Presentation on theme: "Why use multilevel modelling? Jon Rasbash GSOE November 2006 Clio Day."— Presentation transcript:
Why use multilevel modelling? Jon Rasbash GSOE November 2006 Clio Day
A simple question How much of the variability in pupil attainment is attributable to schools level factors and how much to pupil level factors? First of all we have to define what we mean by variability
A toy example – first we calculate the mean Overall mean(0) Two schools each with two pupils. Overall mean= (3+2+(-1)+(-4))/4=0 3 2 -4 attainment School 2School 1
Calculating the variance Overall mean(0) 3 2 -4 attainment 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
The variance of the school means around the overall mean 3 2 -4 attainment School 2School 1 Overall mean(0) 2.5 -2.5 The variance of the school means around the overall mean= Total variance =7.5 (2.5 2 +(-2.5) 2 )/2=6.25
The variance of the pupils scores around their schools mean 3 2 -4 attainment School 2School 1 2.5 -2.5 The variance of the pupils scores around their schools mean= The variance of the school means around overall mean = (2.5 2 +(- 2.5) 2 )/2=6.25 Total variance =7.5=6.25+1.25 ((3-2.5) 2 + (2-2.5) 2 + (-1-(-2.5)) 2 + (-4-(-2.5)) 2 )/4 =1.25
Returning to our question How much of the variability in pupil attainment is attributable to schools level factors and how much to pupil level factors? 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
Now lets do the same thing on real data(65 schools+4000 pupils) Overall mean=0 (attainment scaled to have 0 mean overall) Variance of school means around overall mean=0.15 Variance of pupils attainment scores around school mean=0.85 Total variation = 1 85% of variability due to pupil level factors, 15% due to school level factors
Estimating parameters of distributions The multilevel model assumes the school means and the pupils departures around their school means are Normally distributed. The Normal distribution has two parameters: the mean and the variance. Our model estimates N(0,0.85) for pupil within school effects and N(0,0.15) for school effects We gain a great deal of modelling power and flexibility by making these Normality assumption
Can we explain the variation at the school and pupil levels? With educational data we typically want to take account of pupil intake ability when they enter school. In our data set the plot looks like this:
Can we explain the variation at the school and pupil levels? What might happen to the between school and between pupil within school variances when we correct for prior ability? Prior ability attainment Prior ability attainment Both the between school and between pupil within school variation will be reduced when we model mean attainment as a function of prior ability
And on the real world data… Recall that the between school and between pupil variances before taking account of prior ability were 0.15 and 0.85 respectively. After taking account of prior ability between school and between pupil variances are reduced to 0.092 an 0.566. So accounting for prior ability explains 39% and 35% of between school and between pupil variation. The effect of taking account of prior attainment is important but not entirely surprising. School variability is partly determined by school intake profile and pupil prior attainment is a good predictor of subsequent attainment.
Obvious next questions Are there other school level variables that can explain why schools differ? Does school gender(mixed school, boy school, girl school) explain some of the between school variation? If we (additionally to prior ability) allow the mean to be a function of school gender the between school variance is reduced by 13%.
Focussing on school gender differences mixed school boy school girl school
Problems with traditional techniques How much between school variation is there? What school level predictor variables can explain some of this variation? This line of enquiry is powerful. Traditional statistical analysis techniques can not pursue this exploratory avenue. Estimate the between school variability OR Estimate school level predictors such as school gender But not both
It gets worse If we fit a single level model we get incorrect uncertainty intervals leading to incorrect inferences mixed school boy school girl school This is because the single level model ignores the clustering effect of schools The distributional assumptions made by the multilevel model allows the estimation of between school variance and school level predictors.
Variance is our business We have modelled mean attainment as a function of prior ability and school gender We have simultaneously modelled the total variation in attainment as function of school and pupil levels. Traditional modelling techniques are unable to partition the variation in this way and just estimate a single term and refer to it as error. We think this variation is not error, it contains a lot of interesting structure. Multilevel modelling is a great tool for exploring the structure in the error term.
Is there a family effect? Recent studies in developmental psychology and behavioural genetics(BG) emphasise non-shared environment and genetic influences are much more important in explaining childrens adjustment than shared environment has led to a focus on non- shared environment.(Plomin et al, 1994; Turkheimer&Waldron, 2000) Multilevel modelling can replicate the BG analysis. It can also extend them to more reasonably represent the complexities of family structures and processes. When this is done persistent family effects are found.
Is there a family effect? Recent studies in developmental psychology and behavioural genetics emphasise non-shared environment and genetic influences are much more important in explaining childrens adjustment than shared environment has led to a focus on non- shared environment.(Plomin et al, 1994; Turkheimer&Waldron, 2000) My collaborators from psychology Jenny Jenkins(Toronto University) and Tom OConnor(Rochester University) were concerned that perhaps the analytic techniques being used might have some simplifying assumptions that made it difficult to pick up the shared family context. They were interested to see if applying multilevel models, with the recognised strengths in exploring contextual effects might turn up some different findings.
