1. Katie Reed EPSSA Methods Workshop

2. School environment New Latino destinations Immigrant Incorporation Importance of “context of reception” for immigrants’ incorporation (Portes & Zhou, 1993) School effects-how schools as institutions influence students Geographic spread of Latinos beyond traditional immigrant gateways

3. 1.How does the school context of reception influence students’ educational attainment? 2.How does attending a secondary school that experienced a rapid Hispanic influx influence educational attainment? 3.Do the effects of context of reception and Hispanic influx vary by race/ethnicity or immigrant generation?

4.  Education Longitudinal Study of 2002 (base year and 2006 follow up) ◦ Students sampled within 752 schools  Historical data on school composition ◦ Common Core of Data (public schools) ◦ Private School Universe Survey

5.  Conceptual/Theoretical Reasons ◦ “Because so much of what we study is multilevel in nature, we should use theories and analytic techniques that are also multilevel. If we do not do this, we can run into serious problems.” (Luke, 2004, p. 4)  Statistical reasons ◦ Clustered data ◦ Assumption of independent errors

6.  Clustered Data & Correlated Errors ◦ Multistage sampling in almost every big dataset ◦ NOT random sample of kids from across the country ◦ Students selected from within schools ◦ “this is problematic because individuals belonging to the same context will presumably have correlated errors.” (Luke, 2004, p. 7)  Multilevel modeling relaxes the assumption of independent (i.e., uncorrelated) errors ◦ Key is standard errors: if you have clustered data and DON’T use mutlilevel modeling your standard errors are smaller than they should be  You would be underestimating error and overestimating your effects

7.  Conceptual ◦ Are you conceptually interested in understanding the effects of context?  Statistical ◦ Is your data clustered?  Empirical ◦ Estimating the ICC (null model) will tell you if there is meaningful variation at L2  “robust cluster” workaround in Stata

8.  Conceptual ◦ I was interested in the effects of schools (L2) on students (L1)  Statistical ◦ ELS:2002 data is clustered (i.e., students are not randomly drawn from across the country but from within schools)  Empirical ◦ My null model suggested that about 20% of the variation was the school level  worth proceeding

9.  Looks just like regression!  Variance components can tell you how much variation is due to the levels you specified ◦ E.g., 20% in the variance is due to differences between schools  Interpret coefficients in the same way you interpret regression coefficients  Software: ◦ Stata: xt or gllamm commands ◦ HLM (Hierarchical Linear Modeling)

10. Odds ratios: <1= negative assoc >1= positive

11.  Multilevel Modeling (Luke, 2004)  Hierarchical Linear Models (Raudenbush & Bryk, 2002)  Multilevel and Longitudinal Modeling Using Stata (Rabe Hesketh & Skrondal, 2005)