Moderation

This week is about using regression for testing moderation hypotheses as well as curvilinear hypotheses.

Primary readings

• Kenny, David. Handout on moderation [html]

• UCLA handout on curvilinear relationships [html]

• Spiller, S. A., Fitzsimons, G. J., Lynch Jr, J. G., & McClelland, G. H. (2013). Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression. Journal of Marketing Research, 50(2), 277-288.

  • Ajay Mehra, Martin Kilduff and Daniel J. Brass. The Social Networks of High and Low Self-Monitors: Implications for Workplace Performance. Administrative Science Quarterly. Vol. 46, No. 1 (Mar., 2001), pp. 121-146

  • [pdf]

Secondary readings

• Kromrey, J. D., & Foster-Johnson, L. (1998). Mean centering in moderated multiple regression: Much ado about nothing. Educational and Psychological Measurement, 58(1), 42-67. [^pdf]

• Sutton, R.I. and A. Rafaeli. 1988. "Untangling the relationship between displayed emotions and organizational sales: The case of convenience stores." Academy of Management Journal. 31(3):461-487. [^pdf]

  • in my view, the effect of smiles on sales is moderated by linelength

• Phil Ender handout

Datasets and Do files

• Protest dataset

  • • includes do file for creating interaction plots

• GSS2008 dataset

  • • includes do file for creating interaction plots and for Neyman-Johnson floodlighting

Software tools for SPSS

• PROCESS macro for SPSS and SAS

  • • Download latest from http://www.processmacro.org or use this (possibly old) zip file (zip)

    • Documentation:

      • Brief handout [html]

    • Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis. New York: The Guilford Press. [web page]

• After installing PROCESS, we will be working with the gss2008 dataset. A few commands we will execute:

• • compute revhappy = 4 - happy

    • filter the cases to retain only sibs < 20 and income > 0 and income < 13

  • process vars=sex age sibs income childs revhappy/y=revhappy/x=childs/m=income/model=1/jn=1/quantile=1/plot=1.

  • We will also be working with the protest dataset. A few commands we will execute:

  • process vars = protest liking respappr /y=liking/x=protest/m=respappr/total=1/model=4/boot=5000/effsize=1/normal=1.

  • process vars=protest liking sexism/y=liking/x=protest/m=sexism/model=1/jn=1/quantile=1/plot=1.

Old Slides

• moderation.pdf