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From | David Hoaglin <dchoaglin@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: about residuals and coefficients |
Date | Fri, 6 Sep 2013 22:12:11 -0230 |
Yuval, Most experiments in social science cannot collect data that allows all variables to be held constant. A good design, however, may include all combinations of two or more factors, so that one can study the effect of one factor without changing the other factors. Usually, many additonal variables can only be observed. Those are analyzed as covariates (and adjusted for). Some surveys use stratification. Analysis of variance (ANOVA) and regression analysis both use linear models, but ANOVA usually focuses on designs with a substantial amount of balance. That structure allows ANOVA to deal with aspects of analysis that do not fit into the framework of regression analysis. ANOVA has its own extensive literature. One classic reference is the book by Scheffe (1959). For a statistician, ANOVA and regression analysis are not by any means identical. David Hoaglin Scheffe, H. (1959). The Analysis of Variance. New York: Wiley. On Fri, Sep 6, 2013 at 11:42 AM, Yuval Arbel <yuval.arbel@gmail.com> wrote: > David, > > Unlike Federico, it is not clear to me how can you design a social > science experiment that keeps all the predictors constant (except for > the one you would like to check). > > The only thing that comes to my mind - is to ask subjects to fulfill a > detailed questionnaire with background questions prior to > participation in the experiment, and then to use regression analysis. > > I also don't understand the difference between analysis of variance > and regression analysis. i believe these terms are identical (i.e., > ANOVA equals regression analysis) * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/