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PPM-104: Quantitative Methods

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Normed Course Outlines

PPM-104: Quantitative Methods

Description: This normed course outline covers the fundamentals of quantitative research methods and statistical analysis techniques essential to the investigation of public policy issues. It provides an introduction to probability, statistics, large data sets, empirical research designs, and statistical computer software (such as STATA or SPSS). An important segment of the course focuses on program evaluation. This includes the design and analysis of experiments that aim to measure policy effectiveness, and the use of non-experimental data to evaluate policy effectiveness.

Learning Outcomes: On successful completion of this course, students will have the skills and knowledge to be able to appropriately utilize and interpret results of the following theories and principles, taking account of the concepts noted below, to the analysis of public policy and management problems.

  • Descriptive Statistics
  • Looking at Data
  • Probability Concepts
  • Sampling
  • Confidence Intervals and Hypothesis Testing
  • Simple Regression
  • Multivariate Analysis
  • Omitted Variable Bias
  • Randomized Trials
  • Research Design

Concepts to be Learned: Data Set, Frequency DistributionVariance; Standard Deviation; Variable; Statistical Spread (Dispersion); Indicator; Quantitative Research; Quartile; Unit of Analysis; Discrete Random Variable; Likelihood (in relation to Risk); Predictability; Central Limit Theorem; Sample Size; Sample Space; Sampling Error; Standardized Variable; Classification;  Influential Observations; Ordinary Least Squares Estimator; Regression Analysis; Regression Line; Multiple Regression Analysis; Environmental Validity; Data Collector Bias; External Validity; Hawthorne Effect; John Henry Effect; Natural Experiment; Omitted Variable Bias; Selection Bias; Environmental Validity; External Validity; Hawthorne Effect; John Henry Effect; Natural Experiment; Random Control Trials (RCTs); Focus Groups; Econometrics; Quasi-experimental Design; Research Design; Single Observation Design; Qualitative Methods; Qualitative Research; Time-Series Model; Bootstrapping; Panel Data; Time Series Data

Normed Topics in this Normed Course Outline

Like other normed topics on the Atlas, each of these has a topic description, links to core concepts relevant to the topic, learning outcomes, a reading list drawn from available course syllabi, and a series of assessment questions.

Course Syllabi Sources for this Normed Course Outline: University of Toronto: PPG-1004 & PPG-1008; Carleton PADM-5113 & PADM-5114; Harvard Kennedy School: API-201; George Washington: PPPA-6002; NYU Wagner School: GP-1011; Rutgers: 34:833:530 & 34:833:630; UCLA: PP-203 & PP-208; Saskatchewan-Regina: JSGS-803; American: PUAD-601 & PUAD-605

Recommended Readings:

Week 1: Descriptive Statistics

Moore, D., McCabe G., & Craig, B. (2009). Introduction to the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company. Sections 1.1, 2.1, 1.2, 2.2, 2.6.

Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34.

Week 2: Looking at Data

Healey, Joseph F. (2013). The Essentials of Statistics: A Tool for Social Research, Second edition. Wadsworth/Cengage Learning. Chapters 1 - 3.

Moore, D., McCabe G., & Craig, B. (2009). Introduction to the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company. Rest of chapters 1 – 2.

Week 3: Probability Concepts

Moore, D., McCabe G., & Craig, B. (2009). Introduction to the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company. Chapter 4.

Week 4: Sampling

Healey, Joseph F. (2013). The Essentials of Statistics: A Tool for Social Research, Second edition. Wadsworth/Cengage Learning. Chapter 7 (pp. 146-154).

Moore, D., McCabe G., & Craig, B. (2009). Introduction to the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company. Chapter 5.

Week 5: Confidence Intervals and Hypothesis Testing

Healey, Joseph F. (2013). The Essentials of Statistics: A Tool for Social Research, Second edition. Wadsworth/Cengage Learning. Chapters 7 (pp. 154-172), 8, 9.

Moore, D., McCabe G., & Craig, B. (2009). Introduction to the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company. Sections 3.3, 6.1, 6.2, 7.1, 7.2, 8.2

Stock, James H. and Mark W. Watson. 2011. “Introduction to Econometrics.” 3rd edition. Pearson/Addison-Wesley. Chapter 3 (p. 70-90 and p. 91-96) and Chapter 4 (p. 107-112).

Week 6: Simple Regression

Eaton, B. Curtis, and Mukesh Eswaran, “Differential Grading Standards and Student Incentives,” Canadian Public Policy 34:2 (June 2008), pp. 215-36. (http://library.usask.ca/scripts/remote?URL=http://www.jstor.org/stable/pdfplus/25463608.pdf?acceptTC=true)

Gerber, Linda. “Urban Diversity: Riding Composition and Party Support in the Canadian Federal Election of 2004,” Canadian Journal of Urban Research15:2 Supplement (2006), pp. 105-118.

