# Does the knowledge that the rated person will hear their rating (and perhaps be hurt) lead participants to give higher (or lower) ratings (pity effect)?

Conduct and report the appropriate statistics for the resubmission data set you are given based on the
scenario below as one would for an academic journal. Be sure to report the means, group sizes, and standard
deviations of the discrete variables in a table and to make a plot of all the significant effects.

Scenario: A group of researchers (although the data are made up, this is based on a real study that was
published in Psychological Science: Joel, Teper, & MacDonald, 2014) wants to investigate how likely people are
to agree to date unattractive people out of pity. In order to do this, they asked 40 heterosexual female
participants (raters) to rate 20 male confederates (rated) of different attractiveness. The confederates were
also present in the lab and were introduced as participants in the same study. Participants rated the
confederates in terms of how likely they would be to go on a date with each man (on a scale from 0 =
extremely unlikely to 100 = extremely likely). For half of the rated confederates, the confederate had left the
room when participants gave the rating to the experimenter (absent condition). For the other half of the rated
confederates, the confederates were present in the room and listening when the participants gave the rating
to the experimenter (present condition). In order to see if attractiveness played a role, the researchers also
obtained attractiveness ratings (from 0 = extremely unattractive to 10 = extremely attractive) for the 20
photographs from a different group of participants.

Instructions: Conduct and report the appropriate statistics using the data provided as one would for a Results
section an academic journal. References are not necessary. Be sure to report the means, group sizes, and
standard deviations of the discrete variables in a table and to make a plot of all the significant effects. Perform
and report three sets of analyses, each testing all of the three relevant null hypotheses about the fixed effects
(There is no main effect of rating condition; there is no main effect of attractiveness; there is no interaction
between rating condition and attractiveness):
1. Two repeated‐measures Analysis of Variance, one over raters (F1) and one over rated individuals (F2)
with rating condition and attractiveness as discrete predictors.
2. A standard multiple regression model with rating condition as a discrete predictor and attractiveness
as a continuous predictor (ignoring the random effects of rater and rated individual).
3. A linear mixed model with rating condition as a discrete predictor and attractiveness as a continuous
predictor and random intercepts for both participant and rated person (as we want to be able to
generalise the results beyond the 40 raters and the 20 rated individuals).

IMPORTANT: In order to make everyone’s analyses comparable, please perform your analyses on all the data
points provided and do not remove outliers or transform the data. For the standard multiple regression model
and the LMM, use treatment contrasts (where the baseline is represented 0 and the other condition is
represented as 1) for the experimental condition with the “absent” condition as the baseline.

You should address the following issues: Are the results of the three analyses similar? If not, explain (in nontechnical terms) why not. Which analysis is more appropriate to the data? In layperson (non‐academic)language describe the results and summarise the answers to the following questions referring to the three hypotheses tested: Does the knowledge that the rated person will hear their rating (and perhaps be hurt) lead participants to give higher (or lower) ratings (pity effect)? Is the date rating affected by the attractiveness of the rated person? Does the pity effect disappear (or maybe get stronger) for very attractive people? Explain how the results of these analyses affect your interpretation.

Important: there are three files for each user name, but they all contain data from the same dataset. The “YourID_Assignment_data.csv” files contain the unaggregated data (i.e. by rater and rated), for use in the multiple regression and the linear mixed model analyses. The “YourID_Assignment_data_by_RATER.csv” files contain the same data aggregated (averaged) by rater, for use in the F1 ANOVA. The “YourID_Assignment_data_by_RATED.csv” files contain the same data aggregated (averaged) by rated, for use in the F2 ANOVA. The latter files have within-subjects conditions as columns as required by the repeated-measures module in jamovi and SPSS. This saves you the step of using Pivot Tables in Excel to generate these files.