The Importance of Affirmative Action in the 21st Century: Building Transformational Alliances with Constituencies Across the Power Structure for Scholars, Students and Staff of Color at Predominantly European American Institutions.

The purpose of this session is to reject the notion that proactive Affirmative Action strategic plans are no longer needed at predominantly European American Institutions. Data reveal an inverse relationship between creating successful strategic plans for inclusion and negative reactions from the power structure.

Talent Management in Higher Education: Developing Emerging Leaders Within the Administration at Private Colleges and Universities

This research focused on identifying a series of successful practices relating to administrative talent management within the higher education setting. The field study included a thorough examination of seven small to mid-size private colleges and universities that have incorporated employee development strategies. These strategies were aimed at growing future leaders from within the organization in order to achieve continuity and support institutional priorities. Specifically, several focus areas were investigated including presidential vision, leadership commitment, talent management’s place among institutional priorities, program characteristics, and program evaluation. Among the commonalities that were gathered included support at the senior officer level who serve as advocates, mentors, and program facilitators, a strong connection between talent management and the institutions’ strategic plans, and a holistic approach to developing talent at all levels of the organizations. In addition, both coaching and opportunities for growth in the work environment were evident within several of the institutions. Also, academic leadership development was considered to be a part of the talent management strategy within three of the colleges and universities. The key differentiators included the incorporation of organizational and leadership competencies to provide focus toward the performance development process at two institutions, the implementation of a succession planning model at another institution, and the location of human resource generalists in departments across two of the institutions to identify learning opportunities for both individuals and work teams. Based on both the findings from the field study and the literature review, a comprehensive procedural model is introduced that serves to support human resource departments and higher education professionals, in general, who are looking to either begin or broaden their own talent management approach. However, despite the progress that has been made across several institutions noted throughout the research study, much more must be learned in terms of how the time and resources invested in talent management translates to institutional success. Advisor: James O‘Hanlon

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.