Written in everyday language for non-statisticians, this book provides all the information needed to successfully conduct nonparametric analyses. This ideal reference book provides step-by-step instructions to lead the reader through each analysis, screenshots of the software and output, and case scenarios to illustrate of all the analytic techniques.
Written in everyday language for non-statisticians, this book provides all the information needed to successfully conduct nonparametric analyses. This ideal reference book provides step-by-step instructions to lead the reader through each analysis, screenshots of the software and output, and case scenarios to illustrate of all the analytic techniques.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Carl Siebert, PhD, MBA, is an Assistant Professor for the Department of Curriculum, Instruction, and Foundational Studies in the College of Education at Boise State University. His research interests include nonparametric statistical analysis, psychometrics, data modeling, and instrument development and item performance when dealing with small samples. Darcy Clay Siebert, PhD, is Associate Professor in the School of Social Work at Rutgers University. Her research focuses on personal and professional impairment among social workers and other helping professionals. This work entails the utilization of identity theories, the development and validation of new measures, and the employment of specialized research methods tailored to the collection of sensitive data from cautious research participants.
Inhaltsangabe
Chapter 1 - Introduction to Nonparametrics Chapter 2 - Analyzing Single Variables and Single Groups Chapter 3 - Comparing Two or More Independent Groups Chapter 4 - Comparing Two or More Related Groups Chapter 5 - Predicting with Multiple Independent Variables Appendix Index
Chapter 1 - Introduction to Nonparametrics Chapter 2 - Analyzing Single Variables and Single Groups Chapter 3 - Comparing Two or More Independent Groups Chapter 4 - Comparing Two or More Related Groups Chapter 5 - Predicting with Multiple Independent Variables Appendix Index
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