Re: Confusing SPSS outputs in Linear Regression Analysis

Posted by David Greenberg on Jan 14, 2016; 2:41am
URL: http://spssx-discussion.165.s1.nabble.com/Confusing-SPSS-outputs-in-Linear-Regression-Analysis-tp5731271p5731274.html

You are totally  mistaken. The point is not to do the correction on
the overall regression. That needs no correction. But you are doing 7
tests on the coefficients. Imagine a world in which, in the
population, all those coefficients are zero. If you use a nominal
alpha of .05 the probability of getting any one estimate significant
by chance is 1 in 20, but with 7 tests, the probability of 2 in 7 is
elevated. It is quite a bit higher than .05. David Greenberg

On Wed, Jan 13, 2016 at 9:23 PM, E. Bernardo <[hidden email]> wrote:

> Dear David,
>
> All the seven predictors were entered together into a multiple regression
> model (using ENTER method). The overall F was nonsignificant at the same
> time two of the seven predictors were significant (p<.05). Bonferroni
> correction is out of context in this discussion because all predictors were
> entered into the model simultaneously. That is, only one multiple regression
> was analyzed.
>
> Thank you.
> E.
>
>
> On Thursday, January 14, 2016 9:54 AM, David Greenberg <[hidden email]> wrote:
>
>
> The overall F not being significant should tell you to stop there.
> With seven individual predictors each being tested individually, you
> are multiplying the chances of obtaining 2 significant t tests by
> chance. In other words, you think you are testing at alpha = .05, but
> actually are testing with a larger value of alpha. Many researchers
> correct for this by doing a Bonferroni correction.. Chances are that
> your significant findings will not be significant once that is done.
> David Greenberg, Sociology Department, New York U.
>
> On Wed, Jan 13, 2016 at 8:45 PM, E. Bernardo <[hidden email]>
> wrote:
>> Dear members,
>>
>> My linear regression analysis has seven binary predictors, n=47, and (of
>> course) a continuous dependent.  The overall regression anova is
>> nonsignificant (F=1.489, p = .200). The confusing is that two out of the
>> seven predictors are significant (p<.05).  I dont think there is
>> multicollinearity problem because the collinearity diagnostics statistics
>> seem look fine. For example, no beta coefficients of predictors greater
>> than
>> 1.0; Tolerance of the predictors range between .559 and .814; VIF of
>> predictors range from 1.224 and 1.669; correlation coefficients among
>> predictors are between .009 and .757 but most are below .30.
>>
>> Any comments are welcome.
>>
>> Thank you.
>> E.
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