2023-03-26
Variable names:
- Experiment: exp3_
- Data (_d_)
- d = main df
- Models (_m_)
- like = Likeability ratings
- acc = Accomplishment ratings
- imp = Importance ratings
- ratings = build model with all 3 ratings
Load data and select columns used in model. See data/exp3_data_about.txt for more details.
exp3_d <- read.csv("../data/exp3_data.csv", stringsAsFactors = TRUE) %>%
rename("Participant" = "SubjID", "Item" = "Name") %>%
select(
Participant, Condition, GenderRating, Item,
He, She, Other,
Likeable, Accomplished, Important
)
str(exp3_d)## 'data.frame': 8904 obs. of 10 variables:
## $ Participant : Factor w/ 1272 levels "Exp3_P1","Exp3_P10",..: 974 974 974 974 974 974 974 330 330 330 ...
## $ Condition : Factor w/ 3 levels "first","full",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ GenderRating: num 5.22 1.24 5.86 3.75 6.78 4.34 2.41 6.24 2.61 6.82 ...
## $ Item : Factor w/ 63 levels "Ashley Cook",..: 6 9 13 43 47 52 62 2 16 20 ...
## $ He : int 0 1 0 0 0 0 1 0 1 0 ...
## $ She : int 0 0 1 0 1 1 0 0 0 1 ...
## $ Other : int 1 0 0 1 0 0 0 1 0 0 ...
## $ Likeable : int 2 2 2 2 2 1 2 2 1 2 ...
## $ Accomplished: int 2 1 1 2 1 1 2 3 1 1 ...
## $ Important : int 2 1 2 2 1 1 2 2 1 1 ...
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.
exp3_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(exp3_d$Condition) <- centerfactor(
exp3_d$Condition, c("last", "first")
)
contrasts(exp3_d$Condition)## [,1] [,2]
## first 0.4009434 -0.48113208
## full 0.4009434 0.51886792
## last -0.5990566 0.01886792
Flip ratings from 1=most likeable/accomplished/important to 7=most L/A/I, to make interpreting models easier, then mean-center.
exp3_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(exp3_d)## 'data.frame': 8904 obs. of 17 variables:
## $ Participant : Factor w/ 1272 levels "Exp3_P1","Exp3_P10",..: 974 974 974 974 974 974 974 330 330 330 ...
## $ Condition : Factor w/ 3 levels "first","full",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:3, 1:2] 0.401 0.401 -0.599 -0.481 0.519 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:3] "first" "full" "last"
## .. .. ..$ : NULL
## $ GenderRating : num 5.22 1.24 5.86 3.75 6.78 4.34 2.41 6.24 2.61 6.82 ...
## $ Item : Factor w/ 63 levels "Ashley Cook",..: 6 9 13 43 47 52 62 2 16 20 ...
## $ He : int 0 1 0 0 0 0 1 0 1 0 ...
## $ She : int 0 0 1 0 1 1 0 0 0 1 ...
## $ Other : int 1 0 0 1 0 0 0 1 0 0 ...
## $ Likeable : int 2 2 2 2 2 1 2 2 1 2 ...
## $ Accomplished : int 2 1 1 2 1 1 2 3 1 1 ...
## $ Important : int 2 1 2 2 1 1 2 2 1 1 ...
## $ GenderRatingCentered: num [1:8904, 1] 1.014 -2.966 1.654 -0.456 2.574 ...
## ..- attr(*, "scaled:center")= num 4.21
## $ LikeableFlip : num 6 6 6 6 6 7 6 6 7 6 ...
## $ AccomplishedFlip : num 6 7 7 6 7 7 6 5 7 7 ...
## $ ImportantFlip : num 6 7 6 6 7 7 6 6 7 7 ...
## $ LikeableCentered : num [1:8904, 1] 0.271 0.271 0.271 0.271 0.271 ...
## ..- attr(*, "scaled:center")= num 5.73
## $ AccomplishedCentered: num [1:8904, 1] 0.147 1.147 1.147 0.147 1.147 ...
## ..- attr(*, "scaled:center")= num 5.85
## $ ImportantCentered : num [1:8904, 1] 0.585 1.585 0.585 0.585 1.585 ...
## ..- attr(*, "scaled:center")= num 5.42
Summary statistics:
summary(exp3_d$Likeable)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.271 3.000 7.000
summary(exp3_d$LikeableFlip)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 6.000 5.729 7.000 7.000
sd(exp3_d$Likeable)## [1] 1.316101
Does the Likeability rating of the character predict the likelihood of she as opposed to he and other responses? The maximal model includes all interactions, then random intercepts by item but not by participant.
