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exp4_analysis_ratings.Rmd
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exp4_analysis_ratings.Rmd
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---
title: 'Experiment 4: Ratings Analyses'
date: "`r Sys.Date()`"
output:
github_document:
toc: true
toc_depth: 3
pdf_document:
toc: true
toc_depth: 3
editor_options:
markdown:
wrap: 72
---
```{r setup, include=FALSE}
library(tidyverse)
options(dplyr.summarise.inform = FALSE)
library(magrittr)
library(lme4)
library(lmerTest)
library(buildmer)
library(kableExtra)
```
# Setup
Variable names:
- Experiment: exp4\_
- Data (\_d\_)
- d = main df
- Models (\_m\_)
- like = Likeability ratings
- acc = Accomplishment ratings
- imp = Importance ratings
- ratings = build model with all 3 ratings
```{r load-data}
exp4_d <- read.csv("../data/exp4_data.csv",
stringsAsFactors = TRUE) %>%
rename("Participant" = "SubjID", "Item" = "Name") %>%
select(
Participant, Condition,
GenderRating, Item, Male, Female, Other,
Likeable, Accomplished, Important
)
str(exp4_d)
```
Center gender rating for names: Original scale from 1 to 7, with 1 as
most masculine and 7 as most feminine. Mean-centered with higher still
as more feminine.
```{r center-gender-rating}
exp4_d %<>% mutate(GenderRatingCentered = scale(GenderRating, scale = FALSE))
```
Set contrasts for name conditions, now weighted to account for uneven
sample sizes. This uses Scott Fraundorf's function for weighted
contrasts. (The psycholing package version doesn't support doing 2v1
comparisons, only 1v1.) Condition1 is Last vs First+Full. Condition2 is
First vs Full.
```{r contrast-coding}
source("centerfactor.R")
contrasts(exp4_d$Condition) <- centerfactor(
exp4_d$Condition, c("last", "first")
)
contrasts(exp4_d$Condition)
```
Flip ratings from 1=most likeable/accomplished/important to 7=most
L/A/I, to make interpreting models easier, then mean-center.
```{r flip-ratings}
exp4_d %<>% mutate(
LikeableFlip = recode(Likeable,
"1" = 7, "2" = 6, "3" = 5, "4" = 4, "5" = 3, "6" = 2, "7" = 1
),
AccomplishedFlip = recode(Accomplished,
"1" = 7, "2" = 6, "3" = 5, "4" = 4, "5" = 3, "6" = 2, "7" = 1
),
ImportantFlip = recode(Important,
"1" = 7, "2" = 6, "3" = 5, "4" = 4, "5" = 3, "6" = 2, "7" = 1
),
LikeableCentered = scale(LikeableFlip, scale = FALSE),
AccomplishedCentered = scale(AccomplishedFlip, scale = FALSE),
ImportantCentered = scale(ImportantFlip, scale = FALSE)
)
str(exp4_d)
```
# Likeability
Summary statistics:
```{r means-likeable}
summary(exp4_d$Likeable)
summary(exp4_d$LikeableFlip)
sd(exp4_d$Likeable)
```
Does the Likeability rating of the character predict how likely the
character is to be recalled as female, as opposed to male or other? The
maximal model includes random intercepts by item, but not by
participant.
```{r model-likeable, cache=TRUE}
exp4_m_like <- buildmer(
formula = Female ~ Condition * GenderRatingCentered *
LikeableCentered + (1 | Participant) + (1 | Item),
data = exp4_d, family = binomial,
buildmerControl(direction = "order", quiet = TRUE)
)
summary(exp4_m_like)
```
- Characters who are rated more Likeable are more likely to be
recalled as female across conditions
- Interaction with Name Gender Rating: stronger effect of Likeability
rating for more feminine names
- Interaction with Condition (F vs F): n.s. after multiple comparisons
- No other interactions significant
# Accomplishment
Summary statistics:
```{r means-accomplishment}
summary(exp4_d$Accomplished)
summary(exp4_d$AccomplishedFlip)
sd(exp4_d$Accomplished)
```
Does the Accomplishment rating of the character predict how likely the
character is to be recalled as female, as opposed to male or other? The
maximal model includes random intercepts by item, but not by
participant.
```{r model-accomplishment, cache=TRUE}
exp4_m_acc <- buildmer(
formula = Female ~ Condition * GenderRatingCentered *
AccomplishedCentered + (1 | Participant) + (1 | Item),
data = exp4_d, family = binomial,
buildmerControl(direction = "order", quiet = TRUE)
)
summary(exp4_m_acc)
```
- Characters who were rated more Accomplished were more likely to be
recalled as female, but this is n.s. after correction for multiple
comparisons.
- Interaction with Name Gender Rating: stronger effect of
Accomplishment rating for more feminine names
- Interaction between Condition (L vs F+F), Name Gender Rating, and
Accomplishment: n.s. after correction for multiple comparisons
# Importance
Summary statistics:
```{r means-importance}
summary(exp4_d$Important)
summary(exp4_d$ImportantFlip)
sd(exp4_d$Important)
```
Does the Importance rating of the character predict how likely the
character is to be recalled as female, as opposed to male or other? The
maximal model includes random intercepts by item, but not by
participant.
```{r model-importance, cache=TRUE}
exp4_m_imp <- buildmer(
formula = Female ~ Condition * GenderRatingCentered *
ImportantCentered + (1 | Participant) + (1 | Item),
data = exp4_d, family = binomial,
buildmerControl(direction = "order", quiet = TRUE)
)
summary(exp4_m_imp)
```
- No main effect of Importance like there was for other ratings
- Interaction with Name Gender Rating: stronger effect of Importance
rating for more feminine names
# All Ratings
```{r model-ratings-all, cache=TRUE}
exp4_m_ratings <- buildmer(
formula = Female ~ Condition * GenderRatingCentered * LikeableCentered *
AccomplishedCentered * ImportantCentered +
(1 | Participant) + (1 | Item),
data = exp4_d,
family = binomial,
buildmerControl(direction = c("order", "backward"), quiet = TRUE)
)
summary(exp4_m_ratings)
```