Data analysis with SPSS : a first course in applied statistics

著者

書誌事項

Data analysis with SPSS : a first course in applied statistics

Stephen Sweet, Karen Grace-Martin

Allyn & Bacon, c2012

4th ed

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注記

Includes bibliographical references (p. 274-275) and index

内容説明・目次

内容説明

Data Analysis with SPSS is designed to teach students how to explore data in a systematic manner using the most popular professional social statistics program on the market today. Written in ten manageable chapters, this book first introduces students to the approach researchers use to frame research questions and the logic of establishing causal relations. Students are then oriented to the SPSS program and how to examine data sets. Subsequent chapters guide them through univariate analysis, bivariate analysis, graphic analysis, and multivariate analysis. Students conclude their course by learning how to write a research report and by engaging in their own research project. Each book is packaged with a disk containing the GSS (General Social Survey) file and the States data files. The GSS file contains 100 variables generated from interviews with 2,900 people, concerning their behaviors and attitudes on a wide variety of issues such as abortion, religion, prejudice, sexuality, and politics. The States data allows comparison of all 50 states with 400 variables indicating issues such as unemployment, environment, criminality, population, and education. Students will ultimately use these data to conduct their own independent research project with SPSS. Note: MySearchLab does not come automatically packaged with this text. To purchase MySearchLab, please visit: www.mysearchlab.com or you can purchase a ValuePack of the text + MySearchLab with Pearson eText (at no additional cost). ValuePack ISBN-10: 0205863728 / ValuePack ISBN-13: 9780205863723

目次

TABLE OF CONTENTS: 1. BRIEF 2. COMPREHENSIVE BRIEF TABLE OF CONTENTS Chapter 1 * Key Concepts in Social Science Research Chapter 2 * Getting Started: Accessing, Examining, and Saving Chapter 3 * Univariate Analysis: Descriptive Statistics Chapter 4 * Constructing Variables Chapter 5 * Assessing Association through Bivariate Analysis Chapter 6 * Comparing Group Means through Bivariate Analysis Chapter 7 * Modeling Relationships of Multiple Variables with Linear Regression Chapter 8 * Logistic Regression Chapter 9 * Writing a Research Report Chapter 10 * Research Projects COMPREHENSIVE TABLE OF CONTENTS Chapter 1 * Key Concepts in Social Science Research Overview Framing Topics Into Research Questions Theories and Hypotheses Population and Samples Relationships and Causality Data Sets Parts of a Data Set Reliability and Validity Summary Key Terms Exercises Chapter 2 * Getting Started: Accessing, Examining, and Saving Data Overview The Layout of SPSS Types of Variables Initial Settings Defining and Saving a New Data Set Managing Data Sets: Dropping and Adding Variables, Merging Data Sets Dropping and Adding Variables Merging and Importing Files Loading and Examining an Existing File Summary Key Terms Exercises Chapter 3 * Univariate Analysis: Descriptive Statistics Overview Why Do Researchers Perform Univariate Analysis? Exploring Distributions of Scale Variables Exploring Distributions of Categorical Variables Summary Key Terms Exercises Chapter 4 * Constructing Variables Overview Why Construct New Variables From Existing Data? Recoding Existing Variables Computing New Variables Recording Computations Using Syntax Minimizing Missing Values in Computing New Variables Summary Key Terms Exercises Chapter 5 * Assessing Association through Bivariate Analysis Overview Why Do We Need Significance Tests? Analyzing Bivariate Relationships Between Two Categorical Variables Analyzing Bivariate Relationships Between Two Scale Variables Summary Key Terms Exercises Chapter 6 * Comparing Group Means through Bivariate Analysis Overview One-Way Analysis of Variance Post-hoc Tests Assumptions of ANOVA Graphing the Results of ANOVA T tests Summary Key Terms Exercises Chapter 7 * Modeling Relationships of Multiple Variables with Linear Regression Overview The Advantages of Modeling Relationships in Multiple Regression Linear Regression: A Bivariate Example Multiple Linear Regression Other Concerns In Applying Linear Regression Building Multiple Variable Models Summary Key Terms Exercises Chapter 8 * Logistic Regression Overview What Is Logistic Regression? When Can I Use a Logistic Regression? Understanding Relationships through Probabilities Logistic Regression: A Bivariate Example Multiple Variable Logistic Regression: An Example Summary Key Terms Exercises Chapter 9 * Writing a Research Report Overview Writing Style and Audience The Structure of a Report Summary Key Terms Exercises Chapter 10 * Research Projects Potential Research Projects Research Project 1: Racism Research Project 2: Suicide Research Project 3: Criminality Research Project 4: Welfare and Other Public Aid Consumption Research Project 5: Sexual Behavior Research Project 6: Education Research Project 7: Health Research Project 8: Happiness Research Project 9: Your Topic

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