Gaining Access to Data Sources in Statutory Social Work Agencies: The Long and Winding Road

This paper details a researcher's experience of gaining access to three statutory social work agencies in order to conduct a study examining how social workers respond to family support cases and how parents and carers experience the intervention of social workers in these cases. The stages in gaining access are outlined, the gate-keepers involved at each stage are identified and some of the difficulties encountered are highlighted and discussed. The paper concludes that researchers need to give greater priority to access considerations and that social work agencies need to give greater priority to co-operation with researchers.

Extracting Policy Positions from Political Texts Using words as Data

We present a new way of extracting policy positions from political texts that treats texts not as discourses to be understood and interpreted but rather, as data in the form of words. We compare this approach to previous methods of text analysis and use it to replicate published estimates of the policy positions of political parties in Britain and Ireland, on both economic and social policy dimensions. We “export” the method to a non-English-language environment, analyzing the policy positions of German parties, including the PDS as it entered the former West German party system. Finally, we extend its application beyond the analysis of party manifestos, to the estimation of political positions from legislative speeches. Our “language-blind” word scoring technique successfully replicates published policy estimates without the substantial costs of time and labor that these require. Furthermore, unlike in any previous method for extracting policy positions from political texts, we provide uncertainty measures for our estimates, allowing analysts to make informed judgments of the extent to which differences between two estimated policy positions can be viewed as significant or merely as products of measurement error.

REporting and calibration of post-bomb C-14 data

The definitive paper by Stuiver and Polach (1977) established the conventions for reporting of 14C data for chronological and geophysical studies based on the radioactive decay of 14C in the sample since the year of sample death or formation. Several ways of reporting 14C activity levels relative to a standard were also established, but no specific instructions were given for reporting nuclear weapons testing (post-bomb) 14C levels in samples. Because the use of post-bomb 14C is becoming more prevalent in forensics, biology, and geosciences, a convention needs to be adopted. We advocate the use of fraction modern with a new symbol F14C to prevent confusion with the previously used Fm, which may or may not have been fractionation corrected. We also discuss the calibration of post-bomb 14C samples and the available datasets and compilations, but do not give a recommendation for a particular dataset.

Risk adjustment for hospital use using social security data: cross sectional small area analysis

Objectives: To identify demographic and socioeconomic determinants of need for acute hospital treatment at small area level. To establish whether there is a relation between poverty and use of inpatient services. To devise a risk adjustment formula for distributing public funds for hospital services using, as far as possible, variables that can be updated between censuses. Design: Cross sectional analysis. Spatial interactive modelling was used to quantify the proximity of the population to health service facilities. Two stage weighted least squares regression was used to model use against supply of hospital and community services and a wide range of potential needs drivers including health, socioeconomic census variables, uptake of income support and family credit, and religious denomination. Setting: Northern Ireland. Main outcome measure: Intensity of use of inpatient services. Results: After endogeneity of supply and use was taken into account, a statistical model was produced that predicted use based on five variables: income support, family credit, elderly people living alone, all ages standardised mortality ratio, and low birth weight. The main effect of the formula produced is to move resources from urban to rural areas. Conclusions: This work has produced a population risk adjustment formula for acute hospital treatment in which four of the five variables can be updated annually rather than relying on census derived data. Inclusion of the social security data makes a substantial difference to the model and to the results produced by the formula.

Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.