Multilevel modelling of refusal and noncontact nonresponse in household surveys: evidence from six UK government surveys

This paper analyses household unit nonresponse and interviewer effects in six major UK government surveys using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of survey dependent and independent effects of household and interviewer characteristics, providing an empirical exploration of the leverage-salience theory. The analysis is based on the 2001 UK Census Link Study, a unique data source containing an unusually rich set of auxiliary variables, linking the response outcome of six surveys to census data, interviewer observation data and interviewer information, available for respondents and nonrespondents

Alternative approaches to multilevel modelling of survey noncontact and refusal

We review three alternative approaches to modelling survey noncontact and refusal: multinomial, sequential and sample selection (bivariate probit) models. We then propose a multilevel extension of the sample selection model to allow for both interviewer effects and dependency between noncontact and refusal rates at the household and interviewer level. All methods are applied and compared in an analysis of household nonresponse in the UK, using a dataset with unusually rich information on both respondents and nonrespondents from six major surveys. After controlling for household characteristics, there is little evidence of residual correlation between the unobserved characteristics affecting noncontact and refusal propensities at either the household or the interviewer level. We also find that the estimated coefficients of the multinomial and sequential models are surprisingly similar, which further investigation via a simulation study suggests is due to there being little overlap between the predictors of noncontact and refusal

The effect of school resources on pupil attainment: a multilevel simultaneous equation modelling approach

Improving educational achievement in UK schools is a priority, and of particular concern is the low achievement of specific groups, such as those from lower socio-economic backgrounds. An obvious question is whether we should be improving the outcomes of these students by spending more on their education. The literature on the effect of educational spending on pupil achievement has a number of methodological difficulties, in particular the endogeneity of school resource levels, and the intra-school correlations in student responses. In this paper, we adopt a multilevel simultaneous equation modelling approach to assess the impact of school resources on student attainment at age 14. This paper is the first to apply a simultaneous equation model to estimate the impact of school resources on pupil achievement, using the newly available National Pupil Database (NPDB)

Changes in the Relationship between the Outcomes of Cohabiting Partnerships and Fertility among Young British Women: Evidence from the 1958 and 1970 Birth Cohort Studies

We investigate the effects of a range of time-varying fertility indicators, including pregnancy, and the presence and characteristics of children, on the outcomes of nonmarital unions for two cohorts of British women. We compare the effect of conceptions and births on the odds that a cohabiting partnership is dissolved or that it is converted to marriage for women born in 1958 and 1970. The analysis uses a multilevel competing risks model to allow for multiple partnerships and conceptions, and to distinguish between two outcomes of cohabiting unions (separation and marriage). We also use a multiprocess model, in which the outcomes of cohabitation are modelled simultaneously with fertility, to allow for the potential joint determination of partnership and childbearing decisions. The analysis is based on partnership and birth histories between the ages of 16 and 29, and social background, in the National Child Development Study and the 1970 British Birth Cohort Study

Fertility expectations and residential mobility in Britain

BACKGROUND It is plausible that people take into account anticipated changes in family size in choosing where to live. But estimation of the impact of anticipated events on current transitions in an event history framework is challenging because expectations must be measured in some way and, like indicators of past childbearing, expected future childbearing may be endogenous with respect to housing decisions. OBJECTIVE The objective of the study is to estimate how expected changes in family size affect residential movement in Great Britain in a way which addresses these challenges. METHODS We use longitudinal data from a mature 18-wave panel survey, the British Household Panel Survey, which incorporates a direct measure of fertility expectations. The statistical methods allow for the potential endogeneity of expectations in our estimation and testing framework. RESULTS We produce evidence consistent with the idea that past childbearing mainly affects residential mobility through expectations of future childbearing, not directly through the number of children in the household. But there is heterogeneity in response. In particular, fertility expectations have a much greater effect on mobility among women who face lower costs of mobility, such as private tenants. CONCLUSIONS Our estimates indicate that expecting to have a(nother) child in the future increases the probability of moving by about 0.036 on average, relative to an average mobility rate of 0.14 per annum in our sample. Contribution: Our contribution is to incorporate anticipation of future events into an empirical model of residential mobility. We also shed light on how childbearing affects mobility

Trajectories of functional disability for the elderly in Britain

This study uses an innovative approach to characterise trajectories of functional disability over the final stages of the life course. We use data from the British Household Panel Survey (BHPS), an annual household survey of all adults in a representative sample of British households from 1991-2008. The analysis focuses on the sub-sample of elderly household members who were aged from 65 to 74 in any of the 18 waves of data, with a final sample of 3,671 individuals contributing a total of 13,982 person years. As in previous research, we estimate latent growth curves, but extend the standard model to incorporate a measurement model for the latent outcome variable ‘functional disability’. We identify accelerating trajectories of functional disability for a representative sample of elderly individuals separately by gender. We show that socio-occupational classification is associated with the level of initial functional disability and to a less extent the change in functional disability with age. The contribution of this paper is to explore the use of a measurement model to exploit the variation between items in discriminatory power for identifying an individual’s functional disability. Further we are able to explicitly test for temporal measurement invariance in functional disability i.e. to what extent the items consistently measure the latent variable as people age

The Choice Between Fixed and Random Effects Models: Some Considerations for Educational Research

We discuss fixed and random effects models in the context of educational research and set out the assumptions behind the two approaches. To illustrate the issues, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude that a fixed effects approach will be preferable in scenarios where the primary interest is in policy-relevant inference of the effects of individual characteristics, but the process through which pupils are selected into schools is poorly understood or the data are too limited to adjust for the effects of selection. In this context, the robustness of the fixed effects approach to the random effects assumption is attractive, and educational researchers should consider using it, even if only to assess the robustness of estimates obtained from random effects models. When the selection mechanism is fairly well understood and the researcher has access to rich data, the random effects model should be preferred because it can produce policy-relevant estimates while allowing a wider range of research questions to be addressed. Moreover, random effects estimators of regression coefficients and shrinkage estimators of school effects are more statistically efficient than those for fixed effects.fixed effects, random effects, multilevel modelling, education, pupil achievement

The Choice between fixed and random effects models: some considerations for educational research.

We discuss the use of fixed and random effects models in the context of educational research and set out the assumptions behind the two modelling approaches. To illustrate the issues that should be considered when choosing between these approaches, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude that a fixed effects approach will be preferable in scenarios where the primary interest is in policy-relevant inference about the effects of individual characteristics, but the process through which pupils are selected into schools is poorly understood or the data are too limited to adjust for the effects of selection. In this context, the robustness of the fixed effects approach to the random effects assumption is attractive, and educational researchers should consider using it, even if only to assess the robustness of estimates obtained from random effects models. On the other hand, when the selection mechanism is fairly well understood and the researcher has access to rich data, the random effects model should naturally be preferred because it can produce policy-relevant estimates while allowing a wider range of research questions to be addressed. Moreover, random effects estimators of regression coefficients and shrinkage estimators of school effects are more statistically efficient than those for fixed effects.fixed effects, random effects, multilevel modelling, education, pupil achievement