THREE-LEVEL MODEL Two views The intractable statistical complexity that is occasioned by unduly...
-
Upload
natalie-wade -
Category
Documents
-
view
217 -
download
3
Transcript of THREE-LEVEL MODEL Two views The intractable statistical complexity that is occasioned by unduly...
THREE-LEVEL MODELTwo views
• “The intractable statistical complexity that is occasioned by unduly ambitious three-level models” (Bickel, 2007, 246) AND
• “higher levels may have substantial effects, but without the guidance of well-developed theory or rich substantive literature, unproductive guesswork, data dredging and intractable statistical complications come to the fore” (Bickel, 2007, 219)
•But technically, a three-level model is a straightforward development of 2-level model; substantively research problems are not confined to 2 levels!
THREE-LEVEL MODEL•|Unit and classification diagrams, dataframes• Some example of applied research• Algebraic specification of 3 level random-intercepts model• Various forms of the VPC • Specifying models in MLwiN • Residuals
• Applying the model- the repeated cross-sectional model; changing school
performance
• Further levels:- as structures etc- in MLwin
Student
School
Class
Student St1 St2 St3 St1 St2 St1 St2 St3 St1 St2 St3 St4
School Sc1 Sc2 Sc3
Class C1 C2 C1 C2
• Student achievement affected by student characteristics, class characteristics and school characteristics
•Need more than 1 class per school; imbalance allowed
•Need lots of pupils in lots of classes in lots of schools!
Three-level modelsUnit and classification diagrams
Data Frame for 3 level modelClassifications or levels
Response Explanatory variables
NB categorical and continuous variables can be included at any level
Student
iClass j
School k
Current Exam scoreijk
Student previousExamination scoreijk
Student genderijk
Class teaching stylejk
School typek
1 1 1 75 56 M Formal State
2 1 1 71 45 M Formal State
3 1 1 91 72 F Formal State
1 2 1 68 49 F Informal State
2 2 1 37 36 M Informal State
1 1 2 67 56 M Formal Private
2 1 2 82 76 F Formal Private
3 1 2 85 50 F Formal Private
1 1 3 54 39 M Informal State
NB must be sorted correctly for MLwiN, recognises units by change in higher-level indices
Some examples (with references)• West, B T et al (2007) Linear mixed models, Chapman
and Hall, Boca Raton• Dependent variable: student’s gain in Maths score,
kindergarten to first grade• Explanatory variables- 1: Student (1190): Maths score in kindergarten, Sex,
Minority, SES- 2: Classroom (312) Teacher’s years of teaching
experience, Teacher’s maths experience, teacher’s maths knowledge
- 3: School (107) % households in n’hood of school in poverty
NB lacks power to infer to specific classes/schools?
Some examples continued• Bickel, R (2007) Multilevel analysis for
applied research, Guildford Press, New York
• Dependent variable: Maths score for 8th graders in Kentucky
• Explanatory variables- 1: Student (50,000): Gender, Ethnicity,- 2: Schools (347) School size, % of school
students receiving free/reduced cost lunch - 3: Districts (107) District school size
Some examples continued• Ramano, E et al (2005) Multilevel correlates of
childhood physical aggression and prosocial behaviour Journal of Abnormal Child Psychology, 33, 565-578
- individual, family and neighbourhood
• Wiggins, R et al (2002) Place and personal circumstances in a multilevel account of women’s long-term illness Social Science & Medicine, 54, 827-838
- Large scale study, 75k+ women in 9539 wards in 401 districts; used PCA to construct level-2 variables from census data
Algebraic specification of random intercepts model
ijkjkkjk
ijkijkijk
euv
xy
00000
10
i level 1 (e.g. pupil), j level 2 (e.g. class) , k level 3 (e.g. school)
kv0 is the random effect at the school level, an allowed-to-vary departure from the grand mean;
jku0 is the random effect at the class level, a departure from the school effect;
ijke is the random effect at the pupil level, a departure from the class effect within a school Variance between schools = Var (v0k) = 2
0v Variance between classes within schools = Var (u0jk) = 2
0u Variance between pupils within classes within schools = Var (eijk) = 2
e
Variance between classes = 20v + 2
0u Random effects at different levels assumed to be uncorrelated
Various forms of the VPC for random intercepts model
Proportion variance due to differences between schools
= intra-school correlation = 220
20
20
euv
v
Proportion variance due to differences between classes
= intra-class correlation = 220
20
20
20
euv
uv
Correlation structure of
3 level modelS 1 1 1 2 2 2 2 3 3 3
C 1 1 2 1 1 2 2 1 1 2
P 1 2 3 1 2 3 4 1 2 3
1 1 1 1 0 0 0 0 0 0 0
1 1 2 1 0 0 0 0 0 0 0
1 2 3 1 0 0 0 0 0 0 0
2 1 1 0 0 0 1 0 0 0
2 1 2 0 0 0 1 0 0 0
2 2 3 0 0 0 1 0 0 0
2 2 4 0 0 0 1 0 0 0
3 1 1 0 0 0 0 0 0 0 1
3 1 2 0 0 0 0 0 0 0 1
3 2 3 0 0 0 0 0 0 0 1
Intra-class correlation (within same school & same class)
Intra-school correlation (within same school, different class)
Example: pupils within classes within schools (Snijder & Bosker data)
Response is score on maths test at age 14
Estimate S.E. Fixed β0 7.96 0.23 Random
20v (school) 2.124 0.546
20u (class) 1.746 0.226
2e (pupil) 7.816 0.186
Total variance is 2.124 + 1.746 + 7.816 = 11.686
Intra-school correlation = 2.124/11.686 = 0.18 (similarity of pupils in same school).
