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What Works Clearinghouse Rating
Peer reviewedMulaik, Stanley A. – Psychometrika, 1981
It is proved for the common factor model that, under certain conditions maintaining the distinctiveness of each factor, a given factor will be determinate if there exists an unlimited number of variables in the model, each having an absolute correlation with the factor greater than some arbitrarily small quantity. (Author/JKS)
Descriptors: Data Analysis, Factor Analysis, Mathematical Models, Statistics
Peer reviewedChen, Guoqing; And Others – Journal of the American Society for Information Science, 1992
Discusses the problem of data redundancy in a fuzzy relational data model and describes three existing frameworks of fuzzy data representation and redundancy treatment. A new treatment based on Zadeh's extension principle is proposed. (18 references) (MES)
Descriptors: Data, Data Analysis, Databases, Mathematical Models
Peer reviewedDunn, Terrence R.; Harshman, Richard A. – Psychometrika, 1982
The kinds of individual differences in perceptions permitted by the weighted euclidean model for multidimensional scaling are more restrictive than those allowed by models developed by Tucker or Carroll. It is shown how problems which occur when using the more general models can be removed. (Author/JKS)
Descriptors: Data Analysis, Individual Differences, Mathematical Models, Multidimensional Scaling
Peer reviewedShapiro, Alexander – Psychometrika, 1982
The extent to which one can reduce the rank of a symmetric matrix by only changing its diagonal entries is discussed. Extension of this work to minimum trace factor analysis is presented. (Author/JKS)
Descriptors: Data Analysis, Factor Analysis, Mathematical Models, Matrices
Peer reviewedHedges, Larry V.; Olkin, Ingram – Psychometrika, 1981
Commonality components have been defined as a method of partitioning squared multiple correlations. The asymptotic joint distribution of all possible squared multiple correlations is derived. The asymptotic joint distribution of linear combinations of squared multiple correlations is obtained as a corollary. (Author/JKS)
Descriptors: Correlation, Data Analysis, Mathematical Models, Multiple Regression Analysis
Hwang, Heungsun; Takane, Yoshio – Psychometrika, 2004
A multivariate reduced-rank growth curve model is proposed that extends the univariate reduced rank growth curve model to the multivariate case, in which several response variables are measured over multiple time points. The proposed model allows us to investigate the relationships among a number of response variables in a more parsimonious way…
Descriptors: Multivariate Analysis, Mathematical Models, Psychometrics, Matrices
Beliefs and Practices of Expert Respiratory Care Faculty on Critical-Thinking Learning: A Case Study
Hulse, James Leland – ProQuest LLC, 2009
Problem. The development of critical-thinking skills during the professional training of respiratory therapists is imperative for good practice. Research evidence suggests that interactive instructional strategies are far more effective than traditional lectures. Missing from the literature are thick descriptions of how faculty organize the…
Descriptors: Educational Strategies, Socialization, Peer Evaluation, Mathematical Models
Rubin, Donald B. – 1973
Most articles on missing values assume the missing data are "missing at random" and ignore the process that "caused" the missing values. The condition under which this procedure is justified is explored here: the concept of missing at random is precisely defined, several examples are discussed, and two simple conditions are given which are…
Descriptors: Bulletins, Data Analysis, Mathematical Models, Research Reports
Peer reviewedDickinson, Terry L. – Educational and Psychological Measurement, 1976
Linear models are available for describing dominance and composition judgements. These models include parameters for the scale values of the stimuli, response order bias, and interaction between stimuli, all of which may be evaluated with analysis of variance procedures. (Author)
Descriptors: Analysis of Variance, Computer Programs, Data Analysis, Mathematical Models
Peer reviewedHuck, Schuyler W.; Malgady, Robert G. – Educational and Psychological Measurement, 1978
The computation of F ratios from cell means and standard deviations is shown for a two-way analysis of variance. (JKS)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Mathematical Models
Peer reviewedCohen, Patricia – Multiple Linear Regression Viewpoints, 1978
Commentary is presented on the preceding articles in this issue of the journal. Critical commentary is made article by article, and some general recommendations are made. (See TM 503 664 through 670). (JKS)
Descriptors: Data Analysis, Mathematical Models, Multiple Regression Analysis, Research Design
Peer reviewedRyan, Thomas P. – Multiple Linear Regression Viewpoints, 1978
The problem of selecting regression variables using cost criteria is considered. A method is presented which approximates the optimal solution of one of several criterion functions which might be employed. Examples are given and the results are compared with the results of other methods. (Author/JKS)
Descriptors: Cost Effectiveness, Data Analysis, Mathematical Models, Multiple Regression Analysis
Peer reviewedPennell, Roger – Educational and Psychological Measurement, 1972
Author argues that simplistic and/or heuristic approaches to the Tucker and Messick model (an individual differences model for multidimensional scaling, 1963) are often inadequate. (Author/CB)
Descriptors: Data Analysis, Evaluation, Individual Differences, Mathematical Models
Peer reviewedFerligoj, Anuska; Batagelj, Vladimir – Psychometrika, 1982
Using constraints with cluster analysis limits the possible number of clusters. This paper deals with clustering problems where grouping is constrained by a symmetric and reflexive relation. Two approaches, along with illustrations, are presented. (Author/JKS)
Descriptors: Algorithms, Cluster Analysis, Data Analysis, Mathematical Models
Peer reviewedDesarbo, Wayne S. – Psychometrika, 1982
A general class of nonhierarchical clustering models and associated algorithms for fitting them are presented. These models generalize the Shepard-Arabie Additive clusters model. Two applications are given and extensions to three-way models, nonmetric analyses, and other model specifications are provided. (Author/JKS)
Descriptors: Algorithms, Cluster Analysis, Data Analysis, Mathematical Models

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