WOMEN PRESIDENTS DEFINE THE COLLEGE CULTURE THROUGH STORY

The concept of the leader's need to leave a record so that others know that she was present at a community college at a given point in time, working on behalf of the communities she served, is the theme of this article, which consists of stories from the lives of seven current and former community college chief executive officers, Vivian B. Blevins, Pamila Fisher, Sylvia Ramos, Carol Tatsey-Murray, Jerry Sue Thornton, Carolyn G. Williams, and Parker Williams give accounts of how they came to their positions as chief executive officers, the challenges and joys of that work, and the legacies they hope to leave. Their accounts as academic laborers are important tools for exploring the meaning of their roles and the roles of those they serve, for educating the uninitiated in the mission of the community college, and for reinforcing the value of that mission to the lives of community college students past, present, and future.     

Technology Usage Among Community College Faculty

Community colleges are increasing their use of part-time faculty who are taking a leading role in utilizing technology for instructional purposes. Part-time faculty are less likely than their full-time counterparts to use technology for instructional purposes and are less likely to teach non-face-to-face classes. For community colleges to be successful in expanding technology-based education, they must promote technology use by all faculty. This quantitative, national study reports factors found to predict faculty use of technology for instructional purposes.     

Rural Dislocated Women in Career Transition: The Importance of Supports and Strategies

Job loss researchers have focused on the physical and mental well being of White working and middle class men, their families, friends, and coworkers to with immediate reemployment as the outcome. This study focused on low-income rural women dislocated workers and their decision to enroll in community college for retraining or seek immediate reemployment. Participants were 125 women (86 white, 39 African American; x = 42 years) who held a high school diploma or GED and who were laid off from manufacturing jobs within the last 2 years. Differences between two groups of women based on demographic variables and Transition Guide and Questionnaire-Modified (TGQ-M) scores were examined. The TGQ-M was developed to assess an individual's ability to cope with life transitions, based on Schlossberg's (1995 Schlossberg , N. K. , Waters , E. B. , & Goodman , J. ( 1995 ). Counseling adults intransition: Linking practice with theory. , 2nd ed. , New York : Springer . [Google Scholar]) model of 4Ss (situation, self, supports, and strategies). One group enrolled in community college for retraining while the other group sought immediate reemployment. Findings revealed significant relationships between level of education and race/ethnicity and community college retraining. African-American women in this sample perceived themselves as having less support in coping with job loss. Women who chose community college had higher TGQ-M scores overall. Race/ethnicity and perception of support from others were the best predictors of community college retraining. Women, especially older women of color, have the hardest time finding quality employment after layoff. This study raised questions about the reasons why fewer African-American women in this sample enrolled in community college for retraining and had lower Supports scores than white women. Implications for counseling and future research are discussed.     

MENTORING BY THE COMMUNITY COLLEGE PROFESSOR: ONE ROLE AMONG MANY

The process of mentorship in relationship to the community college professor and the potential to improve and enhance the teaching and learning process is explored in this article. This article suggests that not all community college professors are psychological or intellectually disposed to being or becoming a good mentor. It is put forth that mentoring is a subrole of the community college professor. A framework for understanding the “complete mentor” process, as developed by Cohen (1993) Cohen, N. H. 1993. The development and validation of the principles of adult mentoring scale for faculty mentoring in higher education Philadelphia, PA Unpublished doctoral dissertation, Temple University [Google Scholar], is presented. Implications for practice are offered in a manner that connects the community college professor as mentor with enhancing the instructional process through the nurturing teaching perspective.     

THE FUTURE OF TWO‐YEAR COLLEGES: AN ADMINISTRATOR'S‐EYE VIEW

Individuals in eight different types of administrative positions in two‐year colleges were asked to rate the importance of a variety of external and internal issues in the next five years. The purpose of the study reported here was first to identify those internal and external issues that administrators viewed as being of high importance. A second purpose was to assess the degree of consensus among differing types of administrative positions. The third objective was to identify differences between public and independent two‐year college, between men and women, and minority and white administrators perceptions of issues identified as being most important. There was a high degree of agreement across administrative groups as to the importance of five external issues; and when asked to indicate the two most critical issues, administrators were almost unanimous in their assessment. There is less agreement on the two critical internal issues. Some differences by gender and racial/ethnic group were noted, but the differences were not as great as might have been expected.     