Healey, Joseph F. (2013). The Essentials of Statistics: A Tool for Social Research, Second edition. Wadsworth/Cengage Learning.  Ch. 13.

Moore, D., McCabe G., & Craig, B. (2009). Introduction to the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company. Pp. 545-560.

Richard, John, Jennifer Hove, and Kemi Afolabi, “Understanding the Aboriginal/Non-Aboriginal Gap in Student Performance” C.D. Howe Institute Commentary, No. 276 (December 2008), available online at http://www.cdhowe.org/pdf/commentary_276.pdf.

Stock, James H. and Mark W. Watson. 2011. Introduction to Econometrics, 3rd ed. Pearson/Addison-Wesley. Ch. 4-7.

Week 7: Multivariate Analysis

Kahane, Leo H. Regression Basics. Thousand Oaks: Sage, 2008. Chapter 6, pp. 110-119.

Moore, D., McCabe G., & Craig, B. (2009). Introduction to the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company. Chapter 11.

Schroeder, Larry D., Sjoquist, David L., & Stephan, Paula E. (1986). “Understanding Regression Analysis: An Introductory Guide”. Quantitative Applications in the Social Sciences 57. Newbury Park: Sage Publications.

Stock, James H. and Mark W. Watson. 2011. Introduction to Econometrics, 3rd ed. Pearson/Addison-Wesley. Chapters: 8 (pp. 252-296), 9 (pp. 312-341), and 11 (pp. 381-394, 398-408)      

Week 8: Omitted Variable Bias

Healey (2011). Statistics: A Tool for Social Research, 9th Edition. Cengage Learning. Chapter 16.

Moore, David S (2011). The Basic Practice of Statistics, 5th edition. W.H. Freeman and Company. Chapters 4 and 5.

Stock, James H. and Mark W. Watson. 2011. Introduction to Econometrics, 3rd ed. Pearson/Addison-Wesley. Chapters 8 (p. 252-296) and 9 (p. 312-341).

Week 9: Randomized Trials

Angrist, Joshua D. and Jorn-Steffen Piscke. 2014. Mastering Metrics. Princeton University Press. Chapter 2.

Healey (2011). Statistics: A Tool for Social Research, 9th Edition. Cengage Learning. Chapters 6 and 7. 

Schroeder, Larry D., Sjoquist, David L., & Stephan, Paula E. (1986). “Understanding Regression Analysis: An Introductory Guide”. Quantitative Applications in the Social Sciences 57. Newbury Park: Sage Publications.

Stock, James H. and Mark W. Watson. 2011. Introduction to Econometrics, 3rd ed. Pearson/Addison-Wesley. Chapter 13 (p. 469-506).

Week 10: Research Design

Bryman, Alan, James Teevan, and Edward Bell. 2009. Social Research Methods. 2nd Canadian edition. Oxford UP, pp, 21-41.

King, Gary, Robert Keohane, and Sidney Verba. 1994. Designing Social Inquiry. Princeton UP, pp. 12-19.

Moore, D., McCabe G., & Craig, B. (2014). Introduction to the Practice of Statistics, 8th Revised Edition. New York: W. H. Freeman and Company. Chapter 9.1-9.3. 

Neuman, Lawrence W. 2011. Social Research Methods: Qualitative and Quantitative Approaches. 7th ed. Allyn & Bacon, pp. 124-125; 133-143.  

Week 11: Ethical Issues in Research

Chambliss, Daniel F., and Russell K. Schutt. Making sense of the social world: Methods of investigation. Sage, 2012. Chapters 1 and 2.

Loaeza, Soledad, Randy Stevenson, and Devra C. Moehler. 2005. “Symposium: Should Everyone  Do Fieldwork?,” APSA-CP Newsletter of the American Political Science Association Organized  Section on Comparative Politics 16(2): 8-18.

Siplon, Patricia. 1999. “Scholar, Witness, or Activist? The Lessons and Dilemmas of an AIDS Activist.” PS, 577-581.

Wood, Elisabeth. 2006. “The Ethical Challenges of Field Research in Conflict Zones,” Qualitative Sociology 29.3: 373-386.

Zirakzadeh, Cyrus Ernesto. 2009. “When Nationalists Are Not Separatists: Discarding and Recovering Academic Theories While Doing Fieldwork in the Basque Region of Spain,” in Edward Schatz, ed.,Political Ethnography: What Immersion Contributes to the Study of Power.  Chicago UP., pp. 97-117.

Week 12: Panel Data, Fixed Effects, and Instrumental Variables

Angrist, Joshua D. and Jorn-Steffen Piscke. 2014. Mastering Metrics. Princeton University Press. Chapter 3.

Pétry, Francois, Louis M. Imbeau, Jean Crête, and Michel Clavet, “Explaining the Evolution of Government Size in the Canadian Provinces,” Public Finance Review 28:1 (Jan., 2000), pp. 26-47.

