First things first! Time and Day for Ed260X were incorrect in Axess listing prior to 3/30/02 (for unknown reasons SUSE staff chose not to enter the correct information). Correct Listing (thanks to Kristina and Fred Spitz) EDUC 260X Popular Advanced Statistical Methods McCull 117 Tue 2:15PM - 5:05PM Rogosa,David R
SUSE Computing Lab in CERAS (as of 3/30/02)
LISREL and HLM are both installed in CERAS computer lab on the first three machines on your left as you enter. SAS in process of being added.
BALLAD OF THE CASUAL MODELER Lyrics
Music: Open RealPlayer 7 (or equivalent) to
A partially knowledgable observer could describe this course by the buzz-words "LISREL and HLM" and that concise phrase is somewhat informative. A main objective is to take a serious look at some of these advanced (and heavily marketed) statistical procedures that have become widely used (for better or worse) in education and social science. The broader perspective is to start with the data analysis (and substantive) settings that these procedures purport to address (if not solve):
1. Analysis of Multilevel Data (e.g., kids within classrooms within schools)
The point being that there is much much much more to these important topics than what is covered by LISREL and HLM (programs or writings) and the challenge of organizing this course is to weave in the larger issues.
Scientific Software International http://www.ssicentral.com/home.htm
Centre for Multilevel Modelling (H Goldstein)
NLME: Software for mixed-effects models
SAS PROC MIXED and NLMIXED
SAS PROC CALIS
Amos by James L. Arbuckle
Stanford Social Sciences Data Resources
Historical and Current references on Causality
David Freedman on Social Science and Causal Inference
Aggregation, Multilevel Data
Some Rogosa Papers
Paul Allison, U Penn, Sociology 611
Education 231E, Spring 2002 UCLA B. Muthen
The Inter-university Consortium for Political and Social Research (ICPSR)
1. April 2. Because of chaos and misinformation on scheduling, the most I hope for is to assemble as many prospective students as possible and organize a real launch of the course next week. Organize, meet and greet, discuss student interests.
Current Event for discussion: TV watching, aggression linked in study of teens, young adults
some links: http://www.latimes.com/news/printedition/front/la-032902tv.story http://www.bayarea.com/mld/bayarea/news/nation/2959217.htm
through news.google.com I also found
actual report and review in Science http://www.sciencemag.org/cgi/reprint/295/5564/2468.pdf
supplementary table for the TV-aggression article is: http://www.sciencemag.org/cgi/content/full/295/5564/2468/DC1
Data Adventure #1. Multilevel school data taken from the MlWin manual.
The sequence of the variables and the coding are as follows:
Obtain within school regressions of normexam on standlrt (post on pre) and compare across schools. Any systematic differences for different schgend? Goldstein et al pose two substantive questions for these data under the heading of "Contextual effects"
2. April 9.
3. April 16.
Data Adventure # 2 (From Paul Allison course notes)
class 1.00 famsize -.33 1.00 ability .39 -.33 1.00 esteem .14 -.14 .19 1.00 achieve .43 -.28 .67 .22 1.00 Do the indicated path analysis and interpret.
4. April 23.
5. April 30.
Data Adventure # 3 (From SAS Proc Calis documentation, Joreskog papers)
Conduct the structural equation model analysis for these data and depicted model. If ambitious also look at the possibility of correlated erorrs in the manifest variables.
"Data Matrix of WHEATON, MUTHEN, ALWIN & SUMMERS (1977)";
label v1='Anomia (1967)' v2='Anomia (1971)' v3='Education' v4='Powerlessness (1967)' v5='Powerlessness (1971)' v6='Occupational Status Index';
v1 11.834 . . . . . v2 6.947 9.364 . . . . v3 6.819 5.091 12.532 . . . v4 4.783 5.028 7.495 9.986 . . v5 -3.839 -3.889 -3.841 -3.625 9.610 . v6 -21.899 -18.831 -21.748 -18.775 35.522 450.288
6. May 7.
Data Adventure # 4
For the data in Adventure 1, use normexam as outcome and standirt (pretest) as precitor. Obtain directly the three regression slopes discussed in contextual analysis: total between-school, within-school pooled. Verify the Duncan-Cuzort-Duncan relationship. Verify the relations for what Kreft terms the contextual reegression model (Y on X, Xbar) and for the Cronbach model (Y on X-bar, X - Xbar).
