Date: Mon, 11 May 2009 16:14:03 +0200
Reply-To: Marta García-Granero <firstname.lastname@example.org>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Marta García-Granero <email@example.com>
Subject: Re: Regression Analysis
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Caroline Davis wrote:
> Hello list!
> I would like to perform a regression analysis for 4, however here are
> my constraints:
> Var 1: Prototype rating (Likert scale 1 to 5), not normally distributed
> Var 2: Influence rating (Likert scale 1 to 5), not normally distributed
> Var 3: Accuracy (binary 0 or 1)
> Var 4: Score on test (normally distributed).
> The goal is to determine how well Var 1-3 predict Var 4. Is a
> regression analysis the best way to get at this question? It seems
> tricky, because Var 1 & 2 are not normally distributed.
Normality of the IV (or "predictor variables") is NOT a condition for
linear regression. Anyway, check for linearity in the response to your
Likert predictors. Binary (0/1 coded) variables are also OK, you don't
have to worry about Accuracy (Var 3).
> If I do a regression analysis for the accuracy and test variables,
> should it be a binary logistic regression, with accuracy as the
> dependent variable and score as the independent variable?
Then you would be predicting Accuracy as a function of the other
variables, including Var 4 (clearly not your goal).
> Thanks for any suggestions you may have.
1) Is sample size enough? (you don't mention it). As a rule of thumb,
there should be 10 to 20 cases for each IV (30 to 60 cases for your study)
2) Plot (scatter plot) Var 4 against Var1 first, then Var 4 against Var
2. Visually check for for departures from linearity. Recode Var 1 and/or
Var 2 if necessary.
3) Are there any missing values? A listwise deletion might lower your
sample size a lot.
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