Statistics for Social Data Analysis

ISBN-10: 0875814484
ISBN-13: 9780875814483
Edition: 4th 2002 (Revised)
List price: $283.95 Buy it from $30.13 Rent it from $19.80
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Description: The fourth edition of STATISTICS FOR SOCIAL DATA ANALYSIS continues to show students how to apply statistical methods to answer research questions in various fields. Throughout the text, the authors underscore the importance of formulating  More...

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Book details

List price: $283.95
Edition: 4th
Copyright year: 2002
Publisher: Wadsworth
Publication date: 1/1/2002
Binding: Hardcover
Pages: 544
Size: 7.00" wide x 9.00" long x 1.00" tall
Weight: 2.244
Language: English

The fourth edition of STATISTICS FOR SOCIAL DATA ANALYSIS continues to show students how to apply statistical methods to answer research questions in various fields. Throughout the text, the authors underscore the importance of formulating substantive hypotheses before attempting to analyze quantitative data. An important aspect of this text is its realistic, hands-on approach. Actual datasets are used in most examples, helping students understand and appreciate what goes into the research process. The book focuses on the continuous-discrete distinction in considering the level at which a variable is measured. Rather than dwelling on the four conventional levels-of-measurement distinctions, the authors discuss statistics for analyzing continuous and discrete variables separately and in combination.

Davd Knoke (Ph.D., University of Michigan 1972) coauthored Network Analysis (1982) and has published fifteen books and more than a hundred articles and book chapters, primarily on organizations, networks, politics, and social statistics. He was principal co-investigator on several National Science Foundation-funded projects on voluntary associations, lobbying organizations in national policy domains, and organizational surveys of diverse establishments. A current project on the Global Information Sector examines strategic alliance network evolution among international corporations. At Minnesota, he teaches a graduate network analysis seminar that attracts students from diverse disciplines.

Preface
Basic Concepts and Measures
Statistics in the Research Process
Ideas into Research Projects
Concepts into Propositions
Variables into Hypotheses
Observations into Records
Data into Numbers
Statistical Analysis
The General Linear Model
Describing Variables
Frequency Distributions for Discrete and Continuous Variables
Grouped and Cumulative Distributions
Graphing Frequency Distributions
Measures of Central Tendency
Measures of Dispersion
Percentiles and Quantiles
Standardized Scores (Z Scores)
Exploratory Data Analysis Methods for Displaying Continuous Data
Statistical Inference
Making Statistical Inferences
Drawing Inferences About Populations from Samples
Some Basic Probability Concepts
Chebycheff's Inequality Theorem
The Normal Distribution
The Central Limit Theorem
Sample Point Estimates and Confidence Intervals
The t Distribution
Hypothesis Testing
Testing Hypotheses About Single Means
Properties of Estimators
The Chi-Square and F Distributions
Analyzing Bivariate Relationships
Analysis of Variance
The Logic of ANOVA
ANOVA Tables: Sums of Squares, Mean Squares, F Ratio
Tests for Two Means
The Correlation Ratio: Eta-Squared
Testing Differences Among Means (Post Hoc)
Analyzing Categoric Data
Bivariate Crosstabulation
Using Chi-Square to Test Significance
Measuring Association: Q, Phi, Gamma, Tau c, Somer's d[subscript yx]
Odds and Odds Ratios
Bivariate Regression and Correlation
Scatterplots and Regression Lines
Estimating a Linear Regression Equation
R-Square and Correlation
Significance Tests for Regression Parameters
Standardizing Regression Coefficients
Comparing Two Regression Equations
Multivariate Models
The Logic of Multivariate Contingency Analysis
Controlling Additional Variables
Controlling for a Third Variable in 2 x 2 Tables
The Partial Correlation Coefficient
Multiple Regression Analysis
An Example of a Three-Variable Regression Problem
The Three-Variable Regression Model
Multiple Regression with K Independent Variables
Significance Tests for Parameters
Comparing Nested Equations
Dummy Variable Regression: ANCOVA with Interactions
Comparisons Across Populations
Nonlinear and Logistic Regression
Nonlinear Regression
Dichotomous Dependent Variables
The Logistic Transformation and Its Properties
Estimating and Testing Logistic Regression Equations
The Multinomial-Logit Model
Advanced Topics
Log-Linear Analysis
Log-Linear Models for 2 x 2 Tables
Log-Linear Models for Three-Variable Tables
More Complex Models
Special Topics in Log-Linear Analysis
Causal Models and Path Analysis
Causal Assumptions
Causal Diagrams
Path Analysis
Structural Equation Models
Review of Correlation and Covariance
Reliability and Validity in Measurement Theory
Factor Analysis
Multiple-Indicator Casual Models
Models with Ordered Indicators
Appendices
The Use of Summations
Critical Values of Chi Square (table)
Areas Under the Normal Curve (table)
Student's t Distribution (table)
F Distribution (table)
Fisher's r-to-Z Transformation (table)
Glossary of Terms
List of Mathematical and Statistical Symbols
Answers to Problems
Index

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