Evaluative Mindsets Can Protect Against the Influence of False Information – Experiment 3 Analysis
Load data
data <- readr::read_csv("evaluation-exp3-long.csv", col_names = TRUE) ## Rows: 17920 Columns: 22
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (15): ID, ReadingCheck1, LookUp?, Gender, Education, Race, Race2, Commen...
## dbl (7): Duration, ReadingCheck2, Age, Version, Correct, Error, Unsure
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Incorrect lure and correct answer descriptive stats (interest-only judgment, accuracy-only judgment, mixed interest judgment, mixed accuracy judgment) for summaries in Table 3
# Descriptive table
summary1 <- data %>%
group_by(ID,Study,Instructions,Validity,Difficulty) %>%
summarise(
mean_error = mean(Error,na.rm=TRUE),
mean_correct = mean(Correct, na.rm=TRUE),
) %>%
group_by(Study,Instructions,Validity, Difficulty) %>%
summarise(
mean_err = mean(mean_error),
sd_err = sd(mean_error),
se_error = sd(mean_error)/sqrt(n()),
mean_corr = mean(mean_correct),
sd_corr = sd(mean_correct),
se_corr = sd(mean_correct)/sqrt(n())
)## `summarise()` has grouped output by 'ID', 'Study', 'Instructions', 'Validity'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'Study', 'Instructions', 'Validity'. You
## can override using the `.groups` argument.print(summary1)## # A tibble: 16 × 10
## # Groups: Study, Instructions, Validity [8]
## Study Instructions Validity Difficulty mean_err sd_err se_error mean_corr
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Mix Accuracy Accurate Easy 0.00282 0.0167 0.00198 0.886
## 2 Mix Accuracy Accurate Hard 0.0268 0.0506 0.00600 0.528
## 3 Mix Accuracy Inaccura… Easy 0.0704 0.107 0.0127 0.745
## 4 Mix Accuracy Inaccura… Hard 0.193 0.172 0.0204 0.324
## 5 Mix Interest Accurate Easy 0.00704 0.0258 0.00306 0.861
## 6 Mix Interest Accurate Hard 0.0352 0.0563 0.00668 0.475
## 7 Mix Interest Inaccura… Easy 0.0944 0.137 0.0163 0.724
## 8 Mix Interest Inaccura… Hard 0.194 0.180 0.0214 0.293
## 9 Separate Accuracy Accurate Easy 0.00592 0.0163 0.00187 0.853
## 10 Separate Accuracy Accurate Hard 0.0309 0.0374 0.00429 0.522
## 11 Separate Accuracy Inaccura… Easy 0.0684 0.0647 0.00742 0.751
## 12 Separate Accuracy Inaccura… Hard 0.178 0.122 0.0140 0.291
## 13 Separate Interest Accurate Easy 0.00455 0.0145 0.00165 0.821
## 14 Separate Interest Accurate Hard 0.0240 0.0340 0.00388 0.533
## 15 Separate Interest Inaccura… Easy 0.158 0.156 0.0178 0.612
## 16 Separate Interest Inaccura… Hard 0.213 0.151 0.0172 0.301
## # … with 2 more variables: sd_corr <dbl>, se_corr <dbl>Incorrect lure and correct answer descriptive stats (interest-only judgment, accuracy-only judgment, mixed jugment) following false information only for summaries in text
# Collapse across interest and accuracy items in mixed judgment condition
data <- data %>%
mutate(JudgmentCondition = ifelse(Condition == "Interest_Mixed" | Condition == "Accuracy_Mixed","Mixed Judgment",ifelse(Condition == "Interest_Separate","Interest-Only","Accuracy-Only")))
# Descriptive table
summary2 <- data %>%
filter(Validity == "Inaccurate") %>%
group_by(ID,Difficulty,JudgmentCondition) %>%
summarise(
mean_error = mean(Error,na.rm=TRUE),
mean_correct = mean(Correct, na.rm=TRUE),
) %>%
group_by(Difficulty,JudgmentCondition) %>%
summarise(
mean_err = mean(mean_error),
sd_err = sd(mean_error),
se_error = sd(mean_error)/sqrt(n()),
mean_corr = mean(mean_correct),
sd_corr = sd(mean_correct),
se_corr = sd(mean_correct)/sqrt(n())
)## `summarise()` has grouped output by 'ID', 'Difficulty'. You can override using
