Promoting Completion Through Organizational Development and Process Improvement

In 2007, Tulsa Community College (TCC) joined the national Achieving the Dream (ATD) network, which is dedicated to developing data-informed interventions to increase persistence and completion among community college students. TCC’s participation in the national initiative set it down a path for positive institutional change, but it was the College’s restructuring of their internal work teams and their process improvements since 2011–2012 that have resulted in a sustainable system that will help retain knowledge and ensure successful leadership transitions. In 2013, TCC rebranded its modified ATD system as Completing the Dream, combining the goals of Complete College America with the action research principles of Achieving the Dream. Because of these efforts, the College is in a stronger position today than it has ever been to serve our students using evidence-based strategies, ultimately resulting in more college-educated citizens in the Tulsa area.     

Social Media,Higher Education,and Community Colleges: A Research Synthesis and Implications for the Study of Two-Year Institutions

This research project used a large database to examine factors associated with full-time faculty at 2-year institutions who have secured or not secured external funds. Specifically, the research examined demographic characteristics and other variables of faculty at 2-year institutions.     

The surprising viability of a simple alternate estimation procedure for construction of large‐scale structural equation measurement models

As part of developing a comprehensive strategy for structural equation model building and assessment, a large‐scale Monte Carlo study of 7,200 covariance matrices sampled from 36 population models was conducted. This study compared maximum likelihood with the much simpler centroid method for the confirmatory factor analysis of multiple‐indicator measurement models. Surprisingly, the contribution of maximum likelihood to model analysis is limited to formal evaluation of the model. No statistically discernible differences were obtained for the bias, standard errors, or mean squared error (MSE) of the estimated factor correlations, and empirically obtained maximum likelihood standard errors for the pattern coefficients were only slightly smaller than their centroid counterparts. Further supporting the recommendations of Anderson and Gerbing (1982), the considerably faster centroid method may have a useful role in the analysis of these models, particularly for the analysis of large models with 50 or more input variables. These results encourage the further development of a comprehensive research paradigm that exploits the relative strengths of both centroid and maximum likelihood as complementary estimation procedures along an integrated exploratory‐confirmatory continuum of model specification, revision, and formal evaluation.     

An Early History of the Rural Community College Initiative: Reflections on the Past and Implications for the Future

The $17.2 million Rural Community College Initiative (RCCI) demonstration grant program funded by the Ford Foundation which ran from 1994 to 2001 represents the largest philanthropic project specifically aimed at rural community colleges in United States history. While a good deal of literature has been published about this initiative, much was specifically commissioned by the foundation and can be seen as coming from a public relations perspective. This paper provides a historical perspective from an independent point of view. Its purpose is to create a documentary record for future community college leaders and potential funders interested in reaching the nation's 553 rural community colleges.     

Factor Analysis with Ordinal Indicators: A Monte Carlo Study Comparing DWLS and ULS Estimation

Factor analysis models with ordinal indicators are often estimated using a 3-stage procedure where the last stage involves obtaining parameter estimates by least squares from the sample polychoric correlations. A simulation study involving 324 conditions (1,000 replications per condition) was performed to compare the performance of diagonally weighted least squares (DWLS) and unweighted least squares (ULS) in the procedure's third stage. Overall, both methods provided accurate and similar results. However, ULS was found to provide more accurate and less variable parameter estimates, as well as more precise standard errors and better coverage rates. Nevertheless, convergence rates for DWLS are higher. Our recommendation is therefore to use ULS, and, in the case of nonconvergence, to use DWLS, as this method might converge when ULS does not.     

Detecting Misspecification in Mean Structures for Growth Curve Models: Performance of Pseudo R 2s and Concordance Correlation Coefficients

This study examined the performance of 4 correlation-based fit indexes (marginal and conditional pseudo R ²s; average and conditional concordance correlations) in detecting misspecification in mean structures in growth curve models. Their performance was also compared to that of 4 traditional SEM fit indexes. We found that the marginal pseudo R ² and average concordance correlation were able to detect misspecification in the marginal mean structure (average change trajectory). The conditional pseudo R ² and concordance correlation could detect misspecification when it occurred in the conditional mean structure (individual change trajectory) or in both mean structures. Compared to the SEM fit indexes, the correlation-based fit indexes were more robust to sample size but were less robust to data properties such as magnitude of population mean and measurement error. Theoretical and practical implications of the results and directions for future research are discussed.     

The Role of Set for Variability in Irregular Word Reading: Word and Child Predictors in Typically Developing Readers and Students At-Risk for Reading Disabilities

ABSTRACT

In a quasiregular orthography like English, children inevitably encounter irregular words during reading. Previous research suggests successful reading of an irregular word depends at least partially on a child’s ability to address the mismatch between decoded form and stored word pronunciation, referred to as a child’s set for variability, and the word’s relative transparency, measured here using a spelling to pronunciation transparency rating. Item-level analyses were used to explore the relationship between general child performance on the set for variability mispronunciation task, word specific set for variability (predicting reading of that word), spelling to pronunciation transparency rating, and irregular word reading. Significant predictors included general word reading, general set for variability performance, and item-specific set for variability performance; word frequency and spelling to pronunciation transparency rating; and an interaction between word reading and the transparency rating. Results underscore the importance of considering both general and item-specific factors affecting irregular word reading.