2023-03-26
Variable names:
- Experiment: exp4_
- Data (_d_)
- d = main df
- count =sums of response types
- Models (_m_)
- count =sums of response types
- all = effect of Condition and Name Gender Rating, including other responses
- cond = effect of Condition only
- FF = dummy coded with First + Full Name conditions as 0, Last Name condition as 1
- L = dummy coded with Last Name condition as 0, First + Full Name conditions as 1
- first = dummy coded with First Name condition as 0, Full Name and Last Name conditions as 1
- full = dummy coded with Full Name condition as 0, First Name and Last Name conditions as 1
Load data and select columns used in model. See data/exp4_data_about.txt for more details.
exp4_d <- read.csv("../data/exp4_data.csv",
stringsAsFactors = TRUE) %>%
rename("Participant" = "SubjID", "Item" = "Name") %>%
select(
Participant, Condition, GenderRating,
Item, Male, Female, Other
)
str(exp4_d)## 'data.frame': 8771 obs. of 7 variables:
## $ Participant : Factor w/ 1253 levels "Exp4_P1","Exp4_P10",..: 520 520 520 520 520 520 520 1143 1143 1143 ...
## $ Condition : Factor w/ 3 levels "first","full",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ GenderRating: num 6.24 2.61 6.82 5.34 1.28 4.39 3.87 5.22 1.24 5.86 ...
## $ Item : Factor w/ 63 levels "Ashley Cook",..: 1 18 21 22 25 28 50 5 7 15 ...
## $ Male : int 0 1 0 0 1 1 1 1 1 0 ...
## $ Female : int 1 0 1 1 0 0 0 0 0 1 ...
## $ Other : int 0 0 0 0 0 0 0 0 0 0 ...
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.
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.
source("centerfactor.R")
contrasts(exp4_d$Condition) <- centerfactor(
exp4_d$Condition, c("last", "first")
)
contrasts(exp4_d$Condition)## [,1] [,2]
## first 0.3312051 -0.497605746
## full 0.3312051 0.502394254
## last -0.6687949 0.002394254
Responses by condition.
exp4_d %<>% mutate(ResponseAll = case_when(
Male == 1 ~ "Male",
Female == 1 ~ "Female",
Other == 1 ~ "Other"
))
exp4_d_count <- exp4_d %>%
group_by(Condition, ResponseAll) %>%
summarise(n = n()) %>%
pivot_wider(
names_from = ResponseAll,
values_from = n
) %>%
mutate(
Female_MaleOther = Female / (Male + Other),
Female_Male = Female / Male
)
kable(exp4_d_count)| Condition | Female | Male | Other | Female_MaleOther | Female_Male |
|---|---|---|---|---|---|
| first | 1381 | 1511 | 62 | 0.8779402 | 0.9139643 |
| full | 1380 | 1416 | 116 | 0.9007833 | 0.9745763 |
| last | 1292 | 1529 | 84 | 0.8009919 | 0.8449967 |
- First name condition has second-most (slightly) female responses
- Full name condition has most female responses
- Last name condition has fewest female responses
Because Experiment 4 always introduces the character with a full name, then manipulates the name form in the subsequent 3 references, the main analysis is 1 model, as opposed to the 2 for Experiments 1 and 2.
Effects of Name Condition (first name, last name, full name) and first name Gender Rating (centered, + fem, -masc) on the likelihood of female responses, as opposed to male and other responses. Participant and Item are included as random intercepts, with items defined as the unique first, last and first + last name combinations. Condition1 is the contrast between last and first+full. Condition2 is the contrast between first and full.
exp4_m_all <- glmer(
Female ~ Condition * GenderRatingCentered + (1 | Participant) + (1 | Item),
data = exp4_d, family = binomial
)
summary(exp4_m_all)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Female ~ Condition * GenderRatingCentered + (1 | Participant) +
## (1 | Item)
## Data: exp4_d
##
## AIC BIC logLik deviance df.resid
## 9145.4 9202.1 -4564.7 9129.4 8763
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4531 -0.5754 -0.2627 0.5724 5.4529
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.2014 0.4488
## Item (Intercept) 0.3599 0.5999
## Number of obs: 8771, groups: Participant, 1253; Item, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.25606 0.08161 -3.138 0.001703 **
## Condition1 0.12636 0.06170 2.048 0.040565 *
## Condition2 0.06835 0.07245 0.943 0.345471
## GenderRatingCentered 0.76408 0.04590 16.647 < 2e-16 ***
## Condition1:GenderRatingCentered 0.13147 0.03451 3.809 0.000139 ***
## Condition2:GenderRatingCentered -0.10288 0.04204 -2.447 0.014401 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cndtn1 Cndtn2 GndrRC C1:GRC
## Condition1 0.012
## Condition2 -0.012 -0.016
## GndrRtngCnt -0.028 0.002 0.011
## Cndtn1:GnRC 0.001 -0.121 0.016 0.035
## Cndtn2:GnRC 0.011 0.016 -0.112 -0.030 -0.046
-
Less likely to recall character as female overall
-
More likely to recall character as female in the First and Full Name conditions than in the Last Name condition
-
More likely to recall character as female as first names become more feminine
Double check the directions of the interactions:
Dummy code to get the gender rating effect for just the First and Full Name conditions.