## the `.groups` argument.
## `summarise()` has grouped output by 'Difficulty'. You can override using the
## `.groups` argument.print(summary2)## # A tibble: 6 × 8
## # Groups: Difficulty [2]
## Difficulty JudgmentConditi… mean_err sd_err se_error mean_corr sd_corr se_corr
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Easy Accuracy-Only 0.0684 0.0647 0.00742 0.751 0.234 0.0268
## 2 Easy Interest-Only 0.158 0.156 0.0178 0.612 0.272 0.0310
## 3 Easy Mixed Judgment 0.0824 0.103 0.0122 0.735 0.259 0.0307
## 4 Hard Accuracy-Only 0.178 0.122 0.0140 0.291 0.249 0.0285
## 5 Hard Interest-Only 0.213 0.151 0.0172 0.301 0.257 0.0292
## 6 Hard Mixed Judgment 0.194 0.158 0.0187 0.308 0.272 0.0323Model to analyze incorrect lure responses
# Set referent interest-only judgment condition
data <- within(data, JudgmentCondition <- as.factor(JudgmentCondition))
data <- within(data, JudgmentCondition <- relevel(JudgmentCondition, ref = "Interest-Only"))
# Incorrect lure model
modelincorrect <- data %>%
filter(Validity == "Inaccurate") %>%
dplyr::mutate(Difficulty = ifelse(Difficulty == "Easy",1,0)) %>%
glmer(Error ~ JudgmentCondition*Difficulty + (1 + Difficulty | ID) + (1 | Item), ., family = binomial, control = glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e5)))
summary(modelincorrect)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Error ~ JudgmentCondition * Difficulty + (1 + Difficulty | ID) +
## (1 | Item)
## Data: .
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 6535.7 6606.7 -3257.8 6515.7 8950
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9678 -0.4041 -0.2576 -0.1464 7.0909
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ID (Intercept) 0.8564 0.9254
## Difficulty 0.7922 0.8900 -0.35
## Item (Intercept) 0.8091 0.8995
## Number of obs: 8960, groups: ID, 224; Item, 80
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7056 0.1929 -8.844 < 2e-16
## JudgmentConditionAccuracy-Only -0.2381 0.1819 -1.309 0.190678
## JudgmentConditionMixed Judgment -0.1839 0.1855 -0.991 0.321702
## Difficulty -0.5228 0.2546 -2.053 0.040035
## JudgmentConditionAccuracy-Only:Difficulty -0.7603 0.2227 -3.414 0.000640
## JudgmentConditionMixed Judgment:Difficulty -0.7540 0.2272 -3.319 0.000902
##
## (Intercept) ***
## JudgmentConditionAccuracy-Only
## JudgmentConditionMixed Judgment
## Difficulty *
## JudgmentConditionAccuracy-Only:Difficulty ***
## JudgmentConditionMixed Judgment:Difficulty ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) JdCA-O JdgCMJ Dffclt JCA-O:
## JdgmntCnA-O -0.461
## JdgmntCndMJ -0.451 0.481
## Difficulty -0.603 0.188 0.182
## JdgmnCA-O:D 0.202 -0.442 -0.210 -0.380
## JdgmntCMJ:D 0.197 -0.210 -0.441 -0.365 0.437tab_model(modelincorrect)| Error | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.18 | 0.12 – 0.27 | <0.001 |
|
JudgmentCondition [Accuracy-Only] |
0.79 | 0.55 – 1.13 | 0.191 |
|
JudgmentCondition [Mixed Judgment] |
0.83 | 0.58 – 1.20 | 0.322 |
| Difficulty | 0.59 | 0.36 – 0.98 | 0.040 |
|
JudgmentCondition [Accuracy-Only] * Difficulty |
0.47 | 0.30 – 0.72 | 0.001 |
|
JudgmentCondition [Mixed Judgment] * Difficulty |
