Advanced Methods and Models in Behavioral Research – 2010/2011 AMMBR course design CONTENT METHOD Y is 0/1 conjoint analysis logistic regression multi-level methods our own survey

Advanced Methods and Models in Behavioral Research – 2010/2011 MULTI – LEVEL ANALYSIS

Advanced Methods and Models in Behavioral Research – 2010/2011 Multi-level models or... dealing with clustered data. One solution: the variance component model Bayesian hierarchical models mixed models (in SPSS) hierarchical linear models random effects models random coefficient models subject specific models variance component models variance heterogeneity models

Advanced Methods and Models in Behavioral Research – 2010/2011 Clustered data / multi-level models Pupils within schools (within regions within countries) Firms within regions (or sectors) Vignettes within persons [copy to blackboard]

Advanced Methods and Models in Behavioral Research – 2010/2011 Two issues with clustered data Your estimates will (in all likelihood) be too precise: you find effects that do not exist in the population [further explanation on blackboard] You will want to distinguish between effects within clusters and effects between clusters [see next two slides]

Advanced Methods and Models in Behavioral Research – 2010/2011 On individual vs aggregate data For instance: X = introvertX = age of McDonald’s employee Y = school resultsY = like the manager

Advanced Methods and Models in Behavioral Research – 2010/2011 Had we only known, that the data are clustered! So the effect of an X within clusters can be different from the effect between clusters! Using the school example: lines represent schools. And within schools the effect of being introvert is positive!

Advanced Methods and Models in Behavioral Research – 2010/2011 MAIN MESSAGES Be able to recognize clustered data and deal with it appropriately (how you do that will follow) Distinguish two kinds of effects: those at the "micro-level" vs those at the aggregate level (and... do not test a micro-hypothesis with aggregate data)

Advanced Methods and Models in Behavioral Research – 2010/2011 A toy example – two schools, two pupils Overall mean(0) Two schools each with two pupils. We first calculate the means. Overall mean= (3+2+(-1)+(-4))/4= exam score School 2School 1 (taken from Rasbash)

Advanced Methods and Models in Behavioral Research – 2010/2011 Now the variance Overall mean(0) exam score School 2School 1 The total variance is the sum of the squares of the departures of the observations around mean divided by the sample size (4) = ( )/4=7.5

Advanced Methods and Models in Behavioral Research – 2010/2011 The variance of the school means around the overall mean exam score School 2School 1 Overall mean(0) The variance of the school means around the overall mean= ( (-2.5) 2 )/2=6.25 (total variance was 7.5)

Advanced Methods and Models in Behavioral Research – 2010/2011 The variance of the pupils scores around their school’s mean exam score School 2School The variance of the pupils scores around their school’s mean= ((3-2.5) 2 + (2-2.5) 2 + (-1-(-2.5)) 2 + (-4-(-2.5)) 2 )/4 =1.25

Advanced Methods and Models in Behavioral Research – 2010/2011 -> So you can partition the variance in individual level and school level How much of the variability in pupil attainment is attributable to factors at the school and how much to factors at the pupil level? In terms of our toy example we can now say 6.25/7.5= 82% of the total variation of pupils attainment is attributable to school level factors 1.25/7.5= 18% of the total variation of pupils attainment is attributable to pupil level factors And this is important; we want to know how to explain (in this example) school attainment, and appararently the differences are at the school level more than the pupil level

Advanced Methods and Models in Behavioral Research – 2010/2011 Standard multiple regression won't do YD1D2D3D4D5id… ……………3 +2……………3 -3……………4 +4……………4 ………………… So you can use all the data and just run a multiple regression, but then you disregard the clustering effect, which gives uncorrect confidence intervals (and cannot distinguish between effects at the cluster vs at the school level) Possible solution (but not so good) You can aggregate within clusters, and then run a multiple regression on the aggregate data. Two problems: no individual level testing possible + you get less data points. So what can we do?

Advanced Methods and Models in Behavioral Research – 2010/2011 Multi-level models The usual multiple regression model assumes... with the subscript "i" defined at the case-level.... and the epsilons independently distributed with covariance matrix I. With clustered data, you know these assumptions are not met.

Advanced Methods and Models in Behavioral Research – 2010/2011 Solution 1: add dummy-variables per cluster So just multiple regression, but with as many dummy variables as you have clusters (minus 1)... where, in this example, there are j+1 clusters. IF the clustering is (largely) due to differences in the intercept between persons, this might work. BUT if there are only a handful of cases per person, this necessitates a huge number of extra variables

Advanced Methods and Models in Behavioral Research – 2010/2011 Solution 2: split your micro-level X-vars Say you have: then create: and add both as predictors (instead of x 1 ) Make sure that you understand what is happening here, and why it is of use.

Advanced Methods and Models in Behavioral Research – 2010/2011 Solution 3: the variance component model In the variance component model, we split the randomness in a "personal part" and a "rest part"

Advanced Methods and Models in Behavioral Research – 2010/2011 Now: how do you do this in Stata? [note to CS: use age and schooling as examples to split at restaurant level] relevant commands xtset and xtreg bys : egen = mean( ) gen dvarB = - convenience commands tab, gen()drop orderdes editsum

Advanced Methods and Models in Behavioral Research – 2010/2011 Up next How do we run the "Solution 1”, "Solution 2”, and “Solution 3” analysis and compare which works best? What about assumption checking? Random intercept we now saw, but how about random slopes?

Advanced Methods and Models in Behavioral Research – 2010/2011 When you have multi-level data (2 levels) 1.If applicable: consider whether using separate dummies per group might help (use only when this does not create a lot of dummies) 2.Run an empty mixed model (i.e., just the constant included) in Stata. Look at the level on which most of the variance resides. 3.If applicable: divide micro-variables in "group mean" variables and "difference from group mean" variables. 4.Re-run your mixed model with these variables included (as you would a multiple regression analysis)

To Do If you haven’t done so yet: fill out the survey Make an invitation list using the Excel template and send it to me Invite the persons on your invitation list, and keep track of who actually participates (see instructions online) Make sure you understand the multi-level concept with random intercepts (check online: there you can see a practice exam assignment) Make sure that you are up to speed with Stata. Next time we will have a practice session with Stata. Advanced Methods and Models in Behavioral Research – 2010/2011