exp4_d %<>% mutate(Condition_FF = case_when(
Condition == "first" ~ 0,
Condition == "full" ~ 0,
Condition == "last" ~ 1
))
exp4_d$Condition_FF %<>% as.factor()
exp4_m_all_FF <- glmer(
Female ~ Condition_FF * GenderRatingCentered +
(1 | Participant) + (1 | Item),
data = exp4_d, family = binomial
)
summary(exp4_m_all_FF)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Female ~ Condition_FF * GenderRatingCentered + (1 | Participant) +
## (1 | Item)
## Data: exp4_d
##
## AIC BIC logLik deviance df.resid
## 9147.7 9190.2 -4567.8 9135.7 8765
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2676 -0.5751 -0.2669 0.5734 4.9543
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.2005 0.4477
## Item (Intercept) 0.3603 0.6002
## Number of obs: 8771, groups: Participant, 1253; Item, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.21216 0.08437 -2.515 0.011917 *
## Condition_FF1 -0.12797 0.06164 -2.076 0.037889 *
## GenderRatingCentered 0.80513 0.04766 16.893 < 2e-16 ***
## Condition_FF1:GenderRatingCentered -0.12944 0.03446 -3.756 0.000172 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cn_FF1 GndrRC
## Conditn_FF1 -0.253
## GndrRtngCnt -0.032 0.026
## Cnd_FF1:GRC 0.027 -0.120 -0.272
Then dummy code to get the gender rating effect just in the Last Name condition.
exp4_d %<>% mutate(Condition_Last = case_when(
Condition == "first" ~ 1,
Condition == "full" ~ 1,
Condition == "last" ~ 0
))
exp4_d$Condition_Last %<>% as.factor()
exp4_m_all_L <- glmer(
Female ~ Condition_Last * GenderRatingCentered +
(1 | Participant) + (1 | Item),
data = exp4_d, family = binomial
)
summary(exp4_m_all_L)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Female ~ Condition_Last * GenderRatingCentered + (1 | Participant) +
## (1 | Item)
## Data: exp4_d
##
## AIC BIC logLik deviance df.resid
## 9147.7 9190.2 -4567.8 9135.7 8765
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2676 -0.5751 -0.2669 0.5734 4.9543
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.2005 0.4477
## Item (Intercept) 0.3603 0.6002
## Number of obs: 8771, groups: Participant, 1253; Item, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.34013 0.09101 -3.737 0.000186 ***
## Condition_Last1 0.12797 0.06164 2.076 0.037896 *
## GenderRatingCentered 0.67569 0.05066 13.338 < 2e-16 ***
## Condition_Last1:GenderRatingCentered 0.12944 0.03446 3.757 0.000172 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cnd_L1 GndrRC
## Condtn_Lst1 -0.442
## GndrRtngCnt -0.049 0.057
## Cndt_L1:GRC 0.056 -0.120 -0.425
exp4_m_all_FF %>%
tidy() %>%
filter(term == "GenderRatingCentered") %>%
pull(estimate)## [1] 0.8051317
exp4_m_all_L %>%
tidy() %>%
filter(term == "GenderRatingCentered") %>%
pull(estimate)## [1] 0.6756906
Interaction indicates Gender Rating has a larger effect in the First and Full Name conditions (0.81) than in the Last Name condition (0.67). This makes sense because the gendered first name is repeated all 4x in the First and Full name conditions, but only once in the Last Name condition.
Dummy code to get the gender rating effect for just the First Name condition.
exp4_d %<>% mutate(Condition_First = case_when(
Condition == "first" ~ 0,
Condition == "full" ~ 1,
Condition == "last" ~ 1
))
exp4_d$Condition_First %<>% as.factor()
exp4_m_all_first <- glmer(
Female ~ Condition_First * GenderRatingCentered +
(1 | Participant) + (1 | Item),
data = exp4_d, family = binomial
)
summary(exp4_m_all_first)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Female ~ Condition_First * GenderRatingCentered + (1 | Participant) +
## (1 | Item)
## Data: exp4_d
##
## AIC BIC logLik deviance df.resid
## 9151.6 9194.0 -4569.8 9139.6 8765
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4660 -0.5784 -0.2629 0.5803 5.4716
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.2059 0.4538
## Item (Intercept) 0.3592 0.5994
## Number of obs: 8771, groups: Participant, 1253; Item, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.24885 0.09227 -2.697 0.00699 **
## Condition_First1 -0.01315 0.06303 -0.209 0.83475
## GenderRatingCentered 0.85944 0.05280 16.277 < 2e-16 ***
## Condition_First1:GenderRatingCentered -0.14454 0.03661 -3.948 7.88e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cnd_F1 GndrRC
## Cndtn_Frst1 -0.468
## GndrRtngCnt -0.055 0.065
## Cndt_F1:GRC 0.064 -0.125 -0.497
Dummy code to get the gender rating effect for just the Full Name condition.
