Methods

=Methods and Statistical Techniques in Stratification= What methods should stratification students be required to learn?

Data Collection

 * 1) Participant Observation
 * 2) Non-participant Observation
 * 3) Field Notes
 * 4) Reflexive Journals
 * 5) Structured Interview
 * 6) Unstructured Interview
 * 7) Analysis of documents and materials
 * 8) Objective versus subjective measurements
 * 9) Others

Approaches

 * 1) Ethnographic Research
 * 2) Critical Social Research (communication and development of symbolic meanings)
 * 3) Historical Research
 * 4) Grounded Theory (inductive research, based in the observations or data from which it was developed)
 * 5) Phenomenological Research (describes “subjective reality”)
 * 6) Experimental
 * 7) laboratory experiments
 * 8) field experiments
 * 9) survey-based experiments
 * 10) Others?

Causal inferences
In teaching 699I, I (Reeve) found that one theme that kept coming up and deserved more complete treatment was the increasing complexity of research designs in order to strengthen causal claims. We identified the rough hierarchy of designs:
 * 1) multivariate regression
 * 2) fixed effects methods
 * 3) instrumental variables
 * 4) experimental and quasi experimental designs (e.g., Moving to Opportunity)

Quantitative Methods
Quantitative research refers to the investigation of quantitative properties and phenomena and their relationships. The objective of quantitative research is to develop and employ mathematical models, theories and/or hypotheses pertaining to phenomena. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships.

Data Collection

 * 1) Survey data
 * Government data
 * 1) Cross-national economic and social data

Measurement

 * 1) Wealth & income
 * 2) Social class
 * 3) Segregation
 * 4) Ethnicity
 * 5) Gender
 * 6) Political status
 * 7) Religion
 * 8) Labor force
 * 9) Immigration
 * 10) Poverty
 * 11) Health
 * 12) Education
 * 13) Social capital
 * 14) Power

Statistics

 * 1) General Linear Model
 * 2) Limited dependent variable models

=Readings= Belsley, D.A., Kuh, E., and Welsch, R.E. 1980. Regression Diagnostics. New York: John Wiley & Sons. 1. Introduction and Overview 2. Detecting Influential Observations and Outliers 3. Detecting and Assessing Collinearity

2008. Kleinbaum, David G., Lawrence L. Kupper, Keith E. Muller and Azhar Nizam. Applied Regression Analysis and Multivariable Methods, 4th edition. Duxbury Press, Belmont, CA. 1. Concepts and Examples of Research. 2. Classification of Variables and the Choice of Analysis. 3. Basic Statistics: A Review. 4. Introduction to Regression Analysis. 5. Straight-Line Regression Analysis. 6. The Correlation Coefficient and Straight-Line Regression Analysis. 7. The Analysis-of-Variance Table. 8. Multiple Regression Analysis: General Considerations. 9. Testing Hypotheses in Multiple Regression. 11. Confounding and Interaction in Regression. 12. Dummy Variables in Regression. 14. Regression Diagnostics. 15. Polynomial Regression. 16. Selecting the Best Regression Equation. 22. Logistic Regression Analysis. 23. Polytomous and Ordinal Logistic Regression. 27. Sample Size Planning for Linear and Logistic Regression and Analysis Of Variance.

Hosmer, David W. and Stanley Lemeshow. 1989. Applied Logistic Regression. New York: John Wiley & Sons. 1. Introduction to the Logistic Regression Model 2. The Multiple Logistic Regression Model 3. Interpretation of the Coefficients of the Logistic Regression Model 5. Assessing the Fit of the Model

=Miscellaneous= @http://www.nytimes.com/packages/html/national/20050515_CLASS_GRAPHIC/index_01.html