Intra-class correlation = (2.124+1.746)/11.686 = 0.33 (similarity of pupils in same class, in same school).
The similarity of classes within the same school (correlation between mean maths score for 2 randomly selected classes in a randomly selected school) is 2.124/(2.124+1.746) = 0.55.
Variance Partition Coefficients:pupils within classes within schools (Snijder & Bosker data)
Specifying models in MLwiN• Three-level variance components for attainment
Specifying models in MLwiN• Are there classes and/or schools where the gender gap is large, small or
inverse?• Student gender in fixed part and Variance functions at each level
21
211010
20
20 2 ijkuijkijkuuijku xxxx
21
211010
20
20 2 ijkijkijkijk xxxx
ijkijkeeijke xxx 101020
20 2
Level 2 variance
Level 3 variance
Level 1 variance
Specifying models in MLwiN• Is the Gender gap differential according to teaching style?• Cross-level interactions between Gender and Teaching style in the
fixed part of the model• IE main effects for gender & style, and first order interaction between
Student Gender and Class Teaching Style
Fixed partCons: mean score for Male in Formally-taught class
Female: differential for female in formal class
Informal: differential for male in informal class
Female*Informal: differential for female in informal class
Residuals
• Key notion is that highest level residual is a random, allowed-to-vary departure from general relationship
• Each lower level residual is allowed-to-vary random departure from the higher-level departure
Level 3 residuals: school departures from grand mean line
ijkkijk xy 10
ijkijk xy 10
ijkkijk xy 10
kv0
Level 2 residuals: class departures from the associated school line
ijkjkijk xy 10
jku0
ijkjkijk xy 10
ijkjkijk xy 10
ijkjkijk xy 10
Level-1 residuals: student departures from the associated class line
ijkjkijk xy 10
ijke
Applying the model: the repeated cross-sectional model; changing school performance
Sc1 Sc2 Sc3....
1985 1986 1987 1985 1987 1986 1987
St1…St9 St1… St25 St1 …St32 St1… St22 St1… St12 St1… St29 St1… St13
School
Student
Cohort
• Modelling Exam scores for groups of students who entered school in 1985 and a further group who entered in 1986.• In a multilevel sense we do not have 2 cohort units but 2S cohort units where S is the number of schools. • The model can be extended to handle an arbitrary number of cohorts with imbalance
Applying the model: the repeated cross-sectional model; changing school performance
• Modelling Exam scores aged 16 for Level 3 139 state schools from the Inner London Education Authority, Level 2 304 cohorts with a maximum of 3 cohorts in any one school, and Level 1 115,347 pupils with a maximum of 135 pupils in any one school cohort
•pupil level variables: Sex, Ethnicity, Verbal Reasoning aged 11
• cohort-level variables: % of pupils in each school who were receiving Free-school meals in that year, % of pupils in the highest VRband in that year, the year that the cohort graduated
• school level variables: the ‘sex’ of the school (Mixed Boys and Girls); the schools’ religious denomination (Non-denominational, CofE, Catholic)
Further levels - as structures, etcSome examples of 4-level nested structures:
• student within class within school within LEA• people within households within postcode sectors within regions•Finally, Repeated measures within students within cohorts within schools
O1 O2 O1 O2 O1 O2 O1 O2 O1 O2 O1 O2 O1 O2 O1 O2
Cohorts are now repeated measures on schools and tell us about stability of school effects over time Measurement occasions are repeated measures on students and can tell us about students’ learning trajectories.
1990 1991 1990 1991
Sc1 Sc2...
Cohort
Msmnt occ
student
School
St1 St2... St1 St2.. St1 St2.. St1 St2..
Further levels - in MLwiN• Click on extra subscripts!
• Default is a maximum of 5 but can be increased