Synopses & Reviews
Without question, this text will be the most authoritative source of information on statistics in the human services. From my point of view, it is a definitive work that combines a rigorous pedagogy with a down to earth (commonsense) exploration of the complex and difficult issues in data analysis (statistics) and interpretation. I welcome its publication. -Praise for the First Edition
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice.
The first section introduces basic concepts and terms to provide a solid foundation in statistics. It also addresses tools used by researchers to describe and summarize data ranging from single variables to assessing the relationship between variables and cause and effect among variables. The second section focuses on inferential statistics, describing how researchers draw conclusions about whole populations based on data from samples. This section also covers confidence intervals and a variety of significance tests for examining relationships between different types of variables. Additionally, tools for multivariate analyses and data interpretation are presented. Key Features:
Addresses the role of statistics in evidence-based practice and program evaluation Features examples of qualitative and quantitative analysis Each chapter contains exercise problems and questions to enhance student learning Includes electronic data sets taken from actual social work arenas Offers a full ancillary digital packet including a student guide to SPSS with accompanying Data Set, an Instructor's Manual, PowerPoint slides, and a Test Bank
Synopsis
Without question, this text will be the most authoritative source of information on statistics in the human services. From my point of view, it is a definitive work that combines a rigorous pedagogy with a down to earth (commonsense) exploration of the complex and difficult issues in data analysis (statistics) and interpretation. I welcome its publication.
- Praise for the First Edition
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy to understand language. It includes numerous examples grounded in social work practice, data sets, and issues that students will encounter in social work practice.
This updated second edition newly addresses the role that statistics plays in evidence-based practice and program evaluation, includes data sets and examples from new social work domains, and includes examples of qualitative and quantitative analysis.
Four digital ancillaries, including an instructor's guide, PowerPoint slides, a student manual, and a data set will also be made available for use with this text.Key Features:
Contains dozens of relevant problems each chapterIncludes electronic data sets taken from actual social work arenasComes with PowerPoint slides and Instructor's Manual for professorsOffers both SPSS and ExcelDesigned for stand-alone statistics courses or as an ancillary text
Synopsis
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Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice.
The first section introduces basic concepts and terms to provide a solid foundation in statistics. It also addresses tools used by researchers to describe and summarize data ranging from single variables to assessing the relationship between variables and cause and effect among variables. The second section focuses on inferential statistics, describing how researchers draw conclusions about whole populations based on data from samples. This section also covers confidence intervals and a variety of significance tests for examining relationships between different types of variables. Additionally, tools for multivariate analyses and data interpretation are presented.
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