Introduction To The Futures Focus Issue: The New Rules Of Business

The Community College Futures Assembly has served as a national independent policy think tank since 1995. Its purpose is to articulate the critical issues facing American community colleges and recognize leading-edge trendsetting programs. Convening annually in January in Orlando, Florida, the Assembly offers a learning environment where tough questions are raised, critical issues are discussed, and policy implications are challenged. The 2006 Community College Futures Assembly focused on “The New Rules of Business.”

This special focus issue of the Community College Journal of Research and Practice highlights the “best practices” of 30 Bellwether trend-setting institutions. These institutions competitively presented their “lessons learned” to representatives of colleges interested in replicating their award winning programs. In each of the three award categories, Instructional Program and Services; Planning, Governance and Finance; and Workforce Development; 10 Bellwether finalists were competitively selected. Finalists were featured as presenters at the assembly to share their leading-edge ideas with participants. Three institutions then won the prestigious Bellwether Award in their respective category.     

Culturally Responsive Pedagogy and Online Learning: Implications for the Globalized Community College

Implicit in the open-door mission of the community college is the mandate that every learning experience should offer full equity and inclusion for all learners, including those of diverse cultural backgrounds. This mission is paramount, given the cultural diversity represented among learners served by the globalized community college. This paper presents a cross-section of research, practice, and discourse focused on expanding our knowledge and understanding of the diverse learning needs of community college learners. The relevance of the literature to the planning, design, and implementation of distance-learning is discussed. In the end, the authors offer distance-learning instructional strategies that may accommodate the unique needs of Hispanic/Latino learners. Implications for community college educators are discussed.     

Unconstrained Structural Equation Models of Latent Interactions: Contrasting Residual- and Mean-Centered Approaches

Little, Bovaird and Widaman (2006) proposed an unconstrained approach with residual centering for estimating latent interaction effects as an alternative to the mean-centered approach proposed by Marsh, Wen, and Hau (2004, 2006). Little et al. also differed from Marsh et al. in the number of indicators used to infer the latent interaction factor and how they were represented, but this issue is separate from the mean versus residual centering distinction that was their primary focus. However, their implementation of the Marsh et al. mean-centered approach failed to incorporate the mean structure that Marsh et al. argued was necessary to obtain unbiased estimates. One might suppose that their new approach would suffer this same problem, an issue not addressed by Little et al. However, we demonstrate here why the Little et al. approach obviates this requirement that heretofore was thought to be necessary for all constrained, partially constrained, and unconstrained approaches. Both the Marsh et al. and Little et al. unconstrained approaches typically result in similar results and are much easier to implement than traditional constrained approaches. They differ primarily in that the Little et al. approach is a 2-step approach involving a potentially large number of separate analyses prior to estimating the structural equation model that apparently does not require the estimation of a mean structure, whereas the Marsh et al. approach is a 1-step approach that includes a mean structure.     

Validation of the child sex abuse attitude scale through confirmatory factor analysis

A validation study was conducted on the Child Sex Abuse Attitude Scale (CSAAS) using confirmatory factor analysis (CFA) to examine factor structure. The CSAAS was developed based on Festinger's (1957) theory of attitude development resulting in a 4‐factor first‐order structure (cognition, value, affect, and behavior) and a single‐factor 2nd‐order structure (attitude). A sample of 215 school psychologists, members of the National Association of School Psychologists, responded to the CSAAS survey. CFA results supported the hypothesized factor structure of the CSAAS, thus indicating the plausibility of a 4‐factor 1st‐order and a single‐factor higher order structure of the CSAAS.     

Residual Structures in Latent Growth Curve Modeling

This article presents relevant research on Bayesian methods and their major applications to modeling in an effort to lay out differences between the frequentist and Bayesian paradigms and to look at the practical implications of these differences. Before research is reviewed, basic tenets and methods of the Bayesian approach to modeling are presented and contrasted with basic estimation results from a frequentist perspective. It is argued that Bayesian methods have become a viable alternative to traditional maximum likelihood-based estimation techniques and may be the only solution for more complex psychometric data structures. Hence, neither the applied nor the theoretical measurement community can afford to neglect the exciting new possibilities that have opened up on the psychometric horizon.     

