This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is…

The process of discriminant analysis is reviewed in four different aspects: focusing on formulations, interpretations, and uses of discrimination, estimation and classification. Other issues, problems, and recent developments in discriminant analysis are also reviewed. (Author/DEP)

A brief historical background of discriminant analysis is given, with a description of the variety of roles that discriminant analysis can perform. Focus is on the classification role of discriminant analysis and how it can be performed by using Fisher's classification functions or the canonical discriminant functions. A small hypothetical data…

Either linear or quadratic rules may be used to derive classification equations in discriminant analysis for the purpose of predicting group membership. Generally, the decision about which rule to use is governed by the degree to which the separate group covariance matrices are unequal. An example is presented that supports the superior internal…

A method for comparing the cross validated classification accuracies of linear and quadratic classification rules is presented under varying data conditions for the k-group classification problem. With this method, separate-group as well as total-group proportions of correct classifications can be compared for the two rules. McNemar's test for…

Most successful vocational education programs are identified in 16 community colleges through the use of the Delphi method. The design provided for a reliability check on the Delphi technique through the use of two independent Delphi panels on each campus. Hard data on 36 "most successful" and 36 "other" programs on 12 campuses…

A technique for classifying hospitals on a multidimensional basis was developed. Three major sets of attributes were examined: patient case mix, facility mix, and personnel mix. Using multivariate techniques (factor analysis, cluster analysis, and discriminant analysis), 83 variables were examined for 547 hospitals. A total of six factors were…

Numerical classification techniques were used to explore the conjecture that inconsistent results of many studies of disabled readers could result from samples being composed of subgroups of children with different characteristics. Some five subgroups were identified using ITPA scores from a subsample of 37 poor readers. (Author)

This paper is concerned with the identification and testing of salient variables which show potential for explaining client use of evaluation information. A single-page interview instrument was devised which aggregated the factors and factor categories collected following an extensive review of the literature. Data were collected from a…

Compared the efficacy of two methods of cluster analysis, the unweighted pair-groups method using arithmetic averages (UPGMA) and Ward's method, for students grouped on intelligence, achievement, and social adjustment by both clustering methods. Found UPGMA more efficacious based on output, on cophenetic correlation coefficients generated by each…

Classification studies are important for practitioners who need to identify individuals for specialized treatment or intervention. When interventions are irreversible or misclassifications are costly, information about the proficiency of different classification procedures becomes invaluable. This study furnishes information about the relative…

This book in intended to train students in reading a research report critically. It uses actual research articles as examples including both good and flawed studies in each category and provides interpretation and evaluation of the appropriateness of the statistical analyses in each study. Individual chapters usually include two sample studies and…