exp4_d %<>% mutate(Condition_Full = case_when(
Condition == "first" ~ 1,
Condition == "full" ~ 0,
Condition == "last" ~ 1
))
exp4_d$Condition_Full %<>% as.factor()
exp4_m_all_full <- glmer(
Female ~ Condition_Full * GenderRatingCentered +
(1 | Participant) + (1 | Item),
data = exp4_d, family = binomial
)
summary(exp4_m_all_full)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Female ~ Condition_Full * GenderRatingCentered + (1 | Participant) +
## (1 | Item)
## Data: exp4_d
##
## AIC BIC logLik deviance df.resid
## 9164.2 9206.7 -4576.1 9152.2 8765
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0410 -0.5746 -0.2710 0.5694 4.7906
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.2015 0.4489
## Item (Intercept) 0.3602 0.6001
## Number of obs: 8771, groups: Participant, 1253; Item, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.180223 0.091385 -1.972 0.0486 *
## Condition_Full1 -0.114778 0.062004 -1.851 0.0642 .
## GenderRatingCentered 0.755873 0.051389 14.709 <2e-16 ***
## Condition_Full1:GenderRatingCentered 0.006064 0.035112 0.173 0.8629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cnd_F1 GndrRC
## Condtn_Fll1 -0.450
## GndrRtngCnt -0.035 0.038
## Cndt_F1:GRC 0.038 -0.104 -0.451
exp4_m_all_first %>%
tidy() %>%
filter(term == "GenderRatingCentered") %>%
pull(estimate)## [1] 0.8594397
exp4_m_all_full %>%
tidy() %>%
filter(term == "GenderRatingCentered") %>%
pull(estimate)## [1] 0.7558726
The effect of name gender rating is larger in the First Name condition (0.86) than in the Full Name condition (0.76).
exp(get_intercept(exp4_m_all))## [1] 0.7740991
exp(-get_intercept(exp4_m_all))## [1] 1.291824
0.77x less likely to recall as female overall (or: 1.29x more likely to recall as male overall), p<.01
exp4_m_all %>%
tidy() %>%
filter(term == "Condition1") %>%
pull(estimate) %>%
exp()## [1] 1.134692
1.13x more likely to recall as female in First + Full compared to Last, p<.05
Model with just Condition (to more directly compare to Exp 2).
exp4_m_cond_L <- glmer(
Female ~ Condition_Last + (1 | Participant) + (1 | Item),
data = exp4_d, family = binomial
)
summary(exp4_m_cond_L)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Female ~ Condition_Last + (1 | Participant) + (1 | Item)
## Data: exp4_d
##
## AIC BIC logLik deviance df.resid
## 9265.9 9294.2 -4628.9 9257.9 8767
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0475 -0.5940 -0.2737 0.5750 4.4732
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.1961 0.4428
## Item (Intercept) 2.2443 1.4981
## Number of obs: 8771, groups: Participant, 1253; Item, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.36280 0.19565 -1.854 0.0637 .
## Condition_Last1 0.15844 0.06154 2.574 0.0100 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Condtn_Lst1 -0.211
exp(get_intercept(exp4_m_cond_L))## [1] 0.695726
exp(-get_intercept(exp4_m_cond_L))## [1] 1.437347
0.17x times less likely to recall as female in the Last Name condition (or: 5.72x more likely to recall as male in the Last Name condition), p=0.06
Dummy code with First and Full Name as 0, so that intercept is average for these two conditions. Model with just Condition (to more directly compare to Exp 2).
exp4_m_cond_FF <- glmer(
Female ~ Condition_FF + (1 | Participant) + (1 | Item),
data = exp4_d, family = binomial
)
summary(exp4_m_cond_FF)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Female ~ Condition_FF + (1 | Participant) + (1 | Item)
## Data: exp4_d
##
## AIC BIC logLik deviance df.resid
## 9265.9 9294.2 -4628.9 9257.9 8767
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0475 -0.5940 -0.2737 0.5750 4.4732
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.1961 0.4428
## Item (Intercept) 2.2443 1.4981
## Number of obs: 8771, groups: Participant, 1253; Item, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.20436 0.19231 -1.063 0.288
## Condition_FF1 -0.15843 0.06154 -2.574 0.010 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Conditn_FF1 -0.105
exp(get_intercept(exp4_m_cond_FF))## [1] 0.8151685
exp(-get_intercept(exp4_m_cond_FF))## [1] 1.22674
0.82x less likely to recall as female in First and Full Name conditions (or: 1.23x more likely to recall as male in First and Full Name conditions), p=.29