Please Note: This page has been updated to Stata 12 as of May, 2012.
use https://stats.idre.ucla.edu/stat/data/crf24, clear
* descriptive statistics by group or cell:
table a b, contents(freq mean y sd y) row col
* alternative method
egen grp=group(a b), label tabstat y, by(grp) stat(n mean sd var)
* graph interaction/plot means by cell:
anova y a b a#b margins a#b marginsplot, x(a) // place a on the x-axis marginsplot, x(b) // place b on the x-axis
* Levene’s test of heterogeneity of variance:
robvar y, by(grp) // W0 is the Levene’s test
* visual check of normality
* histograms histogram y, by(grp) normal
* kernal density plots forvalues i = 1/8 { kdensity y if grp==`i’, normal name(cell`i’, replace) }
* normal probability plot forvalues i = 1/8 { pnorm y if grp==`i’, name(pnorm`i’, replace) }
* oneway anova:
anova y b
* factorial anova:
anova y a b a#b anova y a b / a#b / // using interaction as error term
* nested anova:
anova y a b|a // using residual as error term for both a and b|a anova y a / b|a / // using b|a as error term for a and residual for b|a
* repeated measures anova: * data are long
use https://stats.idre.ucla.edu/stat/data/spf24, clear
* randomized block design anova y b s, repeated(b) mat lis e(Srep) // check variance-covariance matrix
* Tukeys test for additivity in randomized block designs: anova y b s predict yhat generate ystar = (yhat – 5.375)^2 /* 5.375 is the grand mean */ anova y a s c.ystar // if ystar is nonsignificant then model is additive
* split-plot factorial design anova y a / s|a b a#b/, repeated(b)
* tests of simple main effects:
use https://stats.idre.ucla.edu/stat/data/crf24, clear
anova y a b a#b contrast a@b contrast b@a
* pairwise comparisons:
pwcompare b, mcompare(tukey) effects
* trend analysis contrast p.b
* user defined contrasts:
contrast{b -3 1 1 1} // 1 vs average of 2, 3, and 4
* analysis of covariance:
use https://stats.idre.ucla.edu/stat/stata/examples/kirk/crac4, clear
anova y a c.x
graph slopes sum x // find min and max margins a, at(x=(32 89)) marginsplot, x(x)
* adjusted cell means margins a
* pairwise comparisons of adjusted means pwcompare a, mcompare(tukey) effects
* check homogeneity of slopes anova y a c.x a#c.x