This is the first book to show the capabilities of Microsoft Excel to teach social science statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need t
This is the first book to show the capabilities of Microsoft Excel to teach social science statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need t
Success in Seminars and Tutorials is the first and only guide to improving academic performance that is specifically designed for social science seminars and tutorials. Combining coverage of writing,
This book offers basic guidelines on writing effectively for academic purposes. It reminds students that writing is an integral part of the learning process, and shows them how to write clear sentence
Why use qualitative methods? What kinds of questions can qualitative methods help you answer? How do you actually do rigorous and reflective qualitative research in the real world?Written by a team of
Why use qualitative methods? What kinds of questions can qualitative methods help you answer? How do you actually do rigorous and reflective qualitative research in the real world?Written by a team of
Like the sound of the proverbial free falling in a forest with no human audience, research that is not ultimately published is 'unheard' and forever lost. Moreover, published research that is not repo
This accessible guide walks readers through all the steps of completing a social science project, from choosing the topic to delivering the finished written product. Whether creating a short memorandu
Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of inte
Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of inte
This is a complete guide to statistics and SPSS for social science students. Statistics with SPSS for Social Science provides a step-by-step explanation of all the important statistical concepts, test
This is a complete guide to statistics and SPSS for social science students. Statistics with SPSS for Social Science provides a step-by-step explanation of all the important statistical concepts, test
What is a focus group? Why do we use them? When should we use them? When should we not? Focus Groups for the Social Science Researcher provides a step-by-step guide to undertaking focus groups, whether as a stand-alone method or alongside other qualitative or quantitative methods. It recognizes the challenges that focus groups encounter and provides tips to address them. The book highlights three unique, inter-related characteristics of focus groups. First, they are inherently social in form. Second, the data emerge organically through conversation; they are emic in nature. Finally, focus groups generate data at three levels of analysis: the individual, group, and interactive level. The book builds from these three characteristics to explain when focus groups can usefully be employed in different research designs. This is an essential text for students and researchers looking for a concise and accessible introduction to this important approach to data collection.
What is a focus group? Why do we use them? When should we use them? When should we not? Focus Groups for the Social Science Researcher provides a step-by-step guide to undertaking focus groups, whether as a stand-alone method or alongside other qualitative or quantitative methods. It recognizes the challenges that focus groups encounter and provides tips to address them. The book highlights three unique, inter-related characteristics of focus groups. First, they are inherently social in form. Second, the data emerge organically through conversation; they are emic in nature. Finally, focus groups generate data at three levels of analysis: the individual, group, and interactive level. The book builds from these three characteristics to explain when focus groups can usefully be employed in different research designs. This is an essential text for students and researchers looking for a concise and accessible introduction to this important approach to data collection.
Event History Modeling, first published in 2004, provides an accessible guide to event history analysis for researchers and advanced students in the social sciences. The substantive focus of many social science research problems leads directly to the consideration of duration models, and many problems would be better analyzed by using these longitudinal methods to take into account not only whether the event happened, but when. The foundational principles of event history analysis are discussed and ample examples are estimated and interpreted using standard statistical packages, such as STATA and S-Plus. Critical innovations in diagnostics are discussed, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. The treatment of complicated events includes coverage of unobserved heterogeneity, repeated events, and competing risks models. The authors point out common problems in the analysis of time-to-event data in the social sciences and make
A Guide to R for Social and Behavioral Science Statistics is a short, accessible book for learning R. This handy guide contains basic information on statistics for undergraduates and graduate students
