Problem set 11

Complete this task in teams of up to three students.

Submission information: please submit on ZoneCours

Task 1

Read Rohrer et al. (2022) and write a summary.

Task 2

Liu et al. (2023) postulated in their Experiment 5b that the underestimation of the appreciation of initiators relative to recipients was mediated by the degree of surprise of the recipient. Their data can be obtained from LRMM23_S5b in the R package hecedsm. You can also download the SPSS database via this link.

  1. Fit the linear mediation model using the PROCESS macro or the mediate function from the mediation R package1
    • use the nonparametric bootstrap with the percentile method to get confidence intervals.
    • compare the coefficients with those of Figure 1 from the paper.
  2. Zhao et al. (2010) review the typology of mediation. Identify the type of mediation (complementary, competitive or indirect only) based on coefficients.
    • complementary mediation when both direct and indirect effects are of the same sign and non-zero.
    • competitive mediation when direct and indirect effects are of opposite signs.
    • indirect-only mediation when the direct effect of \(X \to Y\) is null, but the effect \(X \to M \to Y\) isn’t.
  3. The paper does not discuss any of the assumptions needed to make causal claims. List the latter and explain how some may fail to be valid, thus casting doubt on the conclusions drawn by Liu et al. (2023).

References

Liu, P. J., Rim, S., Min, L., & Min, K. E. (2023). The surprise of reaching out: Appreciated more than we think. Journal of Personality and Social Psychology, 124(4), 754–771. https://doi.org/10.1037/pspi0000402
Rohrer, J. M., Hünermund, P., Arslan, R. C., & Elson, M. (2022). That’s a lot to process! Pitfalls of popular path models. Advances in Methods and Practices in Psychological Science, 5(2). https://doi.org/10.1177/25152459221095827
Zhao, X., Lynch, Jr., John G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257

Footnotes

  1. Note that the authors used neither age nor gender in the model fit. For the latter, there are two few other to reliably estimate coefficients, so they should be removed beforehand should one be interested in adding the variable as a potential confounder.↩︎