0.47 | 0.30 – 0.73 | 0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 ID | 0.86 | ||
| τ00 Item | 0.81 | ||
| τ11 ID.Difficulty | 0.79 | ||
| ρ01 ID | -0.35 | ||
| ICC | 0.35 | ||
| N ID | 224 | ||
| N Item | 80 | ||
| Observations | 8960 | ||
| Marginal R2 / Conditional R2 | 0.068 / 0.395 | ||
# test of simple effects
emmeans(modelincorrect, ~ JudgmentCondition * Difficulty) %>%
pairs(., simple = "JudgmentCondition", reverse = TRUE)## Difficulty = 0:
## contrast estimate SE df z.ratio p.value
## (Accuracy-Only) - (Interest-Only) -0.2381 0.182 Inf -1.309 0.3903
## Mixed Judgment - (Interest-Only) -0.1839 0.186 Inf -0.991 0.5826
## Mixed Judgment - (Accuracy-Only) 0.0542 0.187 Inf 0.289 0.9549
##
## Difficulty = 1:
## contrast estimate SE df z.ratio p.value
## (Accuracy-Only) - (Interest-Only) -0.9984 0.216 Inf -4.611 <.0001
## Mixed Judgment - (Interest-Only) -0.9379 0.221 Inf -4.242 0.0001
## Mixed Judgment - (Accuracy-Only) 0.0605 0.232 Inf 0.261 0.9632
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesModel to analyze correct answer responses
# Correct answer model
modelcorrect <- data %>%
filter(Validity == "Inaccurate") %>%
dplyr::mutate(Difficulty = ifelse(Difficulty == "Easy",1,0)) %>%
glmer(Correct ~ JudgmentCondition*Difficulty + (1 + Difficulty | ID) + (1 | Item), ., family = binomial, control = glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e5)))
summary(modelcorrect)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Correct ~ JudgmentCondition * Difficulty + (1 + Difficulty |
## ID) + (1 | Item)
## Data: .
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 8807.7 8878.7 -4393.8 8787.7 8950
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1236 -0.5111 -0.0783 0.5137 4.8020
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ID (Intercept) 2.6339 1.623
## Difficulty 1.7873 1.337 -0.49
## Item (Intercept) 0.3982 0.631
## Number of obs: 8960, groups: ID, 224; Item, 80
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.33265 0.22467 -5.932 3e-09
## JudgmentConditionAccuracy-Only -0.01772 0.28375 -0.062 0.950212
## JudgmentConditionMixed Judgment 0.07530 0.28898 0.261 0.794436
## Difficulty 2.01443 0.23241 8.668 < 2e-16
## JudgmentConditionAccuracy-Only:Difficulty 0.87207 0.26261 3.321 0.000898
## JudgmentConditionMixed Judgment:Difficulty 0.73659 0.26813 2.747 0.006012
##
## (Intercept) ***
## JudgmentConditionAccuracy-Only
## JudgmentConditionMixed Judgment
## Difficulty ***
## JudgmentConditionAccuracy-Only:Difficulty ***
## JudgmentConditionMixed Judgment:Difficulty **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) JdCA-O JdgCMJ Dffclt JCA-O:
## JdgmntCnA-O -0.628
## JdgmntCndMJ -0.617 0.488
## Difficulty -0.578 0.300 0.295
## JdgmnCA-O:D 0.335 -0.533 -0.260 -0.549
## JdgmntCMJ:D 0.329 -0.260 -0.533 -0.538 0.477tab_model(modelcorrect)| Correct | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.26 | 0.17 – 0.41 | <0.001 |
|
JudgmentCondition [Accuracy-Only] |
0.98 | 0.56 – 1.71 | 0.950 |
|
JudgmentCondition [Mixed Judgment] |
1.08 | 0.61 – 1.90 | 0.794 |
| Difficulty | 7.50 | 4.75 – 11.82 | <0.001 |
|
