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.     

Estimation of Controlled Direct Effects in the Presence of Exposure-Induced Confounding and Latent Variables

Estimation of the direct effect of an exposure on an outcome requires adjustment for confounders of the exposure–outcome and mediator–outcome relationships. When some of the latter confounders have been affected by the exposure, then standard regression adjustment is prone to possibly severe bias. The use of inverse probability weighting under so-called marginal structural models has recently been suggested as a solution in the psychological literature. In this article, we show how progress can alternatively be made via G-estimation. We show that this estimation method can be easily embedded within the structural equation modeling framework and could in particular be used for estimating direct effects in the presence of latent variables. Moreover, by avoiding inverse probability weighting, it accommodates the typical problem of unstable weights in the alternative estimation approaches based on marginal structural models. We illustrate the approach both by simulations and by the analysis of a longitudinal study in individiduals who ended a romantic relationship. In this example we explore whether the effect of attachment anxiety during the relationship on mental distress 2 years after the breakup is mediated by rumination or not.     

Explaining Method Effects Associated With Negatively Worded Items in Trait and State Global and Domain-Specific Self-Esteem Scales

Several investigators have interpreted method effects associated with negatively worded items in a substantive way. This research extends those studies in different ways: (a) it establishes the presence of methods effects in further populations and particular scales, and (b) it examines the possible relations between a method factor associated with negatively worded items and several covariates. Two samples were assessed: 592 high school students from Valencia (Spain), and 285 batterers from the same city. The self-esteem scales used were Rosenberg's Self-Esteem Scale, the State Self-Esteem Scale, and Self-Esteem 17. Anxiety was also assessed with the State-Trait Anxiety Inventory, and gender and educational level were taken into account. The models were conducted using a multiple indicators and multiple causes (MIMIC) model framework. The evidence in this research pointed out that method effects were present across the different measures of self-esteem. Moreover, a significant and negative effect of anxiety on method effects was present across scales and samples, whereas no effects of age or educational level where found.     

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.