Download Presentation
## Distances...

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -

**University of Warwick, Department of Sociology, 2012/13SO**201: SSAASS (Surveys and Statistics) (Richard Lampard)Clustering and Scaling(Week 19)**Distances...**• Some quantitative techniques derive and/or use distances between variables, or distances between categories within variables, as the basis for the construction of maps or the division of items into sets of similar items. • These include multidimensional scaling, correspondence analysis, and cluster analysis.**Multidimensional scaling (MDS)**• MDS is applied to a set of distances between all pairs of categories within a set of categories. • See Coxon (1982); Kruskal and Wish (1978)**Cluster analysis**• In cluster analysis, distances between items (cases and/variables) are generated from the raw data, and then used to generate a categorisation of the items. • See Everitt (1993; see also later editions)**Classifying women’s occupations**• Dale et al. (1985: see handout) used cluster analysis to develop an ‘alternative’ set of categories for women’s occupations.**The Cambridge Scale**• The Cambridge Social Stratification scale was originally derived via the application of multidimensional scaling to occupation-based cross-tabulations matching the occupations of individuals and their ‘associates’. • It subsequently moved in the direction of using correspondence analysis (Prandy 1990; Prandy and Bottero 1998: 2.6 - see handout).**‘Marriage and the Social Order’**• Prandy and Bottero (1998: handout) applied correspondence analysis to occupation-based cross-tabulations to locate occupations on a number of (highly correlated) occupational scales.**Correspondence analysis**• Correspondence analysis in effect partitions the relationship in a cross-tabulation (and more specifically the chi-square statistic) into components reflecting a number of underlying dimensions (see Greenacre 2007). • More specifically, the difference between the distributions of values for two categories is split into components reflecting different underlying dimensions.**Association models**• More recently the Cambridge scale and international equivalents have tended to use ‘association models’, which are a form of statistical model that echoes aspects of correspondence analysis. • See Goodman, L.A. 1986. ‘Some useful extensions to the usual correspondence analysis approach and the usual loglinear approach in the analysis of contingency tables (with comments)’,. Int. Statist. Rev. 54: 243-309. • See also: http://www.camsis.stir.ac.uk/**Evaluating the NS-SEC**• In Rose and Pevalin (2003), various chapters (by Mills and Evans [see extract in handout], Coxon and Fisher, and Fisher) involved the application of cluster analysis, multidimensional scaling, and association models to the relationship between employment relations measures and occupational categories.**More references…**• Cluster analysis: Hair, J.F. Jr. and Black, W.C. 2000. ‘Cluster Analysis’. In In L. Grimm and P. R. Yarnold (eds) Reading and Understanding More Multivariate Statistics. Washington, DC: APA Press. • Multidimensional scaling: Stalans, L.J. 1995. ‘Multidimensional scaling’. In L. Grimm and P. R. Yarnold(eds) Reading and Understanding Multivariate Statistics. Washington, DC: APA Press. • Correspondence analysis: Phillips, D. 1995. ‘Correspondence Analysis’, Social Research Update 7. (http://sru.soc.surrey.ac.uk/SRU7.html)**Row and column scores in correspondence analysis**These are chosen in such a way that each successive dimension explains as much of the cross-tabulation’s chi-square statistic as possible, by contributing to a contingency hierarchy (see next slide) which is as small a chi-square ‘distance’ as possible from the residuals of the independence model applied to the original cross-tabulation (i.e. from the expected values within the calculation of the chi-square statistic.)**Table 2/5: First contingency hierarchy (from Lampard 1992:**30; residuals in brackets) Calculation of one of the entries: 35.66 = -0.96 x -0.93 x 0.25 x 0.20 x (n=)774**So what’s left?**• Note that the five biggest discrepancies between the residuals and the contingency hierarchy are in the third row and/or third column; these are consequently the focus of the second contingency hierarchy. • However, the first contingency hierarchy accounts for 131.6 of the original chi-square statistic of 153.2 (i.e. 85.9%), leaving only 21.6 for the subsequent contingency hierarchies.