Simulated work experience: how realistic should it be?

One hundred 10th grade non-college-bound students were randomly assigned to receive two alternative types of materials: (a) experimental—an Electronic Technician Kit which required students to use an actual ammeter in solving problems representative of that occupation; and (b) control—a kit virtually identical with the experimental treatment except that no meter was provided. The experimental subjects reported more enjoyment, wanted more information about an electronic technician career, wrote more questions about careers, and signed up to take more additional career kits than did the control subjects. However, the number of measured exploratory activities during the week after the treatments was too small to test for statistical significance.     

A Chicano/black/white encounter

What happens when members of three ethnic groups spend eighteen hours together in an “Encounter Weekend”? A facilitation style that placed the major responsibility for group progress on the group itself brought about marked behavioral changes among the group participants. The group progressed from distrust, suspicion, and violent rhetoric to effective communication and understanding. The interracial encounter is a powerful social tool that counselors can use to help people develop respect for each other and live together more humanely.     

OPTIONS FOR SMALL SAMPLE SIZES IN VALIDATION: A CASE FOR THE J-COEFFICIENT

One of the continuing problems that employers face is the validation of their personnel decisions. While a major emphasis of validation has concerned hiring decisions, it is also clear from the 1978 Uniform Guidelines that other decisions concerning individuals are subject to scrutiny. The purpose of this paper is to explore the various option available for the validation of selection procedures when criterion-related validity is technically infeasible and to present a conceptual framework, the J -Coefficient, within which all validation options can be considered.     

Bias and error in multiple-choice tests

A formula for estimating real scores on a multiple-choice test from a knowledge of raw scores is derived. This formula does not involve the assumption of a binomial distribution of real scores as does the Calandra formula. Other important formulas derived show: the variance of real scores in terms of the variance of raw scores and the correlation between real scores and raw scores. If the variance of real scores (or of raw scores also) is binomial, the regression of real scores on raw scores is linear; but, otherwise the regression is curvilinear. Yet the linear estimating formula is a close approximation to the curvilinear relationship. Factors affecting the regression of real scores on raw scores and the correlation coefficient are: (1) the number of choices per question; (2) the number of questions answered; (3) the ratio of the average group raw score to the variance of raw scores.