Rachel A. Gordon – författare

This title is concerned with the sweeping changes that took place in public assistance programmes at the end of the 20th century and the way in which the original and reformed versions of these programmes relate to the well-being of children and their families. The authors critically review the original conceptualizations and the new directions of programs offering cash assistance, food assistance, health insurance, and child protection services to low-income and disabled children and their families - thus, changes in the welfare programmes themselves. And throughout, their concern is with whether and how these programs alter the opportunities for the development of the children targeted by these programmes - thus, changes in the welfare of children and their families. The objective of each chapter of the book is to rigorously highlight key theoretical and research issues, including the identification of major empirical findings and unanswered questions. Wherever relevant, the chapters connect theory and research to policy and practice, pointing to recommendations and challenges for the future including alternative approaches for research, policy and practice.

Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with the basic skills that they need to estimate, interpret, present, and publish statistical models using contemporary standards. The book targets the social and health science branches such as human development, public health, sociology, psychology, education, and social work in which students bring a wide range of mathematical skills and have a wide range of methodological affinities. For these students, a successful course in statistics will not only offer statistical content but will also help them develop an appreciation for how statistical techniques might answer some of the research questions of interest to them. This book is for use in a two-semester graduate course sequence covering basic univariate and bivariate statistics and regression models for nominal and ordinal outcomes, in addition to covering ordinary least squares regression.Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature thorough integration of teaching statistical theory with teaching data processing and analysis teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.This book is for a two-semester course. For a one-semester course, see http://www.routledge.com/9780415991544/

This Element demonstrates how and why the alignment method can advance measurement fairness in developmental science. It explains its application to multi-category items in an accessible way, offering sample code and demonstrating an R package that facilitates interpretation of such items' multiple thresholds. It features the implications for group mean differences when differences in the thresholds between categories are ignored because items are treated as continuous, using an example of intersectional groups defined by assigned sex and race/ethnicity. It demonstrates the interpretation of item-level partial non-invariance results and their implications for group-level differences and encourages substantive theorizing regarding measurement fairness.

Covering basic univariate and bivariate statistics and regression models for nominal, ordinal, and interval outcomes, Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with fundamental skills to estimate, interpret, and publish quantitative research using contemporary standards.Reflecting the growing importance of "Big Data" in the social and health sciences, this thoroughly revised and streamlined new edition covers best practice in the use of statistics in social and health sciences, draws upon new literatures and empirical examples, and highlights the importance of statistical programming, including coding, reproducibility, transparency, and open science.Key features of the book include:interweaving the teaching of statistical concepts with examples from publicly available social and health science data and literature excerpts; thoroughly integrating the teaching of statistical theory with the teaching of data access, processing, and analysis in Stata; recognizing debates and critiques of the origins and uses of quantitative methods.

Covering basic univariate and bivariate statistics and regression models for nominal, ordinal, and interval outcomes, Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with fundamental skills to estimate, interpret, and publish quantitative research using contemporary standards.Reflecting the growing importance of "Big Data" in the social and health sciences, this thoroughly revised and streamlined new edition covers best practice in the use of statistics in social and health sciences, draws upon new literatures and empirical examples, and highlights the importance of statistical programming, including coding, reproducibility, transparency, and open science.Key features of the book include:interweaving the teaching of statistical concepts with examples from publicly available social and health science data and literature excerpts; thoroughly integrating the teaching of statistical theory with the teaching of data access, processing, and analysis in Stata; recognizing debates and critiques of the origins and uses of quantitative methods.

Beauty has a well-documented impact on labor market outcomes with both legal and policy implications. This monograph investigated whether this stratification is rooted in earlier developmental experiences. Specifically, we explored how high schools' dual roles as contexts of social relations and academic progress contributed to the long-term socioeconomic advantages of being physically attractive. Integrating theories from multiple disciplines, the conceptual model of this study contends that physically attractive youths' greater social integration and lesser social stigma help them accumulate psychosocial resources that support their academic achievement while also selecting them into social activities that distract from good grades. A mixed-methods design, combining statistical analyses of the National Longitudinal Survey of Adolescent Health and qualitative analyses of a single high school, supported and expanded this model. The data revealed that the benefits of attractiveness flowed through greater social integration but were partially offset by social distractions, especially romantic/sexual partnerships and alcohol-related problems. Interview and ethnographic data further revealed that adolescents themselves understood how physical attractiveness could lead to favorable treatment by teachers and classmates while also enticing youth to emphasize socializing and dating, even when the latter took time from other activities (like studying) and marginalized some classmates. These patterns, in turn, predicted education, work, family, and mental health trajectories in young adulthood. The results of this interdisciplinary, theoretically grounded, mixed methods study suggest that adolescence may be a critical period in stratification by physical appearance and that the underlying developmental phenomena during this period are complex and often internally contradictory. The monograph concludes with discussion of theoretical and policy implications and recommendations for future developmental research.

Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include:•interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. •thorough integration of teaching statistical theory with teaching data processing and analysis.•teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include:•interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. •thorough integration of teaching statistical theory with teaching data processing and analysis.•teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.