+use "https://github.com/agrogan1/multilevel-thinking/raw/main/simulate-and-analyze-multilevel-data/simulated_multilevel_data.dta", clear
+Tables for
+
+
+
+
+
+
+
+
+Tables for sem in Stata
+1 Introduction
+A quick demo of making tables with sem in Stata.
2 Get Data
+Data from Multilevel Thinking
+
+
+
+3 Run sem Model
+Outcome is a function of physical punishment, parental warmth and the intervention. Physical punishment is potentially an outcome of the intervention.
+
+
+
+
+sem (outcome <- physical_punishment warmth intervention) ///
+(physical_punishment <- intervention) // sem model
+
+est store M1 // store estimates from this model
+
+Endogenous variables
+ Observed: outcome physical_punishment
+
+Exogenous variables
+ Observed: warmth intervention
+
+Fitting target model:
+Iteration 0: Log likelihood = -23247.767
+Iteration 1: Log likelihood = -23247.767
+
+Structural equation model Number of obs = 3,000
+Estimation method: ml
+
+Log likelihood = -23247.767
+
+------------------------------------------------------------------------------------------
+ | OIM
+ | Coefficient std. err. z P>|z| [95% conf. interval]
+-------------------------+----------------------------------------------------------------
+Structural |
+ outcome |
+ physical_punishment | -.9422686 .0831977 -11.33 0.000 -1.105333 -.7792041
+ warmth | .8360494 .0599923 13.94 0.000 .7184666 .9536322
+ intervention | .6706488 .2266906 2.96 0.003 .2263434 1.114954
+ _cons | 51.49974 .3380981 152.32 0.000 50.83708 52.1624
+ -----------------------+----------------------------------------------------------------
+ physical_punishment |
+ intervention | -.0513873 .0497018 -1.03 0.301 -.1488011 .0460265
+ _cons | 2.503555 .0345895 72.38 0.000 2.435761 2.571349
+-------------------------+----------------------------------------------------------------
+ var(e.outcome)| 38.43413 .992365 36.53753 40.42919
+var(e.physical_punishm~t)| 1.850885 .0477897 1.75955 1.946962
+------------------------------------------------------------------------------------------
+LR test of model vs. saturated: chi2(1) = 0.04 Prob > chi2 = 0.83474 Make Table With etable
+As long as the variables have variable labels, etable (with a few options) will automatically make a nice regression table.
+
+
+
+etable, ///
+estimates(M1) /// using these estimates
+showstars showstarsnote /// show significance stars & note
+showeq // show equations
+
+ 1
+---------------------------------------------
+beneficial outcome
+ physical punishment in past week -0.942 **
+ (0.083)
+ parental warmth in past week 0.836 **
+ (0.060)
+ recieved intervention 0.671 **
+ (0.227)
+ Intercept 51.500 **
+ (0.338)
+physical punishment in past week
+ recieved intervention -0.051
+ (0.050)
+ Intercept 2.504 **
+ (0.035)
+/
+ var(e.outcome) 38.434
+ (0.992)
+ var(e.physical_punishment) 1.851
+ (0.048)
+Number of observations 3000
+---------------------------------------------
+** p<.01, * p<.05