exp3_m_like <- buildmer(
formula = She ~ Condition * GenderRatingCentered *
LikeableCentered + (1 | Participant) + (1 | Item),
data = exp3_d, family = binomial,
buildmerControl(direction = "order", quiet = TRUE)
)
summary(exp3_m_like)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) (p-values based on Wald z-scores) [glmerMod]
## Family: binomial ( logit )
## Formula: She ~ 1 + GenderRatingCentered + LikeableCentered + Condition +
## GenderRatingCentered:Condition + GenderRatingCentered:LikeableCentered +
## LikeableCentered:Condition + GenderRatingCentered:LikeableCentered:Condition +
## (1 | Item)
## Data: exp3_d
##
## AIC BIC logLik deviance df.resid
## 7977.9 8070.1 -3975.9 7951.9 8891
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6061 -0.5430 -0.1465 0.6311 10.0732
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 0.3524 0.5936
## Number of obs: 8904, groups: Item, 63
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) -1.37455 0.08926 -15.39978
## GenderRatingCentered 1.03162 0.05487 18.80241
## LikeableCentered 0.08004 0.02805 2.85361
## Condition1 0.13853 0.07103 1.95029
## Condition2 0.06855 0.08957 0.76530
## GenderRatingCentered:Condition1 0.08800 0.04529 1.94319
## GenderRatingCentered:Condition2 -0.06416 0.05848 -1.09717
## GenderRatingCentered:LikeableCentered 0.02698 0.01736 1.55386
## LikeableCentered:Condition1 -0.06365 0.05656 -1.12521
## LikeableCentered:Condition2 0.06998 0.06931 1.00957
## GenderRatingCentered:LikeableCentered:Condition1 0.06193 0.03485 1.77725
## GenderRatingCentered:LikeableCentered:Condition2 -0.03563 0.04395 -0.81076
## Pr(>|z|) Pr(>|t|)
## (Intercept) 0.000 < 2e-16 ***
## GenderRatingCentered 0.000 < 2e-16 ***
## LikeableCentered 0.004 0.00432 **
## Condition1 0.051 0.05114 .
## Condition2 0.444 0.44409
## GenderRatingCentered:Condition1 0.052 0.05199 .
## GenderRatingCentered:Condition2 0.273 0.27257
## GenderRatingCentered:LikeableCentered 0.120 0.12022
## LikeableCentered:Condition1 0.261 0.26050
## LikeableCentered:Condition2 0.313 0.31270
## GenderRatingCentered:LikeableCentered:Condition1 0.076 0.07553 .
## GenderRatingCentered:LikeableCentered:Condition2 0.418 0.41750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GndrRC LkblCn Cndtn1 Cndtn2 GRC:C1 GRC:C2 GnRC:LC LkC:C1
## GndrRtngCnt -0.287
## LikeblCntrd -0.005 -0.003
## Condition1 0.000 -0.005 0.051
## Condition2 -0.017 0.018 -0.072 0.017
## GndrRtnC:C1 0.001 0.016 -0.044 -0.601 0.000
## GndrRtnC:C2 0.016 -0.023 0.048 0.001 -0.586 0.008
## GndrRtnC:LC 0.002 0.005 -0.623 -0.044 0.050 0.044 -0.090
## LkblCntr:C1 0.029 -0.030 -0.054 -0.073 -0.055 0.052 0.037 0.044
## LkblCntr:C2 -0.034 0.028 0.077 -0.056 -0.013 0.038 0.000 -0.042 0.094
## GndRC:LC:C1 -0.029 0.032 0.044 0.053 0.039 -0.047 -0.071 -0.006 -0.626
## GndRC:LC:C2 0.023 -0.042 -0.041 0.038 0.001 -0.073 0.003 0.093 -0.051
## LkC:C2 GRC:LC:C1
## GndrRtngCnt
## LikeblCntrd
## Condition1
## Condition2
## GndrRtnC:C1
## GndrRtnC:C2
## GndrRtnC:LC
## LkblCntr:C1
## LkblCntr:C2
## GndRC:LC:C1 -0.052
## GndRC:LC:C2 -0.594 0.108
- Characters who are rated as more Likeable are more likely to be referred to with she
Summary statistics:
summary(exp3_d$Accomplished)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.147 3.000 7.000
summary(exp3_d$AccomplishedFlip)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 6.000 5.853 7.000 7.000
sd(exp3_d$Accomplished)## [1] 1.27504
Does the Accomplishment rating of the character predict the likelihood of she as opposed to he and other responses? The maximal model includes all interactions, then random intercepts by item but not by participant.
