1. Interpreting published multivariate analyses (using logistic regression) Friday 11 th March 2011

2. Some articles utilising logistic regression Chatzitheochari, S. and Arber, S. 2009. ‘Lack of sleep, work and the long hours culture: evidence from the UK Time Use Survey’, Work, Employment and Society 23.1: 30-48. Weinberger, M.I., Hofstein, Y. and Krauss Whitbourne, S. 2008. ‘Intimacy in young adulthood as a predictor of divorce in midlife’, Personal Relationships 15: 551-557. Pampel, F.C. 2009. ‘The Persistence of Educational Disparities in Smoking’, Social Problems 56.3: 526-542. Lee, H.J., Elo, I.T., McCollum, K.F. and Culhane, J.F. 2009. ‘Racial/Ethnic Differences in Breastfeeding Initiation and Duration Among Low-Income Inner-City Mothers’, Social Science Quarterly 90.5: 1251-1271.

3. Some questions to ask in relation to the analyses in the articles What comparisons do the quantified effects for each explanatory variable relate to? (e.g. What categories are being compared?) What are the sizes/magnitudes of the effects? Are they statistically significant? What other explanatory/control variables are being taken into account/controlled for? Are there any interactions between the effects of variables? How are the results for the models presented? Are the results for a series of models presented; if so, what can be learned from them? What other aspects of the study have some relevance to the value of the results? (e.g. How may sampling, the way in which concepts have been measured, etc., have impacted upon the validity, reliability and generalisability of the findings)

4. Some specific issues relating to each of the articles Chatzitheochari and Arber: What can be learned from the series of models? Are the variables ideally operationalised? What does the interaction between class and gender show? Weinberger et al.: Why are non-significant variables included in the models? How strong is the evidence for the central, interaction effect finding? Pampel: What can be learned by comparing the models without and with controls? What are the pros and cons of the graphical displays of the trends for different educational levels within different ethnic groups? Lee et al.: What can be learned from the series of models about the nature of the ethnic differences? What sort of data leads the authors to make use of a second, different form of statistical model?

5. Bivariate cross-tabulation: Associate in these can be measured using Cramér’s V (or odds ratios) If we elaborate the cross-tabulation using a third variable: What happens to the Cramér’s V(s)? What happens to Cramér’s V when we control for a third variable? Does it get smaller (or bigger)? - in which case some of the initial association has been explained (or had been suppressed?) Do the Cramér’s V values vary between levels of the third variable? If so, there is an interaction between (the effects of) variables going on… Cramér’s V and multivariate cross-tabular analysis

6. In regression analyses, the logic is similar but the letter is different! (B rather than V…) The impact of an independent variable on a dependent variable: B (or Exp(B) # ) How does this B change when we introduce another independent variable? If we allow for an interaction between the two independent variables, does B vary? # In the case of logistic regression B’s and multivariate regression analysis