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Centre for Longitudinal Studies Incorporating information about non-response into analyses of NCDS data Ian Plewis Centre for Longitudinal Studies Bedford.

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Presentation on theme: "Centre for Longitudinal Studies Incorporating information about non-response into analyses of NCDS data Ian Plewis Centre for Longitudinal Studies Bedford."— Presentation transcript:

1 Centre for Longitudinal Studies Incorporating information about non-response into analyses of NCDS data Ian Plewis Centre for Longitudinal Studies Bedford Group for Lifecourse and Statistical Studies Institute of Education, University of London 29 June 2006

2 NCDS longitudinal target sample, sweeps 0 to 6 SWEEP (AGE) 0 (0)1 (7)2 (11)3 (16)4 (23)5 (33)6 (42) Target sample (100%) (93.6%) (92.2%) (91.1%) (90.1%) (88.3%) (87.6%) Permanent emigrants 0322 (1.8%) 552 (3.1%) 705 (4.0%) 869 (4.9%) 1090 (6.2%) 1190 (6.7%) Deaths 0812 (4.6%) 829 (4.7%) 861 (4.9%) 880 (5.0%) 977 (5.5%) 993 (5.6%) Total 17634

3 NCDS longitudinal target and observed samples, sweeps 0 to 6 SWEEP (AGE) 0 (0)1 (7)2 (11)3 (16)4 (23)5 (33)6 (42) Observed sample (98.8%) (91.2%) (90.8%) (86.6%) (75.8%) (70.6%) (71.1%) Non- response: refusal 080 (0.5%) 783 (4.8%) 1114 (6.9%) 1130 (7.1%) 1735 (11.1%) 2043 (13.2%) Non- response: other 219 (1.2%) 1178 (7.1%) 491 (3.0%) 708 (4.4%) 1705 (10.7%) 1100 (7.1%) 308 (2.0%) Uncertain eligibility 0191 (1.2%) 222 (1.4%) 329 (2.0%) 1006 (6.3%) 1746 (11.2%) 2121 (13.7%) Target sample (100%) (100%) (100%) (100%) (100%) (100%) (100%)

4 The substantive question of interest is whether and how well we can predict whether or not someone has any educational qualifications at age 23 (i.e. at sweep 4) from circumstances in early childhood (up to age 7 or sweep 1). The target sample at age 23 = attrition wave non-response status not known 3

5 So, observed sample at age 23 = Item non-response 1765 So, analysis sample = 10279

6 SUBSTANTIVE MODEL Explanatory variableEstimates.e. Constant In care In social housing Inverse birthweight Mother’s age at birth Mother’s age squared Age*housing Age squared* housing Estimates from probit model for no qualifications at age 23 (n = 10279):

7 RESPONSE MODEL (1): Estimates from multivariate logistic model for response at age 23 (n = 12853): Explanatory variableAttritionWave non-response Estimates.e.Estimates.e. Constant Single mother SEN help No. children No. of moves, birth to Reading score, age

8 This model generates an estimate of the probability of a response at age 23 and we can use the inverse of this probability as a weight. The application of inverse probability weights assumes that data are ‘missing at random’ or that missingness is ignorable. RESPONSE MODEL (1):

9 N.B. n = for ‘no weights’; 9767 for ‘response weights’ SUBSTANTIVE MODEL WEIGHTED FOR NON-RESPONSE FROM (1) Explanatory variableEstimates.e. No weights Response weights No weights Response weights Constant In care In social housing Inverse birthweight Mother’s age at birth Mother’s age squared Age*housing Age squared* housing

10 Estimates from multivariate logistic model for response at age 23 (n = 8072): From Hawkes and Plewis, JRSS(A), 2006, 3, RESPONSE MODEL (2): Explanatory variable AttritionWave non-response Estimates.e.Estimates.e. Constant Sex Social adjustment, age No. of moves, birth to Reading score, age

11 N.B. n = for ‘no weights’; 5996 for ‘response weights’ SUBSTANTIVE MODEL WEIGHTED FOR NON- RESPONSE FROM (2) Explanatory variableEstimates.e. No weights Response weights No weights Response weights Constant In care In social housing Inverse birthweight Mother’s age at birth Mother’s age squared Age*housing Age squared* housing

12 Jointly modelling: (i) the probability of no qualifications at age 23 (probit) and (ii) the probability of being included in the sample at age 23 (probit). Need ‘instruments’ for the selection model – use ‘sex’ and ‘number of family moves, birth to 7’. HECKMAN SELECTION MODEL

13 Model allows for correlated residuals, i.e. for non- ignorable or informative non-response. Obtain ML estimates from ‘heckprob’ in STATA. HECKMAN SELECTION MODEL

14 N.B. n = for ‘no weights’; for ‘selection’ SUBSTANTIVE MODEL ALLOWING FOR SELECTION Explanatory variable Estimates.e. No weights Selection No weights Selection Constant In care In social housing Inverse birthweight Mother’s age at birth Mother’s age squared Age*housing Age squared* housing Residual correlation = -0.58

15 Applying these corrections for non-response has little affect on the substantive conclusions for this particular model. Methodological issues: Inverse probability weighting: (1)Standard errors of the estimates should be adjusted to allow for the fact that the weights are themselves estimated. (2)Might a better adjustment take account of the differences between the attrition cases and the wave non-respondents? CONCLUSIONS

16 (3)Missing weights – assume they are one (rather than zero)? Selection models: (1)Vulnerable to mis-specification (2)Depend on the validity of the instruments. Other approaches: (1)Imputation, especially multiple imputation. CONCLUSIONS


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