Full Description
Applied Cross-Cultural Data Analysis for Social Work is a research guide for examining and interpreting data for the purpose of cultural group comparisons. This book aims to provide practical applications in statistical approaches of data analyses that are commonly used in cross-cultural research and evaluation. Readers are presented with step-by-step illustrations in the use of descriptive, bivariate, and multivariate statistics to compare cross-cultural population using large-scale, population-based survey data. These techniques have important applications in health, mental health, and social science research relevant to social work and other helping professions, especially in providing a framework of evidence to examine health disparities using population-health data. For each statistical approach discussed in this book, Thanh V. Tran and Keith T. Chan explain the underlying purpose, basic assumptions, types of variables, application of the Stata statistical package, the presentation of statistical findings, and the interpretation of results. Unlike previous guides on statistical approaches and data analysis in social work, this book explains and demonstrates the strategies of cross-cultural data analysis using descriptive and bivariate analysis, multiple regression, additive and multiplicative interaction, mediation, SEM and HLM for subgroup analysis and cross-cultural comparisons. This book also includes sample syntax from Stata for social work researchers to conduct cross-cultural analysis with their own research.
Contents
Acknowledgements
Chapter 1. Introduction
Chapter 2. Culture and Social Work
Chapter 3. Data Management and Cross-Cultural Descriptive Analysis
Chapter 4. Applied Multiple Regression Analysis in Cross-Cultural Comparisons
Chapter 5. Comparing A Binary Dependent Variable Across Cultural Groups Using Applied
Logistic Regression
Chapter 6. Applied Structural Equation Modeling for Cross-Cultural Comparison
Chapter 7. Applied Hierarchical Linear Modeling for Cross-Cultural Comparison
Chapter 8. Conclusion and Future Directions
