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The Analysis of Factorial Surveys

Author

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  • JOOP J. HOX

    (University of Amsterdam)

  • ITA G. G. KREFT

    (University of California, Los Angeles)

  • PIET L. J. HERMKENS

    (University of Utrecht)

Abstract

Factorial surveys constitute a specific technique for introducing experimental designs in sample surveys. Respondents are presented with descriptions (vignettes) of a constructed world in which important factors are built in experimentally. Using balanced designs well known from the multivariate experimental tradition, it is possible to build in a relatively large number of factors and levels. Within this context, the normal hypothesis is that responses are consistent on the individual level, but not totally idiosyncratic. In the analysis, it is important to determine the influence of both the vignette and the respondent variables. Analysis models for this type of data should reflect the fact that factorial surveys produce data pertaining to two distinct levels: the individual level and the vignette level. Such models are available and are generally known as multilevel analysis models. This article discusses the properties of factorial survey designs and some analysis models that address the multilevel aspects of the data. An example is presented using data on judgments on the fairness of incomes.

Suggested Citation

  • Joop J. Hox & Ita G. G. Kreft & Piet L. J. Hermkens, 1991. "The Analysis of Factorial Surveys," Sociological Methods & Research, , vol. 19(4), pages 493-510, May.
    • Handle: RePEc:sae:somere:v:19:y:1991:i:4:p:493-510
    DOI: 10.1177/0049124191019004003
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    References listed on IDEAS

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    1. G. Kreft & E. Leeuw, 1988. "The See-Saw Effect: a multilevel problem?," Quality & Quantity: International Journal of Methodology, Springer, vol. 22(2), pages 127-137, June.
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    Cited by:

    1. Thomas Mellewigt & Adeline Thomas & Ingo Weller & Edward J. Zajac, 2017. "Alliance or Acquisition? A Mechanisms‐Based, Policy‐Capturing Analysis," Strategic Management Journal, Wiley Blackwell, vol. 38(12), pages 2353-2369, December.
    2. Osiander, Christopher & Stephan, Gesine, 2018. "Unter welchen Bedingungen würden sich Beschäftigte weiterbilden? : Ergebnisse eines faktoriellen Surveys," IAB-Discussion Paper 201804, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Katrin Auspurg & Annette Jäckle, 2017. "First Equals Most Important? Order Effects in Vignette-Based Measurement," Sociological Methods & Research, , vol. 46(3), pages 490-539, August.
    4. Mariacristina De Nardi & Chao Wei & Liqian Ren, 2000. "On the Distribution of Income in Five Countries," LIS Working papers 227, LIS Cross-National Data Center in Luxembourg.
    5. Sascha Kraus & Fabian Meier & Thomas Niemand & Ricarda B. Bouncken & Paavo Ritala, 2018. "In search for the ideal coopetition partner: an experimental study," Review of Managerial Science, Springer, vol. 12(4), pages 1025-1053, October.
    6. Drewniak, Daniel & Krones, Tanja & Sauer, Carsten & Wild, Verina, 2016. "The influence of patients’ immigration background and residence permit status on treatment decisions in health care. Results of a factorial survey among general practitioners in Switzerland," Social Science & Medicine, Elsevier, vol. 161(C), pages 64-73.

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