Gender Differences in Persistence in Computer-Related Fields

1988 ◽  
Vol 4 (2) ◽  
pp. 185-202 ◽  
Author(s):  
Carolyn M. Jagacinski ◽  
William K. Lebold ◽  
Gavriel Salvendy
Keyword(s):  
Discriminant Function ◽  
Computer Technology ◽  
Function Analysis ◽  
School Science ◽  
Important Variable ◽  
High School Science ◽  
Computer Engineering ◽  
Classification Rate ◽  
Men And Women ◽  
Math Ability

The effectiveness of precollege and college achievement measures in predicting persistence for men and women in computer-related fields was examined. Persistence rates were similar for men and women in computer technology, electrical/computer engineering, and industrial engineering. However, fewer women than men persisted in computer science. Discriminant function analysis was conducted separately for men and women in each field and was equally effective for men and women in correctly classifying persisters and nonper-sisters (64–72%) in each field except computer technology where the classification rate was considerably lower for women (58%). GPA was generally the most important variable followed by a measure of math ability. High school science grades and number of semesters were often selected for the discriminant function for men, but not for women. The potential role of nonachievement factors in persistence such as peer and faculty support and expectancies are also discussed.

Comparison of the Toy-Play Behavior of Autistic, Retarded, and Normal Children: A Reanalysis

1969 ◽  
Vol 25 (1) ◽  
pp. 223-227 ◽  
Author(s):  
Barbara J. Weiner ◽  
Donald R. Ottinger ◽  
James R. Tilton
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Measurement Instrument ◽  
Important Variable ◽  
Normal Children ◽  
Observational Technique ◽  
Toy Play ◽  
Discriminant Function Analyses

Tilton and Ottinger (1964) examined differences among autistic, retarded, and normal children by observing their behavior in a toy-play setting. The purpose of this study was to reanalyze these data using a multiple discriminant function analysis, which allowed consideration of all 10 categories of toy play and their intercorrelations within one analysis. Significant differences ( p < .001) were found among the 3 groups and between the possible pairs of groups (normal-autistic, normal-retarded, autistic-retarded). In addition, information about the statistical classification of individuals was available. In the four discriminant function analyses, the proportions of Ss statistically classified the same as their original psychiatric diagnosis were .96 of the normals, .83 of the autistics, and .89 of the retardates. The combinational category of toy play emerged as the most important variable in discriminating the groups in all four analyses. It was concluded that this observational technique combined with the multiple discriminant function analysis would have practical utility as a diagnostic and evaluative measurement instrument.

What's the Difference between Men and Women? Evidence from Facial Measurement

Perception ◽  
1993 ◽  
Vol 22 (2) ◽  
pp. 153-176 ◽  
Author(s):  
A Mike Burton ◽  
Vicki Bruce ◽  
Neal Dench
Keyword(s):  
Discriminant Function ◽  
Human Performance ◽  
Function Analysis ◽  
Human Subjects ◽  
Three Dimensional ◽  
Picture Plane ◽  
Distance Measurements ◽  
Men And Women ◽  
Key Points ◽  
Full Face

Human subjects are able to identify the sex of faces with very high accuracy. Using photographs of adults in which hair was concealed by a swimming cap, subjects performed with 96% accuracy. Previous work has identified a number of dimensions on which the faces of men and women differ. An attempt to combine these dimensions into a single function to classify male and female faces reliably is described. Photographs were taken of 91 male and 88 female faces in full face and profile. These were measured in several ways: (i) simple distances between key points in the pictures; (ii) ratios and angles formed between key points in the pictures; (iii) three-dimensional (3-D) distances derived by combination of full-face and profile photographs. Discriminant function analysis showed that the best discriminators were derived from simple distance measurements in the full face (85% accuracy with 12 variables) and 3-D distances (85% accuracy with 6 variables). Combining measures taken from the picture plane with those derived in 3-D produced a discriminator approaching human performance (94% accuracy with 16 variables). Performance of the discriminant function was compared with that of human perceivers and found to be correlated, but far from perfectly. The difficulty of deriving a reliable function to distinguish between the sexes is discussed with reference to the development of automatic face-processing programs in machine vision. It is argued that such systems will need to incorporate an understanding of the stimuli if they are to be effective.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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