exp3_m_acc <- buildmer(
formula = She ~ Condition * GenderRatingCentered *
AccomplishedCentered + (1 | Participant) + (1 | Item),
data = exp3_d, family = binomial,
buildmerControl(direction = "order", quiet = TRUE)
)
summary(exp3_m_acc)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) (p-values based on Wald z-scores) [glmerMod]
## Family: binomial ( logit )
## Formula: She ~ 1 + GenderRatingCentered + AccomplishedCentered + Condition +
## GenderRatingCentered:Condition + GenderRatingCentered:AccomplishedCentered +
## AccomplishedCentered:Condition + GenderRatingCentered:AccomplishedCentered:Condition +
## (1 | Item)
## Data: exp3_d
##
## AIC BIC logLik deviance df.resid
## 7975.7 8068.0 -3974.9 7949.7 8891
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6466 -0.5469 -0.1450 0.6222 10.8637
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 0.3595 0.5996
## Number of obs: 8904, groups: Item, 63
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -1.372294 0.089919
## GenderRatingCentered 1.034110 0.055238
## AccomplishedCentered 0.072453 0.028342
## Condition1 0.139473 0.070835
## Condition2 0.073638 0.089326
## GenderRatingCentered:Condition1 0.090036 0.045170
## GenderRatingCentered:Condition2 -0.057030 0.058311
## GenderRatingCentered:AccomplishedCentered 0.029731 0.017426
## AccomplishedCentered:Condition1 -0.083766 0.058528
## AccomplishedCentered:Condition2 -0.069941 0.069994
## GenderRatingCentered:AccomplishedCentered:Condition1 0.084687 0.035961
## GenderRatingCentered:AccomplishedCentered:Condition2 0.002789 0.043500
## z value Pr(>|z|)
## (Intercept) -15.261381 0.000
## GenderRatingCentered 18.720891 0.000
## AccomplishedCentered 2.556329 0.011
## Condition1 1.968970 0.049
## Condition2 0.824375 0.410
## GenderRatingCentered:Condition1 1.993281 0.046
## GenderRatingCentered:Condition2 -0.978038 0.328
## GenderRatingCentered:AccomplishedCentered 1.706129 0.088
## AccomplishedCentered:Condition1 -1.431211 0.152
## AccomplishedCentered:Condition2 -0.999249 0.318
## GenderRatingCentered:AccomplishedCentered:Condition1 2.354960 0.019
## GenderRatingCentered:AccomplishedCentered:Condition2 0.064123 0.949
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GenderRatingCentered <2e-16 ***
## AccomplishedCentered 0.0106 *
## Condition1 0.0490 *
## Condition2 0.4097
## GenderRatingCentered:Condition1 0.0462 *
## GenderRatingCentered:Condition2 0.3281
## GenderRatingCentered:AccomplishedCentered 0.0880 .
## AccomplishedCentered:Condition1 0.1524
## AccomplishedCentered:Condition2 0.3177
## GenderRatingCentered:AccomplishedCentered:Condition1 0.0185 *
## GenderRatingCentered:AccomplishedCentered:Condition2 0.9489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GndrRC AccmpC Cndtn1 Cndtn2 GRC:C1 GRC:C2 GnRC:AC AcC:C1
## GndrRtngCnt -0.284
## AccmplshdCn -0.006 0.003
## Condition1 0.001 -0.006 0.060
## Condition2 -0.017 0.018 -0.013 0.015
## GndrRtnC:C1 -0.001 0.018 -0.054 -0.601 0.001
## GndrRtnC:C2 0.016 -0.023 0.002 0.001 -0.586 0.005
## GndrRtnC:AC 0.006 0.006 -0.623 -0.055 0.002 0.060 -0.022
## AccmplsC:C1 0.041 -0.045 -0.088 -0.045 -0.008 0.031 0.001 0.080
## AccmplsC:C2 -0.001 -0.003 0.021 -0.006 0.026 -0.001 -0.033 0.012 0.055
## GndRC:AC:C1 -0.040 0.048 0.079 0.032 0.001 -0.009 -0.016 -0.069 -0.633
## GndRC:AC:C2 -0.005 -0.002 0.011 -0.001 -0.033 -0.015 0.062 0.023 -0.013
## AcC:C2 GRC:AC:C1
## GndrRtngCnt
## AccmplshdCn
## Condition1
## Condition2
## GndrRtnC:C1
## GndrRtnC:C2
## GndrRtnC:AC
## AccmplsC:C1
## AccmplsC:C2
## GndRC:AC:C1 -0.013
## GndRC:AC:C2 -0.592 0.056
-
Characters who are rated as more Accomplished are more likely to be referred to with she, but this is n.s. after correction for multiple comparisons.
