Multilevel Analysis - Princeton University

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.

PU/DSS/OTR. 2. Use multilevel model whenever your data is grouped (or nested) in more than one category (for example, states, countries, etc). Multilevel models allow:

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