Two analyses 1. Understanding the sources of differential parenting: the role of child and family level effects. Jenny Jenkins, Jon Rasbash and Tom OConnor Developmental Psychology 2003(1) 99-113 2. Applying social network models to within family processes. Currently being written up for publication.
Differential parental treatment One key aspect of the non-shared environment that has been investigated is differential parental treatment of siblings. Differential treatment predicts differences in sibling adjustment What are the sources of differential treatment? Child specific/non-shared: age, temperament, biological relatedness Can family level shared environmental factors influence differential treatment?
Parents have a finite amount of resources in terms of time, attention, patience and support to give their children. In families in which most of these resources are devoted to coping with economic stress, depression and/or marital conflict, parents may become less consciously or intentionally equitable and more driven by preferences or child characteristics in their childrearing efforts. Henderson et al 1996. This is the hypothesis we wish to test. We operationalised the stress/resources hypothesis using four contextual variables: socioeconomic status, single parenthood, large family size, and marital conflict The Stress/Resources Hypothesis Do family contexts(shared environment) increase or decrease the extent to which children within the same family are treated differently?
Modelling the mean and variance simultaneously We show a possible pattern of how the mean, within family variance and between family variance might behave as functions of HSES in the schematic diagram below. HSES positive parenting Here are 5 families of increasing HSES(in the actual data set there are 3900 families. We can fit a linear function of SES to the mean. The family means now vary around the dashed trend line. This is now the between family variation; which is pretty constant wrt HSES However, the within family variation(measure of differential parenting) decreases with HSES – this supports the SR hypothesis.
Conclusion on differential parental treatment We have found strong support for the stress/resources hypothesis. That is although differential parenting is a child specific factor that drives differential adjustment, differential parenting itself is influenced by family factors such as HSES. This challenges the current tendency in developmental psychology and behavioural genetics to focus on child specific factors. Multilevel models which model both the mean and the variability simultaneously are needed to uncover these relationships.
Deconstructing relationships: what determines how people get on within a family? family Culture the individual the dyad(the two people relating) genes
Applying models from social network theory to family data Non-Shared Environment Adolescent Development(NEAD) data set, Reiss et al(1994). 2 wave longitudinal family study, designed for testing hypothesis about genetic and environmental effects 277 full-sib pairs, 109 half-sib pairs, 130 unrelated pairs, 93 DZ twins and 99 MZ twins, aged between 9 and 18 years Wave 2 followed 3 years after wave 1 and any families where the older sib was older than 18 were not followed up. A wide range of self-report, parental-report and observer variables were collected. All families had 2 parents and 2 kids of the same sex. We focus here on data on relationship quality collected by observers.
Within family structure Family 1… Relationship: c1 c2 c1 m c1 f c2 c1 c2 m c2 f m c1 m c2 m f f c1 f c2 f m Actor: c1 c2 m f Partner: c1 c2 m f Dyad d1 d2 d3 d4 d5 d6 We start with 12 relationship scores in each family. These can be classified : partner dyadactor This model is the multilevel social relations model-Snijders+Kenny(1999)
Family 1… Actor: c1 c2 m f Relationship: c1 c2 c1 m c1 f c2 c1 c2 m c2 f m c1 m c2 m f f c1 f c2 f m Partner: c1 c2 m f Dyad d1 d2 d3 d4 d5 d6 Useful diagrams for thinking about multilevel structure family dyadactorpartner Relationship score The relationship scores are contained within a cross classification of actor, dyad and partner and all of this structure is nested within families. This can structure can be shown diagramatically with: A unit diagram – one node per unitA classification diagram with one node per classification
Interpretation of variance components Family:the extent to which family level factors effect all the relationships in a family. Actor: the extent to which individuals act similarly across relationships with other family members(actor stability, trait-like behaviour) Partner: We actually have two traits operating, in addition to the trait of common acting to other family members we also have the trait of elicitation from other family members. The greater the partner variance component the greater the evidence for such a trait operating. Dyad: The extent to which relationship quality is specific to the dyad. A high dyad random effect means that the relationship score from joe->fred is similar of that from fred->joe. In social network theory this is known as reciprocity. Reciprocity is a context specific effect(non trait-like) Relationship: residual variation across relationships in relationship quality.
