Thesis (DPhil)--Stellenbosch University, 1977. / ENGLISH ABSTRACT: see item for full text / AFRIKAANSE OPSOMMING: sien item vir volteks.

Subjects were 38 computer programers employed in a national food-retailing corporation. A job analysis provided a basis for criteria development and served to guide the selection of predictors. Ratings of each programmer's job performance by his immediate supervisor, and scores on such tests as the Computer Programer Aptitude Battery (CPAB), clerical tests, and supervisory judgment test were obtained. Relationships between tests and criteria were examined to find the best test combination for predicting programming performance. Statistical treatment of data included a principal components analysis of the criteria and a multiple linear regression analysis. A weighted combination of the CPAB Reasoning, a test of clerical ability, and supervisory judgment test was found to be highly correlated with performance (R = .60).

Subjects were 32 computer programmers employed in a large computerized tax-processing company in the Southwest. Ratings of each programmer's job performance by his/her immediate supervisor and scores on the Aptitude Test for Programmer Personnel (ATPP) were obtained. Relationships between test scores and criteria were examined to identify significant (p < .05) correlations. Statistical treatment of data included zero-order Pearson product-moment correlation, multiple linear regression, and first-order semi-partial correlation analyses. Results indicated that the ATPP did not successfully predict (2 >.05) the rated performance of the programmers.

Subjects were 34 computer programmers employed in a major computerized tax processing company. Scores in the Computer Programmer Aptitude Battery (CPAB) and ratings of each programmer's job performance by his immediate supervisor were obtained. The purpose of the study was to validate a selection test. The relationship between the aptitude battery and performance evaluations was examined to evaluate the test's ability in predicting programming performance. Statistical treatment of data included Pearson product-moment correlations and a multiple linear regression analysis. The total test scores and several of the subtests were found to be significantly correlated with performance.