Stat 209-- Course Files, Readings, Examples

Week 1--Course Introduction; properties of regression models

Lecture topics
Quick Tour of course logistics and course materials
Main Topic: Meaning of regression coefficients: simple and multiple regression (including logistic)
Technical facts and foibles:
a. adjusted variables and regression coefficients--values of coefficients depend crucially on what else is used in the regression fit;   conditioning vs controlling
b. effects of errors in measurement on regression coefficients
c. standardized regression coefficients.

Lecture materials
MT woes of regression coefficients slides
a. Class Handout. Coleman data: adjusted-variables multiple regression (ascii version)      Coleman scanned pdf
       Additional materials: data file, 20 schools      using pairs command
      Adjusted variable plot         Standard regression diagnostic plots, Coleman regression
     Added (adjusted) Variable plots in various R-packages, car,olsrr  Coleman avPlots in car
      nice vignette on regression diagnostics, including added variable plots, from the olsrr package.
slide for regression recursion
b. Class handout: Week 1 Math facts, Measurement error: Basic Results handout
     Also   Faraway book (linked below) Ch.4 single predictor case;    Maindonald-Braun sec6.7 results and R-functions, Stigler example
c. Class handout: Standardized regression coefficients standardized variables
            (aside "beta weights" in Kool-Aid Psychology Scientific American, Jan 2010)
       Hooke's Law example in Statistical Models for Causation: A critical review    

Regression examples, publications:
a.  Do Breast-Fed Baby Boys Grow Into Better Students?   Publication: Breastfeeding Duration and Academic Achievement at 10 Years (Stanford access). Wendy H. Oddy, Jianghong Li, Andrew J. O. Whitehouse, Stephen R. Zubrick, Eva Malacova. Pediatrics; Vol 127, Numb 1, Jan 2011   
        Ohio State breastfeeding study. Is breast truly best? Estimating the effects of breastfeeding on long-term child health and wellbeing in the United States using sibling comparisons Cynthia G. Colen, , David M. Ramey Social Science & Medicine Volume 109, May 2014, Pages 55-65.
Ohio State press release.   Breast-feeding Benefits Appear to be Overstated
b.  Pediatrics 2006;117;1018-1027   Sexy Media Matter: Exposure to Sexual Content in Music, Movies, Television, and Magazines Predicts Black and White Adolescents' Sexual Behavior  (Stanford access)  

Week 1 Readings
Primary Readings
Background piece: Correlation and Causation: A Comment, (Stanford access) Stephen Stigler Perspectives in Biology and Medicine, volume 48, number 1 supplement (winter 2005)
Freedman text Ch. 1 (esp. Yule on paupers, Snow on Cholera and Sec 1.5);(Ch 2-5 are advanced review of regression models)
   Chap 1 exs also in From Association to Causation: Some Remarks on the History of Statistics;  

Additional Resources
Mosteller-Tukey, Chap 13 (Woes of regression coefficients)
Practical Regression and Anova using R Julian J. Faraway,   chapter 4. errors in predictors
MB 3rd ed Ch.6. esp 6.2.2 adjusted variables; 6.2 Interpreting regression coefficients; 6.7 errors in variables
Background info, errors in variables. Short primer on test reliability  (Wm Trochin, Cornell)  Informal exposition in Shoe Shopping and the Reliability Coefficient    extensive technical material in Chap 7 Revelle text
       Source technical papers:   Errors of Measurement in Statistics, W. G. Cochran , Technometrics, Vol. 10, No. 4. (Nov., 1968), pp. 637-666. JStor URL esp sections 8,9,11
Some Effects of Errors of Measurement on Multiple Correlation, W. G. Cochran Journal of the American Statistical Association Vol. 65, No. 329 (Mar., 1970), pp. 22-34 JStor URL esp sec 8 discussion.
An overview of latent variables in Ch 1 of Generalized Latent Variable Modeling Multilevel, Longitudinal, and Structural Equation Models Anders Skrondal and Sophia Rabe-Hesketh Chapman and Hall/CRC 2004

Week 2-- Association vs Causation; Experiments vs observational studies; Neyman-Rubin-Holland formulation

In the news
1.  Depression in girls linked to higher use of social media (Guardian)      Social media linked to higher risk of depression in teen girls (Reuters).   Publication: Social Media Use and Adolescent Mental Health: Findings From the UK Millennium Cohort Study  EClinicalMedicine published by The Lancet, 2019  has multiple regression and path analysis, wow.
2. perennial favorite Spurious Correlation examples   
From 2018
Correlation study.  New study finds sweary people are more honest    Publication: Frankly, We Do Give a Damn: The Relationship Between Profanity and Honesty, Social Psychological and Personality Science.
    Recent (last spring). RCT (cross-over design Week 9). Damn right! The secret of success is swearing: How shouting four letter words can help make you stronger    Swearing can help you boost your physical performance    The full power of swearing is starting to be discovered

Lecture topics
       From week 1, Standardized variables and regression
Week 2
    Third-variable Topics
Class handout: Third Variables
A. Spurious Correlation: some historical notes; partial and part correlations. (class slides)
B. Simpson's paradox wiki page  Kidney stone example (dichotomous outcome slide)
C. Mediating/moderating variables  David Kenny web page   mediation handout   data analysis example   c.f. R-packages multilevel, MBESS, mediation
    First pass: experiments vs observational studies
Class handout: Neyman-Rubin-Holland
D. Design Trumps Analysis.    Rubin paper     Rubin talk .   Other exs: Breast-feeding,   Knee surgery.
E. Surveys of results from experimental and observational studies (see HRT, Mosteller below)
F. Introduction to Neyman-Rubin-Holland formulation (potential outcomes) for causal effects.
       Imbens and Rubin text (linked on main page) Chap 1.
       presentation of NRH formulation for comparative studies based on Appendix of Holland (1988). Class handout.
      shorter (modern) versions of ATE, ATT intros:    Causal inference from Harvard (slides 1-12);
           treatment effects from MIT (pages 1-4; handout pp.2-3);     Wooldridge, estimating average treatment effects from Michigan State.
      Wellesley example, Science Table, pp. 16-22.
G. Encouragement Designs: example of potential outcomes formulation.
       Illustration using encouragement design representation in Holland (1988).    copies of selected overheads.
       Encouragement Designs. Potential outcomes formulation and parameter estimation (Holland, 1988).    Estimation handout

Primary Readings
1. A multi-decade example: Experiments vs Observational studies, Hormone Replacement Therapy
   D.B. Petitti and D.A. Freedman. Invited commentary: How far can epidemiologists get with statistical adjustment? American Journal of Epidemiology vol. 162 (2005) pp. 415-18.       Freedman handout page
2. Freedman text Ch. 1 (esp Snow on Cholera and Sec 1.5) value of modeling Chap 10; response schedules sec 6.4
or online from week 1   Freedman Chap 1 exs also in From Association to Causation: Some Remarks on the History of Statistics;  
or   more on response schedules (text sec 5.4) in Statistical Models for Causation: A critical review    
    and   Statistical Models and Shoe Leather, Sociological Methodology, Vol. 21. (1991), pp. 291-313. JStor link
3. Paul Holland, Causal Effects and Encouragement Designs. Causal Inference, Path Analysis, and Recursive Structural Equations Models Paul W. Holland Sociological Methodology, Vol. 18. (1988), pp. 449-484.
Holland Appendix (esp pp. 475-480) presents the potential outcomes formulation.
Abstract Rubin's model for causal inference in experiments and observational studies is enlarged to analyze the problem of "causes causing causes" and is compared to path analysis and recursive structural equations models. A special quasi-experimental design, the encouragement design, is used to give concreteness to the discussion by focusing on the simplest problem that involves both direct and indirect causation. It is shown that Rubin's model extends easily to this situation and specifies conditions under which the parameters of path analysis and recursive structural equations models have causal interpretations.

