Q1: Using many independent variables does not *cause*
multicollinearity. These are simply different issues. It would lead to
the problem of multiple comparisons, however, with 50k cases, it's hard
to see that as a likely issue here. Given the number of cases, this
must be observational research with archival data. That can lead to a
number of issues if the researcher is trying to make causal claims.
Some good background books for addressing such issues would be:
- Shadish, Cook & Campbell, "Experimental & Quasi-experimental Designs"
- Rosenbaum, "Observational Studies"
- Pearl, "Causality"
- Rothman, et al., "Modern Epidemiology"
Q2: It is quite reasonable to imagine that variables are often, in
reality, related to each other in some way, and all significance tests
do is assess the power of your study. With 2k cases, I would suspect
that the estimate of the association may be fairly accurate (although
this could depend of various factors regarding how the data were
handled). The question of practical significance is an important one.
An easy first step is to look at Kirk's famous paper, Kirk, "Practical
significance; An idea whose time has come", a google scholar search
could tell you about subsequent papers that cited that, and give you a
start through the related literature.
Best,
Jeff
On 3/21/2012 4:59 AM, Iasonas Lamprianou 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
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