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- All Subjects: Power Analysis
- Creators: Thompson, Marilyn S.
Description
The purpose of this mixed-methods study was to understand the key constructs and processes underlying the mentoring relationships between doctoral students and their mentors. First, exploratory and confirmatory factor analyses were used to evaluate the measurement structure underlying the 34-item Ideal Mentor Scale (IMS; Rose, 2003), followed by an examination of factorial invariance and differences in latent means between graduate students differing by gender, age, and Master's vs. Doctoral status. The IMS was administered to 1,187 graduate students from various departments across the university at Arizona State University (ASU); this sample was split into two independent samples. Exploratory factory analysis on Sample 1 (N = 607) suggested a new four-factor mentoring model consisting of Affective Advocacy, Academic Guidance, Scholarly Example, and Personal Relationship. Subsequent confirmatory factor analysis on Sample 2 (N = 580) found that this four-factor solution was superior to the fit of a previously hypothesized three-factor model including Integrity, Guidance, and Relationship factors (Rose, 2003). Latent mean differences were evaluated for the four-factor model using structured means modeling. Results showed that females placed more value on factors relating to Affective Advocacy, Academic Guidance, and Scholarly Example, and less value on Personal Relationship than males. Students 30 and older placed less value on Scholarly Example and Personal Relationship than students under 30. There were no significant differences in means for graduate students pursuing a Master's versus a Doctoral degree. iii Further study qualitatively examined mentoring relationships between doctoral students and their faculty mentor using the Questionnaire on Supervisor Doctoral Student Interaction (QSDI) coupled with semi-structured interviews. Graduate support staff were interviewed to gather data on program characteristics and to provide additional context. Data were analyzed using Erickson's Modified Analytical Inductive method (Erickson, 1986). Findings showed that the doctoral students valued guidance, advocacy and constructive, timely feedback but realized the need to practice self-reliance to complete. Peer mentoring was important. Most of the participants valued a mentor's advocacy and longed to co-publish with their advisor. All students valued intellectual freedom, but wished for more direction to facilitate timelier completion of the degree. Development of the scholarly identity received little or no overt attention.
ContributorsGarrett, Pamela S (Author) / Smith, Mary Lee (Thesis advisor) / Potts, Shelly A. (Thesis advisor) / Thompson, Marilyn S. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Designing studies that use latent growth modeling to investigate change over time calls for optimal approaches for conducting power analysis for a priori determination of required sample size. This investigation (1) studied the impacts of variations in specified parameters, design features, and model misspecification in simulation-based power analyses and (2) compared power estimates across three common power analysis techniques: the Monte Carlo method; the Satorra-Saris method; and the method developed by MacCallum, Browne, and Cai (MBC). Choice of sample size, effect size, and slope variance parameters markedly influenced power estimates; however, level-1 error variance and number of repeated measures (3 vs. 6) when study length was held constant had little impact on resulting power. Under some conditions, having a moderate versus small effect size or using a sample size of 800 versus 200 increased power by approximately .40, and a slope variance of 10 versus 20 increased power by up to .24. Decreasing error variance from 100 to 50, however, increased power by no more than .09 and increasing measurement occasions from 3 to 6 increased power by no more than .04. Misspecification in level-1 error structure had little influence on power, whereas misspecifying the form of the growth model as linear rather than quadratic dramatically reduced power for detecting differences in slopes. Additionally, power estimates based on the Monte Carlo and Satorra-Saris techniques never differed by more than .03, even with small sample sizes, whereas power estimates for the MBC technique appeared quite discrepant from the other two techniques. Results suggest the choice between using the Satorra-Saris or Monte Carlo technique in a priori power analyses for slope differences in latent growth models is a matter of preference, although features such as missing data can only be considered within the Monte Carlo approach. Further, researchers conducting power analyses for slope differences in latent growth models should pay greatest attention to estimating slope difference, slope variance, and sample size. Arguments are also made for examining model-implied covariance matrices based on estimated parameters and graphic depictions of slope variance to help ensure parameter estimates are reasonable in a priori power analysis.
ContributorsVan Vleet, Bethany Lucía (Author) / Thompson, Marilyn S. (Thesis advisor) / Green, Samuel B. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2011