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E-raamat: Microsoft Excel(R) Companion to Political Analysis

(University of Central Florida, USA), (University of Georgia)
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  • Ilmumisaeg: 17-Jan-2022
  • Kirjastus: CQ Press
  • Keel: eng
  • ISBN-13: 9781071813324
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Microsoft Excel(R) Companion to Political AnalysisSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 17-Jan-2022
  • Kirjastus: CQ Press
  • Keel: eng
  • ISBN-13: 9781071813324
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The trusted series of workbooks by Philip H. Pollock III and Barry C. Edwards continues with A Microsoft Excel®Companion to Political Analysis. In this new guide, students dive headfirst into actual political data working with the ubiquitous Excel software. Students learn by doing with new guided examples, annotated screenshots, step-by-step instructions, and exercises that reflect current scholarly debates in varied subfields of political science, including American politics, comparative politics, law and courts, and international relations. Chapters cover all major topics in political data analysis, from descriptive statistics through logistic regression, all with worked examples and exercises in Excel. No matter their professional goals, students can gain a leg up for their future careers by developing a working knowledge of statistics using Excel. By encouraging students to build on their existing familiarity with the Excel program, instructors can flatten the statistics learning curve and take some of the intimidation out of the learning process. Gain lost time usually spent troubleshooting software to provide students with a smooth transition into political analysis.

Arvustused

This text is a welcoming addition to the political analysis companions series by Pollock and colleagues. Its lucid writing style, hands-on

pedagogical approach, and ample carefully curated examples and exercises will be appreciated by research methods instructors

and students alike. It really teaches the students how to conduct empirical political analysis using Microsoft Excel. -- Yi Edward Yang Pollocks book is very intuitive, straightforward, and clear. By using this textbook, students should be able to deepen their learning with more hands-on practices. -- Soyoung Kwon A textbook that makes statistics in Excel very approachable and easy to understand even for non-STEM majors. -- Volodymyr Gupan

List of Figures
xiii
Preface xvii
Getting Started xxiii
Downloading the Companion Workbooks xxiv
What Version of Excel Is Needed? xxv
Watch Tutorial Videos on Demand xxvi
Chapter 1 Using Excel for Data Analysis
1(1)
1.1 Workbooks and Worksheets
2(2)
1.2 Activating Data Analysis Add-Ins
4(2)
1.3 Formulas, Functions, and Tools
6(3)
1.4 Defining and Using Names
9(2)
A Closer Look: Keyboard Shortcuts
11(1)
1.5 Separating Analysis from Data
11(1)
1.6 Printing and Saving Your Work
12(2)
1.7 Formatting Tables and Figures
14(1)
1.8 Getting Help
15(4)
Chapter 1 Exercises
19(4)
Chapter 2 Descriptive Statistics
23(20)
2.1 Identifying Levels of Measurement
24(1)
2.2 Describing Nominal Variables
24(5)
2.2.1 Frequency Distribution Tables
25(3)
2.2.2 Bar Charts
28(1)
2.2.3 Central Tendency and Dispersion
28(1)
2.3 Describing Ordinal Variables
29(2)
2.3.1 High Dispersion Example
29(1)
2.3.2 Low Dispersion Example
30(1)
2.4 Describing Interval Variables
31(4)
2.4.1 Descriptive Statistics with the Data Analysis ToolPak
31(2)
2.4.2 Histograms
33(2)
A Closer Look: Editing Charts with Purpose
35(1)
2.5 Case-Level Information
35(2)
Chapter 2 Exercises
37(6)
Chapter 3 Creating and Transforming Variables
43(20)
3.1 Using Formulas and Functions to Generate New Variables
44(1)
3.2 Creating Indicator Variables ("Dummy Variables")
45(3)
3.3 Recoding Interval-Level Variables into Simplified Categories
48(4)
3.4 Centering or Standardizing a Numeric Variable
52(5)
Chapter 3 Exercises
57(6)
Chapter 4 Making Comparisons
63(18)
4.1 Cross-Tabulation Analysis
64(3)
4.1.1 Comparing States Example
64(1)
4.1.2 Making Cross-Tabulations with Pivot Tables
65(2)
4.1.3 Interpreting Cross-Tabulations
67(1)
4.2 Mean Comparison Analysis
67(4)
4.2.1 Comparing Means with Pivot Tables
68(1)
4.2.2 Adding Counts and Standard Deviations
69(1)
4.2.3 Interpreting Results and Grouping Rows Together
70(1)
4.3 Making Comparisons with Interval-Level Independent Variables
71(1)
4.4 Creating Maps for Geographic Comparisons
