SOCI209 - NOTES FOR FUTURE EDITIONS

• Generally try to replace some of the substantive examples in NKNW by more substantively interesting (sometimes more technically interesting) examples, from Sociology and other fields of the social/behavioral sciences (although not strictly restricted to these).  Obtain substantive examples either
• as published results only
• as data (with or without published results) so original analysis can be replicated or extended in the course
• List of possible examples/data sets include
• estimate standard type of status attainment model using the GSS data (either the entire data set or one year)
• National Organizations Survey; replicate analysis of contextual and structural characteristics of organizations in the book by Marsden, Kalleberg, and Aldrich; this is a good example for data transformations (many nonlinear relationships), nonparametric regression, etc.; Amy Davis has downloaded data from IRSS site
• Afifi & Clark survey data provided with SYSTAT, with 294 respondents with depression scores; adapt to illustrate weighted least squares, in lieu of blood pressure example in NKNW; I had abandoned this example when SYSTAT printed many outlier warnings in the weighted least squares regression, but SYSTAT does this with the NKNW example too so it is not a problem with this example but a quirk of SYSTAT's implementation of WLS interacting with the automatic outlier detection feature
• find a good analysis of time series of yearly divorce rate in the U.S.; or, time series of crime rate; or time series of birth rate/death rate during the demographic transition; to illustrate techniques for correcting serial correlation in time series data (Cochrane-Orcutt, Hildreth-Lu, first-differences) in lieu of the Blaisdell company data in NKNW; for divorce see South, Scott.  1985.  "Economic Conditions and the Divorce rate: A Time-Series Analysis of the Post-War U.S.", Journal of Marriage and the Family 47:31-41.
• Wilkinson, Blank, and Gruber (1996) use data from Lerner (1984) on nonlinear relationship of number of applications for disability benefits as function of Handicapped Rights Movement activity to illustrate data transformation and piecewise linear regression; can replicate their piecewise linear regression using NONLIN (WBG 1996, pp. 241-254)
• Also consider WBG data set on air pollution in context of collinearity (WBG 1996, pp. 254-267)
• Also consider WBG data  on GDP as function of military expenditures, population, gross domestic investment, govt expenditures, in context of heteroskedasticity (WBG 1996, pp. 270-285); in that data set variance of errors is proportional to ^Y
• for robust regression consider reanalyzing the Bollen & Jackman (1985 - diagnostics) data set
• find a data set with a significant (and substantial) interaction effect
• to illustrate simple regression see if can get Richard Alexander's data on relationship of sexual dimorphism with harem size in various genera of mammals
• Find a practical way to do bootstrap analysis with a robust regression example (perhaps using the Yule data); I was not able to complete that example in Module 13 because it does not seem to be possible to save the estimated coefficients only in a file in NONLIN; see if version 8 of SYSTAT does this; or email the SYSTAT support list to ask for a workaround; if not sit down in secluded place and write a BASIC program to extract the estimated coefficients from the text output
• Provide better discussion of variety of automatic indicator variables coding in SYSTAT (e.g., EFFECT vs. DUMMY) and relate to ANOVA; cf Statistics V6.0 p. 297, but this is not a very good discussion
• Try to find way to cover materials more quickly to be able to get to Module 16 on Missing Values & Selectivity Bias; develop that module to include at least discussion of EM algorithm ("Expectation"-"MLE") and mention Allison's latest work on mutiple imputations; develop illustration using SYSTAT's EM algorithm in the CORR module (Statistics v6.0 pp. 313-316), then how to do the regression from SSCP, COVA, or CORR matrix (Statistics v6.0 pp. 295-297); retrieve email exchange on SYSTAT support list about how to save imputed values for missing data to be able to do the regression directly using imputed data rather than the COVA matrix.  [Following quote from message from Lee Wilkinson to SYSTAT support list:  " Actually, Rick, it does. Here:


corr
use ourworld
save junk/data
covar urban gnp_82 mil/EM
use junk
list

This is a little-known feature that does the same thing as the SPSS module.
I should point out that the EM routines in CORR were based on Rick's work.
Rick didn't know that I put the SAVE / DATA option in."]
 

• Move materials in Section 8 of Module 2 on using SYSTAT to replace statistical tables to its own module; add an explanation of principles involved in using the cumulative density function and inverse cdf, with examples for a symmetric distribution (t) and a non-symmetric distribution (F), with graphs showing the density function above, the cumulative density function below, with horizontal axes aligned, and arrows linking a critical value of the variate on the horizontal axis of density function above to the same value on axis of cdf below, so as to relate area to the left of critical value in density function with value of ordinate in cdf; and arrows showing how cdf and inverse cdf allow going back and forth between vertical and horizontal axes in cdf graph
• Develop Module 15 on Model Building & Specification that I didn't have time to cover in Spring 1999
• To improve class presentation of the material using Netscape, find software (if it exists) to
• mask the Netscape display to make it possible to reveal the display in stages (as if masking a transparency with a sheet of paper)
• write freehand on an electronic "blackboard" with the mouse, in lieu of using the actual blackboard which is much les brilliant than the projection screen
• To provide a better sense of the overall "big picture" add a Section 0 at beginning of each module containing
• an outline of the topics of the module
• the readings/references corresponding to the module material
• In Module 10 on regression diagnostics for outliers and influential cases, add reference to the article by Tukey and Jones <> on an alternative to Bonferroni correction in multiple-testing of the significance of the studentized-deleted residual in detecting outliers in the Y-dimension
• Consider reorganizing the presentation of multiple regression to move materials on the ANOVA table and statistical inference (significance tests and/or confidence intervals) from Module 4 to Module 5 so this material is better motivated in the context of the standard multiple regression printout
• Reduce emphasis on statistical inference for mean response ^Y and Y_h(new)
• New paperback edition of Applied Linear Regression Models found at varsitybooks.com for $21.42 (ISBN 0256119872)  [Turns out that this paperback edition was never published.  This is why I had to order the (expensive) hardback edition at the bookstore for Spring 2000.]