Quality & Quantity

, Volume 48, Issue 2, pp 697–712 | Cite as

The inverse operationalisation of concepts for the secondary analysis of quantitative data: an example from the study of parental collaboration

  • Stephen HinchliffeEmail author


Properly validated scalar variables are often viewed as the gold standard for the operationalisation of concepts in quantitative data. This is a sensible approach at the planning stage of the survey process. However, when working with data that has already been collected for another purpose, such variables cannot always be expected. This is particularly the case when one wishes to analyse a concept that has not previously been studied in a particular context. This paper provides an example of the construction of a binary variable for the concept of parental collaboration, using data from the Growing Up in Scotland study. It examines the decision-making process for the “inverse operationalisation” of the concept, an innovative method which starts with the assumption that all cases in the dataset demonstrate a particular property (parental collaboration), and gradually chips away at those which provide sufficient evidence to suggest otherwise, until a working variable is created.


Secondary data analysis Operationalisation of concepts Indicator methodology Coparenting Collaborative parenting 


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  1. Abidin R.R., Brunner J.F.: Development of a parenting alliance inventory. J. Clin. Child Psychol. 24, 31–40 (1995)CrossRefGoogle Scholar
  2. Adcock R., Collier D.: Measurement validity: a shared standard for qualitative and quantitative research. Am. Polit. Sci. Rev. 95, 529–546 (2001)CrossRefGoogle Scholar
  3. Bollen K., Lennox R.: Conventional wisdom on measurement: a structural equation perspective. Psychol. Bull. 110, 305–314 (1991)CrossRefGoogle Scholar
  4. Bradley R.H., Corwyn R.F.: Socioeconomic status and child development. Annu. Rev. Psychol. 53, 371–399 (2002)CrossRefGoogle Scholar
  5. Buckley C.K., Schoppe-Sullivan S.J.: Father involvement and coparenting behavior: parents’ nontraditional beliefs and family earner status as moderators. Pers. Relat. 17, 413–431 (2010)CrossRefGoogle Scholar
  6. Clark L.A., Watson D.: Constructing validity: basic issues in objective scale development. Psychol. Assess. 7, 309–319 (1995)CrossRefGoogle Scholar
  7. Cortina J.M.: What is coefficient alpha? An examination of theory and applications. J. Appl. Psychol. 78, 98–104 (1993)CrossRefGoogle Scholar
  8. Cronbach L.J.: Coefficient alpha and the internal consistency of tests. Psychometrika 16, 297–334 (1951)CrossRefGoogle Scholar
  9. Dale A., Arber S., Proctor M.: Doing Secondary Analysis. Unwin Hyman Ltd., London (1988)Google Scholar
  10. Deutsch F.M.: Halving it All: How Equally Shared Parenting Works. Harvard University Press, Cambridge (1999)Google Scholar
  11. DeVellis R.F.: Scale Development: Theory and Applications. 2nd edn. Sage Publications Inc., Thousand Oaks (2003)Google Scholar
  12. Diamantopoulos A., Winklhofer H.M.: Index construction with formative indicators: an alternative to scale development. J. Mark. Res. 38, 269–277 (2001)CrossRefGoogle Scholar
  13. Dillenbourg P.: What do you mean by collaborative learning?. In: Dillenbourg, P. (ed.) Collaborative-learning: Cognitive and Computational Approaches, pp. 1–19. Elsevier, Oxford (1999)Google Scholar
  14. Fagan J., Lee Y.: Do coparenting and social support have a greater effect on adolescent fathers than adult fathers?. Fam. Relat. 60, 247–258 (2011)CrossRefGoogle Scholar
  15. Feinberg M.E.: Coparenting and the transition to parenthood: a framework for prevention. Clin. Child Fam. Psychol. Rev. 5, 173–195 (2002)CrossRefGoogle Scholar
  16. Feinberg M.E.: The internal structure and ecological context of coparenting: a framework for research and intervention. Parent. Sci. Pract. 3, 95–132 (2003)CrossRefGoogle Scholar