Stock, James H. and Mark W. Watson. 2011. Introduction to Econometrics, 3rd ed. Pearson/Addison-Wesley. Chapter 10 (p. 347-371) and Chapter 12 (p. 419-456).

Sample Assessment Questions:

1a) Define the following terms: Data Set; Frequency Distribution; Variance; Standard Deviation; Variable; Statistical Spread (Dispersion). 1b)Create a graph or figure to illustrate an interesting set of statistics that may be useful for policymakers to understand. 1c) What are the differences between descriptive and inferential statistics? 1d) Consider the following three data sets A, B and C: A = {9,10,11,7,13}; B = {10,10,10,10,10}; C = {1,1,10,19,19}. 1di) Calculate the mean of each data set. 1dii) Calculate the standard deviation of each data set. 1diii) Which set has the largest standard deviation? 1div) Is it possible to answer question 1diii) without calculations of the standard deviation?

2a) Define the following terms: Data; Dataset; Frequency Distribution; Indicator; Quantitative Research; Quartile; Unit of Analysis b) Why are graphs and figures sometimes useful in helping audiences understand data? 2c) How have software programs like STATA and SPSS changed data analysis? 2d) What is a frequency distribution?

3a) Define the following terms: Discrete Random Variable; Likelihood (in relation to Risk); Predictability b) What is a random variable in statistics?  

4a) Define the following terms: Central Limit Theorem; Sample Size; Sample Space; Sampling Error; Standardized Variable; Classification 4b) What is simple random sampling? Describe an alterntive sample technique. 4c) Define the term “sampling distribution.” 4d) What is the central limit theorem? Why is this concept important? 4e) What is a sampling error?

5a) Define the following terms: Causal Relationship; Histogram; Standard Error; Validity (internal and external). 5b) What is a P-value? Why is this statistic important for confidence testing? 5c)What does the term "statistical significance at the 5 percent level" mean?

6a) Define the following terms:  Influential Observations; Ordinary Least Squares Estimator; Regression Analysis; Regression Line 6b) Why is simple regression a potentially useful tool for policy analysis? 6c) Describe the possible shortcomings of a simple regression model as a tool for understanding the causal relationship between a dependent and independent variable.

7a) Define the following terms: Multiple Regression Analysis; Environmental Validity; Data Collector Bias; External Validity; Hawthorne Effect; John Henry Effect; Natural Experiment; Omitted Variable Bias; Selection Bias 7b) How is multivariate regression analysis different from simple regression analysis? 7c) What does it mean to “control for” a specific variable when conducting a regression analysis? 7d) A researcher is interested in studying the causal impact of higher education on lifetime earnings. Describe how the researcher might use multiple regression analysis to estimate the impact of education on earnings. What variables might the researcher control for and why?

8a) Define the following terms: Environmental Validity; External Validity; Hawthorne Effect; John Henry Effect; Natural Experiment; Omitted Variable Bias; Selection Bias 8b) What is meant by the saying “correlation does not equal causation?” 8c) Define omitted variable bias. 8d) Describe two strategies that researchers can use in an attempt to deal with omitted variable bias and produce an estimate of the causal effect of one variable on another.

9a) Define the following term: Random Control Trials (RCTs) 9b) What are the advantages of randomized trials as opposed to other research designs in studying the causal relationship between two variables? 9c) Provide an example of a policy-relevant social science question for which it may be difficult to design a randomized trial (either for practical or ethical reasons). What would the challenges be? 9d) What is a “natural experiment?”

10a) Define the following terms: Focus Groups; Econometrics; Quasi-experimental Design; Research Design; Single Observation Design; Qualitative Methods; Qualitative Research 10b) What is a quasi-experimental research design? Provide one example (real or a hypothetical one that you make up) that illustrates what a quasi-experimental research project is. 10c) What is the difference between a random control experiment and a quasi-experimental design? 10d) What is a longitudinal study, and when might a researcher decide to use this research design for a research project? 10e) What type of research design is sometimes called the “gold standard” for social science research, and why? 10f) What are some of the advantages and disadvantages of the case study approach to social science research?

11a) Describe a hypothetical random control experiment that may produce useful information for policymakers but may raise ethical issue that would make it impossible to conduct. 11b) What is “cherry picking?” Why are researchers ethically obliged to avoid this practice?

12a) Define the following terms: Time-Series Model; Bootstrapping; Panel Data; Time Series Data 12b) What is a fixed effect model? When is the fixed effect model useful? 12c) What is panel data? Why is panel data valuable and what types of research questions can it be used to analyze? 12d) What is an instrumental variable? Why do researchers make use of instrumental variables?

Page created by: James Ban on 3 July 2015 and updated by Ian Clark on 3 July 2015.


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© University of Toronto 2008
School of Public Policy and Governance