Variation within households in consent to link survey data to administrative records: evidence from the UK Millennium Cohort Study

This study expands our knowledge of consent in linking survey and administrative data by studying respondents’ behaviour when consenting to link their own records and when consenting to link those of their children. It develops and tests a number of hypothesised mechanisms of consent, some of which were not explored in the past. The hypotheses cover: parental pride, privacy concerns, loyalty to the survey, pre-existing relations with the agency holding the data, and interviewer effects. The study uses data from the longitudinal Millennium Cohort Study to analyse the correlates of consent in multiple domains (i.e. linkage of education, health and economic records). It relies on a multivariate probit approach to model the different consent outcomes, and uses fixed and random effects specifications to estimate the effects of interviewers. The findings show that respondent’s behaviour vary depending on the consent domain (i.e. education, health, and economic records) and on the person for whom consent is sought (i.e. main respondent vs. cohort member). In particular, the cohort member’s cognitive skills and the main respondent’s privacy concerns have differential effects on consent. On the other hand, loyalty to the survey proxied by the longitudinal response history has a significant and strong impact on consent irrespective of the outcome. The findings also show that interviewers account for a large proportion of variations in consent even after controlling for the characteristics of the interviewer’s assignment area. In total, it is possible to conclude that the significant impact of some of the correlates will lead to sample bias which needs to be accounted for when working with linked survey and administrative data

The rise of endogeneity in multilevel models: A theoretical assessment of the role of stratification

This paper studies the role of stratification in the rise of endogeneity bias in multilevel models. The theory is illustrated using educational stratification and its implications on the estimation of multilevel education production functions. Educational stratification results from the functioning of the education market; it transforms a continuum of student characteristics into a continuum of tuition fees. These fees enter students' utility functions and determine the school they attend and its quality. In other words, student characteristics are the major determinants of school quality and the two are correlated. In this paper, I analyze how these correlations arise and what their implications are for multilevel estimation of education production functions. The major problem posed by such correlations is cross-level endogeneity bias. The theory developed in this paper can be extended to any economic phenomenon that exhibits stratification or nesting of smaller units within larger units (employees within firms, residents within neighborhoods, etc.)

Phase control of electromagnetically induced transparency and its applications to tunable group velocity and atom localization

We show that, by simple modifications of the usual three-level $\Lambda$-type scheme used for obtaining electromagnetically induced transparency (EIT), phase dependence in the response of the atomic medium to a weak probe field can be introduced. This gives rise to phase dependent susceptibility. By properly controlling phase and amplitudes of the drive fields we obtain variety of interesting effects. On one hand we obtain phase control of the group velocity of a probe field passing through medium to the extent that continuous tuning of the group velocity from subluminal to superluminal and back is possible. While on the other hand, by choosing one of the drive fields to be a standing wave field inside a cavity, we obtain sub-wavelength localization of moving atoms passing through the cavity field.Comment: To Appear in SPIE Proceedings Volume 573

Correlation of Preston-tube data with laminar skin friction (Log No. J12984)

Preston tube data within laminar boundary layers obtained on a sharp ten-degree cone in the NASA Ames eleven-foot transonic wind tunnel are correlated with the corresponding values of theoretical skin friction. Data were obtained over a Mach number range of 0.30 to 0.95 and unit Reynolds numbers of 9.84, 13.1, and 16.4 million per meter. The rms scatter of skin friction coefficient about the correlation is of the order of one percent, which is comparable to the reported accuracy for calibrations of Preston tubes in incompressible pipe flows. In contrast to previous works on Preston tube/skin friction correlations, which are based on the physical height of the probe's face, this satisfactory correlation for compressible boundary layer flows is achieved by accounting for the effects of a variable "effective" height of the probe. The coefficients, which appear in the correlation, are dependent on the particular tunnel environment. The general procedure can be used to define correlations for other wind tunnels

Correlation of transonic-cone preston-tube data and skin friction

Preston-tube measurements obtained on the Arnold Engineering Development Center (AEDC) Transition Cone have been correlated with theoretical skin friction coefficients in transitional and turbulent flow. This has been done for the NASA Ames 11-Ft Transonic Wind Tunnel (11 TWT) and flight tests. The developed semi-empirical correlations of Preston-tube data have been used to derive a calibration procedure for the 11 TWT flow quality. This procedure has been applied to the corrected laminar data, and an effective freestream unit Reynolds number is defined by requiring a matching of the average Preston-tube pressure in flight and in the tunnel. This study finds that the operating Reynolds number is below the effective value required for a match in laminar Preston-tube data. The distribution of this effective Reynolds number with Mach number correlates well with the freestream noise level in this tunnel. Analyses of transitional and turbulent data, however, did not result in effective Reynolds numbers that can be correlated with background noise. This is a result of the fact that vorticity fluctuations present in transitional and turbulent boundary layers dominate Preston-tube pressure fluctuations and, therefore, mask the tunnel noise eff ects. So, in order to calibrate the effects of noise on transonic wind tunnel tests only laminar data should be used, preferably at flow conditions similar to those in flight tests. To calibrate the effects of transonic wind-tunnel noise on drag measurements, however, the Preston-tube data must be supplemented with direct measurements of skin friction

Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks