Survey Research and Analysis, 2nd ed. - Print

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Pages: 724

Dimensions: 10 × 8 × 1 in

Binding Type: Paperback

Survey Research and Analysis (2nd ed.) employs the standards of science for addressing both theoretical and applied questions. Early chapters illustrate how social science theory can be used to define and shape the content of surveys, and clarify conceptual distinctions. Without this initial understanding of the concepts (e.g., values, attitudes, norms) used by parks, recreation, and human dimensions researchers, it is impossible to write survey items that measure those concepts. Subsequent chapters examine the processes researchers go through when conceptualizing and measuring variables in a survey and the levels of measurement.

Since the 1950s, levels of measurement have been controversial. Chapter 5 addresses the issues by (a) outlining the different positions in the measurement–statistics relationship debate, (b) offering an alternative measurement hierarchy, and (c) providing practical guidelines for determining which statistics are appropriate given different measurement levels.

Chapter 6 (a) reviews the basic steps in hypothesis testing (i.e., statistical significance), (b) highlights the major problems with the approach, and (c) discusses the strengths and weaknesses of three procedures commonly used to supplement hypothesis tests (i.e., effect sizes, confidence intervals, and odds ratios). Chapters 7 and 8 outline a series of general guidelines and recommendations for ensuring that a survey is scientifically rigorous and that the results are representative of the sampled population.

Data collected from surveys can result in hundreds of variables and thousands of respondents. This implies that time and energy must be devoted to (a) carefully entering the data into a database, (b) running preliminary analyses to identify any problems (e.g., missing data, potential outliers), (c) checking the reliability and validity of the data, and (d) transforming the data to create indices of the underlying concepts. This book devotes several chapters to these topics.

Statistics provide a systematic way of summarizing what has been learned from the data. The appropriate statistics depend on the types of research questions asked (e.g., descriptive, difference, or associational). Statistical chapters in this book cover descriptive questions (e.g., frequencies), difference questions (e.g., chi-square, t tests, analysis of variance), and associational questions (e.g., correlation, regression, logistic regression). Two new chapters address multivariate topics (i.e., factor analysis, cluster analysis).