Measurement and data quality in longitudinal research

  • L. R. Bergman


The importance of paying attention to scale levels is emphasized and it is pointed out that Steven's hierarchy of ratio, interval, ordinal, and nominal scales is too narrow; other important scale properties have to be considered. For instance, sometimes a carefully constructed variable on a nominal scale contains more information than a variable at a higher scale level. Direct versur indirect measurement and relative versus absolute measurement are also discussed and the effects of errors of measurement on the results are considered. It is not infrequent in a longitudinal setting to disregard sampling considerations, which can be very unfortunate. Such considerations, as well as the use of modern sampling theory, can considerably enhance the quality of a longitudinal study. Finally, a number of conclusions and recommendations are given for the carrying out of longitudinal research in relation to measurement issues.

Key words

Longitudinal measurement 


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  1. 1.
    Bergman LR (1972) Inferential aspects of longitudinal data in studying developmental problems. Human Development 15:287–293Google Scholar
  2. 2.
    Bergman LR (1993) Some methodological issues in longitudinal research: looking ahead. In: Magnusson D, Caesar P (eds) Longitudinal Research on Human Development: Present Status and Future Perspectives. Cambridge University Press, CambridgeGoogle Scholar
  3. 3.
    Bergman LR (1995) The measurement, evaluation and development laboratory at Statistics Sweden: Background, present work and prospects. Journal of Official StatisticsGoogle Scholar
  4. 4.
    Bergman LR, Magnusson D (1990) General issues about data quality in longitudinal research. In: Magnusson D, Bergman LIZ (eds) Data Quality in Longitudinal Research. Cambridge University Press, CambridgeGoogle Scholar
  5. 5.
    Bergman LR, Magnusson D (1991) Stability and change in patterns of extrinsic adjustment problems. In: Magnusson D et al (eds) Problems and Methods in Longitudinal Research: Stability and Change. Cambridge University Press, CambridgeGoogle Scholar
  6. 6.
    Burr JA, Nesselroade JR (1990) Change measurement. In: von Eye A (ed) Statistical Methods in Longitudinal Research. Academic Press, Boston, Vol IGoogle Scholar
  7. 7.
    Chatterjee S, Yilmaz MR (1992) Chaos, fractals and statistics. Statistical Science 7:49–67Google Scholar
  8. 8.
    Clogg CG (1992) The impact of sociological methodology on statistical methodology. Statistical Science 7:183–207Google Scholar
  9. 9.
    Forsyth BH, Lessler JT (1991) Cognitive laboratory methods: a taxonomy. In: Biemer P et al (eds) Measurement Errors in Surveys. Wiley, New YorkGoogle Scholar
  10. 10.
    Goldstein H (1979) The Design and Analysis of Longitudinal Studies. Academic Press, LondonGoogle Scholar
  11. 11.
    Groves W (1989) Survey Errors and Survey Costs. Wiley, New YorkGoogle Scholar
  12. 12.
    Hahn GJ, Meeker WQ (1993) Assumptions for statistical inference. The American Statistician, 47:1–11Google Scholar
  13. 13.
    Janson CG (1990) Retrospective data, undesirable behavior, and the longitudinal perspective. In: Magnusson D, Bergman LR (eds) Data Quality in Longitudinal Research. Cambridge University Press, CambridgeGoogle Scholar
  14. 14.
    Jöreskog KG (1979) Statistical estimation of structural models in longitudinal developmental investigations. In: Nesselroade JR, Baltes PB (eds) Longitudinal Research in the Study of Behavior and Development. Academic Press, New YorkGoogle Scholar
  15. 15.
    Lepkowski JM (1989) Treatment of wave nonresponse in panel surveys. In: Kasprzyk et al (eds) Panel Surveys. Wiley, New YorkGoogle Scholar
  16. 16.
    Mosteller F, Turkey JW (1977) Data analysis and regression. Addison Wesley, BostonGoogle Scholar
  17. 17.
    Nesselroade JR, Ford (1985) P-technique comes of age: multivariate, replicated, single-subject designs for research on older adults. Research on Ageing 7:46–80Google Scholar
  18. 18.
    Norlen U (1977) Response errors in the answers to retrospective questions. Statistisk Tidskrift/Statistical Review 15:331–341Google Scholar
  19. 19.
    Stevens SS (1951) Mathematics, measurement, and psychophysics. In: Stevens SS (ed) Handbook of Experimental Psychology. Wiley, New YorkGoogle Scholar
  20. 20.
    Velleman PF, Wilkinson L (1993) Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician 47:65–72Google Scholar

Copyright information

© Steinkopff Verlag 1996

Authors and Affiliations

  • L. R. Bergman
    • 1
  1. 1.Department of PsychologyUniversity of StockholmStockholmSweden

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