Roger Tarling

Statistical modelling for social researchers : principles and practice

1. Statistical Modelling: An Overview 2. Research Designs and Data 3. Statistical Preliminaries 4. Multiple Regression for Continuous Response Variables 5. Logistic Regression for Binary Response Variables 6. Multinomial Logistic Regression for Multinomial Response Variables 7. Loglinear Modelling 8. Ordinal Logistic Regression for Ordered Categorical Response Variables 9. Multilevel Modelling 10. Latent Variables and Factor Analysis 11. Causal Modelling: Simultaneous Equation and Structural Equation Models 12. Longitudinal Data Analysis 13. Event History Models

The volunteering activities of children aged 8 - 15

Despite policies to encourage childrens sense of citizenship and to increase young peoples participation in the voluntary sector, there has been very little research on volunteering by the under-16s, and scant attention has been paid to existing evidence. This paper uses the United Kingdom Time Use Survey, 2000 to explore the formal and informal volunteering of children aged 8 to 15: their participation rates; the time they spend volunteering; the volunteering activities they do; and the characteristics of child volunteers. It is shown that children are a core group of active volunteers who should no longer be sidelined in voluntary or fourth sector research and policy, and nor should research on children ignore volunteering as an aspect of their lives. The conceptualisation of volunteering can be enriched by a better understanding of childrens experience, and the ways in which current conceptions of volunteering may themselves obscure childrens contribution.

Harnessing paradata and multilevel multiple imputation when analysing survey data: a case study

Abstract Missing data (attrition and non-response) are a feature of most surveys especially longitudinal/panel studies. And many such studies now have multilevel designs and hence multilevel data structures. Recent advances in imputation methodology now offer social researchers opportunities to address issues of missing data in a statistically principled way. Paradata can offer great insights in understanding the nature and causes of missingness and can be used to construct auxiliary variables to be included in imputation models. In this paper we present multilevel multiple imputation which has recently extended MI to incorporate multilevel data, making it a flexible and robust strategy for many research settings. We illustrate the procedures by analysing data drawn from a longitudinal study of prisoners. We show how paradata of that study was instrumental in guiding our approach and subsequent analysis.