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# Re: st: Question: Ordered logit- suspicious odds ratio

 From Richard Williams To statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu Subject Re: st: Question: Ordered logit- suspicious odds ratio Date Mon, 16 Apr 2012 10:27:48 -0500

```At 06:12 AM 4/16/2012, Stefanie Kneer wrote:
```
```Dear Statalist

I am encountering running an ordered logit regression with the
following panel data ranging from 1992-2010.

describe  p11101 regionunempl unempl_empl interaction

storage  display  value

variable name   type   format label variable label

p11101          byte   %8.0g        life satisfaction -discrete from 0-10

regionunempl    float  %9.0g        Region unempl. %

```
unempl_empl float %9.0g dummy for being 1=unemployed and 0=employed
```
```
interaction float %9.0g regionalunemployment rate*unemployment dummy
```
I am trying to identify the link between unemployment and happiness
and in particular whether there exists a social norm effect. P11101 is
thus my dependent variable and as it is discrete I was planning to run
an ordered logit but I am encountering difficulties when trying to
calculate the odds ratio. As can be seen the social norm effect, and
thus whether you feel more comfortable when other people around you
are not working, too is massively big (27). That looks kind of
suspicious to me.  Could you give me an advice on why this is the case
and what I should do about it?
```
```
```
I think it is hard to say that an effect is "massively big" without knowing something about the scaling. If you rescale the variables, e.g. measure income in dollars rather than 1000s of dollars, the coefficients will get rescaled too. If regionunempl is coded on a scale that ranges from 0 to 1, then a 1 unit increase is a HUGE increase (100 percentage points), indeed probably a much bigger increase than actually occurs in your data. If it is coded 0-100, then a 1 unit increase is just a 1 percentage point increase, which is much smaller. So, consider rescaling your variables so a 1 pt increase is a meaningful number. Or, compute the predicted values, or use -adjust- or -margins- to compute predicted values for various combinations of values, and see if the results seem plausible to you.
```

-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
WWW:    http://www.nd.edu/~rwilliam

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```