Additional Resources
Spurious correlation?
R-Package ppcor October 29, 2012 Title Partial and Semi-partial (Part) correlation
Correlations Genuine and Spurious in Pearson and Yule, John Aldrich Statistical Science, Vol. 10, No. 4. (Nov., 1995), pp. 364-376.  Jstor link
Spurious Correlation: A Causal Interpretation. Herbert A. Simon Journal of the American Statistical Association, Vol. 49, No. 267. (Sep., 1954), pp. 467-479. Jstor link

Simpson's Paradox.
R-package Simpsons.   Frontiers in Psychology. 2013; 4: 513. Simpson's paradox in psychological science: a practical guide

Experiments vs Observational studies:
Mosteller-Tukey Ch. 13 (esp sec 13G)
Intent-to-treat Analysis of Randomized Clinical Trials Michael P. LaValley Boston University ACR/ARHP Annual Scientific Meeting Orlando 10/27/2003
Bringing Evidence-Driven Progress To Education:    Coalition for Evidence-Based Policy          
Overdoing a good thing? Evidence-based medicine.    Hazardous journey Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials Gordon C S Smith, professor, Jill P Pell, consultant BMJ 2003;327:1459-1461
Classic paper on Medical experimentation. Statistics and Ethics in Surgery and Anesthesia. John P. Gilbert; Bucknam McPeek; Frederick Mosteller Science, New Series, Vol. 198, No. 4318. (Nov. 18, 1977), pp. 684-689.     JTSOR link

mediating/moderating variables
R-implementations: Barron-Kenny method via Sobel function in the multilevel package.  More extensive implementation (incl BCa bootstrapping) function mediation in package MBESS Ken Kelley; power and sample size calculations in package powerMediation
NEW and 'improved'  mediation package. Causal Mediation Analysis Using R   This package (and pubs) takes the topic up a large level of complexity/capabilities
additional technical papers. Causal Mediation Analysis Using R K. Imai, L. Keele, D. Tingley, and T. Yamamoto    American Political Science Review Vol. 105, No. 4 November 2011 Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies
Mediation Analysis David P. MacKinnon, Amanda J. Fairchild, and Matthew S. Fritz Department of Psychology, Arizona State University, Tempe, Arizona 85287-1104; Annu. Rev. Psychol. 2007. 58:593–614
Mediators and Moderators of Treatment Effects in Randomized Clinical Trials Helena Chmura Kraemer; G. Terence Wilson; Christopher G. Fairburn; W. Stewart Agras Arch Gen Psychiatry. 2002;59:877-883
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M.,West, S. G., Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.

Neyman-Rubin-Holland models for comparative experiments (causal inference)
Rosenbaum Ch 2 (esp 2.5)
Statistics and Causal Inference, Paul W. Holland pp. 945-960 JASA 1986, another JSTOR link
Commentaries Donald Rubin, David Cox
Rubin, D. B., 1974, Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies, Journal of Educational Psychology, 66, 688-701.
Direct and Indirect Causal Effects via Potential Outcomes Donald B. Rubin Scandinavian Journal of Statistics Volume 31, Issue 2, Page 161-170, Jun 2004 .
Causal Inference, Annotated Bibliography - Oregon Research Institute Winship's repository Counterfactual Causal Analysis in Sociology (link is broken even from his own webpage)
Counterfactuals Stanford Encyclopedia of Philosophy Counterfactual Theories of Causation   wiki page     long Nancy Cartwright

Week 3-- Path analysis and causal modeling  multiple regression with pictures

In the news
1.  NYTimes:How Exercise May Help Keep Our Memory Sharp .   Publication: Exercise-linked FNDC5/irisin rescues synaptic plasticity and memory defects in Alzheimer's models Nature Medicine volume 25, pages165-175 (2019)
2. From 2018   Breastfeeding, good for mom?     Longer breastfeeding tied to lower diabetes risk for mothers     Mothers who breastfeed for at least six months HALVE their diabetes risk, decades-long study shows     Women who breastfeed less likely to develop type 2 diabetes     Publication:  Lactation Duration and Incident Diabetes in Young Women ...   JAMA Internal Medicine, online January 16, 2018.

Lecture topics
0. Complete Week 2 parts F,G. (Holland, 1988) Encouragement Designs. Potential outcomes formulation and parameter estimation.
     Appendix of Holland (1988). Class handout.           copies of selected overheads.      Estimation handout
1. Traditional Path Analysis introduction and examples (incl Blau-Duncan from Freedman chap 5).   class handouts; basics and examples
        [time permitting a little on Structural equation models: introduction and examples.   old class handout]
2. Three-strikes against these causal models. Does path analysis identify causal effects? Demonstrations of failure for Holland's encouragement design, Rogosa longitudinal examples (Goldstein, simplex).        class handout      Encouragement design slides
3. Causal Graphs, DAG (directed acyclic graph). Quick overview.

Week 3 Readings
Primary Readings
Freedman text Chap 5 (Chap 6 in revised ver). (Freedman Ch.4 has technical background on regression)
   response schedules, path analysis examples and potential outcomes in Statistical Models for Causation: A critical review    
Paul Holland: Encouragement design results; sections 3-5 Causal Inference, Path Analysis, and Recursive Structural Equations Models Paul W. Holland Sociological Methodology, Vol. 18. (1988), pp. 449-484.
David Rogosa. Casual Models Do Not Support Scientific Conclusions: A Comment in Support of Freedman.
Journal of Educational Statistics, Vol. 12, No. 2. (Summer, 1987), pp. 185-195. Jstor link

