Invisible and Indispensable: Parttime Teachers in California Community Colleges

Abstract

Although parttime instructors constitute a substantial portion—often a majority—of community college faculties, they are accorded a low status on campus, with less than half the salary and none of the benefits of fulltime faculty members. This study points up a number of misconceptions that administrators have been using to justify discriminatory treatment of parttime teachers.     

Integrating Language,Literacy, and Academic Development: Alternatives to Traditional English as a Second Language and Remedial English for Language Minority Students in Community Colleges

This article argues for the importance of integrating a focus on language, literacy, and academic development for United States-educated language minority (US-LM) students, sometimes called Generation 1.5. It describes four initiatives at community colleges in California that aim to do so. US-LM students have completed some K–12 schooling in the United States, but their English is considered by community college faculty, staff, or assessment measures to be inadequate for college-level instruction. Although enacting effective language and literacy support for Generation 1.5 has centered on debates about whether these students belong in English as a Second Language (ESL) or remedial English courses, how they can best be identified and tested, or whether they should be taught in separate classes, we argue that more fundamental shifts are needed. Instead of conceiving of students’ language and literacy development solely in terms of progress through ESL or remedial English sequences, educators designing support for US-LM students must also consider larger contexts of students’ academic progress, promoting students’ development of language and literacy for success in academic and professional settings as well as progress toward completing credits required for associate degrees, certificates, and transfer to four-year institutions.     

Measurement Bias Detection Through Factor Analysis

Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes have been developed. The result of these developments is that structural equation modeling packages are now producing a large list of fit measures. One would think that this progression has led to a clear understanding of evaluating models with respect to model misspecifications. In this article we question the validity of approaches for model evaluation based on overall goodness-of-fit indexes. The argument against such usage is that they do not provide an adequate indication of the “size” of the model's misspecification. That is, they vary dramatically with the values of incidental parameters that are unrelated with the misspecification in the model. This is illustrated using simple but fundamental models. As an alternative method of model evaluation, we suggest using the expected parameter change in combination with the modification index (MI) and the power of the MI test.     

Diagnosing Global Case Influence on MAR Versus MNAR Model Comparisons

When missingness is suspected to be not at random (MNAR) in longitudinal studies, researchers sometimes compare the fit of a target model that assumes missingness at random (here termed a MAR model) and a model that accommodates a hypothesized MNAR missingness mechanism (here termed a MNAR model). It is well known that such comparisons are only interpretable conditional on the validity of the chosen MNAR model’s assumptions about the missingness mechanism. For that reason, researchers often perform a sensitivity analysis comparing the MAR model to not one, but several, plausible alternative MNAR models. In the social sciences, it is not widely known that such model comparisons can be particularly sensitive to case influence, such that conclusions drawn could depend on a single case. This article describes two convenient diagnostics suited for detecting case influence on MAR–MNAR model comparisons. Both diagnostics require much less computational burden than global influence diagnostics that have been used in other disciplines for MNAR sensitivity analyses. We illustrate the interpretation and implementation of these diagnostics with simulated and empirical latent growth modeling examples. It is hoped that this article increases awareness of the potential for case influence on MAR–MNAR model comparisons and how it could be detected in longitudinal social science applications.     

Modeling Ability Differentiation in the Second-Order Factor Model

In this article we present factor models to test for ability differentiation. Ability differentiation predicts that the size of IQ subtest correlations decreases as a function of the general intelligence factor. In the Schmid–Leiman decomposition of the second-order factor model, we model differentiation by introducing heteroscedastic residuals, nonlinear factor loadings, and a skew-normal second-order factor distribution. Using marginal maximum likelihood, we fit this model to Spanish standardization data of the Wechsler Adult Intelligence Scale (3rd ed.) to test the differentiation hypothesis.     

Fitting Nonlinear Latent Growth Curve Models With Individually Varying Time Points

Individual growth trajectories of psychological phenomena are often theorized to be nonlinear. Additionally, individuals’ measurement schedules might be unique. In a structural equation framework, latent growth curve model (LGM) applications typically have either (a) modeled nonlinearity assuming some degree of balance in measurement schedules, or (b) accommodated truly individually varying time points, assuming linear growth. This article describes how to fit 4 popular nonlinear LGMs (polynomial, shape-factor, piecewise, and structured latent curve) with truly individually varying time points, via a definition variable approach. The extension is straightforward for certain nonlinear LGMs (e.g., polynomial and structured latent curve) but in the case of shape-factor LGMs requires a reexpression of the model, and in the case of piecewise LGMs requires introduction of a general framework for imparting piecewise structure, along with tools for its automation. All 4 nonlinear LGMs with individually varying time scores are demonstrated using an empirical example on infant weight, and software syntax is provided. The discussion highlights some advantages of modeling nonlinear growth within structural equation versus multilevel frameworks, when time scores individually vary.     

SERVING AND LEARNING ATA UNIVERSITY LABORATORY SCHOOL: STUDENTS FROM BIRTH THOUGH ADULTHOOD BENEFIT WHEN SERVICE LEARNING IS INTEGRATED INTO THE PRESERVICE CURRICULUM

Service‐Learning (S‐L) pedagogy is attracting increased attention in teacher education. This article describes the implementation of S‐L in the preservice early childhood curriculum, which placed students in a birth‐5 years setting. It identifies a continuum of learning to care, which begins in infancy, as caring capacities emerge, through to adulthood, when preservice teachers learn from experienced mentors how caring characterizes their interactions with learners of all ages. Students’ journals indicate they believe S‐L experiences contribute to their personal, academic and professional development. Carefully implemented S‐L experiences validate students’ caring interactions, give them first‐hand experiences with young children during which they can apply theoretical constructs, provide experiences which help them clarify their own career goals, and make them feel better prepared to manage instructional and classroom management dimensions of working with young children.