Popular for its speed, flexibility, and attractive graphics, Stata is a powerful tool for political science students. With Philip Pollock's Fourth Edition of A Stata® Companion to Political Analysis, students quickly learn Stata via step-by-step instruction, more than 50 exercises, customized datasets, annotated screen shots, boxes that highlight Stata's special capabilities, and guidance on using Stata to read raw data. This attractive and value-priced workbook, an ideal complement to Pollock's Essentials of Political Analysis, is a must-have for any political science student working with Stata.…mehr
Popular for its speed, flexibility, and attractive graphics, Stata is a powerful tool for political science students. With Philip Pollock's Fourth Edition of A Stata® Companion to Political Analysis, students quickly learn Stata via step-by-step instruction, more than 50 exercises, customized datasets, annotated screen shots, boxes that highlight Stata's special capabilities, and guidance on using Stata to read raw data. This attractive and value-priced workbook, an ideal complement to Pollock's Essentials of Political Analysis, is a must-have for any political science student working with Stata.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Philip H. Pollock III is a professor of political science at the University of Central Florida. He has taught courses in research methods at the undergraduate and graduate levels for more than thirty years. His main research interests are American public opinion, voting behavior, techniques of quantitative analysis, and the scholarship of teaching and learning. His recent research has been on the effectiveness of Internet-based instruction. Pollock's research has appeared in the American Journal of Political Science, Social Science Quarterly, and the British Journal of Political Science. Recent scholarly publications include articles in Political Research Quarterly, the Journal of Political Science Education, and PS: Political Science and Politics.
Inhaltsangabe
Figures and Tables Preface Introduction: Getting Started About Companion Datasets Chapter 1 Introduction to Stata Information About a Dataset Information About Variables General Syntax of Stata Commands Do-files Printing Results and Copying Output Log Files Getting Help Customizing Your Display Exercises Chapter 2 Descriptive Statistics Interpreting Measures of Central Tendency and Variation Describing Nominal Variables A CLOSER LOOK: Weighting the GSS and NES Datasets Describing Ordinal Variables Describing Interval Variables Bar Charts for Nominal and Ordinal Variables A CLOSER LOOK: Stata's Graphics Editor Histograms for Interval Variables Obtaining Case-Level Information With sort and list Exercises Chapter 3 Transforming Variables Creating Indicator Variables Working With Variable Labels Collapsing Variables Into Simplified Categories Centering or Standardizing a Numeric Variable Creating an Additive Index Exercises Chapter 4 Making Comparisons Cross-Tabulation Analysis Visualizing Comparisons With Nominal or Ordinal Dependent Variables A CLOSER LOOK: The replace Command Mean Comparison Analysis A CLOSER LOOK: The format Command Visualizing Comparisons With Interval-Level Dependent Variables Strip Charts: Graphs for Small-N Datasets Exercises Chapter 5 Making Controlled Comparisons Cross-Tabulation Analysis With a Control Variable A CLOSER LOOK: The "If " Qualifier Visualizing Controlled Comparisons With Categorical Dependent Variables Mean Comparison Analysis With a Control Variable Visualizing Controlled Mean Comparisons Exercises Chapter 6 Making Inferences About Sample Means Finding the 95 Percent Confidence Interval of a Sample Mean Testing a Hypothetical Claim About the Population Mean Testing the Difference Between Two Sample Means A CLOSER LOOK: Inferences About Means With Unweighted Data Extending the mean and lincom Commands to Other Situations Making Inferences About Sample Proportions A CLOSER LOOK: Inferences About Proportions With Unweighted Data Exercises Chapter 7 Chi-Square and Measures of Association Analyzing Ordinal-Level Relationships A CLOSER LOOK: Analyzing Unweighted Data With The tabulate Command Analyzing an Ordinal-Level Relationship With a Control Variable Analyzing Nominal-Level Relationships Exercises Chapter 8 Correlation and Linear Regression Correlation Analysis Regression Analysis A CLOSER LOOK: Treating Census as a Sample A CLOSER LOOK: R-Squared and Adjusted R-Squared: What's the Difference? Creating a Scatterplot With a Linear Prediction Line Multiple Regression A CLOSER LOOK: Bubble Plots Correlation and Regression Analysis With Weighted Data Exercises