Additional Resources
Path Analysis, special issue: Journal of Educational Statistics Publication Info Vol. 12, No. 2, Summer, 1987 Issue        As Others See Us: A Case Study in Path Analysis(pp. 101-128) D. A. Freedman
Useful classnotes:     Notre Dame
Technical details on Rogosa longitudinal examples:
     Rogosa, D. R. (1993). Individual unit models versus structural equations: Growth curve examples.
     In Statistical modeling and latent variables, K. Haagen, D. Bartholomew, and M. Diestler, Eds. Amsterdam: Elsevier North Holland, 259-281.
     Rogosa, D. R., & Willett, J. B. (1985). Satisfying a simplex structure is simpler than it should be.
     Journal of Educational Statistics, 10, 99-107. Jstor link
     original publication on the longitudinal path analysis:   Some Models for Analysing Longitudinal Data on Educational Attainment. Harvey Goldstein         Journal of the Royal Statistical Society. Series A (General), Vol. 142, No. 4. (1979), pp. 407-442.  Jstor link
      Theme Song Ballad of the casual modeler
MB 13.1. Composite scores from multiple indicators (incl principal components). online version, a bit in sec6.1

Path analysis intros
Path Analysis: Sociological Examples. Otis Dudley Duncan The American Journal of Sociology, Vol. 72, No. 1. (Jul., 1966), pp. 1-16. Jstor link
D.A. Freedman, Comments on Standardizing Path Diagrams: What Are the Parameters?
A recent reconsideration by a wise psychologist: The Path Analysis Controversy: A new statistical approach to strong appraisal of verisimilitude Meehl, Paul E; Waller, Niels G Psychological Methods. Vol 7(3), Sep 2002, pp. 283-300.  available from SU APA pubs
     G. David Garson Path Analysis: Statnotes, from North Carolina State University, Public Administration Program (seems to have moved behind a paywall as a book).

Structural equation modeling is a major industry in social and behavioral science with many texts (such as Principles and Practice of Structural Equation Modeling 2nd Edition Rex B. Kline; here's a long list), specialized courses, dedicated journals (Structural Equation Modeling: A Multidisciplinary Journal), and specialized computer programs (e.g., LISREL, EQS, AMOS).
Maximum likelihood factor analysis: A General Method for Analysis of Covariance Structures, K. G. Joreskog, Biometrika, Vol. 57, No. 2. (Aug., 1970), pp. 239-251.
Structural equation modeling from Scientific Software International
home of * Structural Equation Modeling (LISREL) Student editions, documentation, examples, etc
R resources (see also social science and psychometrics task views) John Fox sem exposition   talk format   another talk   also Sec.5 Stats with R
Structural Equation Models package in R,   sem manual    OpenMx - Advanced Structural Equation Modeling   Using R for Structural Equation Model:
Two good structural equation model reviews:
Structural Equation Models William T. Bielby; Robert M. Hauser Annual Review of Sociology, Vol. 3. (1977), pp. 137-161. JStor link
Breckler, S. J. (1990). Applications of Covariance Structure Modeling in Psychology: Cause for Concern? Psychological Bulletin, 107, 260-273. here's a link that may be permanent

Graphical Models, Causal Diagrams.
Original Epi exposition.  Greenland S., Pearl J., and Robins J.M. Causal diagrams for epidemiologic research. Epidemiology, 10(1):37-48, 1999.
Richardson and Robbins attempts at unification.   Single World Intervention Graphs: A Primer   Longer version    
Graphical Markov Models: Overview   Nanny Wermuth and D.R. Cox
C. Shalizi. Advanced Data Analysis from an Elementary Point of View, 2017; Chapter 24 (except 24.2)
R-implementations. CRAN Task View: gRaphical Models in R .   Peter Buehlmann and pcalg package.

Week 4-- Group Comparisons and Causal Inference with Multilevel data:
Contextual effects, aggregation bias, Mixed-effects (lmer) models

Lecture topics
1. Background: nested data, ecological fallacy, aggregation bias, levels of analysis. levels of analysis handout
2. Traditional approaches to multilevel analysis: contextual effects, school effects.  Multilevel regressions     NELS example (NELS data schools in CMatching package; larger subset in influence.ME package)
3. Advanced multilevel analyses: mixed effects models, linear and non-linear (via lme4).
  i. Berk example, contextual effects
  ii.  UK Exam data.   Coed schools exam data   Gender gap data analysis.  ascii version   scanned class handout
             extended version 'start to finish' in Stat196 week1
  iii.    Lab 2, High School and Beyond (HSB) data.    Collection of HSB data analyses from various text sources
                Teaching document from Indiana, HSB from every statistical package
       HSB analyses in R, class and Lab2.         complete Bryk dataset     first pass, Bryk data:   session    plots
         Lecture slide, lme lmer for Bryk data   side-by-side boxplots, SFYS analysis
              for week 5 ancova HSB example HSB ancova handout (ascii version)      data for HSB ancova     HSB ancova, scanned pdf

Week 4 Readings
Primary Readings
        Aggregation bias, Ecological fallacy.
D.A. Freedman. "Ecological inference and the ecological fallacy." International Encyclopedia for the Social and Behavioral Sciences. Elsevier (2001) vol. 6 pp. 4027-30. N. J. Smelser and Paul B. Baltes, eds. A one-page version: D.A. Freedman. "The ecological fallacy." In the Encyclopedia of Social Science Research Methods. Sage Publications (2004) Vol. 1 p. 293. M. Lewis-Beck, A. Bryman, and T. F. Liao, eds
        Current statistical analyses in social science: multilevel models.  Also Lab2
History of multilevel models from Scientific Software International, Inc
Maindonald-Braun Chap 10, esp 10.2, 10.5, 10.7-9   online version Ch.10
Using R, lme, nlme.    John Fox lme tutorial   Fitting linear mixed models in R Using the lme4 package Douglas Bates (pp.27-30)

Additional Resources
Aggregation bias, Ecological fallacy.
D.A. Freedman. "The ecological fallacy." In the Encyclopedia of Social Science Research Methods. Sage Publications (2004) Vol. 1 p. 293. M. Lewis-Beck, A. Bryman, and T. F. Liao, eds
A Rule for Inferring Individual-Level Relationships from Aggregate Data, Glenn Firebaugh American Sociological Review Vol. 43, No. 4 (Aug., 1978), pp. 557-572   JStor URL
The original: Ecological Correlations and the Behavior of Individuals W. S. Robinson American Sociological Review Vol. 15, No. 3, Jun., 1950 . One of many followups: Some Alternatives to Ecological Correlation Leo A. Goodman American Journal of Sociology Vol. 64, No. 6, May, 1959
A good sociological/medical overview. Ecological effects in multi-level studies. Blakely TA, Woodward AJ. J Epidemiol Community Health. 2000 May;54(5):367-74.  pubmed   full text
American Journal of Epidemiology Vol. 139, No. 8: 747-760 Invited Commentary: Ecologic Studies -- Biases, Misconceptions, and Counterexamples S Greenland, J Robins
The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology J. Michael Oakes Social Science and Medicine 58 (2004) 1929–1952
R-package eiPack: R x C Ecological Inference and Higher-Dimension Data Management.  R News Oct 2007  
Educational multilevel data.
The Analysis of Multilevel Data in Educational Research and Evaluation Leigh Burstein Review of Research in Education, Vol. 8. (1980), pp. 158-233. Jstor link
Methodological Advances in Analyzing the Effects of Schools and Classrooms on Student Learning, Stephen W. Raudenbush; Anthony S. Bryk Review of Research in Education, Vol. 15. (1988 - 1989), pp. 423-475. Jstor link
Analyzing Multilevel Data in the Presence of Heterogeneous within-Class Regressions Leigh Burstein; Robert L. Linn; Frank J. Capell    Journal of Educational Statistics, Vol. 3, No. 4. (Winter, 1978), pp. 347-383. Jstor link

