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Secondary Data in Mixed Methods Research

Daphne C. Watkins

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Sozialwissenschaften, Recht, Wirtschaft / Methoden der empirischen und qualitativen Sozialforschung

Beschreibung

Secondary Data in Mixed Methods Research by Daphne C. Watkins, the latest contribution to the Mixed Methods Research Series, offers unique and necessary instruction in this growing topic. With the increasing amount of secondary data available through journals and repositories, researchers have a trove of sources for new investigations at their fingertips, but few books to guide them. This brief text provides readers with a step-by-step procedure for incorporating secondary data into various mixed methods research designs, as well as identifying key characteristics of existing datasets that make them good candidates for mixed methods projects and giving ideas for new uses of secondary data. Introductory chapters help the reader understand the “what” and “why” of secondary data. Subsequent chapters address the use of secondary data in convergent, exploratory sequential, explanatory sequential, and other complex research designs. The final chapters delve into writing and reporting on projects before, during, and after the project. Quotes throughout the chapter help readers remember key bits of knowledge, while learning objectives and summaries in each chapter structure the reading experience. Application questions at the end of each chapter help readers recall information and apply it to their own research projects. By emphasizing how to use existing qualitative and quantitative datasets in mixed methods research,  Secondary Data in Mixed Methods Research will help readers answer new and ongoing questions in social science research.

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mixed-methods, Exploratory Sequential Design, mixed methods research series, social science research methods, social science methods, social work research methods, data analysis, mmrs, convergent design, education research methods, big data, health research methods, sociology research methods, qualitative quantitative, data retrieval, existing data, qual-quant, Explanatory Sequential Design, research methods