Writing Experiences of Community College Students with Self-Reported Writing Anxieties and Linguistic Insecurity: A Perspective for College Counselors

This explanatory case study researched the writing experiences of 11 community college students who differed subculturally and who were all part of the African diasporic community. The theoretical perspectives used for this study were Arthur Chickering's (1969 Chickering , A. ( 1969 ). Education and identity , (2nd ed.) . San Francisco , CA : John Wiley & Sons . [Google Scholar]) classical concept of academic competence and community dialect theory (Baxter & Holland, 2007 Baxter , M. , & Holland , R. (2007). Addressing the needs of students who use a nonstandard dialect. Adult Basic Education and Literacy Journal , 1(3), 145–153. [Google Scholar]). Academic competence is a stage in college that explores how students learn acquired skills for educational attainment. Community dialect theory argues that African American dialect writers have a low-to-some awareness of how their dialect differs from standard English in the area of subject-verb agreement. Two research questions guided this inquiry: (a) How does Black English influence writing anxiety in academic settings? (b) Why should college counselors have a strategy to assist students who express fears of enrolling in remedial English? A mixed method strategy was used to gather the data. This strategy consisted of a qualitative interview, and two educational tests. The study found that most of the students had a low awareness of how their dialect differed from standard English. Furthermore, Black English directly induced writing anxiety in academic settings for this group of community college students. Their writing anxieties and insecurities were detected by the words and situational experiences these students related during the interviews. In addition, college counselors should have an intervention strategy in place for this population of students to reduce the gap between academic competence and students of African descent who are dialect writers with writing anxieties and linguistic insecurities in academic settings.     

Learning Together: A Family-Centered Literacy Program

ABSTRACT

Blue Ridge Community College in Flat Rock, North Carolina serves a rapidly growing Hispanic population through its Family-Centered Literacy Program. The Hispanic population in the region has been increasing at a staggering rate of 50% per year, most of which is in-migration. These newcomers frequently face challenges adjusting to their jobs, schools, and living environment. The Family-Centered Literacy Program is teaching new skills to new residents to minimize the “culture shock”, and to make community immersion a less difficult process. The program offers evening classes 2 nights a week for parents and their children. Parents attend classes in basic and conversational English and General Education Development (GED) preparation; school-age children receive tutoring and help with homework; and preschool children learn from fluency-building games and activities. The college also provides Spanish instruction for school personnel who want to better communicate with Hispanic students and their parents. The program currently operates in 5 elementary schools. Each school has taken on the role of “community center” for Hispanic families.     

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.     

VOCATIONAL PREPARATION: HUMAN RESOURCE DEVELOPMENT SPECIALISTS’ ASSESSMENT

This study investigated the perceptions of Human Resource Development (HRD) Specialists regarding the preparation of Illinois community college vocational/ technical program graduates. Two hundred and forty‐five respondents answered a mail questionnaire concerning the technical, human relations, problem solving and basic skills of program graduates. The graduates were employed in Nursing and Allied Health, Industrial Technology, Secretarial and Clerical, Agriculture, Marketing and Management, Accounting and Data Processing, and Social and Public Services. The perceptions of these HRD Specialists toward community college program graduates are positive. Graduates of Nursing and Allied Health programs were consistently rated higher than graduates of other programs. Overall, graduates were ranked highest on human relations skills and lowest on problem solving skills. Finally, implications for practice included suggestions for involving HRD Specialists in community college vocational programs.     

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.     

Longitudinal confirmatory factor analysis: Common,time‐specific,item‐specific,and residual‐error components of variance

Following Cronbach (1970) and others, it is useful to decompose test score variation into common factor, time‐specific, item‐specific, and residual components. In the traditional approach to factor analysis, only two sources of variance can be estimated: common factor variance and a uniqueness term that confounds specific sources of variation and residual error. When the same items are measured on different occasions, however, it is possible to separate specific variance and residual error. Two approaches, the first‐order approach described by Raffalovich and Bohrnstedt (1987) and a second‐order approach based on Jöreskog and Sörbom (1989; Jöreskog, 1974) are considered initially. The two approaches, although based on different rationales, both suffer a similar weakness in that two of the four sources of variance are confounded. In the Raffalovich and Bohrnstedt approach, time‐specific variance is confounded with common factor variance that generalizes across items and time. In the second‐order approach based on Jöreskog and Sörbom, time‐specific variance is confounded with residual error. Here we demonstrate that by combining features from both approaches we can eliminate these weaknesses and estimate all four of Cronbach's sources of variance, and that this combined approach is easily generalized to a wide variety of applications.     

Software review

STATISTICA 5.0. StatSoft, 2325 East 13th Street, Tulsa, OK 74104, (918) 583–4149. $995 retail, academic site license—$2,000 for 10 copies. Requirements: 386 or better, 4 Meg RAM, Microsoft Windows 3.1 or 95.     

Binomial index of model fit: An elaboration

This article is an elaboration on the use of the binomial test of model fit value, which in this article will be referred to as the binomial index of model fit value, to gauge the degree that the data fit a path analytic or structural equation model. In addition, this article responds to the criticisms and comments made by Hsu (this issue), Drezner and Drezner (this issue), and Raykov and Penev (this issue) regarding the use of this approach to measuring the degree of model fit. We appreciate the comments provided by these authors. Their comments have assisted us in clarifying our reason for developing the binomial index of model fit procedure as well as our perception of its use.     

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.     

General Growth Mixture Analysis of Adolescents' Developmental Trajectories of Anxiety: The Impact of Untested Invariance Assumptions on Substantive Interpretations

Substantively, this study investigates potential heterogeneity in the developmental trajectories of anxiety in adolescence. Methodologically, this study demonstrates the usefulness of general growth mixture analysis (GGMA) in addressing these issues and illustrates the impact of untested invariance assumptions on substantive interpretations. This study relied on data from the Montreal Adolescent Depression Development Project (MADDP), a 4-year follow-up of more than 1,000 adolescents who completed the Beck Anxiety Inventory each year. GGMA models relying on different invariance assumptions were empirically compared. Each of these models converged on a 5-class solution, but yielded different substantive results. The model with class-varying variance–covariance matrices was retained as providing a better fit to the data. These results showed that although elevated levels of anxiety might fluctuate over time, they clearly do not represent a transient phenomenon. This model was then validated in relation to multiple predictors (mostly related to school violence) and outcomes (grade-point average, school dropout, depression, loneliness, and drug-related problems).