Data Adventure # 5 (Nels data from Kreft text)
Table 2.1 Ten selected schools from NELS-88: within-school means
School Size Math mean Homework mean
1 23 45.8 1.39 2 20 42.2 2.35 3 24 53.2 1.83 4 22 43.6 1.64 5 22 49.7 0.86 6 20 46.4 1.15 7. 67 62.8 3.30 8 21 49.6 2.10 9 21 46.3 1.33 10 20 47.8 1.60
Table 2.1 gives the mean math score (number correct) amounts of homework (in hours per week),
Table 2.2 Ten selected schools from NELS-88:within-school dispersions and correlations
School Dispersion Correlation
A 55.2 -4.24 -0.52 -4.24 1.19
B 65.1 -4.65 -0.45 -4.65 1.63
C 126.3 9.62 0.77 9.62 1.22
D 94.1 11.9 0.84 11.9 2.14 .
E 69.2 -2.71 -0.43 -2.71 0.57
F 17.0 -1.56 -0.48 -1.56 0.63
G 31.2 3.24 0.34 3.24 2.92 .
H 101.1 7.94 0.71 7.94 1.22 .
I 86.6 4.61 0.56 4.61 0.79 .
J 120.9 12.3 0.80 12.3 1.94 .
7. May 14.
Data Adventure # 6 HSB data from Bryk-Raundenbush, Singer
a. replicate the analysis using cSES as Level 1 predictor and SES, Sector as Level 2 predictors shown for example in Singer pp.336-338.
8. May 21.
Data Adventure # 7 Rogosa 3-wave longitudinal examples
Data Adventure # 8 Rogosa, 5-wave simplex model analysis examples
9. May 28.
Data Adventure #9. Bernoulli outcome data taken from the MlWin manual. Data mlwinsbrit.dat . These data come from the longitudinal component of
10. June 4, Dead Week meeting.
Final Problems. Assignment available here Due in Rogosa's Sequoia Hall mailbox 3 PM Friday June 14 (graduating students due 5PM Tues 6/11).
Causal Inference, Structural Equation Models
Alwin, D. F. (1988). Structural equation models in research on human development and aging. In K. W. Schaie, R. T. Campbell, W. M. Meredith, & S. M. Rawlings (Eds.), Methodological issues in aging research (pp. 71-170). New York: Springer Publishing Co.
Breckler, S. J. (1990). Applications of Covariance Structure Modeling in Psychology: Cause for Concern? Psychological Bulletin, 107, 260-273.
David Freedman. From Association to Causation: Some Remarks on the History of Statistics
Holland, P. W. (1988). Causal inference, path analysis and recursive structural equation models. In C. Clogg (Ed.), Sociological Methodology 1988 (pp. 449-484). Washington, D.C.: ASA
Joreskog, K. & Sorbom, D. (1979). Advances in Factor Analysis and Structural Equations Models. Cambridge MA: ABT Books.
Rogosa, D. R., & Willett, J. B. (1985). Satisfying a simplex structure is simpler than it should be. Journal of Educational Statistics, 10, 99-107.
Werts C.E Linn, R. L. and Joreskog (1977). A simplex model for analyzing academic growth. Educational and Psychological Measurement, 37, 745-756.
multilevel/hierarchical data, aggregation, HLM
Burstein, Leigh. (1980) Issues in the Aggregation of Data. Review of Research in Education, 8, 158-236.
Bryk, A.S. & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147-58
D. A. Freedman S. P. Klein M. Ostland M. Roberts. On "Solutions" to the Ecological Inference Problem 10 June 1998 Technical Report No. 515 Statistics Department UC Berkeley
Harvey Goldstein (1995). Multilevel Statistical Models.
Kreft, I.G., de Leeuw J., & Kim, K.S. (1990). Comparing Four Different Statistical Packages for Hierarchical Linear Regression: Genmod, HLM, ML2, and VARCL. CSE Technical Report 311, UCLA Center for Research on Evaluation, Standards, and Student Testing.
Rogosa, D. R., and Saner, H. M. (1995). Longitudinal data analysis examples with random coefficient models. Journal of Educational and Behavioral Statistics, 20, 149-170.
Judith D. Singer. Fitting multilevel models using SAS PROC MIXED