Logit regression is one of the fundamental tools in econometrics for modeling binary outcome variables. This article is primarily helpful for students learning STATA to interpret the results of a logit regression, especially in the context of assignments as well as coursework. We
will use a practical example as well as provide coding illustrations for making the process clear and engaging.
Introduction to Logit Regression
Logit
regression, also known as logistic regression, is in use when your dependent
variable is binary (e.g., yes/no, 1/0). Thus, instead of predicting a
continuous outcome, it also models the probability of a certain outcome that is
occurring. This is done by transforming the results from a linear regression
into probabilities using the logistic function.
Understanding the Basics
Logits and Odds Ratios: This indicates that the basic form of logit regression does not
actually predict probabilities. Instead,
it models the log-odds of an event happening.
The log-odds are then exponentiated to find the odds ratio to get more
interpretations. The odds ratio can be
understood to provide a measure of how much the odds of your outcome either
increase or decrease with a one-unit change of your predictor variable.
Coefficients: From STATA
software, the change arising from a unit increase in your predictor as
described in the logit regression output is presented as Coef. When the coefficient sign is positive, it means
odds of the outcome are higher. When the
coefficient sign is negative, it means odds of the outcome are lower.
At the end
of this guide, you should be able to comprehend various elements of logit
regression analysis, especially applied when interpreting results obtained from
analyzing econometrics data typically given in your course work
assignments. Ok, let’s do an example now
and do the coding side a bit and keep it as simple as we want.
How to Perform a Logit Regression in STATA
Let
us walk through how to perform a logit regression in STATA using a practical
example. We will examine the relationship between education level and support
for gay marriage using the dataset GSS2016.DTA.
First,
make sure your dependent variable is binary. In our case, the dependent
variable is support for gay marriage. If this variable isn’t binary, you’ll
need to recode it.
recode
marhomo (1/2=1 "Favor")(3/5=0 "Neutral or oppose"),
gen(marhomo_r) label variable marhomo_r "Favorable view toward gay
marriage"
To
estimate a logit regression, use the following command:
logit
marhomo_r educ, nolog
Also,
Read our blog on Linear
Regression in STATA for one-of-a-kind assignment help.
Menu Method:
1. Click
on "Statistics" > "Binary outcomes" > "Logistic
regression".
2. Fill
in the dependent and independent variables.
3. Click
on the "Reporting" tab to choose "Report estimated
coefficients".
4. Click
"OK".
Interpreting the Output
Here is a sample output:
Coefficients of the model. For educ, the coefficient is 0.153, indicating the log-odds of supporting gay marriage increase by 0.153 for each extra year of education.
Converting to Odds Ratios
Log-odds are not intuitive.
Convert coefficients to odds ratios using the or option:
logit marhomo_r educ, nolog or
Odds Ratio: For educ, the odds ratio is 1.165,
meaning each extra year of education increases the odds of supporting gay
marriage by nearly 16.5%.
To control for additional variables like age
and gender, extend the model:
logit marhomo_r educ age female, nolog or
Interpreting the output:
educ: Still significant with an odds ratio of 1.165.
age: Odds ratio of 0.977, indicating older individuals are less likely to support gay marriage.
female: Not significant in this model.
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Additional Resources
Books:
• "Logistic Regression Using STATA" by Scott Long and Jeremy Freese.
• "An Introduction to Statistics and Data Analysis Using STATA" by Lisa Daniels and Nicholas Minot.
• Stata Documentation: The logit documentation is your official reference.
• Statisticshelpdesk.com for help with Stata assignments.