examples from analyses of voting data.
Bias in ecological regression   Stephen Ansolabehere and Douglas Rivers
David A. Freedman et al., "Ecological Regression and Voting Rights," Evaluation Review 1991, pp. 673-711, Berkeley Law postimg[broken, see alternatives below]
Klein, S. P. and Freedman, D. A. (1993), "Ecological regression in voting rights cases" Chance, 6, 38-43.
D.A. Freedman, S.P. Klein, M. Ostland, and M.R. Roberts. "Review of 'A Solution to the Ecological Inference Problem.' " Journal of the American Statistical Association, vol. 93 (1998) pp. 1518-22; with discussion, vol. 94 (1999) pp. 352–57.

Current statistical analyses in social science: multilevel models.
Using SAS PROC mixed:   
Judith Singer HLM/PROC Mixed papers: Multilevel Modelling Newsletter ; JEBS1998 Using SAS PROC MIXED to Fit Multilevel Models, Jstor
HLM - Hierarchical Linear and Nonlinear Modeling (HLM): descriptions and student edition HLM6
Freedman, D. A. (census adjustments). Hierarchical Linear Regression
Using R: lme4 (lmer and nlme) and mlmRev.    John Fox lme tutorial
Doug Bates draft book (Feb 2010)     Doug Bates SASmixed package   
Fitting linear mixed models in R Using the lme4 package Douglas Bates (pp.27-30)
London exam data example in Examples from Multilevel Software Comparative Reviews Douglas Bates
Regression diagnostics for lmer models. Package influence.ME   
mlmRev data examples. Also, Tennessee's Student Teacher Achievement Ratio (STAR) from Creating an R data set from STAR Douglas Bates
STATA does it also
HLM7.03 student edition   HLM setup for HSB example
lmer for SAS PROC MIXED Users Douglas Bates Department of Statistics University of Wisconsin Madison

Week 5.--The many uses and forms of analysis of covariance (including heterogeneous treatment effects and regression discontinuity designs)

In the news
Any way you slice it, nutrition studies are controversial   Stanford Medicine
More Ioannides mentions: Not just observational studies have problems.               Medical studies are almost always bogus NYPost
From 2018.  Cell phones and cancer once again.    Cellphone Radiation Linked To Tumors In Male Rats, Government Study Says       NIH Tech Reports

Lecture topics
0. Recap Mixed Model example, model formulation in Lab 2, HW4.
1. Review: formulation and purposes of analysis of covariance (including role in multilevel analysis)
    basic (old) ancova exposition slides    ancova and extensions, math notes
  revisit week 4 HSB example HSB ancova handout (ascii version)      data for HSB ancova     HSB ancova, scanned pdf
2. Heterogeneous Treatment Effects (CATE).
    aka Moderating variables or Analyzing treatment effects as a function of covariate(s):      CNRL, including Johnson-Neyman technique   cnrl data   cnrl analysis (extended)
      moderation, mediation recap slide
3. Uses of ancova with haphazard and with systematic assignment.
  a. Failures of ancova regression adjustments in observational studies.    Regression adjustments in quasiexperiments handout
  b. Non-random assignment on the basis of the covariate, such as regression discontinuity designs.     Regression Discontinuity handout     Example from rdd manual

Week 5 Readings
Primary Readings
Ancova and extensions   
Rogosa, D. R. (1980). Comparing nonparallel regression lines.   Psychological Bulletin, 88, 307-321. [a better quality scan from the APA site]
Regression Discontinuity Designs  Useful primers by Wm Trochin:  The regression-discontinuity design   regression-discontinuity analysis
Rubin, D. B., (1977), "Assignment to a Treatment Group on the Basis of a Covariate", Journal of Educational Statistics, 2, 1-26.   Jstor link

Moderation examples:
1. An example of interactions [old].  Aspirin may be less effective heart treatment for women than men
         Aspirin Resistance in Patients with Stable Coronary Artery Disease, in the Annals of Pharmacotherapy April 2007
2. Statistics is the only friend you need; OR moderating variables can be your friend           music: I've got friends in low places
Wash Post: Why smart people are better off with fewer friends . Publication: Country roads, take me home... to my friends: How intelligence, population density, and friendship affect modern happiness.   British Journal of Psychology 2016

Additional Resources
Ancova and extensions
NEW! Improving Present Practices in the Visual Display of Interactions Advances in Methods and Practices in Psychological Science
  Freedman section 5.6   (sec 6.6 p.103 in revised edition).    MB sec 7.3 ("fitting multiple lines"); online version, sec 5.7
      analysis of covariance: Background/historical papers:
Weisberg, H. I. Statistical adjustments and uncontrolled studies. Psychological Bulletin, 1979, 86, 1149-1164.
Covariance Adjustment in Randomized Experiments and Observational Studies Paul R. Rosenbaum Statistical Science, Vol. 17, No. 3. (Aug., 2002), pp. 286-304.   Jstor
Some Aspects of Analysis of Covariance, A Biometrics Invited Paper with Discussion. D. R. Cox; P. McCullagh Biometrics, Vol. 38, No. 3, (Sep., 1982), pp. 541-561.   Jstor
Analysis of Covariance: Its Nature and Uses William G. Cochran Biometrics, Vol. 13, No. 3, Special Issue on the Analysis of Covariance. (Sep., 1957), pp. 261-281. Jstor
The Use of Covariance in Observational Studies W. G. Cochran Applied Statistics, Vol. 18, No. 3. (1969), pp. 270-275. Jstor
Estimation of the Slope and Analysis of Covariance when the Concomitant Variable is Measured with Error James S. Degracie; Wayne A. Fuller Journal of the American Statistical Association, Vol. 67, No. 340. (Dec., 1972), pp. 930-937. Jstor
Deep background Neter-Wasserman text (Applied linear statistical models. Neter, Kutner, Nachtsheim and Wasserman 1996. Fifth edition. Homewood IL: Irwin, Inc.) chapters 22 and 8.