Chapter 9 Dummy Variables and Interaction Effects Regression With Multiple Dummy Variables Interaction Effects in Multiple Regression Graphing Linear Prediction Lines for Interaction Relationships Changing the Reference Category Exercises Chapter 10 Logistic Regression Thinking About Odds, Logged Odds, and Probabilities Estimating Logistic Regression Models Logistic Regression With Multiple Independent Variables A CLOSER LOOK: Comparing Logistic Regression Models With the estimates and lrtest Commands Graphing Predicted Probabilities With One Independent Variable Graphing Predicted Probabilities With Multiple Independent Variables Exercises Chapter 11 Doing Your Own Political Analysis Seven Doable Ideas Importing Data Into Stata Writing It Up Appendix Table A-1: Variables in the GSS Dataset in Alphabetical Order Table A-2: Variables in the NES Dataset in Alphabetical Order Table A-3: Variables in the States Dataset by Topic Table A-4: Variables in the World Dataset by Topic
Figures and Tables Preface Introduction: Getting Started About Companion Datasets Chapter 1 Introduction to Stata Information About a Dataset Information About Variables General Syntax of Stata Commands Do-files Printing Results and Copying Output Log Files Getting Help Customizing Your Display Exercises Chapter 2 Descriptive Statistics Interpreting Measures of Central Tendency and Variation Describing Nominal Variables A CLOSER LOOK: Weighting the GSS and NES Datasets Describing Ordinal Variables Describing Interval Variables Bar Charts for Nominal and Ordinal Variables A CLOSER LOOK: Stata's Graphics Editor Histograms for Interval Variables Obtaining Case-Level Information With sort and list Exercises Chapter 3 Transforming Variables Creating Indicator Variables Working With Variable Labels Collapsing Variables Into Simplified Categories Centering or Standardizing a Numeric Variable Creating an Additive Index Exercises Chapter 4 Making Comparisons Cross-Tabulation Analysis Visualizing Comparisons With Nominal or Ordinal Dependent Variables A CLOSER LOOK: The replace Command Mean Comparison Analysis A CLOSER LOOK: The format Command Visualizing Comparisons With Interval-Level Dependent Variables Strip Charts: Graphs for Small-N Datasets Exercises Chapter 5 Making Controlled Comparisons Cross-Tabulation Analysis With a Control Variable A CLOSER LOOK: The "If " Qualifier Visualizing Controlled Comparisons With Categorical Dependent Variables Mean Comparison Analysis With a Control Variable Visualizing Controlled Mean Comparisons Exercises Chapter 6 Making Inferences About Sample Means Finding the 95 Percent Confidence Interval of a Sample Mean Testing a Hypothetical Claim About the Population Mean Testing the Difference Between Two Sample Means A CLOSER LOOK: Inferences About Means With Unweighted Data Extending the mean and lincom Commands to Other Situations Making Inferences About Sample Proportions A CLOSER LOOK: Inferences About Proportions With Unweighted Data Exercises Chapter 7 Chi-Square and Measures of Association Analyzing Ordinal-Level Relationships A CLOSER LOOK: Analyzing Unweighted Data With The tabulate Command Analyzing an Ordinal-Level Relationship With a Control Variable Analyzing Nominal-Level Relationships Exercises Chapter 8 Correlation and Linear Regression Correlation Analysis Regression Analysis A CLOSER LOOK: Treating Census as a Sample A CLOSER LOOK: R-Squared and Adjusted R-Squared: What's the Difference? Creating a Scatterplot With a Linear Prediction Line Multiple Regression A CLOSER LOOK: Bubble Plots Correlation and Regression Analysis With Weighted Data Exercises Chapter 9 Dummy Variables and Interaction Effects Regression With Multiple Dummy Variables Interaction Effects in Multiple Regression Graphing Linear Prediction Lines for Interaction Relationships Changing the Reference Category Exercises Chapter 10 Logistic Regression Thinking About Odds, Logged Odds, and Probabilities Estimating Logistic Regression Models Logistic Regression With Multiple Independent Variables A CLOSER LOOK: Comparing Logistic Regression Models With the estimates and lrtest Commands Graphing Predicted Probabilities With One Independent Variable Graphing Predicted Probabilities With Multiple Independent Variables Exercises Chapter 11 Doing Your Own Political Analysis Seven Doable Ideas Importing Data Into Stata Writing It Up Appendix Table A-1: Variables in the GSS Dataset in Alphabetical Order Table A-2: Variables in the NES Dataset in Alphabetical Order Table A-3: Variables in the States Dataset by Topic Table A-4: Variables in the World Dataset by Topic
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