A Stata companion to political analysis. (2023)

• Record Type: Book
• Other Titles: Companion to political analysis; Political analysis
• Further Information: Note: Philip H. Pollock, Barry Clayton Edwards.
• Authors: III, Pollock, Philip H; Edwards, Barry Clayton
• Contents: Figures and Tables; Preface; Introduction: Getting Started with Stata; I.1Datasets for Stata Companion; I.2A Quick Tour of Stata; I.3Running Commands in Stata; I.4Quick Access to Tutorials and Resources; Chapter 1 Using Stata for Data Analysis; 1.1General Syntax of Stata Commands; 1.2Using Stata’s Graphic User Interface Effectively; 1.3Do-files; 1.4Printing Results and Copying Output; 1.5Customizing Your Display; 1.6Log Files; 1.7Getting Help; Chapter 1 Exercises; Chapter 2 Descriptive Statistics; 2.1Identifying Levels of Measurement; 2.2Describing Nominal Variables; A Closer Look: Weighted and Unweighted Analysis: What’s the Difference?; 2.3Describing Ordinal Variables; 2.4Bar Charts for Nominal and Ordinal Variables; 2.5Describing Interval Variables; A Closer Look: Stata’s Graphics Editor; 2.6Histograms for Interval Variables; 2.7Obtaining Case-Level Information; Chapter 2 Exercises; Chapter 3 Transforming Variables; 3.1Creating Dummy Variables; 3.2Applying Math Operators to Variables; 3.3Managing Variable Descriptions and Labels; 3.4Collapsing Variables into Simplified Categories; 3.5Centering or Standardizing a Numeric Variable; 3.6Creating an Additive Index; Chapter 3 Exercises; Chapter 4 Making Comparisons; 4.1Cross-Tabulation Analysis; A Closer Look: The replace Command; 4.2Mean Comparison Analysis; A Closer Look: The format Command; 4.3Making Comparisons with Interval-Level Independent Variables; Chapter 4 Exercises; Chapter 5 Graphing Relationships and Describing Patterns; 5.1Graphs for Binary Dependent Variables; 5.1.1Simple Bar Charts with Nominal-Level Independent Variables; 5.1.2 Area Chart with Ordinal-Level Independent Variables; 5.1.3Graphs with Interval-Level Independent Variables; 5.2Graphs for Nominal-Level Dependent Variables; 5.2.1Clustered Bar Charts with Nominal-Level Independent Variables; 5.2.2 Multiple Line Plots with Ordinal-Level Independent Variables; 5.2.3Graphs with Interval-Level Independent Variables; 5.3Graphs for Ordinal-Level Dependent Variables; 5.3.1Using Bars to Represent Select Values; 5.3.2Stacked Bar Chart for Ordinal-Ordinal Relationship; 5.3.3Graphs with Interval-Level Independent Variables; 5.4Graphs for Interval-Level Dependent Variables; 5.4.1Plotting Means with Bars or Lines; 5.4.2Box Plots; 5.4.3Scatterplots; Chapter 5 Exercises; Chapter 6 Random Assignment and Sampling; 6.1Random Assignment; 6.1.1Two Groups with Equal Probability; 6.1.2Multiple Groups with Varying Probabilities; 6.1.3Random Assignment to Predetermined-Size Groups; 6.2Analyzing the Results of an Experiment; 6.2.1Assessing Random Assignment; 6.2.2Evaluating the Effect of Treatment; 6.3Random Sampling; 6.3.1Simple Random Sampling with Replacement; 6.3.2Simple Random Sampling without Replacement; 6.3.3Systematic Random Samples; 6.3.4Clustered and Stratified Random Samples; 6.4Selecting Cases for Qualitative Analysis; 6.4.1Most Similar Systems; 6.4.2Most Different Systems; 6.5Analyzing Data Ethically; 6.5.1Ethical Issues in Data Analysis; 6.5.2Ten Tips for Writing Replication Code; Chapter 6 Exercises; Chapter 7 Making Controlled Comparisons; 7.1Cross-Tabulation Analysis with a Control Variable; 7.1.1Start with a Basic Cross-Tabulation; 7.1.2Controlling for Another Variable; 7.1.3Interpreting Controlled Cross-Tabulations; A Closer Look: The If Qualifier; 7.2Visualizing Controlled Comparisons with Categorical Dependent Variables; 7.3Mean Comparison Analysis with a Control Variable; 7.3.1Start with Basic Mean Comparison Table; 7.3.2Adding Control Variables; 7.3.3Interpreting a Controlled Mean Comparison; 7.4Visualizing Controlled Mean Comparisons; Chapter 7 Exercises; Chapter 8 Foundations of Inference; 8.1Estimating Population Parameters with Simulations; 8.2Expected Shape of Sampling Distributions; 8.2.1Central Limit Theorem and the Normal Distribution; 8.2.2Normal Distribution of Sample Proportions; 8.2.3Normal Distribution of Sample Means; 8.2.4The Standard Normal Distribution; 8.2.5The Empirical Rule (68-95-99 Rule); 8.3Confidence Interval and Margins of Error; 8.3.1Critical