  17. Feinberg M.E., Kan M.L.: Establishing family foundations: intervention effects on coparenting, parent/infant well-being, and parent-child relations. J. Fam. Psychol. 22, 253–263 (2008)CrossRefGoogle Scholar
  18. Goodman R.: The strengths and difficulties questionnaire a research note. J. Child Psychol. Psychiatry 38, 581–586 (1997)CrossRefGoogle Scholar
  19. Hsieh C.M.: To weight or not to weight: the role of domain importance in quality of life measurement. Soc. Indic. Res. 68, 163–174 (2004)CrossRefGoogle Scholar
  20. Kiecolt K.J., Nathan L.E.: Secondary Analysis of Survey Data. Sage, London (1985)Google Scholar
  21. Leamer E.E.: Sensitivity analyses would help. Am. Econ. Rev. 75, 308–313 (1985)Google Scholar
  22. Marsh C.: The Survey Method: The Contribution of Surveys to Sociological Explanation. George Allen & Unwin, London (1982)Google Scholar
  23. McHale J.P.: Overt and covert coparenting processes in the family. Fam. Process 36, 183–201 (1997)CrossRefGoogle Scholar
  24. Miller B.: making measures capture concepts: tools for securing correspondence between theoretical ideas and observations. In: Gschwend, T., Schimmelfennig, F. (eds.) Research Design in Political Science, pp. 83–102. Palgrave MacMillan, Basingstoke (2007)Google Scholar
  25. Murphy J.W., Schlaerth C.A.: Where are your data? A critique of secondary data analysis in sociological research. Humanit. Soc. 34, 379–390 (2010)CrossRefGoogle Scholar
  26. Netemeyer R.G., Bearden W.O., Sharma S.: Scaling Procedures: Issues and Applications. Sage Publications, Inc, Thousand Oaks (2003)Google Scholar
  27. Oakley J.E., O’Hagan A.: Probabilistic sensitivity analysis of complex models: a Bayesian approach. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 66, 751–769 (2004)CrossRefGoogle Scholar
  28. Oxford R.L.: Cooperative learning, collaborative learning, and interaction: three communicative strands in the language classroom. Mod. Lang. J. 81, 443–456 (1997)CrossRefGoogle Scholar
  29. Saltelli A., Tarantola S., Campolongo F.: Sensitivity analysis as an ingredient of modeling. Stat. Sci. 15, 377–395 (2000)CrossRefGoogle Scholar
  30. Smith E.: Pitfalls and promises: the use of secondary data analysis in educational research. Br. J. Educ. Stud. 56, 323–339 (2008)CrossRefGoogle Scholar
  31. Smith A.K., Ayanian J.Z., Covinsky K.E., Landon B.E., McCarthy E.P., Wee C.C., Steinman M.A.: Conducting high-value secondary dataset analysis: an introductory guide and resources. J. Gen. Intern. Med. 26, 920–929 (2011)CrossRefGoogle Scholar
  32. Spector P.E.: Summated Ratings Scale Construction: An Introduction. Sage Publications, Inc., Newbury Park (1992)Google Scholar
  33. Teubert D., Pinquart M.: The association between coparenting and child adjustment: a meta-analysis. Parent. Sci. Pract. 10, 286–307 (2010)CrossRefGoogle Scholar
  34. Van Egeren L.A., Hawkins D.P.: Coming to terms with coparenting: implications of definition and measurement. J. Adult Dev. 11, 165–178 (2004)CrossRefGoogle Scholar
  35. Vartanian T.P.: Secondary Data Analysis. Oxford University Press, New York (2011)Google Scholar
  36. Wang Y., Xiang Z.: Toward a theoretical framework of collaborative destination marketing. J. Travel Res. 46, 75–85 (2007)CrossRefGoogle Scholar
  37. Weissman S., Cohen R.: The parenting alliance and adolescence. Ann. Am. Soc. Adolesc. Psychiatry 12, 24–45 (1985)Google Scholar
  38. Wood D.J., Gray B.: Toward a comprehensive theory of collaboration. J. Appl. Behav. Sci. 27, 139–162 (1991)CrossRefGoogle Scholar
  39. Yorke M.: Analysing existing datasets: some considerations arising from practical experience. Int. J. Res. Method Educ. 34, 255–267 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Centre for Research on Families and RelationshipsUniversity of EdinburghEdinburghUK

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