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Kiers, Henk A. L.; Vicari, Donatella; Vichi, Maurizio – Psychometrika, 2005
For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are…
Descriptors: Classification, Multidimensional Scaling, Multivariate Analysis, Models
Amir, Tamar; Gati, Itamar; Kleiman, Tali – Journal of Career Assessment, 2008
This research develops and tests a procedure for interpreting individuals' responses in multiscale career assessments, using the Career Decision-Making Difficulties Questionnaire (CDDQ). In Study 1, criteria for ascertaining the credibility of responses were developed, based on the judgments of 39 career-counseling experts. In Study 2, the…
Descriptors: Career Choice, Decision Making Skills, Career Development, Questionnaires
Seok, Soonhwa; Meyen, Edward; Poggio, John C.; Semon, Sarah; Tillberg-Webb, Heather – Quarterly Review of Distance Education, 2008
This article addresses how evaluation criteria improve educational Web-information system design, and the tangible and intangible benefits of using evaluation criteria, when implemented in an educational Web-information system design. The evaluation criteria were developed by the authors through a content validation study applicable to…
Descriptors: Evaluation Criteria, Information Systems, Internet, Computer System Design
Sireci, Stephen G. – 1998
Multidimensional scaling (MDS) is a versatile technique for understanding the structure of multivariate data. Recent studies have applied MDS to the problem of evaluating content validity. This paper describes the importance of evaluating test content and the logic of using MDS to analyze data gathered from subject matter experts employed in…
Descriptors: Content Validity, Evaluation Methods, Multidimensional Scaling, Research Methodology
Peer reviewedNishisato, Shizuhiko; Arri, P. S. – Psychometrika, 1975
A modified technique of separable programming was used to maximize the squared correlation ratio of weighted responses to partially ordered categories. The technique employs a polygonal approximation to each single-variable function by choosing mesh points around the initial approximation supplied by Nishisato's method. Numerical examples were…
Descriptors: Algorithms, Linear Programing, Mathematical Models, Matrices
Peer reviewedLund, Thorleif – Psychometrika, 1975
Among the criticisms of Micko's Halo Model are: 1) it is too restrictive to fit empirical data, 2) it misrepresents unrelated percepts as bipolar structures, 3) it requires all dimensions to be bipolar, and 4) it causes the interpretations of orthogonality of factors and factor loadings to become problematic. (Author/BJG)
Descriptors: Mathematical Models, Multidimensional Scaling, Ratios (Mathematics), Research Problems
Peer reviewedBurton, Michael L. – Multivariate Behavioral Research, 1975
Three dissimilarity measures for the unconstrained sorting task are investigated. All three are metrics, but differ in the kind of compensation which they make for differences in the sizes of cells within sortings. Empirical tests of the measures are done with sorting data for occupations names and the names of behaviors, using multidimensional…
Descriptors: Classification, Cluster Analysis, Correlation, Matrices
Peer reviewedZinnes, Joseph L.; Griggs, Richard A. – Psychometrika, 1974
Probabilistic assumptions are added to single and multidimensional versions of the Coombs unfolding model for preferential choice (Coombs, 1950) and practical ways of obtaining maximum likelihood estimates of the scale parameters and goodness-of-fit tests of the model are presented. A Monte Carlo experiment is discussed. (Author/RC)
Descriptors: Goodness of Fit, Multidimensional Scaling, Probability, Statistical Analysis
Peer reviewedKrus, David J.; Bart, William M. – Educational and Psychological Measurement, 1974
Descriptors: Item Analysis, Matrices, Multidimensional Scaling, Response Style (Tests)
Peer reviewedMatch, Joel; Wiggins, Nancy – Educational and Psychological Measurement, 1974
Descriptors: Individual Differences, Multidimensional Scaling, Personality Measures, Social Influences
Clark, W. Crawford; Ferrer-Brechner, Theresa – 1985
Multidimensional scaling (MDS) offers a rigorous approach to many problems in perception, emotion, personality, and cognition, where the stimuli are too complex to be quantified by other means. In these procedures similarity ratings of the stimulus objects are modeled as points in multidimensional space, such that perceived similarity is…
Descriptors: Affective Measures, Cancer, Multidimensional Scaling, Sensory Experience
Peer reviewedCohen, Harvey S.; Jones, Lawrence E. – Psychometrika, 1974
Descriptors: Algorithms, Correlation, Models, Multidimensional Scaling
Peer reviewedSjoberg, Lennart – Multivariate Behavioral Research, 1975
An analysis of preferences with respect to silhouette drawings of nude females is presented. Systematic intransitivities were discovered. The dispersions of differences (comparatal dispersons) were shown to reflect the multidimensional structure of the stimuli, a finding expected on the basis of prior work. (Author)
Descriptors: Correlation, Dimensional Preference, Multidimensional Scaling, Psychological Patterns
Peer reviewedLundberg, Ulf; Devine, Bernard – Educational and Psychological Measurement, 1975
Results showed the relation between an experiment with only positive estimations of emotional terms and one that included negative similarity estimations of emotional terms. Negative similarities were found to have been represented mainly as zero ratings when only a positive scale was available for the subjects to rate the words. (Author/BJG)
Descriptors: Affective Behavior, Comparative Analysis, Emotional Response, Multidimensional Scaling
Peer reviewedLangeheine, Rolf – Educational and Psychological Measurement, 1978
A computer program which analyzes sociometric data is presented. The SDAS program provides classical sociometric analysis. Multi-dimensional scaling and cluster analysis techniques may be combined with the MSP program. (JKS)
Descriptors: Computer Programs, Data Analysis, Multidimensional Scaling, Sociometric Techniques

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