     Johnson-Neyman technique and aptitude-treatment interaction (ATI)
There is an R-project jnt that's never really gotten started.
Regions of Significant Criterion Differences in Aptitude-Treatment-Interaction Research Leonard S. Cahen; Robert L. Linn American Educational Research Journal, Vol. 8, No. 3. (May, 1971), pp. 521-530. Jstor
Identifying Regions of Significance in Aptitude-by-Treatment-Interaction Research Ronald C. Serlin; Joel R. Levin American Educational Research Journal, Vol. 17, No. 3. (Autumn, 1980), pp. 389-399. Jstor
Defining Johnson-Neyman Regions of Significance in the Three-Covariate ANCOVA Using Mathematica Steve Hunka; Jacqueline Leighton Journal of Educational and Behavioral Statistics, Vol. 22, No. 4. (Winter, 1997), pp. 361-387.  Jstor
discussion of substantive issues: Trait-Treatment Interaction and Learning David C. Berliner; Leonard S. Cahen Review of Research in Education, Vol. 1. (1973), pp. 58-94. Jstor

       Regression Discontinuity Designs
R-package--rdd;   Regression Discontinuity Estimation Author Drew Dimmery
Also Package rdrobust Title Robust data-driven statistical inference in Regression-Discontinuity designs
Journal of Econometrics (special issue) Volume 142, Issue 2, February 2008, The regression discontinuity design: Theory and applications      Regression discontinuity designs: A guide to practice, Guido W. Imbens, Thomas Lemieux
    Also from Journal of Econometrics (special issue) Volume 142, Issue 2, February 2008, The regression discontinuity design: Theory and applications  Waiting for Life to Arrive: A history of the regression-discontinuity design in Psychology, Statistics and Economics, Thomas D Cook
the original paper: Thistlewaite, D., and D. Campbell (1960): "Regression-Discontinuity Analysis: An Alternative to the Ex Post Facto Experiment," Journal of Educational Psychology, 51, 309-317.
Trochim W.M. & Cappelleri J.C. (1992). "Cutoff assignment strategies for enhancing randomized clinical trials." Controlled Clinical Trials, 13, 190-212.  pubmed link
Capitalizing on Nonrandom Assignment to Treatments: A Regression-Discontinuity Evaluation of a Crime-Control Program Richard A. Berk; David Rauma Journal of the American Statistical Association, Vol. 78, No. 381. (Mar., 1983), pp. 21-27. Jstor
Berk, R.A. & de Leeuw, J. (1999). "An evaluation of California's inmate classification system using a generalized regression discontinuity design." Journal of the American Statistical Association, 94(448), 1045-1052.  Jstor
 another econometric treatment

Week 6.-- Instrumental variable methods, simultaneous equations

In the news
1. Screen time rots kids minds. Fox17 Nashville:     Increased screen time in young children associated with developmental delays
Publication:   Association Between Screen Time and Children's Performance on a Developmental Screening Test   JAMA Pediatr. Published online January 28, 2019. doi:10.1001/jamapediatrics.2018.5056
2. Mediated moderation?    Stanford Medicine     Common opioids less effective for patients on SSRI antidepressants   

Lecture topics
0. Recap Regression Discontinuity Designs, Systematic Assignment example from week 5
1. Intro IV (Disattenuation, omitted variables, "selection effects") and other IV applications for broken regression models    IV basics and measurement error example    IV intro Stat266        Music: Wishin' and hopin'
2. Random assignment as an Instrumental Variable (AIR paper).    AIR IV handouts
3. Simultaneous equations (2SLS, IV in butter, peer aspirations, ed and fertility, Freedman), nonrecursive models      Simultaneous equations handouts   Duncan et al ascii
4. Reciprocal effects and non-recursive models in longitudinal data.   Empirical research on reciprocal effects (e.g. TV and ADHD), including cross-lagged correlation. clc slides

Computing resources in R, also for Lab 3.
ivreg in AER package AER: Applied Econometrics with R
   ivmodel package    vignette       Dylan Small ivpack
Two-stage Least Squares in R (tsls in sem package) by John Fox.     older package systemfit)

Week 6 Readings
Primary Readings
Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments. Joshua D. Angrist; Alan B. Krueger, The Journal of Economic Perspectives Vol. 15, No. 4 (Autumn, 2001), pp. 69-85
Technical reference. Joshua D. Angrist; Guido W. Imbens; Donald B. Rubin "Identification of Causal Effects Using Instrumental Variables" Journal of the American Statistical Association, Vol. 91, No. 434. (Jun., 1996), pp. 444-455. JStor note: compliance discussion for week 7
Freedman, text Chap 8 (Chap 9 revised ver)

Additional resources
IV class handout from Basel.
A pretty complete recap of lecture details on IV from the UK
Rindfus example (Freedman Chap 8; paper reprinted in Freedman text). Education and Fertility: Implications for the Roles Women Occupy Ronald R. Rindfuss; Larry Bumpass; Craig St. John American Sociological Review, Vol. 45, No. 3. (Jun., 1980), pp. 431-447.   from Jstor
Instrumental variables, Epidemiology exposition:   An introduction to instrumental variables for epidemiologists, Sander Greenland, International Journal of Epidemiology 2000;29:722-729 note: compliance discussion for week 7
Structural Equation Modeling With the sem Package in R John Fox STRUCTURAL EQUATION MODELING,13(3),465–486     Jox Fox home page
Peer Influences on Aspirations: A Reinterpretation Otis Dudley Duncan, Archibald O. Haller, Alejandro Portes American Journal of Sociology, Vol. 74, No. 2 (Sep., 1968), pp. 119-137   Jstor
Fox, J. (1979) Simultaneous equation models and two-stage least-squares. In Schuessler, K. F. (ed.) Sociological Methodology 1979, Jossey-Bass. Jstor
R-package psych has front-end for sem   Also, brand new is lavaan: an R package for structural equation modeling and more
Application of instrumental variables:
Course case study Does Television Cause Autism? and should instrumental variables (IV) provide the answer? Is Rain the magic IV?
A cautionary comment, including by Nobel-laureate Jim Heckman
Economists' Full paper: Does Television Cause Autism?
Now it's rainfall.    Autism Prevalence and Precipitation Rates in California, Oregon, and Washington Counties Michael Waldman; Sean Nicholson; Nodir Adilov; John Williams Arch Pediatr Adolesc Med. 2008;162(11):1026-1034.
Other applications.   The Effect of File Sharing on Record Sales An Empirical Analysis     

Reciprocal effects: Rogosa, D. R. (1980). A critique of cross-lagged correlation. Psychological Bulletin, 88, 245-258. APA site version
Granger Causality. Nobel 2003. Complete Granger
Relationships--and the Lack Thereof--Between Economic Time Series, with Special Reference to Money and Interest Rates. David A. Pierce Journal of the American Statistical Association, Vol. 72, No. 357. (Mar., 1977), pp. 11-26. Jstor

Week 7.-- Compliance and experimental protocols; intent to treat

In the news
If girls are bad at math, should we blame their mothers?

