Multilevel Analysis - Princeton University

1 PU/DSS/OTRM ultilevel Analysis (ver. )Oscar Torres-ReynaData Multilevel model whenever your data is grouped (or nested) in more than one category (for example, states, countries, etc). Multilevel models allow: Study effects that vary by entity (or groups) Estimate group level averagesSome advantages: Regular regression ignores the average variation between entities. Individual regression may face sample problems and lack of generalizationMotivationPU/DSS/OTR3-40-2 002040y0204060schoolScorey_meanuse : egeny_mean=mean(y)twowayscatter y school, msize(tiny) || connected y_meanschool, connect(L) clwidth(thick) clcolor(black) mcolor(black) msymbol(none) || , ytitle(y)Variation between entitiesPU/DSS/OTR4statsbyinter=_b[_cons ] slope=_b[x1], by(school) saving(ols, replace): regress y x1sort schoolmerge schoolusing olsdrop _mergegen yhat_ols= inter + slope*x1sort school x1separate y, by(school)separate yhat_ols, by(school)twowayconnected yhat_ols1-yhat_ols65 x1 || lfity x1, clwidth(thick) clcolor(black) legend(off) ytitle(y)-20-100102030y-40-2002040 Reading testIndividual regressions (no-pooling approach)PU/DSS/OTRLR test vs.

2 Linear regression: chibar2(01) = Prob >= chibar2 = sd(Residual) .1030214 sd(_cons) .3999163 : Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval] _cons .536272 .9193634 y Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = Prob > chi2 =.

3 Wald chi2(0) = . max = 198 avg = Obs per group: min = 2 Group variable: school Number of groups = 65 Mixed-effects ML regression Number of obs = 4059. xtmixed y || school: , mle nolog5 Standard deviation at the school level (level 2)Standard deviation at the individual level (level 2)Mean of state level )()(_)(_)_()_()_(_222222222=+=+=+=residu alsdconssdconssdesigmausigmausigmancorre latioIntraclass An intraclass correlation tells you about the correlation of the observations (cases) within a cluster ( ) If the interclass correlation (IC) approaches 0 then the grouping by counties (or entities) are of no use (you may as well run asimple regression). If the IC approaches 1 then there is no variance to explain at the individual level, everybody is the model (null)iijiy +=][Ho: Random-effects = 0PU/DSS/OTR6 Standard deviation at the school level (level 2)Standard deviation at the individual level (level 2)Mean of state level )()(_)(_)_()_()_(_222222222=+=+=+=residu alsdconssdconssdesigmausigmausigmancorre latioIntraclass An intraclass correlation tells you about the correlation of the observations (cases) within a cluster ( ) If the interclass correlation (IC) approaches 0 then the grouping by counties (or entities) are of no use (you may as well run asimple regression).

4 If the IC approaches 1 then there is no variance to explain at the individual level, everybody is the model (one level-1 predictor)LR test vs. linear regression: chibar2(01) = Prob >= chibar2 = sd(Residual) .0841759 sd(_cons) .3052516 : Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval] _cons .0238706 .4002258 .8082987 x1 .5633697 .0124654 .5389381 .5878014 y Coef.

5 Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = Prob > chi2 = Wald chi2(1) = max = 198 avg = Obs per group: min = 2 Group variable: school Number of groups = 65 Mixed-effects ML regression Number of obs = 4059. xtmixed y x1 || school: , mle nologiiijixy ++=][Ho: Random-effects = 0PU/DSS/OTR7 Standard deviation at the school level (level 2)Standard deviation at the individual level (level 2)Mean of state level )()1()(_)1()(_)_()_()_(_22222222222=+++= +++=+=residualsdxsdconssdxsdconssdesigma usigmausigmancorrelatioIntraclassVarying -intercept, varying-coefficient modelNote: LR test is conservative and provided only for test vs.

6 Linear regression: chi2(3) = Prob > chi2 = sd(Residual) .0839482 corr(x1,_cons) .4975474 .1487416 .1572843 .7322131 sd(_cons) .3044138 sd(x1) .1205631 .0189827 .0885508 .1641483school: Unstructured Random-effects Parameters Estimate Std. Err. [95% Conf. Interval] _cons .3978336 .6646554 x1 .5567291 .0199367 .5176539 .5958043 y Coef.

7 Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = Prob > chi2 = Wald chi2(1) = max = 198 avg = Obs per group: min = 2 Group variable: school Number of groups = 65 Mixed-effects ML regression Number of obs = 4059. xtmixed y x1 || school: x1, mle nolog covariance(unstructure)iiijijixy ++=][][Ho: Random-effects = 0PU/DSS/OTR8 Standard deviation at the school level (level 2)Standard deviation at the individual level (level 2)Mean of state level interceptsVarying-slope modelLR test vs. linear regression: chibar2(01) = Prob >= chibar2 = sd(Residual).

8 089372 sd( ) .0003388 .1806391 0 ._all: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval] _cons .1263914 .2357746 x1 .5950551 .0127269 .5701108 .6199995 y Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = Prob > chi2 = Wald chi2(1) = max = 4059 avg = Obs per group: min = 4059 Group variable: _all Number of groups = 1 Mixed-effects ML regression Number of obs = 4059.

9 Xtmixed y x1 || _all: , mle nologiiijixy ++=][PU/DSS/OTR9 PostestimationPU/DSS/OTR10 Comparing models using likelihood-ration testUse the likelihood-ratio test (lrtest) to compare models fitted by maximum likelihood. This test compares the log likelihood (shown in the output) of two models and tests whether they are significantly different. /*Fitting random intercepts and storing results*/quietly xtmixedy x1 || school:, mlenologestimates store ri/*Fitting random coefficients and storing results*/quietly xtmixedy x1 || school: x1, mlenologcovariance(unstructure)estimates store rc/*Running the likelihood-ratio test to compare*/lrtestri rcNote: LR test is conservative(Assumption: ri nested in rc) Prob > chi2 = test LR chi2(2) = lrtest ri rcThe null hypothesis is that there is no significant difference between the two models. If Prob>chi2< , then you may reject the null and conclude that there is a statistically significant difference between the models.

10 In the example above we reject the null and conclude that the random coefficients model provides a better fit (it has the lowest log likelihood)PU/DSS/OTR11 Standard deviation at the school level (level 2)Standard deviation at the individual level (level 2)Mean of state level )var()1var()var(_)1var()var(_)_()_()_(_2 =+++=+++=+=residualxconsxconsesigmausigm ausigmancorrelatioIntraclassVarying-inte rcept, varying-coefficient model: postestimationNote: LR test is conservative and provided only for test vs. linear regression: chi2(3) = Prob > chi2 = var(Residual) cov(x1,_cons) .1804036 .0691515 .0448692 .315938 var(_cons) var(x1) .0145355 .0045772 .0078412 .0269446school: Unstructured Random-effects Parameters Estimate Std.