-
Interaction between Accomplishment, Name Gender Rating, and Condition (L vs F+F), but this is n.s. after correction for multiple comparisons, so I’m not going to dig into it.
Summary statistics:
summary(exp3_d$Important)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.585 3.000 7.000
summary(exp3_d$ImportantFlip)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 6.000 5.415 7.000 7.000
sd(exp3_d$Important)## [1] 1.366153
Does the Importance rating of the character predict the likelihood of she as opposed to he and other responses The maximal model includes all interactions, then random intercepts by item but not by participant.
exp3_m_imp <- buildmer(
formula = She ~ Condition * GenderRatingCentered *
ImportantCentered + (1 | Participant) + (1 | Item),
data = exp3_d, family = binomial,
buildmerControl(direction = "order", quiet = TRUE)
)
summary(exp3_m_imp)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) (p-values based on Wald z-scores) [glmerMod]
## Family: binomial ( logit )
## Formula: She ~ 1 + GenderRatingCentered + Condition + ImportantCentered +
## GenderRatingCentered:Condition + GenderRatingCentered:ImportantCentered +
## Condition:ImportantCentered + GenderRatingCentered:Condition:ImportantCentered +
## (1 | Item)
## Data: exp3_d
##
## AIC BIC logLik deviance df.resid
## 7998.6 8090.8 -3986.3 7972.6 8891
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4475 -0.5401 -0.1474 0.6287 9.9493