Lecture topics
1. Compliance background: Intent-to-treat analyses, CACE estimators, research examples
2. Compliance and Dose-response data analysis (Efron-Feldman)
3. Rubin-Holland approach via Booil Jo presentation: Potential Outcomes Approach: A Brief Introduction
    Class handouts:   Compliance examples     Compliance overview     Compliance math notes     Little-Rubin Ann Rev Pub Health formulation

Week 7 Readings
Primary Readings
Compliance Background: Intent-to-Treat (ITT), the FDA mandate.    simple definitions: wiki    Encyclopedia of epidemiology, Volume 1  
Potential outcomes formulation (CACE): Causal Effects in Clinical and Epidemiological Studies Via Potential Outcomes: Concepts and Analytical Approaches Roderick J. Little and and Donald B. Rubin Vol. Annual Review of Public Health, 21: 121-145, May 2000.
Epidemiology exposition:   An introduction to instrumental variables for epidemiologists, Sander Greenland, International Journal of Epidemiology 2000;29:722-729

Additional resources
David Freedman on Compliance Adjustments:      Statistical Models for Causation: What Inferential Leverage Do They Provide?  Evaluation Review 2006; 30: 691-713.       On regression adjustments to experimental data  Advances in Applied Mathematics vol. 40 (2008) pp. 180-93.
Intent-to-treat Analysis of Randomized Clinical Trials Michael P. LaValley Boston University ACR/ARHP Annual Scientific Meeting Orlando 10/27/2003
Compliance as an Explanatory Variable in Clinical Trials. B. Efron; D. Feldman Journal of the American Statistical Association, Vol. 86, No. 413. (Mar., 1991), pp. 9-17. Jstor
Intention to treat--who should use ITT? J. A. Lewis and D. Machin Br J Cancer. 1993 October; 68(4): 647-650.   
Compliance analyses, R-implementations: Imai experiment package     Package icsw,   Inverse Compliance Score Weighting
What is meant by intention to treat analysis? Survey of published randomised controlled trials Sally Hollis and Fiona Campbell British Medical Journal 1999;319;670-674
Booil Jo, Dept of Psychiatry   Estimation of Intervention Effects with Noncompliance Journal of Educational and Behavioral Statistics
   Compliance Publications based on Neyman-Rubin causal models:
Direct and Indirect Causal Effects via Potential Outcomes Donald B. Rubin Scandinavian Journal of Statistics Volume 31, Issue 2, Page 161-170, Jun 2004 .
Imbens GW and Rubin DB (1997) Bayesian Inference for Causal Effects in Randomized Experiments with Noncompliance The Annals of Statistics, 25, 305-327.
Principal Stratification in Causal Inference  Constantine E. Frangakis and Donald B. Rubin, Biometrics, 2002, 58, 21–29.
Addressing Complications of Intention-to-Treat Analysis in the Combined Presence of All-or-None Treatment-Noncompliance and Subsequent Missing Outcomes. Constantine E. Frangakis; Donald B. Rubin Biometrika, Vol. 86, No. 2. (Jun., 1999), pp. 365-379. Jstor link
    Additional Case Studies
Principal Stratification Approach to Broken Randomized Experiments: A Case Study of School Choice Vouchers in New York City Barnard, Frangakis, Hill, and Rubin Journal of the American Statistical Association June 2003, Vol. 98, No. 462, Applications and Case Studies
The British Journal of Psychiatry (2003) 183: 323-331 Estimating psychological treatment effects from a randomised controlled trial with both non-compliance and loss to follow-up graham dunn, and mohammad maracy
Battistin, E. and Rettore, E. (2002). "Testing for Programme Effects in a Regression Discontinuity Design with Imperfect Compliance." Journal of the Royal Statistical Society A, 165(1), 39-57.

Week 8.-- Matching and propensity score methods

In the news
From 2018 week 8, we deserve some good news
Want to live to your 90s? Drink a couple of glasses of wine or beer each night and put on a few pounds         LIVE FAST, DIE OLD Drinking two beers a day 'slashes your risk of dying young by a fifth'

Lecture topics
0. Review: Matching for increased precision, Randomized block designs (see HW8, part 1)   package blockTools
1. Traditional matching methods: subclassification, pair matching, Mahalanobis distance. Matching for increased precision or bias-reduction. Case-control studies.
          handout for smoking ex, Cochran subclassification
2. Modern Implementations of matching methods (also Lab 4).
         optmatch exs, nuclear plants, gender      ascii version for some Ben Hansen matching exs using MatchIt/optmatch
3. The advent/onslaught of propensity score matching methodology for treatment-control comparisons
         propensity score intro      checking balance, aspirin ex
4. Alternative propensity score analyses. Propensity score weighting: Inverse Probability of Treatment Weighting (IPTW).
   twang package from RAND, tutorials and resources.    Also, an exposition using the Lalonde (Lab4) data  and    another exposition

Week 8 Readings
Primary Readings
Non-technical overviews
Donald Rubin Nonrandomized Comparative Clinical Studies   another version,[Lane library from campus] Annals of Internal Medicine, 1997, 15 October 1997, Vol. 127. No. 8_Part_2       Cochran's smoking, subclassification and Rubin's Breast Cancer example also discussed in Rubin "Design Trumps Analysis" linked in week 2.   also set of slides     also Matching Methods for Causal Inference Elizabeth Stuart Donald Rubin [does lalonde lab4 example]
Joffe, Marshall M. and Paul R. Rosenbaum. 1999. "Invited Commentary: Propensity Scores." American Journal of Epidemiology 150(4):327-33.
Rosenbaum and Rubin, Reducing Bias in Observational Studies Using Subclassification on the Propensity Score, JASA 79[387], September 1984, 516-524. JStor  [one of the original technical papers]

Additional resources
Talks and tutorials
Strategies for Using Propensity Scores Well.  A Workshop given by Thomas E. Love, Ph. D., Case Western Reserve University      Love workshop ASA
Aspirin pub. JAMA. 2001 Sep 12;286(10):1187-94. Aspirin use and all-cause mortality among patients being evaluated for known or suspected coronary artery disease: A propensity analysis.    Gum PA1, Thamilarasan M, Watanabe J, Blackstone EH, Lauer MS.
A broad review of matching and bias-reduction methods. Opiates for the Matches: Matching Methods for Causal Inference Jasjeet S. Sekhon. Annual Review of Political Science 2009
UNC, Chapel Hill Social Work: Introduction to Propensity Score Matching: A Review and Illustration     Propensity Score Matching: A New Device for Program Evaluation  UNC, Chapel Hill Social Work 2004     flash version
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies Peter C. Austin Multivariate Behav Res. 2011 May; 46(3): 399-424.
Methods to assess intended effects of drug treatment in observational studies are reviewed  Journal of Clinical Epidemiology 57(2004)1223-1231 [an overview of many of past weeks topics]
Average causal effects from nonrandomized studies: A practical guide and simulated example. Schafer, Joseph L.; Kang, Joseph Psychological Methods, Vol 13(4), Dec 2008, 279-313.
A Primer for Applying Propensity-Score Matching Office of Strategic Planning and Development Effectiveness, Inter-American Development Bank
Tutorial in biostatistics: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group   Statist. Med. 17, 2265-2281 (1998)
MB sec 13.2 "Propensity scores in regression" uses NSW, PSID data, lab4

