The choice between fixed and random effects models: some considerations for educational research Claire Crawford with Paul Clarke, Fiona Steele & Anna Vignoles Motivation • Appropriate modelling of pupil achievement – Pupils clustered within schools → hierarchical models • Two popular choices: fixed and random effects • Which approach is best in which context? – May depend whether primary interest is pupil or school characteristics – But idea is always to move closer to a causal interpretation Outline of talk • Why SEN? • Fixed and random effects models in the context of our empirical question • Data and results • Conclusions Special educational needs (SEN) • One in four Year 6 pupils (25% of 10 year olds) in England identified as having SEN – With statement (more severe): 3.7% – Without statement (less severe): 22.3% • SEN label means different things in different schools and for different pupils – Huge variation in numbers of pupils labelled across schools – Assistance received also varies widely • Ongoing policy interest (recent Green Paper) Why adjust for school effects? • Want to estimate causal effect of SEN on pupil attainment no matter what school they attend • Need to adjust for school differences in SEN labelling – e.g. children with moderate difficulties more likely to be labelled SEN in a high achieving school than in a low achieving school (Keslair et al, 2008; Ofsted, 2004) – May also be differences due to unobserved factors • Hierarchical models can account for such differences – Fixed or random school effects? Fixed effects vs. random effects • Long debate: – Economists tend to use FE models – Educationalists tend to use RE/multi-level models • But choice must be context and data specific Basic model y is 0 1 X is u s e is • FE: us is school dummy variable coefficient • RE: us is school level residual – More flexible and efficient than FE, but: – Additional assumption required: E [us|Xis] = 0 • That is, no correlation between unobserved school characteristics and observed pupil characteristics • Both: models assume: E [eis|Xis] = 0 – That is, no correlation between unobserved pupil characteristics and observed pupil characteristics Relationship between FE, RE and OLS y is 0 1 X is u s e is FE: y is y i 1 ( X is X i ) ( e is e i ) RE: y is y i 1 ( X is X i ) ( e is e i ) Where: 1 1 1 S u / e 2 2 How to choose between FE and RE • Very important to consider sources of bias: – Is RE assumption (i.e. E [us|Xis] = 0) likely to hold? • Other issues: – – – – Number of clusters Sample size within clusters Rich vs. sparse covariates Whether variation is within or between clusters • What is the real world consequence of choosing the wrong model? Sources of selection • Probability of being SEN may depend on: – Observed school characteristics • e.g. ability distribution, FSM distribution – Unobserved school characteristics • e.g. values/motivation of SEN coordinator – Observed pupil characteristics • e.g. prior ability, FSM status – Unobserved pupil characteristics • e.g. education values and/or motivation of parents Intuition I • If probability of being labelled SEN depends ONLY on observed school characteristics: – e.g. schools with high FSM/low achieving intake are more or less likely to label a child SEN • Random effects appropriate as RE assumption holds (i.e. unobserved school effects are not correlated with probability of being SEN) Intuition 2 • If probability of being labelled SEN also depends on unobserved school characteristics: – e.g. SEN coordinator tries to label as many kids SEN as possible, because they attract additional resources • Random effects inappropriate as RE assumption fails (i.e. unobserved school effects are correlated with probability of being SEN) • FE accounts for these unobserved school characteristics, so is more appropriate – Identifies impact of SEN on attainment within schools rather than between schools Intuition 3 • If probability of being labelled SEN depends on unobserved pupil/parent characteristics: – e.g. some parents may push harder for the label and accompanying additional resources; – alternatively, some parents may not countenance the idea of their kid being labelled SEN • Neither FE nor RE will address the endogeneity problem: – Need to resort to other methods, e.g. IV Other considerations • Other than its