1. Comparability of categorical variables in longitudinal survey research
Paul Lambert*, Alison Bowes*, Vernon Gayle*, Guy Warner*, and Tom Doherty**
(*University of Stirling; ** University of Glasgow)
Presented to the Society for Longitudinal & Life Course Studies
Clare College, Cambridge
22-24 September 2010

2. Comparability of categorical variables in longitudinal survey research
Comparability strategies and selected problems
Some analytical prescriptions
The relevance of the ‘GESDE’ services
..Underlying motivation to study patterns/changes over time in distributions measured by categorical instruments..
“..Comparisons Are the Essence..” (Treiman, 2009: 382)

3. 1) Comparability strategies
Harmonisation/standardisation is used to aid in the comparison of data across contexts
Measurement v’s meaning/functional equivalence
[i.e. same absolute qualities of position, v’s same relative meaning of position within the distribution]
Ex post v’s pre-harmonisation
[e.g. Harkness et al. 2003; Hoffmeyer-Zlotnik & Harkness 2005]
Comparability problem: when comparison (over time) is muddled by a lack of equivalence in absolute and/or relative position

5. Categorical variables: coding membership of groups
Interest in this talk
High
Medium
Low
[n.a.]
ordinal
Country of birth UK
India
Poland
…..
nominal
..Please describe your occupation...
Secretary
Lecturer
Shop assistant
No job
NS-SEC 3
1.2 6 8
(recode of 3)
CAMSIS(F)
62.4
94.2
42.8
(scaling of 3)
Sports you enjoy
Football
Cricket
Darts ….
nominal+MR
Most social survey data is categorical!
Importance of: fine levels of detail; boundaries;
recoding strategies; cognitive issues

6. Three important categorical variables
Standards
Key correlates (r2)*
Reference
Educational qualifications
ISCED; GHS;
years of study
year of birth (0.25); gender (0.04);
Schneider (2010); Treiman (2007)
Occupations
NS-SEC; RGSC
gender (0.41); age (0.05); region (0.02);
Rose and Harrison (2010)
Ethnicity
ONS; Years since immigr.
year of birth (0.08); immigrant status (0.26); region (0.09)
Bosveld et al. (2006)
Longitudinal problems of comparison include:
Changing structural contexts (distributions; correlations; sparsity)
Changing measurement practices (e.g. decennial revisions; admin data)
Changing international comparisons (e.g. ISCO88-SOC; ISCED-ONS)
Existing measurement instruments are generally outside the domain of concept formation research (cf. Jowell, 2007)
* 2008 BHPS 20+yrs, qfedhi, jbsoc, ‘xeth’ using race{l} downwt for Wh

7. Selected comparability problems for categorical measures in longitudinal analysis
Operationalisation problems
Interpretation problems (same relative meaning
over
time?)
Sparse categories
Changing distribution of categories
Changing categories
Documentation
Parsimony – multivariate models and interactions
Hidden change in administrative practices (e.g. recoding)
Changing relevant correlates (e.g. with yob)
Timing issues

8. Common practice with longitudinal surveys
Comparative model for categorical data is overwhelmingly one of measurement equivalence
‘Nominal equivalence’: offer, or locate, categories in the same scheme over time (often suppressing further detail in released data)
Distribute explanatory metadata (e.g.
Validity studies may be used to test correlation with expectations (e.g. Rose and Harrison 2010)
This is commonly visible as coding to the ‘lowest common denominator’

9. A measurement equivalence example – comparing ethnicity categories in context of demographic change
Are the compatible categories equivalent?
Who does the work (data distributors and/or analysts)…?
…& who records it (e.g. Mohler et al., 2008)?