72(3)
Chapter 4 Exercises
75(6)
Chapter 5 Graphing Relationships and Describing Patterns
81(24)
5.1 Graphing Relationships with Binary Dependent Variables
83(4)
5.1.1 Simple Bar Charts with Nominal Independent Variables
83(2)
5.1.2 Simple Line and Area Charts with Ordinal Independent Variables
85(1)
5.1.3 Charts with Interval-Level Independent Variables
86(1)
5.2 Graphing Relationships with Nominal-Level Dependent Variables
87(3)
5.2.1 Clustered Bar Charts with Nominal Independent Variables
87(1)
5.2.2 Multiple Line Charts with Ordinal Independent Variables
88(1)
5.2.3 Charts with Interval-Level Independent Variables
89(1)
5.3 Graphing Relationships with Ordinal-Level Dependent Variables
90(3)
5.3.1 Stacked Bar Charts with Nominal Independent Variables
90(1)
5.3.2 Stacked Area Charts with Ordinal Independent Variables
90(2)
5.3.3 Charts with interval-Level Independent Variables
92(1)
5.4 Graphing Relationships with Interval-Level Dependent Variables
93(6)
5.4.1 Box Plots with Nominal Independent Variables
93(2)
5.4.2 Line Charts with Ordinal Independent Variables
95(1)
5.4.3 Scatterplots with Interval-Level Independent Variables
96(3)
Chapter 5 Exercises
99(6)
Chapter 6 Random Assignment and Sampling
105(22)
6.1 Random Assignment
106(2)
6.1.1 Two Groups with Equal Probability
107(1)
6.1.2 Multiple Groups with Varying Probabilities
107(1)
6.2 Analyzing the Results of an Experiment
108(3)
6.2.1 Assessing Random Assignment
108(2)
6.2.2 Evaluating the Effect of Treatment
110(1)
6.3 Random Sampling
111(4)
6.3.1 Simple Random Samples with Replacement
111(1)
6.3.2 Simple Random Samples without Replacement
112(1)
6.3.3 Systematic Random Samples
113(2)
6.3.4 Clustered and Stratified Random Samples
115(1)
6.4 Selecting Cases for Qualitative Analysis
115(2)
6.4.1 Most Similar Systems
115(1)
6.4.2 Most Different Systems
116(1)
6.5 Entering Data from Experiments or Surveys
117(4)
Chapter 6 Exercises
121(6)
Chapter 7 Making Controlled Comparisons
127(38)
7.1 Cross-Tabulation Analysis with a Control Variable
128(3)
7.1.1 Start with a Simple Cross-Tabulation
128(1)
7.1.2 Controlled Comparisons with Pivot Tables
128(2)
7.1.3 Controlled Comparisons with Slicers
130(1)
7.1.4 Interpreting Controlled Cross-Tabulations
131(1)
7.2 Graphing Controlled Comparisons with Categorical Dependent Variables
131(3)
7.3 Mean Comparison Analysis with a Control Variable
134(2)
7.3.1 Adding a Control Variable to Mean Comparison Tables
134
7.3.2 Using Slicers for Controlled Mean Comparisons
131(4)
7.3.3 Interpreting Controlled Mean Comparisons
135(1)
7.4 Visualizing Controlled Mean Comparisons
136(1)
7.5 Controlled Comparisons with an Interval-Level Control Variable
136(3)
Chapter 7 Exercises
139(26)
Chapter 8 Foundations of Statistical Inference
165(2)
8.1 Estimating a Population Proportion
146(4)
8.1.1 Sample of Marbles Example
146(2)
8.1.2 Simulating Many Sample Proportions with Excel
148(1)
A Closer Look: Using Probability Distributions to Simulate Raw Data
149(1)
8.2 Estimating a Population Mean
150(2)
A Closer Look: Estimating the Population Standard Deviation with Sample Data
151(1)
8.3 Expected Shape of Sampling Distributions
152(5)
8.3.1 Central Limit Theorem and the Normal Distribution
153(1)
8.3.2 Normal Distribution of Sample Proportions
154
8.3.3 Normal Distribution of Sample Means
150(5)
8.3.4 The Standard Normal Distribution
155(1)
8.3.5 Empirical Rule (68-95-99 Rule)
156(1)
8.4 Confidence Intervals and Margins of Error
157(3)
8.6.1 Critical Values for Confidence Intervals
157(1)
8.4.2 Excel Worksheet for the Confidence Interval of a Proportion
158(1)
8.4.3 Excel Worksheet for the Confidence Interval of a Mean
159
8.4.4 Excel's Confidence Functions
155(5)
8.5 Student's t-Distribution: When You're Not Completely Normal
160(3)
8.5.1 The t-Distribution's Role in Inferential Statistics
160(1)
8.5.2 Critical Values of t-Distributions
161(2)
Chapter 8 Exercises
163(4)
Chapter 9 Hypothesis Tests with One or Two Samples
167(24)
9.1 Role of the Null Hypothesis
168(1)
9.2 Testing a Hypothesis about One Sample Proportion
169(3)
9.2.1 Start with Descriptive Statistics
169(1)
9.2.2 Confidence Interval Approach
169(1)
9.2.3 P-Value Approach Using the Z Score
170(2)
9.3 Testing the Difference between Two Sample Proportions
172(3)
9.3.1 Start by Comparing Proportions
172(1)
9.3.2 Confidence Interval for the Difference of Proportions
173(1)
9.3.3 Difference of Proportions Test
174(1)
9.4 Testing a Hypothesis about One Sample Mean
175(4)
