Kirjahaku

Sampling is fundamental to nearly every study in the social and policy sciences, yet clear, concise guidance for practitioners and graduate students has been difficult to find. Practical Sampling provides guidance for researchers dealing with the everyday problems of sampling. Using the practical design approach Henry integrates sampling into the overall research design and explains the interrelationships between research design and sampling choices. He lays out alternatives and implications of the choices using four detailed examples to illustrate the alternatives selected and the trade-offs made by applied researchers. The author uses a narrative, conceptual approach throughout the book; mathematical presentations are limited to necessary formulas; and calculations are kept to the absolute minimum, making it an easily approachable book for any researcher, student or professional across the social sciences.

How can applied researchers present and analyze their research data more effectively? By applying the techniques described in this volume, social scientists will be able to make the most efficient use of their data as well as making it more accessible. The book uses many examples to illustrate effective graphical displays of data and offers practical techniques for producing graphs. Beginning with a discussion of what has been learned from visual perception research that relates to graphing data, the book then covers: the use of graphs for summarizing data; the display of multiple units; ways to improve tabular displays; graphical alternatives to tables; two variable scatterplots as graphical support for correlation and regression; how to show variation and the summary statistics together; and how to create more effective legends and titles on a graphical display.

Gary Henry has provided an excellent little book which discusses not only the basic 'techniques for display and analysis' which are available, but offers some very timely hints for their effective use. His audience is the 'applied researcher'. . . anybody engaged in empirical research at all. . . . He does. . . provide empirically grounded and valuable advice throughout on how best to design effective, accurate and reliable displays. . . . he provides a theoretical framework to guide the design of competent graphical displays. . . . Graphing Data. . . is well worth a good read. --Randy Banks in ESRC Data Archive Bulletin "Graphing Data is an accurate and factual presentation of effective techniques for graphically displaying quantitative data. The ideas postulated within the book represent effective techniques for graphing quantitative data and are consistent with the ideas of contemporary authors about the visual display of quantitative information. Graphing Data provides guidelines for researchers with desktop publishing capability who desire to incorporate graphs displaying quantitative data into research and evaluation reports. . . . The book is worthy of consideration as a course text for the introduction to graphing data or as a supplementary text for an introductory course on statistics where communicating research results are discussed. . . . Graphing Data serves as a legitimate reference for selecting appropriate data displays depending on the type of data and the purpose of data. . . . I would recommend this book for researchers and scholars interested in the effective communication of quantitative data to broad audiences." --Robert C. Branch in a prepublication review for Evaluation and Program Planning How can applied researchers present and analyze their research data more effectively? By using the techniques described in this volume, social scientists will be able to make their data more accessible to nontechnical audiences. This book is packed with examples that illustrate the characteristics of effective graphical displays of data and provides solid techniques for producing graphs. Graphing Data begins with a discussion of what has been learned from the visual perception research that relates to graphing data. It then explores the analysis tasks that apply most often: the use of graphs for data summarization, the display of multiple units, ways to improve tabular displays, graphical alternatives to tables, two-variable scatterplots as graphical support for correlation and regression, how to show variation and summary statistics together, and how to create more effective legends and titles on graphical displays.