The problem of this study was to determine the differences in experimentwise and comparisonwise Type I error rate among six multiple comparison procedures when applied to twenty-eight combinations of normally distributed data. These were the Least Significant Difference, the Fisher-protected Least Significant Difference, the Student Newman-Keuls Test, the Duncan Multiple Range Test, the Tukey Honestly Significant Difference, and the Scheffe Significant Difference. The Spjøtvoll-Stoline and Tukey—Kramer HSD modifications were used for unequal n conditions. A Monte Carlo simulation was used for twenty-eight combinations of k and n. The scores were normally distributed (µ=100; σ=10). Specified multiple comparison procedures were applied under two conditions: (a) all experiments and (b) experiments in which the F-ratio was significant (0.05). Error counts were maintained over 1000 repetitions. The FLSD held experimentwise Type I error rate to nominal alpha for the complete null hypothesis. The FLSD was more sensitive to sample mean differences than the HSD while protecting against experimentwise error. The unprotected LSD was the only procedure to yield comparisonwise Type I error rate at nominal alpha. The SNK and MRT error rates fell between the FLSD and HSD rates. The SSD error rate was the most conservative. Use of the harmonic mean of …

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 …

Behavioral data are frequently plagued with highly intercorrelated variables. Collinearity is an indication of insufficient information in the model or in the data. It, therefore, contributes to the unreliability of the estimated coefficients. One result of collinearity is that regression weights derived in one sample may lead to poor prediction in another model. One technique which was developed to deal with highly intercorrelated independent variables is ridge regression. It was first proposed by Hoerl and Kennard in 1970 as a method which would allow the data analyst to both stabilize his estimates and improve upon his squared error loss. The problem of this study was the application of ridge regression in the analysis of data resulting from educational research.

The supply of non-physician clinicians (NPCs), such as physician assistant (PAs), could significantly influence demand requirements in medical workforce projections. This study predicts supply of and demand for PAs from 2006 to 2020. The PA supply model utilized the number of certified PAs, the educational capacity (at 10% and 25% expansion) with assumed attrition rates, and retirement assumptions. Gross domestic product (GDP) chained in 2000 dollar and US population were utilized in a transfer function trend analyses with the number of PAs as the dependent variable for the PA demand model. Historical analyses revealed strong correlations between GDP and US population with the number of PAs. The number of currently certified PAs represents approximately 75% of the projected demand. At 10% growth, the supply and demand equilibrium for PAs will be reached in 2012. A 25% increase in new entrants causes equilibrium to be met one year earlier. Robust application trends in PA education enrollment (2.2 applicants per seat for PAs is the same as for allopathic medical school applicants) support predicted increases. However, other implications for the PA educational institutions include recruitment and retention of qualified faculty, clinical site maintenance and diversity of matriculates. Further research on factors affecting …

The purpose of this study was to examine if there is an association between effective supervision and communication competence in divisions of student affairs at Christian higher education institutions. The investigation examined chief student affairs officers (CSAOs) and their direct reports at 45 institutions across the United States using the Synergistic Supervision Scale and the Communication Competence Questionnaire. A positive significant association was found between the direct report's evaluation of the CSAO's level of synergistic supervision and the direct report's evaluation of the CSAO's level of communication competence. The findings of this study will advance the supervision and communication competence literature while informing practice for student affairs professionals. This study provides a foundation of research in the context specific field of student affairs where there has been a dearth of literature regarding effective supervision. This study can be used as a platform for future research to further the understanding of characteristics that define effective supervision.

Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability. Monte Carlo simulation methodology was used to fulfill the purpose of this study. Initially, data populations with various manipulated conditions were generated (N = 100,000). Subsequently, 500 random samples were drawn with replacement from each population, and data was subjected to canonical correlation analyses. The canonical correlation results were then analyzed using descriptive statistics and an ANOVA design to determine under which condition(s) the squared canonical correlation coefficient was most attenuated when compared to population Rc2 values. This information was analyzed and used to determine what effect, if any, the different conditions considered in this study had on Rc2. The results from this Monte Carlo investigation clearly illustrated the importance of score reliability when interpreting study results. As evidenced by the outcomes presented, the more measurement error (lower reliability) present in the variables included in an analysis, the more attenuation experienced by the effect size(s) produced in the analysis, in this …