Estimation of MIMIC Model Parameters with Multilevel Data

The purpose of this simulation study was to assess the performance of latent variable models that take into account the complex sampling mechanism that often underlies data used in educational, psychological, and other social science research. Analyses were conducted using the multiple indicator multiple cause (MIMIC) model, which is a flexible and effective tool for relating observed and latent variables. The data were simulated in a hierarchical framework (e.g., individuals nested in schools) so that a multilevel modeling approach would be appropriate. Analyses were conducted accounting for and not accounting for the nested data to determine the impact of ignoring such multilevel data structures in full structural equation models. Results highlight the differences in modeling results when the analytic strategy is congruent with the data structure and what occurs when this congruency is absent. Type I error rates and power for the standard and multilevel methods were similar for within-cluster variables and for the multilevel model with between-cluster variables. However, Type I error rates were inflated for the standard approach when modeling between-cluster variables.     

Models and Strategies for Factor Mixture Analysis: An Example Concerning the Structure Underlying Psychological Disorders

The factor mixture model (FMM) uses a hybrid of both categorical and continuous latent variables. The FMM is a good model for the underlying structure of psychopathology because the use of both categorical and continuous latent variables allows the structure to be simultaneously categorical and dimensional. This is useful because both diagnostic class membership and the range of severity within and across diagnostic classes can be modeled concurrently. Although the conceptualization of the FMM has been explained in the literature, the use of the FMM is still not prevalent. One reason is that there is little research about how such models should be applied in practice and, once a well-fitting model is obtained, how it should be interpreted. In this article, the FMM is explored by studying a real data example on conduct disorder. By exploring this example, this article aims to explain the different formulations of the FMM, the various steps in building a FMM, and how to decide between an FMM and alternative models.     

Improving Instructional Practices, Policies, and Student Outcomes for Early Childhood Language and Literacy Through Data-Driven Decision Making

Since the passage of No Child Left Behind, data-driven decision making has become one of the central foci in schools in their attempt to attain and maintain adequate levels of student academic performance. The importance of early childhood education is well established with language and literacy proficiency in the early years being viewed as a leading indicator in children’s educational development. It provides schools with the initial signs of progress towards academic achievement. In this article, a conceptual framework for improving instructional practice and student outcomes in early childhood language and literacy through data-driven decision making was described. Four questions served as the structure around which the conceptual framework was built. These questions include (1) Why do data need to be collected? (2) What kinds of data need to be collected? (3) How are the data collected? (4) How are the data used for making decisions? Responses to these questions serve as tenets for guiding the decision making process.     

Spelling,Reading Abilities and Speech Perception in Deaf Children with a Cochlear Implant

ABSTRACT

This study aims to compare word spelling outcomes for French-speaking deaf children with a cochlear implant (CI) with hearing children who matched for age, level of education and gender. A picture written naming task controlling for word frequency, word length, and phoneme-to-grapheme predictability was designed to analyze spelling productions. A generalized linear mixed model on the percentage of correct spelling revealed an effect of participant’s reading abilities, but no effect of hearing status. Word frequency and word length, but not phoneme-to-grapheme predictability, contributed to explaining the spelling variance. Deaf children with a CI made significantly less phonologically plausible errors and more phonologically unacceptable errors when compared to their hearing peers. Age at implantation and speech perception scores were related to deaf children’s errors. A good word spelling level can be achieved by deaf children with a CI, who nonetheless use less efficiently the phoneme-to-grapheme strategy than do hearing children.     

Orthographic Learning During Oral and Silent Reading

This study examined orthographic learning in oral and silent reading conditions. Dutch third graders read, either aloud or silently, short texts containing novel target (pseudo) words. The acquisition of new word-specific orthographic knowledge was assessed several days later by comparing target spellings with homophonic spellings in tasks requiring orthographic choice, spelling, and naming. It was predicted that orthographic learning would be evident in both oral and silent conditions but stronger in the oral condition. As expected, orthographic learning was evident in both oral and silent conditions. This finding suggests that support for the self-teaching hypothesis of orthographic learning obtained in studies of unassisted oral reading can be generalized to the more common form of independent reading: silent reading. In addition, the results on the naming task provided some evidence for stronger orthographic learning during oral reading, but the two spelling tasks did not.