Poli 101 Outline
Analyzing Political Data UCSC Winter 2024
J. Fletcher
This course focuses upon quantitative data analysis and research methods. It is specifically designed to meet UCSC’s Statistical Reasoning (SR) General Education (GE) requirement. Each week students will complete an exercise in data analysis. Through these assignments students will learn to describe data and statistical relationships, measure complex variables, assess statistical significance and construct multivariate models with both indirect relationships and interactions.
Required readings for each week are indicated by an asterisk (*). All others are recommended.
Please note, any adjustments to the course schedule will be appear on the News page of the course website.
Week 1: Introduction to Social Research
Learning Objectives:
Using the social scientific approach to the study of politics; appreciating its benefits and limits; using the language of theories, concepts and hypotheses.
*Data Lab 1 and Lab 2;
Almquist et al. on SPSS Chapt 1 & 2.2;
Kellstedt & Whitten (K&W) Ch 1 (esp pp 1-9; 15-19) & 2 (22-31; 40-43).
Loveless, Chapt 2 pp 30, 38-42 (available at McHenry short term loan JA 71.7 L68 2023) ;
PPIC Oct 2016 Survey Report:
http://www.ppic.org/content/pubs/survey/S_1016MBS.pdf
Exercise 1 Frequencies & Crosstabs
(see homework exercises menu for details)
Week 2: Describing Variables & Constructing Tables
Learning Objectives:
Understanding how and why political scientists describe groups and compare subgroups with differing degrees of precision; appreciating ways in which information is organized affects interpretations; using summary measures with frequency distributions and crosstabulations.
*Data Labs: Lab 4; Lab 6; Lab 7;
Selecting Measures of Association; Interpreting Measures of Association
*Ludwig-Mayerhofer, http://wlm.userweb.mwn.de/wlmspss.htm
Basics (Syntax, Data Files & Output), Simple Analysis (Frequency Tables), Data Transformation (Recode), Handling Data Files (Missing Values, Labeling Variables and Values; Rename Variables and Selecting Cases), Simple Analysis (Crosstabulation).
Almquist et al. on SPSS Chapts 3.3, 3.4, 4.2, 4.3 & 5.1;
K&W Ch 6, Ch 3 (pp 48-50) & Chapt 8 (pp 134-135; 139-140);
Loveless, Chapt 2 p 37; Chapt 6; Chapt 7 pp 138-9. NB: There are errors of calculation in Chapt 7 and the measures or association used are inappropriate. See: Crosstabs from Loveless
Exercise 2—Univariate & Bivariate Summary Measures
(see assignments menu for details) LINK
Week 3: Measuring, Operationalizing & Indexing
Learning Objectives:
Understanding how and why political scientists measure complex social phenomena; appreciating how concepts relate to measures; using reliability techniques to create multi-item indicators.
*Ludwig-Mayerhoffer, http://wlm.userweb.mwn.de/wlmspss.htm
Data Reduction (Item Analysis), Data Transformation (Compute);
Almquist et al., Chapt 5.2 & 10.2;
Loveless, Chapt 3 pp 51-56; Chapt 15, pp 356-358 only.
Exercise 3—Reliability
(see assignments menu for details). LINK
Week 4: Surveys, Sampling & Significance
Learning Objectives:
Understanding how and why political scientists gather sample data; appreciating sampling’s limits and some compensating strategies; using probability theory to make inferences.
*Ludwig-Mayerhoffer http://wlm.userweb.mwn.de/wlmspss.htm
Handling Data Files (Case Weights);
Almquist et al., Chapt 2.2, 5.5 & 8;
K&W Ch 4 (77-84) 7&8 (pp 136-150).
Loveless, Chapt 10 pp 219-226; Chapt 11 pp 244-256; Chapt 12 pp 261-267.
Graphic Probability Calculator
homepage.divms.uiowa.edu/~mbognar/applets/bin.html
Numeric Probability Calculator
http://graphpad.com/quickcalcs/probability1.cfm
Dan Benjmin 2017. “Let’s Redefine Statistical Significance”
Link
Daniel, T. & Kostic, B. (2017). RStats Tables and Calculators
https://www.missouristate.edu/rstats/Tables-and-Calculators.htm
Exercise 4-Inference LINK
Week 5: Analysis of Variance
Learning Objectives:
Understanding how some subgroups make a significant difference and others don’t.
*Data Lab: Lab 12.
Almquist Chapt 6.1, 6.2 & 7.3.
Exercise 5- Oneway Anova LINK
Week 6: Correlation & Causality
Leaning Objectives:
Understanding how and why political scientists distinguish between association and causation; appreciating some of the challenges of causal inference; using correlational analysis properly.
*Ludwig-Mayerhoffer http://wlm.userweb.mwn.de/wlmspss.htm
Simple Analysis (Correlations)
Almquist et al., Chapt 9 & 13.1;
K&W Ch 3 & 8 (150-55). Ch 9.1;
Loveless Chap 8 pp 165-182; Chap 9 pp 189-204.
Exercise 6–Correlation and Regression LINK
Week 7: Multivariate Regression
Learning Objectives:
Understanding how and why political scientists gauge the relative influence of multiple factors; appreciating the challenge of comparison; using standardization as an approach
*Data Labs: Lab 17; Lab 18 (dummies)
*Ludwig-Mayerhoffer, http://wlm.userweb.mwn.de/wlmspss.htm
More Complex Analysis (Linear Regression);
Almquist et al., Chapt 12.2 & 13.2;
K&W Ch 10;
Loveless, Chapt 14.
Exercise 7 Multiple Regression LINK
Week 8: Experimental & Statistical Control
Learning Objectives:
Understanding how and why political scientists seek control over variables; appreciating alternative theoretical linkages; using statistical controls and similar system designs to mimic control.
Data Lab: Lab 19a.
*Ludwig-Mayerhoffer, http://wlm.userweb.mwn.de/wlmspss.htm
Simple Analysis (T-test & Simple Analysis of Variance);
Almquist et al., Chapt 11;
K&W Ch 4 (67-77) Ch 11 (202-15; 225-32); Ch 12 (244-48).
Exercise 8—Spuriousness/Explanation
Mediation/Interpretation LINK
Week 9: Interaction/Specification
Learning Objectives:
Understanding how and why political scientists look for omitted variables and interactions; appreciating regression’s limitations and extensions; using diagnostic tools.
*Data Labs: Lab 19b.
*Ludwig-Mayerhoffer, http://wlm.userweb.mwn.de/wlmspss.htm
More Complex Analysis (Linear Regression through Method subcommand);
Almquist et al., Chapt 17;
K&W Ch 11 (pp 215-20; 232-43);
Loveless, Chapt 15 pp 341-351; 358-365.
Exercise 9-Interaction/Specification LINK
Week 10: Path Analysis/Preview
Learning Objectives:
Understanding how and why political scientists’ reports bring design elements together; appreciating the open-ended nature of research; using alternative reporting techniques.
K&W, Ch 12.
Data Lab: Lab 21
Exercise 10 -Final Exercise LINK
CJPS_Fletcher-Hove published version (5)
Week 11: Exam slot (Wednesday 20 March 2024 4-7pm).
Final Exercise due prior to 7pm.