Admission Criteria for Educational Leadership Doctoral Students in One U.S. Doctoral Program

Aim/Purpose: The purpose of this study was to explore relationships between preadmission criteria and doctoral student performance ratings and to develop a model to predict student persistence in one doctoral program of educational leadership. Background: Individuals responsible for program admission decisions have a responsibility to minimize bias in the candidate selection process. Despite an interest in doctoral degree completion, few researchers have examined preadmission criteria and the ability to predict doctoral student performance, particularly in education programs. Methodology: Preadmission variables and postacceptance performance ratings were used in this cross-sectional predictive study (Type 5; Johnson, 2001) of 102 doctoral students in one educational leadership program. Analyses included descriptive statistics, a Pearson r correlation matrix, and predictive discriminant analysis. Contribution: In addition to strengthening the extant literature base, we attempted to respond to the charge levied by other researchers for faculty members in educational preparation programs to reassess current practices used to recruit and retain students. Findings: Using predictive discriminant analysis, we determined that separate models for students of color and White students most accurately predicted program performance, indicating that a one-size fits all approach was not optimal. The GRE-Q and undergraduate GPA were useful predictors of doctoral student persistence. Additionally, the GRE-V and graduate GPA were also useful predictors but differentially so for students of color and White students. Recommendations for Practitioners: We found value in using the GPA and GRE in admission decisions with some modifications. Programs directors are advised to evaluate their own selection processes to understand the utility of their preadmission criteria. Recommendation for Researchers: Although the functions that worked best in predicting continuance were grouped by ethnicity in this study for our students, future researchers might consider disaggregation by gender or some other characteristic to optimally identify a model specific to the student groups represented in their sample. Impact on Society: Working from an activist stance, we use our awareness of the positive correlation between degree attainment and socio-economic mobility in the United States, coupled with the existing realities of students of color who seek access to a space within the dominant culture, to urge admission committees to evaluate closely the variables used in their admission selection and to understand to what extent the selection process results in a fair selection across student groups. Future Research: Future studies could be conducted to understand why these differences exist. Other variables for future researchers to consider include time since the candidates obtained their master’s and bachelor’s degrees, the length of time to obtain those degrees, and the type of degree obtained.

The Case for Using the General Linear Model as a Unifying Conceptual Framework for Teaching Statistics and Psychometric Theory

The present paper argues for teaching statistics and psychometric theory using the GLM as a unifying conceptual framework. This helps students understand what analyses have in common, and also provides a firm grounding for understanding that more general cases of the GLM (canonical correlation analysis and SEM) can be interpreted with the same rubric used throughout the GLM. And this approach also helps students better understand analyses that are not part of the GLM, such as predictive discriminant analysis (PDA). The approach helps students understand that all GLM analyses (a) are correlational, and thus are all susceptible to sampling error, (b) can yield r2-type effect sizes, and (c) use weights applied to measured variables to estimate the latent variables really of primary interest.DOI:10.2458/azu_jmmss_v6i2_thompson

The Case for Using the General Linear Model as a Unifying Conceptual Framework for Teaching Statistics and Psychometric Theory

The present paper argues for teaching statistics and psychometric theory using the GLM as a unifying conceptual framework. This helps students understand what analyses have in common, and also provides a firm grounding for understanding that more general cases of the GLM (canonical correlation analysis and SEM) can be interpreted with the same rubric used throughout the GLM. And this approach also helps students better understand analyses that are not part of the GLM, such as predictive discriminant analysis (PDA). The approach helps students understand that all GLM analyses (a) are correlational, and thus are all susceptible to sampling error, (b) can yield r2-type effect sizes, and (c) use weights applied to measured variables to estimate the latent variables really of primary interest.DOI:10.2458/azu_jmmss_v6i2_thompson

Production and perception of a sound change in progress: Tone merging in Hong Kong Cantonese

Cantonese has six lexical tones (T), but some tone pairs appear to be merging: T2 [25] vs. T5 [23], T3 [33] vs. T6 [22], and T4 [21] vs. T6 [22]. Twenty-eight merging participants and thirty control participants in Hong Kong were recruited for a perception experiment. Both accuracy rate and reaction time data were collected. Seventeen merging participants also participated in a production experiment. Predictive discriminant analysis of the fundamental frequency data and judgments by native transcribers were used to assess production accuracy. Results show that the merging participants still had six tone categories in production, although their “tone space” was more reduced. Tones with lower type frequency were more prone to change. The merging group was significantly slower in tone perception than the control group was. In illustrating the patterns of the ongoing tone merging process in Cantonese, this study contributes to a better understanding of the forces of sound change in general.