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 0.3588 0.599
## Number of obs: 8904, groups: Item, 63
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -1.374939 0.089891
## GenderRatingCentered 1.033388 0.055223
## Condition1 0.136688 0.070834
## Condition2 0.076283 0.089473
## ImportantCentered 0.041717 0.026130
## GenderRatingCentered:Condition1 0.087122 0.045131
## GenderRatingCentered:Condition2 -0.054814 0.058223
## GenderRatingCentered:ImportantCentered 0.003465 0.016691
## Condition1:ImportantCentered -0.059863 0.052359
## Condition2:ImportantCentered 0.078479 0.063684
## GenderRatingCentered:Condition1:ImportantCentered 0.065849 0.033480
## GenderRatingCentered:Condition2:ImportantCentered -0.028646 0.041322
## z value Pr(>|z|) Pr(>|t|)
## (Intercept) -15.295564 0.000 <2e-16
## GenderRatingCentered 18.712849 0.000 <2e-16
## Condition1 1.929679 0.054 0.0536
## Condition2 0.852574 0.394 0.3939
## ImportantCentered 1.596514 0.110 0.1104
## GenderRatingCentered:Condition1 1.930431 0.054 0.0536
## GenderRatingCentered:Condition2 -0.941446 0.346 0.3465
## GenderRatingCentered:ImportantCentered 0.207623 0.836 0.8355
## Condition1:ImportantCentered -1.143320 0.253 0.2529
## Condition2:ImportantCentered 1.232319 0.218 0.2178
## GenderRatingCentered:Condition1:ImportantCentered 1.966845 0.049 0.0492
## GenderRatingCentered:Condition2:ImportantCentered -0.693251 0.488 0.4882
##
## (Intercept) ***
## GenderRatingCentered ***
## Condition1 .
## Condition2
## ImportantCentered
## GenderRatingCentered:Condition1 .
## GenderRatingCentered:Condition2
## GenderRatingCentered:ImportantCentered
## Condition1:ImportantCentered
## Condition2:ImportantCentered
## GenderRatingCentered:Condition1:ImportantCentered *
## GenderRatingCentered:Condition2:ImportantCentered
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GndrRC Cndtn1 Cndtn2 ImprtC GnRC:C1 GnRC:C2 GRC:IC Cn1:IC
## GndrRtngCnt -0.285
## Condition1 0.002 -0.006
## Condition2 -0.017 0.018 0.014
## ImprtntCntr -0.022 0.026 0.051 -0.053
## GndrRtnC:C1 -0.001 0.017 -0.601 0.001 -0.052
## GndrRtnC:C2 0.015 -0.021 0.002 -0.588 0.030 0.008
## GndrRtnC:IC 0.026 -0.025 -0.052 0.030 -0.585 0.043 -0.059
## Cndtn1:ImpC 0.033 -0.038 -0.042 -0.039 -0.070 0.046 0.021 0.064
## Cndtn2:ImpC -0.027 0.022 -0.041 0.019 0.041 0.023 -0.017 -0.001 0.065
## GndRC:C1:IC -0.036 0.038 0.046 0.022 0.065 -0.030 -0.046 -0.048 -0.590
## GndRC:C2:IC 0.019 -0.035 0.023 -0.017 -0.001 -0.048 0.021 0.048 -0.018
## Cn2:IC GRC:C1:
## GndrRtngCnt
## Condition1
## Condition2
## ImprtntCntr
## GndrRtnC:C1
## GndrRtnC:C2
## GndrRtnC:IC
## Cndtn1:ImpC
## Cndtn2:ImpC
## GndRC:C1:IC -0.018
## GndRC:C2:IC -0.552 0.074
- Interaction between Important, Name Gender Rating, and Condition (L vs F+F), but this is way too small to be significant after correction for multiple comparisons
exp3_m_ratings <- buildmer(
formula = She ~ Condition * GenderRatingCentered * LikeableCentered *
AccomplishedCentered * ImportantCentered +
(1 | Participant) + (1 | Item),
data = exp3_d,
family = binomial,
buildmerControl(direction = c("order", "backward"), quiet = TRUE)
)
summary(exp3_m_ratings)##
## Call:
## stats::glm(formula = She ~ 1 + GenderRatingCentered + LikeableCentered +
## Condition + ImportantCentered + AccomplishedCentered + Condition:AccomplishedCentered +
## Condition:ImportantCentered + LikeableCentered:Condition +
## LikeableCentered:AccomplishedCentered + LikeableCentered:ImportantCentered +
## ImportantCentered:AccomplishedCentered + LikeableCentered:ImportantCentered:AccomplishedCentered +
## LikeableCentered:Condition:AccomplishedCentered + LikeableCentered:Condition:ImportantCentered +
## Condition:ImportantCentered:AccomplishedCentered + Condition:LikeableCentered:AccomplishedCentered:ImportantCentered,
## family = binomial, data = exp3_d)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9790 -0.7231 -0.3095 0.7728 2.9943
##
## Coefficients:
## Estimate
## (Intercept) -1.183679
## GenderRatingCentered 0.895923
## LikeableCentered 0.110380
## Condition1 0.192791
## Condition2 -0.061019
## ImportantCentered -0.070138
## AccomplishedCentered 0.053692
## Condition1:AccomplishedCentered 0.043019
## Condition2:AccomplishedCentered -0.239237
## Condition1:ImportantCentered -0.035665
## Condition2:ImportantCentered 0.100167
## LikeableCentered:Condition1 -0.052403
## LikeableCentered:Condition2 0.022351
## LikeableCentered:AccomplishedCentered 