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.     

PERSISTENCE AND THE RELATIONSHIP TO ACADEMIC PERFORMANCE MEASURES: A LONGITUDINAL STUDY

The persistence rates of community college transfer students were contrasted to those of native university students over a six year period. A general trend was observed which suggested community college transfer students were less likely to persist when returning to school for their second or third semesters of coursework. However, those transfer students remaining for the third semester of coursework were just as likely to persist as the native students for the next semester. Questions were raised about the validity of the notion of “transfer shock”, which may actually be a reflection of the change in the student population being observed.     

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.     

Technology User Groups and Early Childhood Education: A Preliminary Study

This article presents a preliminary examination of the potential of Technology User Groups as a professional development venue for early childhood education professionals in developing operational and functional competence in using hardware and software components of a Technology toolkit. Technology user groups are composed of varying numbers of participants having an interest in technology, and are led by one or more skilled facilitators who meet with participants across time to help them acquire and demonstrate new technology skill sets. A series of these groups were conducted with seven early education professionals serving young preschool children who were at risk or who had disabilities. The impact of these technology user groups was examined using self-reports subsequent to individual participation. Specific data were collected regarding the types of technologies that had been used, and the types of classroom instructional products that had been created and implemented in classrooms using the technologies. A discussion of the value of technology user groups is presented.     

Increasing Access to Higher Education Among Low-Income Students: The Washington State Achievers Program

The purpose of this study was to assess how a comprehensive precollege intervention and developmental program among low-income high school students contributed to college enrollment outcomes measured in 2006. Our focus was on the Fifth Cohort of the Washington State Achievers (WSA) Program, which provides financial, academic, and college preparation support to 500 high school students who come from the lowest 35% of Washington state income levels. One important feature of the WSA Program is that it provided funding for complete high school curriculum reform among 16 Washington high schools that have a high prevalence of low-income students. The data set contained three groups of students from these 16 high schools: Funded Achievers who were part of the WSA Program and received funding for college; Nonfunded Achievers who were part of the WSA Program and but did not receive funding for college; and Nonrecipients who were neither part of the WSA Program nor received funding for college. Results from generalized multinomial logistic models found two trends (a) early and continuous financial support for college along with being active in the WSA Program nearly guarantees enrollment in college and increases enrollment in 4-year and highly selective colleges; and (b) even in the absence of financial support for college there are still quantifiable and positive effects on college-going for just participating in the WSA Program and receiving its abundant nonfinancial resources and support. These results persist even with strong controls for selection, background, academic, financial, aspiration, and school-level variables.     

What We Know,What We Still Need to Know: Teaching Adolescents to Write

The purpose of this study was to uncover any differences in the early reading and spelling processes of children learning to read in a first language (L1) and children learning to read in a second language (L2). The reading and spelling development of native Dutch-speaking children and minority children in the first two grades of elementary school were compared. The children were given a number of tasks to test their vocabulary knowledge and the efficiency of their word decoding (including grapheme knowledge and word blending), word spelling (including cipher knowledge and phonemic segmentation), and reading comprehension processes. Analyses of variance were used to test for differences between the L1 and L2 learners. LISREL analyses were used to explore the components underlying the reading and spelling processes in the 2 groups of children. The results showed that the minority children kept up with the native Dutch-speaking children on word blending and word decoding tasks. On word spelling and reading comprehension, however, the minority children were found to be less efficient than their monolingual Dutch peers. The structural models for word decoding and word spelling were highly comparable for the 2 groups. For reading comprehension, vocabulary knowledge was found to have more of an impact on the L2 learners than on the L1 learners. This finding suggests that children learning to read in an L2 should be helped to build their lexical knowledge and that reading instruction should be matched to this knowledge.     

Social Networking: Engaging Prospective and Admitted African American and Other Minority Students Before They Arrive on Campus

With the effectiveness of traditional methods of connecting with incoming Wittenberg University minority students waning, Forest B. Wortham spans the virtual and physical worlds to help them find their place.