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.
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 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.
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).
1 The Science of Survey Research
2 Linking Theory and Concepts to Survey Research
3 The Evolution of Theory
4 Conceptualization and Measurement
5 Levels of Measurement: Once Over Again
6 Hypothesis Testing and Effect Size
7 Writing and Constructing Surveys
8 Survey Implementation, Sampling, and Weighting Data
9 An Introduction to IBM SPSS for Windows
10 Constructing IBM SPSS Data Files
11 Frequencies and Descriptive Statistics
12 Data Manipulation Techniques
14 Means and t Tests
15 Analysis of Variance
16 Bivariate Correlation and Regression
17 Logistic Regression and Discriminant Analysis
18 Psychological Scales and Reliability Analysis
19 Missing Data
20 Mediator and Moderator Variables in Path Analysis
21 Principal Components Analysis and Exploratory Factor Analysis
22 Cluster Analysis