Statistics – spss

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). Are the results of the three analyses similar? If not, explain (in non-technical 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. (2000 words maximum)