Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: Poll of polls


From   Clive Nicholas <clivelists@googlemail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Poll of polls
Date   Wed, 23 Jun 2010 00:42:04 +0100

Richard Ohrvall wrote:

> I am sorry if this is a bit too unspecific, but I am currently looking
> into what is usually called "poll of polls", i.e. techniques to take
> estimates from different opinion polls and estimating time series. I
> know, it is outside of established statistical theory, but I am
> playing around with political opinion polls to look at different
> methods to achieve time series. Some of the issues are a) how to
> handle "house effects", i.e. that different pollsters systematically
> diverge from others, b) how to smooth the data over time, e.g. some
> sort of moving average. So, my questions are 1) if any of you have
> seen anything done on this using Stata? 2) Do you have any
> ideas/suggestions about the best way to tackle this (e.g. if -lowess-
> is a path worth exploring)?

[...]

You should be clearer on what your response variable is. Is it voting
intention shares for political parties? Is it party-leader popularity
ratings? Are they measured monthly, quarterly or annually? Either way,
these are measured on the 0-100 scale and can pose their own problems
if their limits are reached. Analysed on their own on a house-by-house
basis would, in my view, call for normalizing the scales and
regressing them on your independent variables in a fractional logit or
probit model, viz:

glm y x, family(binomial) link(logit) robust

However:

(a) if your intention is to model 'polling house' effects on opinion
shares across all polling houses and points of time simultaneously,
then this would call for a pooled TSCS (or CSTS) approach with the
polling houses included as fixed-effect dummies, so look at the family
of -xt- models and pick the one most suitable for your data;

(b) download Nick Cox and Kit Baum's -mvsumm- from SSC for creating
moving-average variables;

and

(c) -lowess- could be used to smooth time-series graphs, but also
check out Cox and Baum's -tsgraph- (SSC) or -help xtline-.

(Much) more details on your data, which reads very interestingly,
would help here.

-- 
Clive Nicholas

[Please DO NOT mail me personally here, but at
<clivenicholas@hotmail.com>. Please respond to contributions I make in
a list thread here. Thanks!]

"My colleagues in the social sciences talk a great deal about
methodology. I prefer to call it style." -- Freeman J. Dyson.
*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index