Parent couples’ participation in speech-language therapy for school-age children with autism spectrum disorder in the United States

This study examined parent couples’ participation in and satisfaction with speech-language therapy for school-age children with autism spectrum disorder in the United States. Responses from 40 father–mother couples (n = 80 parents) were examined across therapy components (i.e. parent–therapist communication, assessment, planning, and intervention). Descriptive frequencies, chi-square tests, intraclass correlations, and dyadic multilevel modeling were used to examine participation across fathers and mothers and within parent couples. Compared to mothers, fathers communicated less with therapists and participated less in assessment and planning. Fathers also had lower satisfaction than mothers with parent–therapist communication and planning. Although few parents participated in school-based therapy sessions, 40% of fathers and 50% of mothers participated in homework. However, few parents received homework support from therapists. Results are discussed in terms of clinical implications for interventionists to more effectively engage both fathers and mothers in family-centered speech-language therapy for school-aged children with autism spectrum disorder

A primer for using and understanding weights with national datasets

ABSTRACT. Using data from the National Study of Postsecondary Faculty and the Early Childhood Longitudinal Study-Kindergarten Class of 1998-99, the author provides guidelines for incorporating weights and design effects in single-level analysis using Windows-based SPSS and AM software. Examples of analyses that do and do not employ weights and design effects are also provided to illuminate the differential results of key parameter estimates and standard errors using varying degrees of using or not using the weighting and design effect continuum. The author gives recommendations on the most appropriate weighting options, with specific reference to employing a strategy to accommodate both oversampled groups and cluster sampling (i.e., using weights and design effects) that leads to the most accurate parameter estimates and the decreased potential of committing a Type I error. However, using a design effect adjusted weight in SPSS may produce underestimated standard errors when compared with accurate estimates produced by specialized software such as AM

Using Nces National Datasets For Evaluation Of Postsecondary Issues

The purpose of this study is to review 10 NCES databases that can be used for researching postsecondary issues and provide lesser-known facts to using the datasets that are important but may not be widely understood. Issues addressed include: (1) access; (2) statistical issues; (3) database nuances; and (4) database training opportunities. A concise review of each database is also provided which includes: (1) a general overview of the survey; (2) formats in which the dataset is available; and (3) research areas (which include key variables that can be used as a basis for research themes along with examples of how the dataset has been used to answer research questions). The databases provide rich sources of information for national as well as international comparative analysis studies. © 2007 Taylor & Francis

A Primer For Using And Understanding Weights With National Datasets

Using data from the National Study of Postsecondary Faculty and the Early Childhood Longitudinal Study-Kindergarten Class of 1998-99, the author provides guidelines for incorporating weights and design effects in single-level analysis using Windows-based SPSS and AM software. Examples of analyses that do and do not employ weights and design effects are also provided to illuminate the differential results of key parameter estimates and standard errors using varying degrees of using or not using the weighting and design effect continuum. The author gives recommendations on the most appropriate weighting options, with specific reference to employing a strategy to accommodate both oversampled groups and cluster sampling (i.e., using weights and design effects) that leads to the most accurate parameter estimates and the decreased potential of committing a Type I error. However, using a design effect adjusted weight in SPSS may produce underestimated standard errors when compared with accurate estimates produced by specialized software such as AM

Analysis Of Data From Complex Samples

Oversampling and cluster sampling must be addressed when analyzing complex sample data. This study: (a) compares parameter estimates when applying weights versus not applying weights; (b) examines subset selection issues; (c) compares results when using standard statistical software (SPSS) versus specialized software (AM); and (d) offers recommendations for analyzing complex sample data. Underestimated standard errors and overestimated test statistics were produced when both the oversampled and cluster sample characteristics of the data were ignored. Regarding subset analysis, marked differences were not evident in SPSS results, but the standard errors of the weighted versus unweighted models became more similar as smaller subsets of the data were extracted using AM. Recommendations to researchers are provided including accommodating both oversampling and cluster sampling

