1. Talk by William Browne Slides by Kelvyn Jones In memory of Jon Rasbash all University of Bristol Monday 5th July 2010, Session 2 WHAT IS: multilevel modelling?

2. What is multilevel modelling? Realistically complex modelling Structures that generate dependent data Data-frames for modelling Distinguishing between variables and levels (fixed and random classifications) Why should we use multilevel modelling as compared to other approaches? Going further

3. Realistically complex modelling Statistical models as a formal framework of analysis with a complexity of structure that matches the system being studied Three KEY Notions Modelling contextuality: micro & macro eg individual house prices varies from neighbourhood to n’hood eg individual house prices varies differentially from neighbourhood to neighbourhood according to size of property Modelling heterogeneity standard regression models ‘averages’, i.e. the general relationship ML models variances E.g. between-n’hood AND between-house, within-n’hood variation Modelling dependent data deriving from complex structure series of structures that ML can handle routinely, ontological depth!

4. 1:Hierarchical structures : model all levels simultaneously a) People nested within places: two-level model b) People nested within households within places: three-level model Modelling data with complex structure Note imbalance allowed! 2

5. So far unit diagrams now…… b) multiple membership with weights a) cross-classified structure Non- Hierarchical structures

6. CLASSIFICATION DIAGRAMS (Browne, Goldstein and Rasbash, 2001) a) 3-level hierarchical structure b) cross-classified structure c) multiple membership structure People Neighbourhoods Regions Students Neighbourhoods Schools Neighbourhoods People

7. School S1 S2 S3 S4 Pupils P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 Area A1 A2 A3 Combining structures: crossed-classifications and multiple membership relationships P1 Pupil 1 moves in the course of the study from residential area 1 to 2 and from school 1 to 2 Now in addition to schools being crossed with residential areas pupils are multiple members of both areas and schools. Pupil 8 has moved schools but still lives in the same area P8 Pupil 7 has moved areas but still attends the same school P7 Student School Area

8. ALSPAC All children born in Avon in 1990 followed longitudinally Multiple attainment measures on a pupil Pupils span 3 school-year cohorts (say 1996,1997,1998) Pupils move between teachers,schools,neighbourhoods Pupils progress potentially affected by their own changing characteristics, the pupils around them, their current and past teachers, schools and neighbourhoods occasions Pupil TeacherSchool Cohort Primary school Area

9. IS SUCH COMPLEXITY NEEDED? Complex models are NOT reducible to simpler models Confounding of variation across levels (eg primary and secondary school variation) M. occasions Pupil TeacherSchool Cohort Primary school Area

10. A data-frame for examining neighbourhood effects on price of houses Classifications or levels ResponseExplanatory variables House i N’hood j House Price ij No of Rooms ij House type ij N’hood Type j 11756SemiSuburb 21718SemiSuburb 31917DetSuburb 12684TerCentral 22376DetCentral 32676TerCentral 13827SemiSuburb 23855DetSuburb 14549TerrCentral 24917TerrCentral 34434SemiCentral 446655DetCentral Questions for multilevel (random coefficient) models What is the between-neighbourhood variation in price taking account of size of house? Are large houses more expensive in central areas? Are detached houses more variable in price Form needed for MLwiN

11. P1 P2 P3..... O1 O2 O3 O4 O1 O2 O1 O2 O3 Person Measurement Occasion Classification diagram Unit diagram Two level repeated measures design: classifications, units and dataframes a) in long form Classifications or levels ResponseExplanatory variables Occasion i Person j Income ij Age ij Gender j 117525F 218526F 319527F 128232M 229133M 138845F 239346F 339647F b) in short form : Person Inc- Occ1 Inc- Occ2 Inc- Occ3 Age- Occ1 Age- Occ2 Age- Occ3 Gender 1758595252627F 28291*3233*M 3889396454647F Form needed for MLwiN

12. House H1 H2 H3 H1 H2 H3 H1 H2 H1 H2 H3 H4 N’hood N1 N2 N1 N2 N’hood type Suburb Central Distinguishing Variables and Levels Classifications or levelsResponseExplanatory Variables House I Nhood jType k Price ijkRooms ijkHouse type ijk ijk 11Suburb756Det 21Suburb714Det 31Suburb917F 12Central689F 22Central376M Etc N’hood type is not a random classification but a fixed classification, and therefore an attribute of a level; ie a VARIABLE Random classification: if units can be regarded as a random sample from a wider population of units. Eg houses and n’hoods Fixed classification is a small fixed number of categories. Eg Suburb and central are not two types sampled from a large number of types, on the basis of these two we cannot generalise to a wider population of types of n’hoods, NO!

13. Analysis Strategies for Multilevel Data I Group-level analysis. Aggregate to level 2 and fit standard regression model. Problem: Cannot infer individual-level relationships from group-level relationships (ecological or aggregation fallacy) Robinson (1950) calculated the correlation between illiteracy and ethnicity in the USA. 2 scales of analysis for 1930 USA - Individual: for 97 million people; - States: 48 units

14. Analysis Strategies continued IIIndividual-level analysis. Fit standard OLS regression model Problem: Assume independence of residuals, but may expect dependency between individuals in the same group; leads to underestimation of SEs; Type I errors Bennet’s (1976) “teaching styles” study uses a single-level model: test scores for English, Reading and Maths aged 11 were significantly influenced by teaching style; PM calls for a return to ‘traditional’ or formal methods Re-analysis: Aitkin, M. et al (1981) Statistical modelling of data on teaching styles (with Discussion). J. Roy. Statist. Soc. A 144, 419-461 Using proto- multilevel models to handle dependence of pupils within classes; no significant effect Also atomistic fallacy………….

