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edited Dec 10, 2012 at 0:36

EDIT:

Here are the set of results from my model -- I've masked the actual variables names because I cannot share them publicly.

variable    controls    proximal variables
(Intercept) 0.212(0.255)    0.172(0.296)
variable 1                  1.038(0.09)
variable 2                  0.721(0.098)**
variable 3                  0.986(0.095)
variable 4                  0.810(0.104)**
variable 5                  0.744(0.109)**
variable 6                  0.981(0.15)
variable 7                  1.317(0.051)**
controls 1  0.769(0.087)**  0.763(0.087)**
controls 2  1.078(0.015)**  1.087(0.015)**
controls 3  0.666(0.098)**  0.838(0.11)
controls 4  0.958(0.122)    1.129(0.127)
controls 5  0.706(0.144)**  0.750(0.146)**
controls 6  1.012(0.086)    0.998(0.089)
controls 7  0.957(0.141)    1.010(0.142)
controls 8  0.694(0.173)**  0.726(0.18)*
controls 9  1.895(0.065)**  1.575(0.072)**
controls 10 1.353(0.085)**  1.279(0.095)**
controls 11 0.907(0.083)    0.949(0.095)
controls 12 0.988(0.135)    0.920(0.145)
Log Likelihood  -3417.879   -3356.028
AIC                   6863.758  6754.056
BIC                   6958.156  6895.653
PCP                     0.702   0.697

EDIT:

Here are the set of results from my model -- I've masked the actual variables names because I cannot share them publicly.

variable    controls    proximal variables
(Intercept) 0.212(0.255)    0.172(0.296)
variable 1                  1.038(0.09)
variable 2                  0.721(0.098)**
variable 3                  0.986(0.095)
variable 4                  0.810(0.104)**
variable 5                  0.744(0.109)**
variable 6                  0.981(0.15)
variable 7                  1.317(0.051)**
controls 1  0.769(0.087)**  0.763(0.087)**
controls 2  1.078(0.015)**  1.087(0.015)**
controls 3  0.666(0.098)**  0.838(0.11)
controls 4  0.958(0.122)    1.129(0.127)
controls 5  0.706(0.144)**  0.750(0.146)**
controls 6  1.012(0.086)    0.998(0.089)
controls 7  0.957(0.141)    1.010(0.142)
controls 8  0.694(0.173)**  0.726(0.18)*
controls 9  1.895(0.065)**  1.575(0.072)**
controls 10 1.353(0.085)**  1.279(0.095)**
controls 11 0.907(0.083)    0.949(0.095)
controls 12 0.988(0.135)    0.920(0.145)
Log Likelihood  -3417.879   -3356.028
AIC                   6863.758  6754.056
BIC                   6958.156  6895.653
PCP                     0.702   0.697

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asked Dec 9, 2012 at 22:59

mike

Interpret statistically significant betas with poorly performing models

I am using logit models to predict whether or not children are unhealthy (binary indictor). Many of my models have statistically significant relationships between predictors of interest (ie, if the child’s household has improved water/sanitation) and the outcome. However, none of my models outperform a model which just uses demographic information (age, region, urban/rural, sex). Furthermore, the models are pretty terrible at predicting unhealthy children (only about ~15% true positives correctly predicted, which are about 23% of the data). Do these two facts render the statistically significant relationships between the predictors and outcome of interest meaningless? How do I explain this to my (hopefully future) readers? Thanks,