multivariate Behavioral Research, 1974
Descriptors: Content Analysis, Item Analysis, Personality Assessment, Psychological Testing
Peer reviewedComrey, Andrew L.; And Others – Multivariate Behavioral Research, 1988
Three methods were used to test the factor structure of the Eysenck Personality Inventory administered to 583 Australians. The preferred method was to extract factors by the minimum residual method, use the Tandem Criteria Method, and then rotate that number of factors by the Tandem Criteria I method. (SLD)
Descriptors: Adults, Factor Analysis, Factor Structure, Foreign Countries
Peer reviewedCollins, Linda M.; And Others – Multivariate Behavioral Research, 1986
The present study compares the performance of phi coefficients and tetrachorics along two dimensions of factor recovery in binary data. These dimensions are (1) accuracy of nontrivial factor identifications; and (2) factor structure recovery given a priori knowledge of the correct number of factors to rotate. (Author/LMO)
Descriptors: Computer Software, Factor Analysis, Factor Structure, Item Analysis
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