Thanks, I will have a look. Judea Pearl's book seems to be famous! Dr. Iasonas Lamprianou Department of Social and Political Sciences University of Cyprus
>________________________________ > From: Donald Pianto <[email protected]> >To: Iasonas Lamprianou <[email protected]> >Cc: "[email protected]" <[email protected]> >Sent: Wednesday, 21 March 2012, 16:50 >Subject: Re: [R-sig-teaching] regerssion issues > > >Dear Jason, > > >In relation to question 1, I believe that what is critical is the final use of >the regression. If one is making causal claims, then it is very important to >understand the causal structure since conditioning on inappropriate variables >can lead to nonsense results. If the use of the regression is purely >descriptive then collinearity may be the only problem, but one must be careful >not to make causal interpretations. The book "Causality: Models, Reasoning and >Inference" by Judea Pearl discusses the causal question and is full of >references. > > >In relation to question 2, I've seen mention of this question in many books. >In Wooldridge's Introductory Econometrics book he draws the distinction >between statistical and economic significance, however I don't know if he >cites any particular paper on the subject. > > >Good teaching, >Donald Pianto >Department of Statistics >University of Brasília > > >On Wed, Mar 21, 2012 at 5:59 AM, Iasonas Lamprianou <[email protected]> >wrote: > >Dear all, >>I have a question which can be expanded to the geeneral context of regression >>modelling in general. If you feel that this question is beyond the scope of >>this list, please say so and I will apologize. However, this has to do with >>teaching. >> >> >>Question 1: I am revieweing a paper and the author uses a sample size of >>around 50,000 cases to run a logistic regression. He is using 22 independent >>variables. Using too many independent variables may cause collinearity >>problems. Beyond this, however, I am not aware of any other problems caused by >>using too many variables in a model. However, this is also related to the >>problem of massively throwing tens of variables in amodel and then waiting >>for statistically significant results. Can anyone suggest relevant literature >>to give to my students to read? >> >> >>Question 2: Some coefficients of a diffrent logistic model in the same paper >>are marginally significant e.g. b=-0.18 and se=0.08. The only reason this is >>signficant is because the researcher used in this model a large sample size >>(around two thousand cases N=2000). The lower bound of the confidence >>interval is almost zero. Can anyone suggest a good reference to say that in >>such a case we should also check the "practical significance" and since the >>lower bound is so close to zero, we should be careful on what we claim about >>the effect? >> >>Thank you for your time >>Jason >> >> >> >>Dr. Iasonas Lamprianou >>Department of Social and Political Sciences >>University of Cyprus >> >> >> >> >>> >> [[alternative HTML version deleted]] >> >> >>_______________________________________________ >>[email protected] mailing list >>https://stat.ethz.ch/mailman/listinfo/r-sig-teaching >> >> > > > [[alternative HTML version deleted]]
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