greater efficiency, the RE model may be favoured over FE where: – Number of observations per cluster is large • e.g. ALSPAC vs. NPD – Most variation is between clusters • e.g. UK (between) vs. Sweden (within) – Have rich covariates Can tests help? • Hausman test: – Commonly used to test the RE assumption • i.e. E [us|Xis] = 0 – But really testing for differences between FE and RE coefficients • Over-interpretation, as coefficients could be different due to other forms of model misspecification and sample size considerations (Fielding, 2004) – Test also assumes: E [eis|Xis] = 0 Data • Avon Longitudinal Study of Parents and Children (ALSPAC) – Recruited pregnant women in Avon with due dates between April 1991 and December 1992 – Followed these mothers and their children over time, collecting a wealth of information: • • • • Family background (including education, income, etc) Medical and genetic information Clinic testing of cognitive and non-cognitive skills Linked to National Pupil Database Looking at SEN in ALSPAC • Why is ALSPAC good for looking at this issue? – Availability of many usually unobserved individual and school characteristics: • e.g. IQ, enjoyment of school, education values of parents, headteacher tenure Descriptive statistics • 17% of sample are identified as having SEN at age 10 Individual characteristics School characteristics Standardised KS1 APS -0.104** % eligible for FSM -0.002** IQ (age 8) -0.003** H’teacher tenure: 1-2 yrs -0.044** SDQ (age 7) 0.012** H’teacher tenure: 3-9 yrs -0.046** Mum high qual vocational -0.028* Mum high qual O-level -0.021 Mum high qual A-level -0.033* Mum high qual degree -0.019 H’teacher tenure: 10+ yrs -0.031 Observations 5,417 Notes: relationship between selected individual and school characteristics and SEN status. Omitted categories are: mum’s highest qualification is CSE level; head teacher tenure < 1 year. SEN results Fixed effects -0.335** [0.025] Random effects -0.330** [0.025] Intra-school correlation 0.175 % difference 1.5 M2: M1 + admin data -0.347** [0.025] -0.342** [0.025] 0.161 1.4 M3: M2 + typical survey data -0.355** [0.025] -0.349** [0.024] 0.086 1.7 M4: M3 + rich survey data -0.321** [0.024] -0.314** [0.024] 0.076 2.2 M5: M4 + school level data -0.321** [0.024] -0.319** [0.024] 0.064 0.6 M1: KS1 APS only Notes: ** indicates significance at the 1% level; * at the 5% level. Robust standard errors are shown in parentheses. Summary of SEN results • SEN appears to be strongly negatively correlated with progress between KS1 and KS2 – SEN pupils score around 0.3 SDs lower • Choice of model does not seem to matter here – FE and RE give qualitatively similar results – Suggests correlation between probability of having SEN and unobserved school characteristics is not important • Consistency across specifications suggests regression assumption is also likely to hold Summary of FSM results • In contrast to the SEN results, the estimated effects of FSM on attainment decrease as richer data is used – Suggests that the regression assumption may fail in models with few controls, such as those based on admin data • There are also relatively larger differences between FE and RE models until we add school characteristics – Suggests that the RE assumption is less likely to hold here Conclusions • Approach each problem with agnostic view on model – Should be determined by theory and data, not tradition • FE should be preferred when the selection of pupils into schools is poorly understood or data is sparse • RE should be preferred when the selection of pupils into schools is well understood and data is rich • Worth remembering that neither FE nor RE deals with correlation between observed and unobserved individual characteristics FSM results Fixed effects -0.157** [0.028] Random effects -0.175 Intra-school correlation 0.145** [0.028] % difference 11.5 M2: M1 + admin data -0.122** [0.028] -0.138 0.161** [0.027] 13.1 M3: M2 + typical survey data -0.089** [0.029] -0.103 0.086** [0.028] 15.7 M4: M3 + rich survey data -0.089** [0.028] -0.102 0.076** [0.028] 14.6 M5: M4 + school level data -0.089** [0.028] -0.095 0.064** [0.028] 6.7 M1: KS1 APS only Notes: ** indicates significance at the 1% level; * at the 5% level. Robust standard errors are shown in parentheses.

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