The purpose of this study is to determine boundary conditions associated with the number of treatment groups (K), the common treatment group sample size (n), and an index of the extent to which the assumption of equality of treatment population variances is violated (Q) with regard to user confidence in application of the one-way analysis of variance F-test for determining equality of treatment population means. The study concludes that the analysis of variance F-test is robust when the number of treatment groups is less than seven and when the extreme ratio of variances is less than 1:5, but when the violation of the assumption is more severe or the number of treatment groups is seven or more, serious discrepancies between actual and nominal significance levels occur. It was also concluded that for seven treatment groups confidence in the application of the analysis of variance should be limited to the values of Q and n so that n is greater than or equal to 10 In (1/2)Q. For nine treatment groups, it was concluded that confidence be limited to those values of Q and n so that n is greater than or equal to (-2/3) + 12 ln (1/2)Q. No definitive …

The purpose of this study was to use a cross categorical scoring method for sociometric data focusing upon those individuals who have made the selections. A cross category selection was defined as choosing an individual on a sociometric instrument who was not within one's own classification. The classifications used for this study were sex, race, and perceived achievement level. A cross category score was obtained by summing the number of cross category selections. The conclusions below are the result of this study. Cross categorical scoring provides a useful method of scoring sociometric data. This method successfully focuses on those individuals who make sociometric choices rather than those who receive them. Each category utilized provides a unique contribution. The categories used in this study were sex, race, and achievement level. These are, however, only reflective of any number of variables which could be used. The categories must be chosen to reflect the needs of the particular study in which they are included. Multiple linear regression analysis can be used in order to provide the researcher with enough scope to handle numerous nominal and ordinal independent variables simultaneously. The sociometric criterion or question does make a difference in the results on cross …