15. What does an individual analysis miss? Re-analysis as a two level model (97m in 48 States) Who is illiterate? Individual model Does this vary from State to State? States People Cross-level interactions?

16. Analysis Strategies (cont.) III Contextual analysis. Analysis individual-level data but include group-level predictors Problem: Assumes all group-level variance can be explained by group-level predictors; incorrect SE’s for group-level predictors Do pupils in single-sex school experience higher exam attainment? Structure: 4059 pupils in 65 schools Response: Normal score across all London pupils aged 16 Predictor: Girls and Boys School compared to Mixed school Parameter Single level Multilevel Cons (Mixed school)-0.098 (0.021) -0.101 (0.070) Boy school 0.122 (0.049) 0.064 (0.149) Girl school 0.245 (0.034) 0.258 (0.117) Between school variance(  u 2 ) 0.155 (0.030) Between student variance (  e 2 ) 0.985 (0.022) 0.848 (0.019) SEs

17. Analysis Strategies (cont.) IV Analysis of covariance (fixed effects model). Include dummy variables for groups Problems What if number of groups very large, eg households? No single parameter assesses between group differences Cannot make inferences beyond groups in sample Cannot include group-level predictors as all degrees of freedom at the group-level have been consumed

18. Analysis Strategies (cont.) VFit single-level model but adjust standard errors for clustering. Problems: Treats groups as a nuisance rather than of substantive interest; no estimate of between-group variance; not extendible to more levels and complex heterogeneity VI Multilevel (random effects) model. Partition residual variance into between- and within-group (level 2 and level 1) components. Allows for un-observables at each level, corrects standard errors, Micro AND macro models analysed simultaneously, avoids ecological fallacy and atomistic fallacy: richer set of research questions

19. Type of questions tackled by ML: fixed AND random effects Even with only ‘simple’ hierarchical 2-level structure E.G. 2-level model: current attainment given prior attainment of pupils(1) in schools(2) Do Boys make greater progress than Girls (F: ie averages) Are boys more or less variable in their progress than girls? (R: modelling variances) What is the between-school variation in progress? (R) Is School X different from other schools in the sample in its effect? (F)……….

20. Type of questions tackled by ML cont. Are schools more variable in their progress for pupils with low prior attainment? (R) Does the gender gap vary across schools? (R) Do pupils make more progress in denominational schools? (F) (correct SE’s) Are pupils in denominational schools less variable in their progress? (R) Do girls make greater progress in denominational schools? (F) (cross-level interaction) (correct SE’s) More generally a focus on variances: segregation, inequality are all about differences between units

21. Sometimes: single level models can be seriously misleading! Why should we use multilevel models?

22. Resources Centre for Multilevel Modelling http://www.cmm.bris.ac.uk Provides access to general information about multilevel modelling and MlwiN. Email discussion group:www.jiscmail.ac.uk/multilevel/www.jiscmail.ac.uk/multilevel/ Lemma training repository http://www.ncrm.ac.uk/nodes/lemma/about. php http://www.ncrm.ac.uk/nodes/lemma/about. php

27. Texts There is also a ‘Useful Books’ guide on the website.

28. The MLwiN manuals are another training resource

29. The Future ? Many of the most recent advances in multilevel models have relied on Monte Carlo Markov chain (MCMC) methods. The ESRC has funded much of this work including early funding of the pioneering WinBUGS package and the bulk of my funding for MCMC and other developments in the MLwiN package. Jon Rasbash, before his tragic death, had many ideas on how to produce software and training materials for the various statistical and computing ability levels that exist in the social science research community. He had a vision of a statistical ladder with researchers working their way up the rungs being supported by more quantitative colleagues. We are hoping to realise this vision by continuing the work in our LEMMA 2 node and our sister E-STAT node in the ESRC NCeSS programme.

30. E-STAT E-STAT node in the ESRC funded NCeSS program began in September 2009 including researchers from Bristol, Southampton, Manchester, IOE and Stirling. Software to cater for all types of users including novice practitioners, advanced practitioners and software developers. Based around a new algebraic processing system and MCMC engine written in Python but also includes interoperability with other packages. A series of model templates for specific model families are being developed with the idea being that advanced users develop their own (domain specific) templates. A browser based user interface is being developed. Website: http://www.cmm.bristol.ac.uk/research/NCESS-EStat/ or see me for a demonstration of current version.http://www.cmm.bristol.ac.uk/research/NCESS-EStat/

31. A last word One of the benefits of living in a world filled with modern computers and video equipment is that much that would be lost is now downloadable. Jon Rasbash was a great friend and also a fantastic teacher and we are fortunate that we still have training materials produced demonstrating this. I therefore want to end by inviting those of you who want to know more on multilevel modelling to visit at your leisure the CMM site and let Jon teach you some of the basics. http://www.cmm.bristol.ac.uk/learning-training/videos/index.shtml