9.4.1 Start with Descriptive Statistics
176(1)
A Closer Look: Treating Census as a Sample
177(1)
9.6.2 Confidence Interval Approach
177(1)
9.4.3 P-Value Approach Using the t-Statistic
178(1)
9.5 Testing the Difference between Two Sample Means
179(8)
9.5.1 Start by Comparing Means
179(1)
9.5.2 Confidence Intervals for the Difference of Means
180(1)
9.5.3 Difference of Means Tests
181(3)
9.5.4 Difference of Means Tests with the Data Analysis ToolPak
184(2)
9.5.5 Difference of Means Tests with the T.TEST Function
186(1)
Chapter 9 Exercises
187(4)
Chapter 10 Chi-Square Test and Analysis of Variance
191(26)
10.1 Chi-Square Test of Independence
192(6)
10.1.1 How the Chi-Square Test Works
193(1)
10.1.2 Creating Tables of Observed and Expected Frequencies
193(2)
10.1.3 Excel's Chi-Square Functions
195(1)
10.1.4 Opinion about Divorce Laws and Marital Status Example
196(1)
A Closer Look: Other Applications of Chi-Square Tests
197(1)
10.2 Measuring the Strength of Association between Categorical Variables
198(5)
10.2.1 Lambda
198(2)
10.2.2 Somers d
200(2)
10.2.3 Cramer's V
202(1)
A Closer Look: Reporting and Interpreting Chi-Square Test Results
203(1)
10.3 Analysis of Variance [ ANOVA)
203(6)
10.3.1 How ANOVA Works
204(1)
10.3.2 Legal Quality and Judicial Selection Method Example
205(2)
A Closer Look: More Applications of ANOVA
207(2)
Chapter 10 Exercises
209(8)
Chapter 11 Correlation and Bivariate Regression
217(22)
11.1 Correlation Analysis
218(3)
11.1.1 Excel Functions for Correlation Analysis
219(1)
11.1.2 Correlation Analysis with the Data Analysis ToolPak
219(1)
11.1.3 Interpreting Correlation Coefficients
220(1)
A Closer Look: Other Types and Applications of Correlation Analysis
221(1)
11.2 Bivariate Regression
221(6)
11.2.1 Organizing Data for Regression Analysis with Excel
222(1)
A Closer Look: What to Do about Missing Data
223(1)
11.2.2 Regression with the Data Analysis ToolPak
224(1)
11.2.3 Interpreting Results
224(3)
A Closer Look: R-Squared and Adjusted R-Squared: What's the Difference?
227(1)
11.3 Creating Scatterplots for Bivariate Regressions
227(6)
11.3.1 Adding Regression Lines to Scatterplots
229(1)
11.3.2 Using Regression Lines to Make Informed Predictions
230(1)
A Closer Look: What If a Scatterplot Doesn't Show a Linear Relationship?
230(1)
11.3.3 Editing Scatterplots for Clarity and Use of Space
230(3)
Chapter 11 Exercises
233(6)
Chapter 12 Multiple Regression Analysis
239(26)
12.1 Multiple Regression Example
240(4)
12.1.1 Organizing Data for Multiple Regression Analysis with Excel
240(2)
12.1.2 Multiple Regression with the Data Analysis ToolPak
242(1)
12.1.3 Interpreting Multiple Regression Results
242(1)
A Closer Look: Reporting Regression Results in Tables
243(1)
12.2 Regression with Multiple Dummy Variables
244(5)
12.2.1 Regression Equations with Dummy Variables
244(1)
12.2.2 State Smoking Rates and Cigarette Taxes Example
245(1)
12.2.3 Creating Multiple Dummy Variables
245(2)
12.2.4 Estimating and Interpreting the Multiple Regression Equation
247(1)
A Closer Look: Changing the Reference Category
248(1)
12.3 Interaction Effects in Multiple Regression
249(3)
12.3.1 Multiple Regression with an Interaction Term Example
249(1)
12.3.2 Organizing Data and Creating Interaction Terms
250(1)
12.3.3 Using the Regression Tool in the Data Analysis ToolPak
251(1)
12.3.4 Interpreting Interaction Effects in Results
252(1)
12.4 Visualizing Multiple Regression with a Bubble Plot
252(2)
12.5 Graphing Interaction Relationships
254(5)
12.5.1 Organizing Data to Plot Distinct Scatterplot Markers
255(1)
12.5.2 Creating a Scatterplot with Distinct Regression Lines
256(3)
Chapter 12 Exercises
259(6)
Chapter 1 Analyzing Regression Residuals
265(24)
13.1 Expected Values, Observed Values, and Regression Residuals
266(3)
13.1.1 Calculating Residuals for Observations
266(1)
13.1.2 Visualizing Residuals on Scatterplots
267(1)
13.1.3 Squared Residuals Measure Model Fit
268(1)
13.2 Assumptions about Regression Residuals
269(1)
13.3 Residuals Options with the Regression Tool
270(3)
13.3.1 Tables of Residuals
270(2)
13.3.2 Plots of Residuals
272(1)
13.4 Analyzing Graphs of Residuals
273(4)
13.6.1 Two Multiple Regression Examples
273(1)
13.4.2 Histograms of Standardized Residuals
274(1)
13.4.3 Scatterplots of Residuals and Expected Values
275(1)
13.4.4 Quantile-Quantile Plots
276(1)
13.5 Statistical Tests about Regression Residuals
277(4)
13.5.1 Testing the Assumption That Residuals Are Normally Distributed
278(1)
13.5.2 Testing the Constant Variance Assumption
279(2)
