- Thread starter Traore Mamadou
- Start date

What is the dependent variable (DV)? and what are the independent variables (IV)?

If for example, you have 30 boys and 30 girls

Do you mean that when you run logistic regression over 30 boys or over 30 girls the IV

And when you run it over 60 children than IV

You didn't answer all my questions , so I will try to have a general answer.

1. Statistical tests with a greater sample size (60) will have greater power to identify smaller effect sizes than tests with a smaller sample size (30).

If you want to get better intuition you may do a simple exercise. Run a two-sample t-test on two sets of numbers with some difference in the average.

(for example

Now run the same test on half of the groups (

2. Maybe If you have only boys or only girls you actually fix one IV, if this IV is relevant to the model the fixed model may not be as good as the wider model.

So the main question is why do you expect the poverty variable to be significant in any of the two models?

Both sexes:

Number of obs = 2569

-----------------Odds Ratio----Std. Err. -------T----------P>t

Poverty_1-------1.585762------0.3234038-----2.26------0.024

Poverty_2-------1.516249------0.298936------2.11-------0.035

Male:

Number of obs = 1317

----------------Odds Ratio----Std. Err. ------T------P>t

Poverty_1------1.430976-------0.3880431---1.32---0.187

Poverty_2------1.35826--------0.3575763----1.16---0.245

Female:

Number of obs = 1252

-----------------Odds Ratio-------Std. Err. -------t---------P>t

Poverty_1----1.967428--------0.7056009-------1.89-------0.060

Poverty_2----1.899081--------0.6526607-------1.87-------0.063

I have put spaces between items with hyphens to make it more readable.

What is poverty_1, poverty_2? dummy variable of categorical variable with 3 values?

What are the possible values of poverty?

The only male regression and the only female regression have less power than both sexes regression.

Generally, a sample size of more than 1000 should be big enough to identify large effects size.

It looks like poverty is potentially significant for females, but may not be significant for males even with larger sample size.

So with larger sample size, at least for females poverty should be significant.

For me, there is no difference between the p-value of 0.05 or 0.06 (although you need to put the border)

The significance level is not important by itself, you should also look at the effect size (better a standardized one)