Values for Confidence Intervals; 8.3.2Reporting the Confidence Interval for a Sample Proportions; 8.3.2Reporting the Confidence Interval for a Sample Means; 8.4Student’s t-Distribution: When You’re Not Completely Normal; 8.4.1The t-Distribution’s Role in Inferential Statistics; 8.4.1Critical Values of t-Distributions; Chapter 8 Exercises; Chapter 9 Hypothesis Tests with One and Two Samples; 9.1Role of the Null Hypothesis; 9.2Testing Hypotheses with Sample Proportions; 9.2.1Testing One Sample Proportion Against Hypothesized Value; 9.2.2Testing Difference Between Two Sample Proportions Using Groups; 9.2.3 Testing Difference Between Two Sample Proportions Using Variables; 9.2.4Testing Hypotheses about Proportions with Weighted Data; 9.3Testing Hypotheses with Sample Means; 9.3.1Testing One Sample Mean Against Hypothesized Value; 9.3.2Testing the Difference Between Two Sample Means Using Groups; 9.3.3Testing the Difference Between Two Sample Means Using Variables; 9.3.4T-Test Variations from Assumptions about Variance; 9.3.5Testing Hypotheses about Means with Weighted Data; Chapter 9 Exercises; Chapter 10 Chi-Square Test and Analysis of Variance; 10.1The Chi-Square Test of Independence; 10.1.1How the Chi-Square Test Works; 10.1.2Conducting a Chi-Square Test; 10.1.3Example with Nominal-Level Independent Variable; A Closer Look: Chi-Square Test with Weighted Data; 10.1.4Reporting and Interpreting Results; A Closer Look: Other Applications of Chi-Square Tests; 10.2Measuring the Strength of Association between Categorical Variables; 10.2.1Lambda; 10.2.2Somers’ D; 10.2.3Cramer’s V; 10.3Chi-Square Test and Measures of Association in Controlled Comparisons; 10.3.1Analyzing an Ordinal-Level Relationship with a Control Variable; 10.3.2 Analyzing a Nominal-Level Relationship with a Control Variable (and Weighted Observations); 10.4Analysis of Variance (ANOVA); 10.4.1How ANOVA Works; 10.4.2 Single Factor ANOVA; 10.4.3 Two Factor ANOVA; 10.4.4 Stata’s F-Distribution Functions; Chapter 10 Exercises; Chapter 11 Correlation and Bivariate Regression; 11.1Correlation Analysis; 11.1.1Correlation between Two Variables; 11.1.2Correlation Among More than Two Variables; A Closer Look: Other Types and Application of Correlation Analysis; 11.2Bivariate Regression Analysis; A Closer Look: Treating Census as a Sample; A Closer Look: R-Squared and Adjusted R-Squared: What’s the Difference?; 11.3Creating a Scatterplot with a Linear Prediction Line; A Closer Look: Creating Graphs with Multiple Layered Elements; A Closer Look: What If a Scatterplot Doesn’t Show a Linear Relationship?; 11.4Correlation and Bivariate Regression Analysis with Weighted Data; A Closer Look: Creating Tables of Regression Results; Chapter 11 Exercises; Chapter 12 Multiple Regression; 12.1Multiple Regression Analysis; 12.1.1Estimating and Interpreting a Multiple Regression Model; 12.1.2Visualizing Multiple Regression with Bubble Plots; 12.1.3Multiple Regression with Weighted Observations; 12.2Regression with Multiple Dummy Variables; 12.2.1Estimating and Interpreting Regression with Multiple Dummy Variables; 12.2.2Changing the Reference Category; 12.2.3Visualizing Regression with Multiple Dummy Variables; 12.3Interaction Effects in Multiple Regression; 12.3.1Estimating Regression Model with Interaction Term; 12.3.2Graphing Linear Prediction Lines for Interaction Relationships; Chapter 12 Exercises; Chapter 13 Analyzing Regression Residuals; 13.1Expected Values, Observed Values, and Regression Residuals; 13.1.1Example from Bivariate Regression Analysis; 13.1.2Residuals from Multiple Regression Analysis; 13.2Squared and Standarized Residuals; 13.2.1Squared Residuals; 13.2.2Standardized Residuals; 13.3Assumptions about Regression Residuals; 13.4Analyzing Graphs of Regression Residuals; 13.4.1Histogram of Regression Residuals; 13.4.2Residual Diagnostic Plots; 13.5Testing Regression Assumptions with Residuals; 13.5.1 Testing Assumption that Residuals are Normally Distributed; 13.5.2Testing the Constant V
• Edition: Fifth edition
• Publisher Details: Washington, D.C : CQ Press
• Publication Date: 2023
• Extent: 1 online resource
• Subjects: 320.028555; Political statistics -- Computer programs; Analysis of variance -- Computer programs
• Languages: English
• ISBNs: 9781071815038
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.