13.6 What If You Diagnose Problems with Residuals?
281(2)
Chapter 1 Exercises
283(6)
Chapter 14 Logistic Regression
289(26)
14.1 Odds, Logged Odds, and Probabilities
290(1)
14.2 Estimating a Logistic Regression Model
291(7)
14.2.1 Logistic Regression Equation and Example
292(1)
14.2.2 Organizing Data for Logistic Regression Analysis
292(1)
14.2.3 Using the Real Statistics Add-In for Logistic Regression
293(1)
14.2.4 Interpreting Logistic Regression Coefficients
294(1)
14.2.5 Interpreting Odds Ratios
295(1)
14.2.6 How Well Does the Model Fit the Data?
296(2)
14.3 Logistic Regression with Multiple Independent Variables
298(2)
14.3.1 Example Equation with Multiple Independent Variables
298(1)
14.3.2 Organizing Data and Using the Real Statistics Add-In
298(2)
14.3.3 Interpreting Results
300(1)
14.4 Graphing Predicted Probabilities with One Independent Variable
300(3)
14.5 Graphing Predicted Probabilities with Multiple Independent Variables
303(6)
14.5.1 Plotting Marginal Effects at Representative Values
303(2)
14.5.2 Plotting Marginal Effects at the Means
305(4)
Chapter 14 Exercises
309(6)
Chapter 15 Doing Your Own Political Analysis
315(20)
15.1 Doable Research Ideas
316(2)
15.1.1 Economic Performance and Election Outcomes
316(1)
15.1.2 Electoral Turnout in Comparative Perspective
317(1)
15.1.3 Religion and Politics
317(1)
15.1.4 Race and Politics
318(1)
15.1.5 Women and Politics
318(1)
15.1.6 Replicate a Published Paper
318(1)
15.2 Importing Data into Excel
318(8)
15.2.1 Microsoft Excel Datasets
319(3)
15.2.2 HTML Table Data
322(3)
15.2.3 Other Supported Data Types
325(1)
15.3 Writing It Up
326(5)
15.3.1 The Research Question
327(1)
15.3.2 Literature Review
328(1)
15.3.3 Data, Hypotheses, and Analysis
328(1)
15.3.6 Conclusions and Implications
329(2)
Chapter 15 Exercises
331(4)
Appendix, Table A-1 Variables in the Debate Experiment Workbook's Dataset in Alphabetical Order 335(1)
Appendix, Table A-2 Variables in the Presidential Elections Workbook s Dataset in Alphabetical Order 336(1)
Appendix, Table A-3 Variables in the States Dataset by Topic 337(9)
Appendix, Table A-4 Variables in the World Dataset by Topic 346
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. Pollocks 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. Barry C. Edwards writes textbooks and works for Fair Trial Analysis, LLC, a company that conducts research on juries and jurors for civil and criminal litigation. He received his B.A. from Stanford University, a J.D. from New York University, and a Ph.D. from the University of Georgia. He taught survey design and analysis, research methods, and prelaw courses at the University of Central Florida and continues to teach occasional courses for the University of Georgia. His political science interests include American politics, public law, and research methods. He founded the Political Science Data Group and created the PoliSciData.com website. His research has been published in American Politics Research, Congress & the Presidency, Election Law Journal, Emory Law Journal, Georgia Bar Journal, Harvard Negotiation Law Review, Journal of Politics, NYU Journal of Legislation and Public Policy, Political Research Quarterly, Presidential Studies Quarterly, Public Management Review, State Politics and Policy Quarterly, and UCLA Criminal Justice Law Review.
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