Title: Data analysis : an introduction /
Authors: Lewis-Beck, Michael S.
Publisher: Sage Publications,
Year: 1995
Subjects: Social sciences Statistical methods; Social Sciences statistics & numerical data
Abstract: "Written at a level appropriate for the advanced undergraduate course on data analysis, this accessible volume introduces the reader to the "art" of data analysis from data-gathering to multiple regression in which a dependent variable is influenced by several independent variables. The book focuses on the interpretation of a statistical result, in particular those that come from nonexperimental social research. Using a consistent data set throughout the book in order to illustrate the various analytic techniques, the author covers such topics as univariate statistics, measures of association, the statistical significance of the relationship between two variables, and simple regression where the dependent variable is influenced by a single independent variable. The last chapter offers analysis recommendations. Data Analysis will provide social science researchers with the tools to select and evaluate statistical tests appropriate for their particular research question."--Pub. desc.
Contents: 1. Introduction -- 2. Data gathering -- The research question -- The sample -- The measures -- Data coding, entry, and checking -- 3. Univariate statistics -- Central tendency -- Dispersion -- Central tendency, dispersion, and outliers -- 4. Measures of association -- Correlation -- Ordinal data: the Tau measure of association -- Nominal data: Goodman and Kruskals' lambda -- Dichotomous variables: flexibility of choice -- Summary and conclusion -- 5. Significance testing -- The logic: a simple example -- Applying the logic: bivariate measures of association -- Summary and conclusion -- 6. Simple regression -- Y as a function of X -- The lease squares principle -- Intercept and slope -- Prediction and goodness-of-fit -- Significance tests and confidence intervals -- Presenting regression results: a summary guide -- 7. Multiple regression -- An example -- The notion of statistical control -- Specification error -- Dummy variables -- Collinearity -- Interaction effects -- Nonlinearity -- Summary and conclusion.