Results of SRM more detail Pos SRM Neg SRM Family0.120.19 Actor0.440.12 Partner0.010.03 Dyad0.180.41 Relat.0.250.24 -2loglike10225.717800.9 Table shows variance partition coefficients For positivity 44% of the variablity is attributable to actors indicating that individuals act in a consistent way across relationships with other family members. There is a strong actor trait component to positivity. For negativity 0.41 of the variability is attributable to dyad. Indicating the dyad is an important structure in determining negativity in relationships. There is a strong context specific component to negativity. There is little evidence of an elicitation or partner trait for either response. At the family level there are stronger effects for negativity than positivity.
Modelling the mean relationship quality in terms of role relation ship Actor_rolePartner_role childmotherfatherchildmotherfather c1 c2 100100 c1 m 100010 c1 f 100001 c2 c1 100100 c2 m 100110 c2 f 100001 m c1 010100 m c2 010100 m f 010001 f c1 001100 f c2 001100 f m 001010 The basic unit, a relationship, has an actor and a partner. Actors and partners are classified into the roles of children, mothers and fathers by the two categorical variables actor_role and partner_role. We use child as the reference category for actor_role and partner_role variables.
Including actor and partner roles-positivity param(se) fixed intercept2.834(0.011)2.263(0.014) a_mother-0.502(0.016) a_father-0.351(0.016) p_mother-0.021(0.011) p_father--0.032(0.011) random family0.034(0.004)0.050(0.004) actor0.124(0.005)0.061(0.004) partner0.003(0.002)0.001(0.002) dyad0.050(0.003)0.051(0.003) relationship0.073(0.002) -2loglike10225.79092.64 Modelling actor and partner role drops likelihood by over 1000 units with 4df. The effect is dominated by the actor role categories. With mothers and then fathers being much more positive as actors than the reference category child. These actor_role role variables explain over 50% of the actor level variance. Adding interactions between actor_role and partner-role does not improve the model. Since we have explained actor level variance this means actor role explains the some of the trait component of relationship positivity.
Graphing actor and partner role effects for positivity actor child actor m actor f The graph shows actor_role having a big effect on relationship quality and partner role having a marginal effect.
Including actor and partner roles-negativity param(se) fixed intercept0.348(0.018)0.729(0.027) a_mother--0.375(0.030) a_father--0.516(0.031) p_mother--0.319(0.028) p_father--0.625(0.028) a_moth*p_fath-0.359(0.040) a_fath*p_moth-0.563(0.040) random family0.137(0.012)0.144(0.012) actor0.082(0.006)0.087(0.006) partner0.022(0.005)0.018(0.004) dyad0.282(0.010)0.239(0.009) relationship0.165(0.005)0.162(0.005) -2loglike17800.917305.18 Modelling actor and partner role and the interaction drops the loglike by 500 units with 6df. Now an interaction is required between actor_role and partner_role. Note the interaction categories a_moth*p_moth and a_fath*p_fath structurally do not exist. } Note the main drop in the variance occurs at the dyad level which reduces by 15%. This means modelling actor and partner roles has explained context specific variation in relationship quality for negativity.
Graphing actor and partner role effects for negativity actor child actor m actor f With respect to actor and partner roles the main context specific effects for relationship quality occur in relationships where the child is an actor.. Note that parents are trait-like wrt actor negativity effects. A possible psychological explanation for this pattern is that negativity is high stakes behaviour. The amount of negativity a child feels safe to express is determined by the power/authority of the partner. Whether the partner is another child, a mother or a father greatly effects the negativity of the predicted relationship quality
Genetic effects Individuals exhibit some trait-like behaviour for both relationship positivity and negativity. With individuals exhibiting stronger trait-like behaviour for relationship positivity. Such trait-like behaviour may have a genetic component. The standard behavioural genetics model for children within families estimates shared environment(family), non-shared environment(individual) and genetic components of variation. Our structure is more complex in that the lowest level is not the individual but a relationship between two individuals. Also we have a dyad component of variation and the individual component of variation is split into actor and partner components. However, we can extend the basic BG model (which incorporates some questionable assumptions) to our structure. The extended model gives heritabilities (genetic variance)/(total variance) of 0.42 and 0.16 for positivity and negativity respectively. The actor and partner variance components were reduced with the inclusion of genetic effects but the family variance component was undiminished.
Stability of effects over time The data has two waves where the same relationships were measured three years later. This allows us to explore the stability of family, actor, partner, dyad and relationship effects over time. We can operationalise the longitudinal structure by fitting a multivariate response social relations model where the first response is the time 1 relationship score and the second the time 2 relationship score. dyad actor partner Relationship score family dyad actor partner Relationship score family time 1 relationship score time 2 relationship score We simultaneously estimate all variance components for each response and the following correlations
Stability – results of two bivariate SRM PositivityNegativity w1 vpcw2 vpc 12 w1 vpcw2 vpc 12 family0.110.120.770.200.170.8 actor0.440.460.870.11 0.67 partner0.01 1.5??0.030.040.88 dyad0.170.120.150.420.410.34 relat.0.260.2188.8.131.520.16 The basic patterns of the vpcs found in wave 1 are repeated in wave 2 for both positivity and negativity. Family effects are very stable over time for both positivity ( 12 = 0.77) and negativity ( 12 =0.8). Family effects are a bit stronger for negativity. Actor effects are stronger for positivity than negativity but stability across time is high for both actor behaviours(0.87 and 0.67) Dyad effects are much stronger for negativity than positivity. But the stability of dyad effects for both behaviours is lower than actor, partner and family effect stabilities. Dyads are more stable for negativity than positivity.
