Analysis of Multiple Respondent Data

Abstract

The nature of family research is such that several family members may be asked to comment on identical items. The purpose of this article is to discuss several approaches for analysis of multiple respondent data applicable to family research. Analysis techniques will focus on one dependent variable. Of special consideration in analysis of multiple respondent data is the violation of statistical assumptions, specifically, the assumption of sphericity. One method of handling this assumption violation will be addressed. An example from a recent family-focused study will be provided to demonstrate differences in significance obtained through use of one-way analysis of variance (ANOVA), repeated measures ANOVA, and Hotellings T².

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References

Bozdogan, H., & Gupta, A. (Eds.). (1987). Multivariate statistical methods and data analysis. Boston: Reidel.

Everitt, B. S., & Dunn, G. (1992). Applied multivariate data analysis. New York: Oxford University Press.

Gray, B. A. (2001). Women’s subjective appraisal of pregnancy risk and the effects of uncertainty, perceived control, coping, and emotions on maternal attachment. Unpublished doctoral dissertation, Case Western Reserve University, Cleveland, OH.

Green, P. E. (1978). Analyzing multivariate data. Hinsdale, IL: Dryden.

Maxwell, S. E., & Delaney, H. D. (2000). Designing experiments and analyzing data: Amodel comparison perspective. Mahwah, NJ: Lawrence Erlbaum.

Rutherford, A. (2001). Introducing ANOVA and ANCOVA. A GLM approach. London: Sage.

Schumm, W. R., Barnes, H. L., Bollman, S. R., Jurich, A. P., & Milliken, G. A. (1983). Approaches to the statistical analysis of family data. Home Economics Research Journal, 14, 112-122.

Stevens, J. (2002). Applied multivariate statistics for the social sciences. Mahwah, NJ: Lawrence Erlbaum.

Timm, N. H. (2002). Applied multivariate analysis. New York: Springer.