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Hi All I have noticed in the emails lately that some people have been having trouble with -svylogitgof- I have been having a problem that I'm hoping somebody has already figured out an answer to... I am using logistic regression with employment status as my dependent variable (dichotomous) and "multimorbidity" (presence of two or more chronic health conditions) as my independent variable.
(Multimorbidity is three levels - 0/1=No multimorbidity, 2=2 chronic health conditions, 3=3 or more chronic health conditions). At first, I had my employment status variable coded as 0=employed 1=not employed. This gave me this output: (Notice the p value of –svylogitgof-) . svy: logistic empstat0 multimorbidity (running logistic on estimation sample) Survey: Logistic regression Number of strata = 1 Number of obs = 8841 Number of PSUs = 8841 Population size = 16015345 Design df = 8840 F( 1, 8840) = 189.94 Prob > F = 0.0000 -------------------------------------------------------------------------------- | Linearized empstat0 | Odds Ratio Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- multimorbidity | 3.491233 .3167115 13.78 0.000 2.922472 4.170683 _cons | .4568083 .0154654 -23.14 0.000 .4274766 .4881526 -------------------------------------------------------------------------------- . svylogitgof Number of observations = 8841 F-adjusted test statistic = F(1,8840) = 0.172 Prob > F = 0.679 Then when I reversed the coding for my employment status variable to 1=employed 0=not employed, the –svylogitgof- result changed: . svy: logistic empstat multimorbidity (running logistic on estimation sample) Survey: Logistic regression Number of strata = 1 Number of obs = 8841 Number of PSUs = 8841 Population size = 16015345 Design df = 8840 F( 1, 8840) = 189.94 Prob > F = 0.0000 -------------------------------------------------------------------------------- | Linearized empstat | Odds Ratio Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- multimorbidity | .2864318 .025984 -13.78 0.000 .2397689 .342176 _cons | 2.189102 .0741126 23.14 0.000 2.04854 2.339309 -------------------------------------------------------------------------------- . svylogitgof Number of observations = 8841 F-adjusted test statistic = F(1,8840) = 0.000 Prob > F = 1.000 Can anybody tell me why this happens, and more specifically what it means in this instance to get a p=1.0? Does it simply mean the model is a bad fit, or is there something else going on?
Also, when I changed the syntax to show the levels of multimorbidity, this happened: . svy: logistic empstat0 i.multimorbidity (running logistic on estimation sample) Survey: Logistic regression Number of strata = 1 Number of obs = 8841 Number of PSUs = 8841 Population size = 16015345 Design df = 8840 F( 2, 8839) = 110.26 Prob > F = 0.0000 ---------------------------------------------------------------------------------------------- | Linearized empstat0 | Odds Ratio Std. Err. t P>|t| [95% Conf. Interval] -----------------------------+---------------------------------------------------------------- multimorbidity | 2 Chronic Illnesses | 4.026233 .4977376 11.27 0.000 3.159772 5.13029 3 or more chronic illnesses | 8.237624 1.707088 10.18 0.000 5.487604 12.36577 | _cons | .4539598 .0154738 -23.17 0.000 .4246188 .4853283 ---------------------------------------------------------------------------------------------- . svylogitgof Number of observations = 8841 F-adjusted test statistic = F(1,8840) = 0.000 Prob > F = 1.000 Does –svylogitgof- not allow for polychotomous IV’s? Thanks in advance! Imogen Jones
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st: differences in -svylogitgof- results
From
Imogen Jones <[email protected]>
To
"[email protected]" <[email protected]>
Subject
st: differences in -svylogitgof- results
Date
Mon, 12 Aug 2013 05:07:26 +0000