10. Here, measurement equivalence is compromised by administrative errors, & meaning equivalence is doubtful due to industrial restructuring (orig. occ. codes not available)

11. [Changing correlates] Occupational measures change over time in total distribution and this interacts with occupational gender segregation

12. …Meaning equivalence is preferable in longitudinal survey research…!!
Collect and preserve the data in its original detail
Preserves information; easier for respondent; better documentation
Allow analysts to harmonise/standardise using flexible/tailored equivalence strategies
Same relative meaning may=diff. absolute properties (e.g. Educ-by-yob)?
Ideally, trying and comparing more than one strategy
Pressures for measurement equivalence from statistics agencies, analysts, consumers
“..goal of standardisation is to enhance comparability; inappropriate standardisation may do just the opposite” (Harkness, 2008: 61)
“..when nominal equivalence is not enough..” (Schneider, 2010)

13. 2) Some analytical prescriptions
Measurement equivalence
..is what we already do
Appealing because we have existing standards/literature on most measures
Improved practice:
Use the published standards when they exist(!)
cf. temptation to recode/collapse recommended measures
Documentation/replication of derivations
Comparison between a few different options
Recognise parsimony/multivariate interactions

14. (Own analysis, for the NeISS project, www.neiss.org.uk)

15. Meaning equivalence
For categorical data, equivalence for comparisons is often best approached in terms of meaning equivalence
(because of non-linear relations between categories and shifting underlying distributions)
(even if measurement equivalence seems possible)
Arithmetic standardisation offers a convenient form of meaning equivalence by indicating relative position with the structure defined by the current context
For categorical data, this can be achieved/approximated by scaling categories in one or more dimension of difference

17. UK born
Not UK born
Whi Iri
0 (base)
Whi Other
-5.4
-3.4
Black Car.
-9.6
-1.0
Black Afr
-12.7
-5.9
Indian
-16.1
Pak/Bang.
-16.8
-3.2
Chinese
-15.8
-6.5
Other
-11.0
-3.9

18. Is scaling useful? ..sometimes.. Revealing exploratory exercise
Parsimonious functional form
If complex categorical measure is a control variable
If interaction effects are considered
If story told by (transformation of) a linear functional form is persuasive (e.g. exponential increase)
Good for Multiple Response data
(e.g. take arithmetic function of related cases)

19. 3) The GESDE services
DAMES ( node seeking to support data management in social research activities
‘Data management’ = tasks involved in organising/ manipulating/enhancing data in preparation for research analysis
Making tasks easier and more consistent
Pre-analysis tasks conducted by researchers and data distributors
Re-coding; harmonising; standardising; cleaning; linking
Recording and distributing metadata for replication

20. Contributions: Preserving/facilitating data management

21. DAMES provides online services for data coordination/organisation
Tools for handing variables in social science data
Recoding measures; standardisation / harmonisation; Linking; Curating
17/MAR/2010
DIR workshop: Handling Social Science Data

22. GESDE – Search and browse supplementary data on occupations; educational qualifications; ethnicity
17/MAR/2010
DIR workshop: Handling Social Science Data

23. Conclusions: What we do and what we ought to do
Research writers routinely select single simplifying sub-optimal collinear categorisations of concepts
Due to coordinated instructions [e.g. Blossfeld et al. 2006]
Due to perceived lack of available alternatives
Due to perceived convenience
To make longitudinal comparative analyses more scientific (cumulative & open to cross-examination) we should…
Acknowledge and discuss our equivalence strategies
Operationalise and deploy various categorisations (measurement equivalence) and scalings/arithmetic measures (meaning equivalence), and explore their distributional properties
… and keep a replicable trail of all these activities..
…ideally by using GESDE…!!

