The D-Lab, 356 Barrows (unless otherwise noted)

Wednesdays from 4-5pm

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*25 September 2014*

This week, we discussed:

Little, R. J., & Rubin, D. B. (2000). Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annual Review of Public Health, 21(1), 121–145.

In this article, Little and Rubin lay down some of their requirements for causal inference, including a variety of approaches. Read the full post for some notes and links to python code and angry physicists.

*30 April 2014*

Laura’s been a great expositor of text analysis methods here in the D-Lab (and beyond). This week, we’ll look at a pair of papers by Grimmer and colleagues:

Required:

Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. doi:10.1093/pan/mps028

Supplemental:

Grimmer, Justin, & King, G. (2011). General purpose computer-assisted clustering and conceptualization. Proceedings of the National Academy of Sciences, 108(7), 2643–2650. doi:10.1073/pnas.1018067108

*16 April 2014*

This week we will focus on theoretical models. There are two required readings:

Chapter 1 of A Model Disipline by Clarke and Primo

The Fall and Rise of Development Economics by Krugman (focus on “The Evolution of Ignorance” and “Metaphors and Models” sections)

All readings are uploaded in Zotero.

Chapters 3 and 6 of A Model Discipline are also included, but are not required reading.

Models are useful. It’s good to think about them!

*19 March 2014*

This week, we shift to the relatively *un*controversial practice of Bayesian
modeling. You can even do frequentist statistics on your results.

For an overview, we’ll read:

Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120(3), 302–321.

And for application, we’ll read:

Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10(7), 309–318. doi:10.1016/j.tics.2006.05.009

It’s all a little cognitively focused, but hopefully we can see how this might be applied to learning in societies, institutions, etc.!

*12 March 2014*

Only last week, we heard scathing (and well-argued) remarks against the use of Bayesian methods for scientific hypothesis testing. Yet, some very smart people argue in precisely the opposite direction…

Required:

Kruschke, J. K. (2011). Bayesian Assessment of Null Values Via Parameter Estimation and Model Comparison. Perspectives on Psychological Science, 6(3), 299–312. doi:10.1177/1745691611406925

Recommended:

Dienes, Z. (2011). Bayesian Versus Orthodox Statistics: Which Side Are You On? Perspectives on Psychological Science, 6(3), 274–290. doi:10.1177/1745691611406920

Supplemental:

Kruschke, John K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573–603. doi:10.1037/a0029146

*05 March 2014*

This week we read some frequentist critiques of Bayesian methods (and statistical practice in general!):

Stark, P. B. (Submitted). Constraints versus Priors. SIAM/ASA Journal on Uncertainty Quantification.

Freedman, D. (1995). Some issues in the foundation of statistics. Foundations of Science, 1, 19–83.

And some supplemental reading:

Freedman, D. A., & Stark, P. B. (2003). What is the chance of an earthquake? In F. Mulargia & R. J. Geller (Eds.), Earthquake Science and Seismic Risk Reduction (Vol. 32, pp. 201–213). Dordrecht, The Netherlands: Kluwer.

Stark, P. B., & Tenorio, L. (2010). A primer of frequentist and Bayesian inference in inverse problems. In L. T. Biegler, G. Biros, O. Ghattas, M. Heinkenschloss, D. Keyes, B. Mallick, … K. Willcox (Eds.), Large Scale Inverse Problems and Quantification of Uncertainty. NY: John Wiley and Sons.

*26 February 2014*

This week, Glenda led us through a pair of papers about event studies:

Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. The American Economic Review, 83(4), 685–709.

MacKinlay, A. C. (1997). Event Studies in Economics and Finance. Journal of Economic Literature, 35(1), 13–39.

Here are Glenda’s Slides.

*19 February 2014*

For this, week, please try to read through all of the Fabrigar paper on Exploratory Factor Analysis. It’s technical, but even if you don’t follow the detail, it raises what I consider to be a fairly complete set of high-level issues - which is what we’ll focus on this week:

- Latent Variables
- Exploratory vs. Confirmatory techniques
- Descriptive / Data Mining techniques vs. Model Theoretic techniques
- Dealing with ordinal scales (e.g., Likert scales; discussed in supplemental materials)

Here’s what we read:

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272.

Flora, D. B., & Curran, P. J. (2004). An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data. Psychological Methods, 9(4), 466–491. doi:10.1037/1082-989X.9.4.466

*12 February 2014*

To kick off the reading group, Ryan presented the following:

Freedman, D. A. (1991). Statistical models and shoe leather. Sociological Methodology, 21, 291–313.

Little, R. J., & Rubin, D. B. (2000). Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annual Review of Public Health, 21(1), 121–145.

These are archived on the Zotero group.