Stata Analyst Guide

* Social science surveys typically require survey weights
svyset psu [pweight=finalweight], strata(stratum)

* Weighted means and proportions
svy: mean income education_years age
svy: proportion race gender marital_status

* Weighted cross-tabulation
svy: tabulate education_cat income_quintile, row se

* Descriptive statistics table for paper
estpost summarize age education_years income ///
    children household_size, detail
esttab using "tables/descriptives.tex", ///
    cells("mean(fmt(2)) sd(fmt(2)) min max count") ///
    label title("Descriptive Statistics") replace

* T-tests with survey weights
svy: mean income, over(gender)
lincom [income]Male - [income]Female

* ANOVA
svy: regress income i.race i.education_cat
testparm i.race
testparm i.education_cat

* Effect sizes (Cohen's d)
esize twosample income, by(gender)

* Model building strategy (nested models for sociology papers)

* Model 1: Bivariate
reg income i.gender [pweight=finalweight], robust
estimates store m1

* Model 2: Add demographics
reg income i.gender age age_sq i.race i.marital [pweight=finalweight], robust
estimates store m2

* Model 3: Add human capital
reg income i.gender age age_sq i.race i.marital ///
    education_years experience experience_sq [pweight=finalweight], robust
estimates store m3

* Model 4: Add job characteristics
reg income i.gender age age_sq i.race i.marital ///
    education_years experience experience_sq ///
    i.occupation i.industry hours_worked [pweight=finalweight], robust
estimates store m4

* Publication-ready table
esttab m1 m2 m3 m4 using "tables/regression_income.tex", ///
    b(3) se(3) star(* 0.05 ** 0.01 *** 0.001) ///
    label title("OLS Regression of Income") ///
    mtitles("Bivariate" "Demographics" "Human Capital" "Full Model") ///
    stats(N r2_a, labels("Observations" "Adjusted R-squared") fmt(0 3)) ///
    addnotes("Standard errors in parentheses." ///
             "All models use survey weights.") ///
    replace

* Binary outcome: employment status
logit employed i.gender age age_sq i.race i.education_cat ///
    children i.marital [pweight=finalweight], robust
estimates store logit1

* Report odds ratios
logit employed i.gender age age_sq i.race i.education_cat ///
    children i.marital [pweight=finalweight], robust or
estimates store logit_or

* Average marginal effects (preferred in sociology)
margins, dydx(*) post
estimates store ame

* Predicted probabilities by group
logit employed i.gender##i.race age education_years [pweight=finalweight], robust
margins gender#race, atmeans
marginsplot, title("Predicted Probability of Employment")

* Gender x education interaction on income
reg income c.education_years##i.gender age i.race [pweight=finalweight], robust

* Visualize interaction
margins gender, at(education_years=(8(2)20))
marginsplot, ///
    title("Returns to Education by Gender") ///
    ytitle("Predicted Income ($)") ///
    xtitle("Years of Education") ///
    legend(order(1 "Male" 2 "Female")) ///
    scheme(s2mono)
graph export "figures/education_gender_interaction.pdf", replace

* Test whether the effect of X on Y varies by moderator Z
reg outcome c.x_var##c.moderator controls [pweight=finalweight], robust

* Simple slopes at meaningful values of moderator
margins, dydx(x_var) at(moderator=(10 25 50 75 90))  // Percentiles
marginsplot, recast(line) recastci(rarea) ///
    title("Effect of X on Y at Different Levels of Moderator")

* Students nested within schools
mixed test_score gender ses || school_id:, ///
    variance mle

* Random slopes
mixed test_score gender c.ses || school_id: ses, ///
    covariance(unstructured) mle

* Calculate ICC
estat icc

* Store and compare models
estimates store mlm1
mixed test_score gender c.ses school_quality || school_id: ses, ///
    covariance(unstructured) mle
estimates store mlm2

lrtest mlm1 mlm2

* Set publication-ready scheme
set scheme s2mono

* Coefficient plot
coefplot m2 m3 m4, ///
    drop(_cons) xline(0) ///
    title("Regression Coefficients Across Models") ///
    legend(order(2 "Demographics" 4 "Human Capital" 6 "Full")) ///
    graphregion(color(white))
graph export "figures/coefplot.pdf", replace

* Distribution comparison
twoway (kdensity income if gender==1, lcolor(navy)) ///
       (kdensity income if gender==2, lcolor(cranberry)), ///
    title("Income Distribution by Gender") ///
    legend(order(1 "Male" 2 "Female")) ///
    xtitle("Annual Income ($)") ytitle("Density") ///
    graphregion(color(white))
graph export "figures/income_density.pdf", replace

* Master do-file structure for replication
* master.do
* ==========================================
* Project: [Title]
* Author: [Name]
* Date: [Date]
* Description: Master script for replication
* ==========================================

version 17
clear all
set more off
set maxvar 10000

global root "~/research/project_name"
global raw "$root/data/raw"
global processed "$root/data/processed"
global tables "$root/tables"
global figures "$root/figures"
global logs "$root/logs"

log using "$logs/master_log.smcl", replace

do "$root/code/01_data_cleaning.do"
do "$root/code/02_descriptives.do"
do "$root/code/03_main_analysis.do"
do "$root/code/04_robustness.do"
do "$root/code/05_tables_figures.do"

log close