A comment on family effects Developmental psychology and behavioural genetics,.(Plomin et al, 1994; Turkheimer&Waldron, 2000). Have suggested that after taking account of genetic and individual level factors there is scant evidence for family level effects. Our work shows strong family level effects, that persist over time, even when genetic, actor, partner, dyad and relationship level variance components are included in the model. Part of the previous failure to find family effects may be the analytical strategy of breaking down families into series of overlapping dyads and analyising each dyad separately. This strategy is probably in part determined by the methodology available to the researchers.
A comment on dyad effects for relationship negativity For relationship negativity we saw large dyad effects and relatively low stability over time. This means that at wave 1 there is a large within family variability in dyad negativity and likewise at wave 2. However the dyads which are most and least negative within the family are to an extent switching around. The next step is to see if we can find some systematic pattern to these dyadic dynamics for relationship negativity.
All the children born in the Avon area in 1990 followed up longitudinally Many measurements made including educational attainment measures Children span 3 school year cohorts(say 1994,1995,1996) Suppose we wish to model development of numeracy over the schooling period. We may have the following attainment measures on a child : m1 m2 m3 m4 m5 m6 m7 m8 primary school secondary school Alspac data – an example of highly complex multilevel structure
Measurement occasions within pupils M. Occasion PupilP. Teacher At each occasion there may be a different teacher P School Cohort Pupils are nested within primary school cohorts Primary schoolArea All this structure is nested within primary school Pupils are nested within residential areas Structure for primary schools
M. occasions PupilP. Teacher P School Cohort Primary school Area Nodes directly connected by a single arrow are nested, otherwise nodes are cross- classified. For example, measurement occasions are nested within pupils. However, cohort are cross-classified with primary teachers, that is teachers teach more than one cohort and a cohort is taught by more than one teacher. T1T2T3 Cohort 1959697 Cohort 2969798 Cohort 3989900 A mixture of nested and crossed relationships
It is reasonable to suppose the attainment of a child in a particualr year is influenced not only by the current teacher, but also by teachers in previous years. That is measurements occasions are multiple members of teachers. m1 m2 m3 m4 t1 t2 t3 t4 M. occasions PupilP. Teacher P School Cohort Primary school Area We represent this in the classification diagram by using a double arrow. Multiple membership
If pupils move area, then pupils are no longer nested within areas. Pupils and areas are cross-classified. Also it is reasonable to suppose that pupils measured attainments are effected by the areas they have previously lived in. So measurement occasions are multiple members of areas M. occasions Pupil P. Teacher P School Cohort Primary school Area M. occasions Pupil P. Teacher P School Cohort Primary school Area Classification diagram without pupils moving residential areas Classification diagram where pupils move between residential areas BUT… What happens if pupils move area?
Classification diagram where pupils move between areas but not schools If pupils move schools they are no longer nested within primary school or primary school cohort. Also we can expect, for the mobile pupils, both their previous and current cohort and school to effect measured attainments M. occasions Pupil P. Teacher P School Cohort Primary school Area M. occasions Pupil P. TeacherP School Cohort Primary school Area Classification diagram where pupils move between schools and areas If pupils move area they will also move schools
And secondary schools… M. occasions Pupil P. TeacherP School Cohort Primary school Area We could also extend the above model to take account of Secondary school, secondary school cohort and secondary school teachers. If pupils move area they will also move schools cntd
So why use multilevel models? It gives the correct answers for the standard errors of regression coefficients(in the presence of clustering). Thereby protecting against incorrect inferences(school gender example). Modelling the variance(in addition to the mean) gives a framework that allows a greater range of questions. For example, how does variability in parental treatment of sibs partition between and within families? Does the within family variance change as a function of social class?(As in the differential parenting example) Multilevel models extend to handle situations where there are multiple classifications arranged in nested, crossed and multiple membership relations. For example in the social relations model with relationship score, actor, partner, dyad, family and genetic effects.
Remember we are partitioning the variability in attainment over time between primary school, residential area, pupil, p. school cohort, teacher and occasion. We also have predictor variables for these classifications, eg pupil social class, teacher training, school budget and so on. We can introduce these predictor variables to see to what extent they explain the partitioned variability. Other predictor variables