Dennis M. Roberts

Reliability and Validity of a Statistics Attitude Survey

A 34 item scale entitled Statistics Attitude Survey (SAS) was developed and administered to three samples of students taking a beginning statistics course. Analyses showed that the scale was highly homogeneous and that total scale scores had moderate correlations with statistics grades.

A Comparison of Methods for Treating Incomplete Data in Selection Research

Data were simulated to conform to covariance patterns taken from the personnel selection literature. Two, six, and ten percent of the values were deleted from one of three predictor variables in sample sizes of 50, 100 and 200. Incomplete data matrices were treated by four methods: (a) elimination of cases with incomplete data records; (b) substitution of missing values with the variable mean; (c) replacement of missing values with an estimate obtained from simple regression; and (d) replacement of missing values with an estimate derived from iterated multiple regression. The treated data matrices were subjected to multiple regression analyses, and the resulting regression equations were compared to the equations obtained from the original, complete data. The two regression based estimation procedures provided the most accurate regression equations, followed by the method of inserting means. Discarding cases with incomplete records was the least accurate method. Although the results supported the practice of using covariate information to estimate missing data, the increases in accuracy were minimal under the conditions investigated.

Validity of a Statistics Attitude Survey: A Follow-Up Study

The validity of the Statistics Attitude Survey (SAS) was further examined in the present study. Students were assessed on a number of pretest and posttest cognitive and non-cognitive variables, including the SAS. SAS scores were found to be significantly related to such cognitive variables as basic mathematics skills, statistics preknowledge, and course grades. Non-cognitive factors with which SAS was significantly correlated were sex, the degree to which students indicated that they had wanted to take the course and that they were glad they had taken the course, number of previous mathematics courses completed, the status of a course being required or elective, calculator attitudes, and course and instructor evaluations. In addition, SAS scores showed a significant positive change from the beginning to the end of the course.

A Comparison of Two Scales Measuring Attitudes towards Statistics

The present investigation compared two statistics attitude scales, the Statistics Attitude Survey (SAS) by Roberts and Bilderback (1980) and the Attitudes Toward Statistics (ATS) by Wise (1985). Wise had claimed that the SAS was partially invalid because many of the items appeared to be achievement oriented rather than attitudinal in nature. The SAS and ATS were combined in one scale and administered to 280 introductory statistics students at the beginning of the course. Course grades were obtained at the end of the course. Analyses showed high reliabilities for both SAS and ATS but a correlation of nearly .9 between the two scales. In addition, none of the correlations between the two scales with several criteria, including course grades, were significantly different. It was concluded that the ATS was essentially an alternate form of the previously developed SAS.

A Scale to Assess Perceptions of Cheating in Examination-Related Situations

The Perceptions of Cheating Scale (POCS) was developed to assess perceptions of academic cheating in examination-related situations. The final form of the POCS contained 30 items depicting cheating scenarios. Respondents were required to assign levels of punishment appropriate for the cheating described. Data were collected from both college students and faculty. As partial validation, a comparison was made between faculty and student response patterns. Results showed clear differences with faculty consistently assigning harsher punishments. Examination of the relationships between the POCS and several criterion variables yielded some small but statistically significant coefficients.

Development and validation of a foreign language attitude scale

The present studies examined the validity of a new foreign language attitude scale (FLAS). One study compared the validity coefficients of the FLAS with those of ability predictors. The FLAS was correlated .25 with college foreign language (FL) grades, while being essentially orthogonal to most ability predictors. High school GPA was correlated .50 with FL grades. The correlations of FL grades with the total scores and subtest scores on the Modern Language Aptitude Test ranged from .12 to .44. College Board Scholastic Aptitude Verbal and Mathematics sub-test scores were not significantly related to FL grades. Stepwise regression analyses indicated that the FLAS made a significant contribution to the prediction of FL grades. In a second study with a different sample of college students, correlations between the FLAS and standardized measures of FL reading and listening proficiency were in the middle .40s. Although further research is needed, the present findings suggest that the moderate relationship between the FLAS and FL proficiency may be used to augment the validity in the prediction of success in FL courses.

A Case against Continuing Use of the Spearman Formula for Rank-Order Correlation

Historical development of the Pearson correlation and the Spearman analog on the ranks was traced to delineate the basis on which the Spearman formula was derived. Following this, recommendations from current statistical textbooks on the use of the Spearman formula were presented. Arguments of computational simplicity, appropriateness when data are not at the interval level, and appropriateness when the relation between X and Y is monotonic but nonlinear for using Spearman were found deficient. Results from a small simulation study also supported this view.

TEACHING INTRODUCTORY STATISTICS FOR PSYCHOLOGY: EFFECTS OF CALCULATORS ON PERFORMANCE AND MOTIVATION

60 college students worked both simple and complex statistical problems under one of three computational conditions: manual (no calculator), elementary calculator, and advanced calculator. Results indicated that calculator usage reduced working times and errors, especially on complex problems, and helped to preserve positive attitudes students held about themselves and the nature of their problem-solving.

Book Reviews : Terry D. TenBrink. Evaluation: A practical guide for teachers. McGraw-Hill, Inc., 1974, Pp. x + 493,

discussion of validity he mentions four types of validity: face validity, content validity, empirical validity and construct validity. But the only one of these four discussed in depth is empirical validity because it is associated with a number of formulas. To a large extent, Quantitative Aspects of Psychological Assessment: An Introduction is a collection of formulas and their derivations which are associated with introductory correlational statistics and with test theory. The notation used in the formulas is acceptable. In his derivations, the step-by-step process is easy to follow. An undergraduate student certainly should be able to understand the formulas and the derivations. The main difficulty with the book is that it provides very little discussion which would be useful to help the introductory student gain a conceptual understanding of the material covered in the formulas. Ley provides only brief prose discussions of the concepts he covers. Discussion of the conceptual issues with which many introductory students have difficulty-such as the notion of true score, regression towards the mean, parallel tests and base rates-are inadequate and inappropriately terse. One method Ley could have used to help introductory students gain a conceptual understanding of why the formulas in the book

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Douglas R. Whitney, James M. Richards, Jr., John D. Roth, Gary R. Hanson, and Dale J. Prediger. Particularly meaningful are the circular planar configurations of Holland’s six Vocational Preference inventory scales, of 50 Strong Vocational Interest Blank occupational scales, and of the 23 Kuder Occupational Interest Survey scales. Figure 7 portrays comparison of the spatial configurations of the Strong Vocational Interest Blank (SVIB), the Kuder Occupational Interest Survey (OIS), and Holland’s Vocational Preference Inventory (VPI). &dquo;Most occupational titles do not represent narrowly defined occupations.... The activities of an engineer may range from building bridges to teaching electrical engineering.&dquo;