1. Today’s program Herwart / Axel: Kiva intro (the Galak et al. paper) Follow-up questions Non-response (and respondent list) Multi-level models in Stata

2. Your follow-up questions See Galak_etal_follow_ups.rtf Advanced Methods and Models in Behavioral Research – 2012

3. Issues Consider doable in short vs long term Consider doable with the same or similar data, and on the same or another site Consider topic (it should not be Kiva) Not just... “this might also be interesting” Advanced Methods and Models in Behavioral Research – 2012

4. Typical paper follow-ups Paper is wrong There is an alternative explanation for the analytical results Paper’s conclusion might be dependent on design/measurement/analysis. Redo with different kind of design/measurement/analysis Paper is right, but conclusions limited to a given time or place or context Paper argues that X’s of a given kind are important  you check several other X’s of that kind, or  you check several X’s of a different kind Relative importance of (kinds of) X’s A connection X  Y is given with a “theory” behind it. You check that theory behind it in more detail (typically with another kind of design/measurement/analysis).

5. Finding other papers on the topic Look at the references in the original paper Search for Kiva related papers (either in Google scholar or directly in Web of Science / Scopus...) Search for more general key-words: “micro- financing” & “decision-making” Other literature: try “matching” (e.g. Literature on online dating), other sites with similar setup (e.g. eBay), persuasion and trust, charitable giving...

6. New analysis/paper: conjoint experiment Tests importance of stimuli directly (allows comparison of importance of different stimuli + choice of stimuli) (also note the analysis on magnitude of donation in Galak et al., and their complicated analysis of the similarity argument) Population consists largely of non-donors No real money involved...

7. Non-response analysis Not all of the ones invited are going to participate Think about selective non-response: some (kinds of) individuals might be less likely to participate. How might that influence the results? sample

8. Back to the (multi-level) statistics... Advanced Methods and Models in Behavioral Research – 2012

9. AMMBR course design CONTENT METHOD Y is 0/1 conjoint analysis logistic regression multi-level methods our own survey

10. Advanced Methods and Models in Behavioral Research – 2012 MULTI – LEVEL ANALYSIS

11. Advanced Methods and Models in Behavioral Research – 2012 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

12. Advanced Methods and Models in Behavioral Research – 2012 Clustered data / multi-level models Pupils within schools (within regions within countries) Firms within regions (or sectors) Vignettes within persons

13. Advanced Methods and Models in Behavioral Research – 2012 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]

14. Advanced Methods and Models in Behavioral Research – 2012 On individual vs aggregate data For instance: X = introvertX = age of McDonald’s employee Y = school resultsY = like the manager

15. Advanced Methods and Models in Behavioral Research – 2012 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!

16. Advanced Methods and Models in Behavioral Research – 2012 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" (within clusters) vs those at the aggregate level (between clusters) (and... do not test a micro-hypothesis with aggregate data)

17. Advanced Methods and Models in Behavioral Research – 2012 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=0 3 2 -4 exam score School 2School 1 (taken from Rasbash)

18. Advanced Methods and Models in Behavioral Research – 2012 Now the variance Overall mean(0) 3 2 -4 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) = (9+4+1+16)/4=7.5

19. Advanced Methods and Models in Behavioral Research – 2012 The variance of the school means around the overall mean 3 2 -4 exam score School 2School 1 Overall mean(0) 2.5 -2.5 The variance of the school means around the overall mean= (2.5 2 +(-2.5) 2 )/2=6.25 (total variance was 7.5)

20. Advanced Methods and Models in Behavioral Research – 2012 The variance of the pupils scores around their school’s mean 3 2 -4 exam score School 2School 1 2.5 -2.5 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

21. Advanced Methods and Models in Behavioral Research – 2012 -> 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

22. Advanced Methods and Models in Behavioral Research – 2012 Standard multiple regression won't do YD1D2D3D4D5id… +4 0101 -311101 +200102 010 102 +1……………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?

23. Advanced Methods and Models in Behavioral Research – 2012 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.

24. Advanced Methods and Models in Behavioral Research – 2012 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

25. Advanced Methods and Models in Behavioral Research – 2012 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.

26. Advanced Methods and Models in Behavioral Research – 2012 Solution 3: the variance component model In the variance component model, we split the randomness in a "personal part" and a "rest part"

27. Advanced Methods and Models in Behavioral Research – 2012 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

28. Advanced Methods and Models in Behavioral Research – 2012 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?

29. Advanced Methods and Models in Behavioral Research – 2012 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) 5.(and note: use regression diagnostics secretly, to find outliers and such)

30. To Do Make an invitation list using the Excel template that I will send to you later this week (don’t invite anyone just yet!) Make sure you understand the multi-level concept with random intercepts (that is: c 0 varies per cluster), and know how to do it in Stata Try the assignment on the website. Next week we will work on that data in class. Check out the practice data “motoroccasion8March2012.dta” on the website as well. It’s practice data. Advanced Methods and Models in Behavioral Research – 2012

31. Data: TVSFP on influencing behavior Advanced Methods and Models in Behavioral Research – 2012

32. Online already (though not visible) motoroccasion8March2012.dta Online already (though not visible) motoroccasion8March2012.dta