0.008091
## LikeableCentered:ImportantCentered 0.005932
## ImportantCentered:AccomplishedCentered 0.000364
## LikeableCentered:ImportantCentered:AccomplishedCentered 0.009798
## LikeableCentered:Condition1:AccomplishedCentered 0.007850
## LikeableCentered:Condition2:AccomplishedCentered 0.017325
## LikeableCentered:Condition1:ImportantCentered 0.008035
## LikeableCentered:Condition2:ImportantCentered 0.078234
## Condition1:ImportantCentered:AccomplishedCentered 0.023376
## Condition2:ImportantCentered:AccomplishedCentered 0.029479
## LikeableCentered:Condition1:ImportantCentered:AccomplishedCentered 0.022956
## LikeableCentered:Condition2:ImportantCentered:AccomplishedCentered 0.057829
## Std. Error
## (Intercept) 0.038223
## GenderRatingCentered 0.021163
## LikeableCentered 0.032207
## Condition1 0.069638
## Condition2 0.087625
## ImportantCentered 0.029469
## AccomplishedCentered 0.034803
## Condition1:AccomplishedCentered 0.071294
## Condition2:AccomplishedCentered 0.089899
## Condition1:ImportantCentered 0.059845
## Condition2:ImportantCentered 0.077861
## LikeableCentered:Condition1 0.065543
## LikeableCentered:Condition2 0.084333
## LikeableCentered:AccomplishedCentered 0.021795
## LikeableCentered:ImportantCentered 0.022602
## ImportantCentered:AccomplishedCentered 0.023053
## LikeableCentered:ImportantCentered:AccomplishedCentered 0.008109
## LikeableCentered:Condition1:AccomplishedCentered 0.043915
## LikeableCentered:Condition2:AccomplishedCentered 0.058238
## LikeableCentered:Condition1:ImportantCentered 0.045577
## LikeableCentered:Condition2:ImportantCentered 0.060974
## Condition1:ImportantCentered:AccomplishedCentered 0.047445
## Condition2:ImportantCentered:AccomplishedCentered 0.058217
## LikeableCentered:Condition1:ImportantCentered:AccomplishedCentered 0.016068
## LikeableCentered:Condition2:ImportantCentered:AccomplishedCentered 0.022892
## z value
## (Intercept) -30.968
## GenderRatingCentered 42.335
## LikeableCentered 3.427
## Condition1 2.768
## Condition2 -0.696
## ImportantCentered -2.380
## AccomplishedCentered 1.543
## Condition1:AccomplishedCentered 0.603
## Condition2:AccomplishedCentered -2.661
## Condition1:ImportantCentered -0.596
## Condition2:ImportantCentered 1.286
## LikeableCentered:Condition1 -0.800
## LikeableCentered:Condition2 0.265
## LikeableCentered:AccomplishedCentered 0.371
## LikeableCentered:ImportantCentered 0.262
## ImportantCentered:AccomplishedCentered 0.016
## LikeableCentered:ImportantCentered:AccomplishedCentered 1.208
## LikeableCentered:Condition1:AccomplishedCentered 0.179
## LikeableCentered:Condition2:AccomplishedCentered 0.297
## LikeableCentered:Condition1:ImportantCentered 0.176
## LikeableCentered:Condition2:ImportantCentered 1.283
## Condition1:ImportantCentered:AccomplishedCentered 0.493
## Condition2:ImportantCentered:AccomplishedCentered 0.506
## LikeableCentered:Condition1:ImportantCentered:AccomplishedCentered 1.429
## LikeableCentered:Condition2:ImportantCentered:AccomplishedCentered 2.526
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## GenderRatingCentered < 2e-16 ***
## LikeableCentered 0.00061 ***
## Condition1 0.00563 **
## Condition2 0.48621
## ImportantCentered 0.01731 *
## AccomplishedCentered 0.12289
## Condition1:AccomplishedCentered 0.54624
## Condition2:AccomplishedCentered 0.00779 **
## Condition1:ImportantCentered 0.55121
## Condition2:ImportantCentered 0.19827
## LikeableCentered:Condition1 0.42398
## LikeableCentered:Condition2 0.79098
## LikeableCentered:AccomplishedCentered 0.71047
## LikeableCentered:ImportantCentered 0.79297
## ImportantCentered:AccomplishedCentered 0.98740
## LikeableCentered:ImportantCentered:AccomplishedCentered 0.22694
## LikeableCentered:Condition1:AccomplishedCentered 0.85814
## LikeableCentered:Condition2:AccomplishedCentered 0.76610
## LikeableCentered:Condition1:ImportantCentered 0.86006
## LikeableCentered:Condition2:ImportantCentered 0.19947
## Condition1:ImportantCentered:AccomplishedCentered 0.62223
## Condition2:ImportantCentered:AccomplishedCentered 0.61260
## LikeableCentered:Condition1:ImportantCentered:AccomplishedCentered 0.15310
## LikeableCentered:Condition2:ImportantCentered:AccomplishedCentered 0.01153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11191.3 on 8903 degrees of freedom
## Residual deviance: 8193.3 on 8879 degrees of freedom
## AIC: 8243.3
##
## Number of Fisher Scoring iterations: 5