24. References
Blossfeld, H. P., Mills, M., & Bernardi, F. (Eds.). (2006). Globalization, Uncertainty and Men's Careers: An International Comparison. Cheltenham: Edward Elgar.
Bosveld, K., Connolly, H., Rendall, M. S., & (2006). A guide to comparing 1991 and 2001 Census ethnic group data. London: Office for National Statistics.
Harkness, J. (2008). Comparative survey research: goals and challenges. In E. De Leeuw, J. Hox & D. A. Dillman (Eds.), International Handbook of Survey Methodology. London: Psychology Press.
Harkness, J., van de Vijver, F. J. R., & Mohler, P. P. (Eds.). (2003). Cross-Cultural Survey Methods. New York: Wiley.
Hoffmeyer-Zlotnik, J. H. P., & Harkness, J. (Eds.). (2005). Methodological Aspects in Cross-National Research. Mannheim: GESIS - ZUMA Zentrum fur Umfragen, Methoden und Analysen.
Jowell, R., Roberts, C., Fitzgerald, R., & Eva, G. (2007). Measuring Attitudes Cross-Nationally. London: Sage.
Lambert, P. S., Prandy, K., & Bottero, W. (2007). By Slow Degrees: Two Centuries of Social Reproduction and Mobility in Britain. Sociological Research Online, 12(1).
Li, Y., & Heath, A. F. (2008). Socio-Economic Position and Political Support of Black and Ethnic Minority Groups in the United Kingdom, [computer file]. 2nd Edition. Colchester: UK Data Archive [distributor], SN: 5666.
Mohler, P. P., Pennell, B.-E., & Hubbard, F. (2008). Survey Documentation: Toward professional knowledge management in sample surveys. In E. De Leeuw, J. Hox & D. A. Dillman (Eds.), International Handbook of Survey Methodology (pp ). Hove: Psychology Press.
Rose, D., & Harrison, E. (Eds.). (2010). Social Class in Europe: An Introduction to the European Socio-economic Classification London: Routledge.
Schneider, S. L. (2010). Nominal comparability is not enough: (In-)Equivalence of construct validity of cross-national measures of educational attainment in the European Social Survey. Research in Social Stratification and Mobility.
Simpson, L., & Akinwale, B. (2006). Quantifying Stablity and Change in Ethnic Group. Manchester: University of Manchester, CCSR Working Paper
Treiman, D. J. (2007). The Legacy of Apartheid: Racial Inequalities in the New South Africa. In A. F. Heath & S. Y. Cheung (Eds.), Unequal Chances: Ethnic Minorities in Western Labour Markets. Oxford: Oxford University Press, for the British Academy.
Treiman, D. J. (2009). Quantitative Data Analysis: Doing Social Research to Test Ideas. New York: Jossey Bass.

25. Abstract
In working with longitudinal social surveys, we sometimes ignore, or at the very least over-simplify, challenges of measurement comparability in variables over time. Often, our approaches to harmonization involve searching for categories with consistent names over time, frequently achieving such ‘nominal equivalence’ by reducing the level of detail of measures to the so-called lowest common denominator.
In this paper we highlight some well-known, and some less well-known, comparability problems for categorical measures in longitudinal surveys. We focus on UK studies and their measures of occupational position, educational qualifications and ethnicity. These are widely measured and important categorical variables, whether being central analytical variables, or relevant controls in analyses with a different focus. We propose contributions in the form of (i) new online services that can assist in harmonizing measures, which we have generated through a recent ESRC-funded project on ‘data management’ ( and (ii) our own suggestions on achieving effective harmonization of longitudinal variables, which focus upon documentation for replication, the recognition of age-cohort distributional differences, and a general advocacy of scaling categories as part of a strategy of ‘functional equivalence’. These cannot solve every comparability challenge in these areas, but, we argue, are steps in the right direction.

26. Data curation tool (for collecting metadata)
17/MAR/2010
DIR workshop: Handling Social Science Data

27. [Timing issues] Relation between time of survey sweep and exact timing of interview
NCDS(March)/BCS (April), & relative age effect in Engl. (Jul/A) & Scotland (Jan/Feb)
In some instances, categorical thresholds make timing matter more (e.g. b/a exam res)
…9/11 effects seem evident in the BHPS, but vanish after controlling for age

29. Predicting poor subjective health, BHPS w15