Applied Multivariate Statistical Concepts

More comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today\u27s research. Ideal for courses on multivariate statistics/analysis/design, advanced statistics or quantitative techniques taught in psychology, education, sociology, and business, the book also appeals to researchers with no training in multivariate methods. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps reader\u27s master key concepts so they can implement and interpret results generated by today\u27s sophisticated software. Annotated screenshots from SPSS and other packages are integrated throughout. Designed for course flexibility, after the first 4 chapters, instructors can use chapters in any sequence or combination to fit the needs of their students. Each chapter includes a \u27mathematical snapshot\u27 that highlights the technical components of each procedure, so only the most crucial equations are included. Highlights include: Outlines, key concepts, and vignettes related to key concepts preview what\u27s to come in each chapter; Examples using real data from education, psychology, and other social sciences illustrate key concepts; Extensive coverage of assumptions including tables, the effects of their violation, and how to test for each technique; Conceptual, computational, and interpretative problems mirror the real-world problems students encounter in their studies and careers; A focus on data screening and power analysis with attention on the special needs of each particular method; Instructions for using SPSS via screenshots and annotated output along with HLM, Mplus, LISREL, and G*Power where appropriate, to demonstrate how to interpret results; Templates for writing research questions and APA-style write-ups of results which serve as models; Propensity score analysis chapter that demonstrates the use of this increasingly popular technique; A review of matrix algebra for those who want an introduction (prerequisites include an introduction to factorial ANOVA, ANCOVA, and simple linear regression, but knowledge of matrix algebra is not assumed); www.routledge.com/9780415842365 provides the text\u27s datasets preformatted for use in SPSS and other statistical packages for readers, as well as answers to all chapter problems, Power Points, and test items for instructors

Changes In Student-Centred Assessment By Postsecondary Science And Non-Science Faculty

Although many appeals for reform include adopting more student-centred assessment, few studies have examined the postsecondary classroom. Using the 1993 and 1999 National Study of Postsecondary Faculty, the results of the current study revealed that faculty in the sciences were less likely to use student-centred assessment practices than faculty in non-sciences. Additionally, while faculty in the non-sciences showed a significant increase in their use of student-centred assessment between the two waves of data collection, no such increase was obtained for faculty in the sciences. Results are discussed in terms of public policy

Beginning English Teacher Attrition, Mobility, And Retention

Although much research on teacher attrition and mobility exists, few researchers have addressed English teachers specifically. The present authors, using the 1999-2000 Schools and Staffing Survey (SASS) and the Teacher Follow-Up Survey (TFS; National Center for Education Statistics, 2005) examined individual and school characteristics and mentoring and induction activities that affect beginning English teachers\u27 attrition, mobility, and retention. The results indicated that only salary was statistically significantly related to increased odds of beginning English teachers\u27 leaving the profession. No factors related to decreased attrition. In terms of mobility, no teacher or school characteristics were associated with migration (i.e., changing schools). Reviewing combined effects of mentoring and induction activities when controlling for teacher and school characteristics, the authors found that the results suggested none of the activities were related to attrition and migration. © 2008 Heldref Publications

Utilization Of Sample Weights In Single-Level Structural Equation Modeling

ABSTRACT. Complex survey designs often employ multistage cluster sampling designs and oversample particular units to ensure more accurate population parameter estimates. These issues must be accommodated in the analysis to ensure accurate parameter estimation. Incorporation of sample weights in some statistical procedures has been studied. However, research on the behavior of sample weights on estimates, standard errors, and fit measures in latent variable models is negligible, and studies examining methodology on latent variable modeling applications using extant data are rare. Using the Beginning Postsecondary Students Longitudinal Study 1990/92/94, the authors found, with mixed results, that a statistically significant difference exists in estimates and fit indices when weights and designs are applied versus when they are ignored. © 2006 Taylor & Francis Group, LLC