Transfer Student Success: Exploring Community College,University, and Individual Predictors

ABSTRACT

This paper investigates community college transfer success by exploring the relationship between individual and institutional-level characteristics at students’ two- and four-year institutions. Using statewide administrative data from North Carolina, this study employs a cross-classified multilevel model to investigate the impact that a student’s community college and four-year transfer institution have on post-transfer success. Our findings offer important and compelling insights into the relationship between transfer students, the community college they attended, the four-year transfer institution, and educational outcomes. While individual effects were small, we find several institutional factors associated with student success. Attendance at a large community college or having a public university in the same county as their community college is positively associated with student success, whereas size of the university is negatively related to grades during the first year and persistence to the second year. While the four-year institution’s selectivity is negatively related to many of our outcomes, transferring to a Historically Black College or University is positively associated with GPA, college persistence, and degree completion.     

Exploratory analyses to improve model fit: Errors due to misspecification and a strategy to reduce their occurrence

Two Lagrange multiplier (LM) methods may be used in specification searches for adding parameters to models: one based on univariate LM tests and respecification of the model (LM‐respecified method) and the other based on a partitioning of multivariate LM tests (LM‐incremental method). These methods may result in extraneous parameters being included in models due to either sampling error or the model being misspecified. A 2‐stage specification search may be used to reduce errors due to misspecification. In the 1st stage, parameters are added to models based on LM tests to maximize fit. Second, parameters added in the 1st stage are deleted if they are no longer necessary to maintain model fit. Illustrations are presented to demonstrate that errors due to misspecification occur with the LM‐respecified method and are even more likely with the LM‐incremental approach. These illustrations also show how the deletion stage can help eliminate some of these errors.     

Principles and Practice of Scaled Difference Chi-Square Testing

We highlight critical conceptual and statistical issues and how to resolve them in conducting Satorra–Bentler (SB) scaled difference chi-square tests. Concerning the original (Satorra & Bentler, 2001 Satorra, A. and Bentler, P. M. 2001. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66: 507–514. [Crossref], [Web of Science ®] , [Google Scholar]) and new (Satorra & Bentler, 2010 Satorra, A. and Bentler, P. M. 2010. Ensuring positiveness of the scaled chi-square test statistic. Psychometrika, 75: 243–248. [Crossref], [Web of Science ®] , [Google Scholar]) scaled difference tests, a fundamental difference exists in how to compute properly a model's scaling correction factor (c), depending on the particular structural equation modeling software used. Because of how LISREL 8 defines the SB scaled chi-square, LISREL users should compute c for each model by dividing the model's normal theory weighted least-squares (NTWLS) chi-square by its SB chi-square, to recover c accurately with both tests. EQS and Mplus users, in contrast, should divide the model's maximum likelihood (ML) chi-square by its SB chi-square to recover c. Because ML estimation does not minimize the NTWLS chi-square, however, it can produce a negative difference in nested NTWLS chi-square values. Thus, we recommend the standard practice of testing the scaled difference in ML chi-square values for models M ₁ and M ₀ (after properly recovering c for each model), to avoid an inadmissible test numerator. We illustrate the difference in computations across software programs for the original and new scaled tests and provide LISREL, EQS, and Mplus syntax in both single- and multiple-group form for specifying the model M ₁₀ that is involved in the new test.     

Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification

This Monte Carlo study examines the performance of fit indices commonly used by applied researchers interested in finite mixture modeling for the purposes of classification. Conditions for the simulation study were selected to reflect conditions found in applied educational and psychological research. The factors included in the investigation were metric level of indicators, sample size, and class prevalence. All models contained a combination of categorical and continuous indicators. All categorical indicators were dichotomous, and continuous indicators were normally distributed. The fit indices examined were Akaike’s information criterion, Bayesian information criterion (BIC), sample size-adjusted Bayesian information criterion (SSBIC), integrated classification likelihood criterion with Bayesian-type approximation, and Lo–Mendell–Rubin likelihood ratio test. Overall, SSBIC tended to identify the correct solution with higher frequency than other indices. BIC tended to identify the correct solution with higher frequency than the other indices in models with more continuous than categorical indicators, or when rare classes were absent.