R packages and examples:
1. Ben Hansen (local hero)   optmatch manual     R News Oct 2007        Hansen presentation: Flexible, Optimal Matching for Comparative Studies Using the optmatch package
Optmatch application paper: Full matching in an observational study of coaching for the SAT.(Scholastic Assessment Test) Journal of the American Statistical Association; 9/1/2004; Hansen, Ben B.
Additional exercises (checking balance) using the nuclearplants data (class handout ex) from Mark Fredrickson here
2. MatchIt: Nonparametric Preprocessing for Parametric Casual Inference Daniel Ho, Kosuke Imai, Gary King, Elizabeth Stuart MatchIt provides a wrapper that can call optmatch or Sekhon's genetic matching]
JSS May 2011 exposition: MatchIt: Nonparametric Preprocessing for Parametric Causal Inference   more R-fun from Gary King, WhatIf: Software for Evaluating Counterfactuals
Another application (including matchit): Attributing Effects to a Get-Out-The-Vote Campaign Using Full Matching and Randomization Inference Jake Bowers and Ben Hansen.    Data archive and computing resources for the New Haven get-out-the-vote
3. Multivariate and Propensity Score Matching Software for Causal Inference Jasjeet S. Sekhon

    Propensity etc Original Technical Publications [jstor links]
Rosenbaum, P. R. And D. B. Rubin, 1983, The Central Role of the Propensity Score in Observational Studies for Causal Effects, Biometrika 70[1], April 1983, 41-55. JStor
P. Rosenbaum, Chapters 2 and 3 (on exact inference for treatment effects) in Observational Studies, New York: Springer, 1995.
Dropping out of High School in the United States: An Observational Study Paul R. Rosenbaum Journal of Educational Statistics, Vol. 11, No. 3. (Autumn, 1986), pp. 207-224.  Jstor
Paul R. Rosenbaum; Donald B. Rubin. "Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score" The American Statistician, Vol. 39, No. 1. (Feb., 1985), pp. 33-38   JStor   Danish downers example
D. Rubin, Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies, Statistical Science 5[4], November 1990, 472-480. JStor
Rubin, D. B., 1974, Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies, Journal of Educational Psychology, 66, 688-701.
Rubin, D. B., 1978, Bayesian Inference for Causal Effects: The Role of Randomization,” Annals of Statistics 6[1], January 1978, 34-58. JStor

Case-control studies
Case-control overview (shown in class) from Encyclopedia of Public Health
Breslow NE. Statistics in epidemiology: the case-control study.J Am Stat Assoc. 1996 Mar;91(433):14-28
Carbonated Soft Drink Consumption and Risk of Esophageal Adenocarcinoma JNCI: Journal of the National Cancer Institute, Volume 98, Issue 1, 4 January 2006, Pages 72-75,
Smoking and Lung Cancer in Chap 18 of HSAUR3 (Handbook of Statistical Analysis Using R). Also driving and backpain data in Chap 7 HSAUR2
Some R-packages and resources: SensitivityCaseControl: Sensitivity Analysis for Case-Control Studies; multipleNCC: Inverse Probability Weighting of Nested Case-Control Data;    Two-phase designs in epidemiology   (Thomas Lumley) ;   Exact McNemar's Test and Matching Confidence Intervals

Week 9. Longitudinal (mainly time-1, time-2) data analysis for Experimental designs and Observational studies .

In the news
1. Pre-post design  Get moving    Can't Focus? 10 Minutes Of Exercise Gives Brain Burst Of Energy        Short-term exercise equals big-time brain boost.   Publication: Executive-related oculomotor control is improved following a 10-min single-bout of aerobic exercise: Evidence from the antisaccade task Neuropsychologia Volume 108, 8 January 2018, Pages 73-81.

Lecture Topics
1. Experimental Designs  Cross-over designs (usually time-1, time-2). Laird-Ware text slides (pdf pages 135-150). Crossover design data from slide 137,    anova for crossover design ex       ascii version, anova for crossover design ex   
   R-resources for crossover designs. package Crossover    Crossover vignette     package crossdes   see Rnews Vol. 5/2, November 2005         also see slides 5-14 Repeated Measures Design Mark Conaway
More crossover designs.
      a.  This time with 3 conditions   For Exercise, Nothing Like the Great Outdoors   Publication: Niedermeier M, Einwanger J, Hartl A, Kopp M (2017) Affective responses in mountain hiking-- randomized crossover trial focusing on differences between indoor and outdoor activity. PLoS ONE 12(5): e0177719.
      b.   Does nutrition science know anything?     Is white or whole wheat bread 'healthier?' Depends on the person    Publication: Bread Affects Clinical Parameters and Induces Gut Microbiome-Associated Personal Glycemic Responses Cell Metabolism, Korem et al DOI: 10.1016/j.cmet.2017.05.002
Another crossover design (from Stat266). RCT (cross-over design). Damn right! The secret of success is swearing: How shouting four letter words can help make you stronger    Swearing can help you boost your physical performance    The full power of swearing is starting to be discovered

2. Experimental Designs  Comparing groups on time-1, time-2 measurements: repeated measures anova vs lmer OR the t-test
Comparative Analyses of Pretest-Posttest Research Designs, Donna R. Brogan; Michael H. Kutner, The American Statistician, Vol. 34, No. 4. (Nov., 1980), pp. 229-232.   JSTOR link
     urea synthesis, BK data       data, long-form
    BK plots (by group)     BK overview
    2017 Analysis handout     Extended BK lmer analysis
Additional stuff
     BK repeated measures analysis      pdf version
    Stat141 analysis
    archival example analyses. SAS and minitab

3. Observational studies    Economist's differences in differences (or diffs in diffs with matching) for observational studies.  class slide
      Austin Nichols slides. Causal inference with observational data A brief review of quasi-experimental methods July 2009
         Angrist Ch 5, MHE. Card and Krueger (1994) data, minumimum wage ex
        R-package wfe (my failures). paper On the Use of Linear Fixed Effects Regression Models for Causal Inference(sec 3.2)

4. Observational studies       Lord's paradox; pre-post group comparisons. Lord notes   Publication: Lord, F. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68, 304-305.       Wainer, H. (1991). Adjusting for differential base rates: Lord's Paradox again. Psychological Bulletin, 109, 147-151.

5. Observational studies       Exogenous Variables and Correlates of Change (use of lagged dependent variables)
   Time-1,time-2 data analysis examples    Measurement of change: time-1,time-2 data
      data example for handout    scan of regression handout      ascii version of data analysis handout    
   Extra material for Correlates and predictors of change: time-1,time-2 data
    Rogosa R-session to replicate handout, demonstrate wide-to-long data set conversion, and descriptive fitting of individual growth curves. Some useful plots from Rogosa R-session
        Technical results: Section 3.2.2 esp Equation 27 in Rogosa, D. R., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50, 203-228.      Talk slides

Additional Special topics (likely unaddressed)
   Observational studies.   Interrupted Time-series designs
      Gene Glass overview      Time Series Analysis with R section 4.6   R package BayesSingleSub: Computation of Bayes factors for interrupted time-series designs
       Current implementations of value-added analysis    American Statistical Association Statement on Using Value-Added Models for Educational Assessment
   Reciprocal effects (from week 6)

     Additional resources
1. Repeated measures analysis of variance
Models for Pretest-Posttest Data: Repeated Measures ANOVA Revisited Earl Jennings Journal of Educational Statistics, Vol. 13, No. 3. (Autumn, 1988), pp. 273-280.  Jstor
A good R-primer on repeated measures (a lots else). Notes on the use of R for psychology experiments and questionnaires Jonathan Baron, Yuelin Li.   Another version
Multilevel package   has behavioral scienes applications including estimates of within-group agreement, and routines using random group resampling (RGR) to detect group effects.
More repeated measures resources: Background primer on analysis of variance (with R); see sections 6.8, 6.9 of Notes on the use of R for psychology experiments and questionnaires Jonathan Baron, Yuelin Li.   Pdf version    The ez package provides extended anova capabities.   Examples (blog notes) : Repeated measures ANOVA with R (functions and tutorials)   Repeated Measures ANOVA using R    Obtaining the same ANOVA results in R as in SPSS - the difficulties with Type II and Type III sums of squares

2. Lord's Paradox, pre-post group comparisons.
Lord, F. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68, 304-305.L
Wainer, H. (1991). Adjusting for differential base rates: Lord's Paradox again. Psychological Bulletin, 109, 147-151.
or Wainer and Brown Three Statistical Paradoxes in the Interpretation of Group Differences: Illustrated with Medical School Admission and Licensing Data
a quick low-level read: Lord's Paradox and the Assessment of Change During College    Journal of College Student Development, May/Jun 2004 by Pike, Gary R
Another time1-time2 reading covering old-fashioned ground including Lord's paradox. Maris, Eric. (1998). Covariance Adjustment Versus Gain Scores--Revisited. Psychological Methods, 3(3) 309-327. apa link  

3. Value-added analysis.
Value-added does New York City. New York schools release 'value added' teacher rankings     Formula uncovers the 'value added'    from the unions: THIS IS NO WAY TO RATE A TEACHER
Chap 9 in Uneducated Guesses: Using Evidence to Uncover Misguided Education Policies.   Howard Wainer (Author) amazon page    available in paper and Kindle
Other versions of the Chap 9 materials Value-Added Models to Evaluate Teachers: A Cry For Help H Wainer, Chance, 2011.         Journal of Consumer Research Vol. 32, No. 2, Sept 2005
More Value-added analysis. Journal of Educational and Behavioral Statistics Vol. 29, No. 1, Spring, 2004 Value-Added Assessment Special Issue
Value-Added Measures of Education Performance: Clearing Away the Smoke and Mirrors, PACE
LA Times Teacher Ratings, summer 2010        NEPC vs LATimes
J.R. Lockwood, Harold Doran, and Daniel F. McCaffrey. Using R for estimating longitudinal student achievement models. R News, 3(3):17-23, December 2003.
Fitting Value-Added Models in R  Harold C. Doran and J.R. Lockwood
Andrew Gelman on Value-added arithmetic: It's no fun being graded on a curve     more NY  Principals rebel against 'value-added' evaluation

4. Interrupted time-series
Interrupted Time Series Quasi-Experiments Gene V Glass Arizona State University
Did fertility go up after the Oklahoma City bombing? An analysis of births in metropolitan counties in Oklahoma, 1990-1999. Demography, 2005.
original publication (ozone data): Box, G. E. P. and G. C. Tiao. 1975. Intervention Analysis with Applications to Economic and Environmental Problems." Journal of the American Statistical Association. 70:70-79. SAS example for ozone data     another ozone analysis with data
Box-tiao time series models for impact assessment Evaluation Quarterly 1979
Interrupted time-series analysis and its application to behavioral data Donald P. Hartmann, John M. Gottman, Richard R. Jones, William Gardner, Alan E. Kazdin, and Russell S. Vaught J Appl Behav Anal. 1980 Winter; 13(4): 543-559.
Segmented regression analysis of interrupted time series studies in medication use research. By: Wagner, A. K.; Soumerai, S. B.; Zhang, F.; Ross-Degnan, D.. Journal of Clinical Pharmacy & Therapeutics, Aug2002, Vol. 27 Issue 4, p299-309,
Interrupted Time Series Designs In Health Technology Assessment: Lessons From Two Systematic Reviews Of Behavior Change Strategies Craig R. Ramsay University Of Aberdeen, International Journal Of Technology Assessment In Health Care, 19:4 (2003), 613-623.

5. Measurement of Change, Correlates of Change, Growth Curve Analysis.   See Stat222 website
Rogosa, D. R., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50, 203-228. available from John Willet's pub page
A growth curve approach to the measurement of change. Rogosa, David; Brandt, David; Zimowski, Michele Psychological Bulletin. 1982 Nov Vol 92(3) 726-748 APA record   direct link
Longitudinal Data Analysis Examples with Random Coefficient Models. David Rogosa; Hilary Saner . Journal of Educational and Behavioral Statistics, Vol. 20, No. 2, Special Issue: Hierarchical Linear Models: Problems and Prospects. (Summer, 1995), pp. 149-170. Jstor
Demonstrating the Reliability of the Difference Score in the Measurement of Change. David R. Rogosa; John B. Willett Journal of Educational Measurement, Vol. 20, No. 4. (Winter, 1983), pp. 335-343. Jstor

Dead Week

Friday, March 15.
Collect TH2. Distribute flash drive of lectures.
Review course content, Weeks 1-9.
Discuss in-class exam March 18.
Collection of scanned course handouts, weeks 1-9.
    note:many handout examples have extended online versions

Final Pass: Courses, Talks and Papers covering Stat209 content:
Dylan Small, Stanford Stat Ph.D. Stat921 at Wharton
A broad review of matching and bias-reduction methods. Opiates for the Matches: Matching Methods for Causal Inference Jasjeet S. Sekhon. Annual Review of Political Science 2009
Average causal effects from nonrandomized studies: A practical guide and simulated example. Schafer, Joseph L.; Kang, Joseph Psychological Methods, Vol 13(4), Dec 2008, 279-313.
The Foundations of Causal Inference Judea Pearl. Sociological Methodology, upcoming.
Experiments and Observational Studies: Causal Inference in Statistics Paul R. Rosenbaum
For Objective Causal Inference, Design Trumps Analysis Don Rubin, Harvard
Causal inference with observational data A brief review of quasi-experimental methods Austin Nichols July 30, 2009
Propensity Score Analysis and Strategies for Its Application to Services Training Evaluation. Shenyang Guo, Ph.D. School of Social Work University of North Carolina at Chapel Hill June 14, 2011  another version

Stat266/CHPR290 texts
Design of Observational Studies, Paul Rosenbaum, 1st Edition (Springer) Available online:   Stanford access
Additional Resources
Causal Inference, Miguel Hernan & Jamie Robins Available online:
Causal Inference in Statistics, Social and Biomedical Sciences: An Introduction, Guido Imbens and Don Rubin, 1st Edition (Cambridge University Press)   Stanford access