JudgmentCondition [Accuracy-Only] * Difficulty |
2.39 | 1.43 – 4.00 | 0.001 |
|
JudgmentCondition [Mixed Judgment] * Difficulty |
2.09 | 1.23 – 3.53 | 0.006 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 ID | 2.63 | ||
| τ00 Item | 0.40 | ||
| τ11 ID.Difficulty | 1.79 | ||
| ρ01 ID | -0.49 | ||
| ICC | 0.46 | ||
| N ID | 224 | ||
| N Item | 80 | ||
| Observations | 8960 | ||
| Marginal R2 / Conditional R2 | 0.216 / 0.581 | ||
# test of simple effects
emmeans(modelcorrect, ~ JudgmentCondition * Difficulty) %>%
pairs(., simple = "JudgmentCondition", reverse = TRUE)## Difficulty = 0:
## contrast estimate SE df z.ratio p.value
## (Accuracy-Only) - (Interest-Only) -0.0177 0.284 Inf -0.062 0.9979
## Mixed Judgment - (Interest-Only) 0.0753 0.289 Inf 0.261 0.9633
## Mixed Judgment - (Accuracy-Only) 0.0930 0.290 Inf 0.321 0.9448
##
## Difficulty = 1:
## contrast estimate SE df z.ratio p.value
## (Accuracy-Only) - (Interest-Only) 0.8544 0.265 Inf 3.229 0.0036
## Mixed Judgment - (Interest-Only) 0.8119 0.270 Inf 3.008 0.0074
## Mixed Judgment - (Accuracy-Only) -0.0425 0.273 Inf -0.155 0.9868
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesIncorrect lure responses by accuracy-only, interest-only, and mixed judgment (Figure 5)
Evaluation_Exp3_Figure5 <- summary2 %>%
ggplot(.) + aes(x = reorder(Difficulty, -mean_err), y = mean_err, fill = reorder(JudgmentCondition, -mean_err)) +
geom_bar(stat = "summary", fun.y = "mean", position = "dodge") +
xlab("Item Difficulty") + ylab("Proportion Lure Reported") + labs(fill = "Judgment Group") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black")) +
geom_errorbar(aes(ymin= mean_err - se_error, ymax= mean_err + se_error), position=position_dodge(width=0.9), width=.1) + geom_text(aes(label=round(mean_err,digits=2)), position=position_dodge(width=.9), vjust=-3) + scale_fill_manual(values=c("#f0f0f0","#bdbdbd","#636363")) + ylim(0,.25)## Warning: Ignoring unknown parameters: fun.yprint(Evaluation_Exp3_Figure5)## No summary function supplied, defaulting to `mean_se()`
ggsave(Evaluation_Exp3_Figure5, file="Evaluation-Exp3-Error.jpeg", width=8, height=4)## No summary function supplied, defaulting to `mean_se()`Correct answer responses by accuracy-only, interest-only, and mixed judgment (Figure 6)
Evaluation_Exp3_Figure6 <- summary2 %>%
ggplot(.) + aes(x = reorder(Difficulty, -mean_err), y = mean_corr, fill = reorder(JudgmentCondition, -mean_err)) +
geom_bar(stat = "summary", fun.y = "mean", position = "dodge") +
xlab("Item Difficulty") + ylab("Proportion Correct Answer Reported") + labs(fill = "Judgment Group") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black")) +
geom_errorbar(aes(ymin= mean_corr - se_corr, ymax= mean_corr + se_corr), position=position_dodge(width=0.9), width=.1) + geom_text(aes(label=round(mean_corr,digits=2)), position=position_dodge(width=.9), vjust=-1.7) + scale_fill_manual(values=c("#f0f0f0","#bdbdbd","#636363")) ## Warning: Ignoring unknown parameters: fun.yprint(Evaluation_Exp3_Figure6)## No summary function supplied, defaulting to `mean_se()`
ggsave(Evaluation_Exp3_Figure6, file="Evaluation-Exp3-Correct.jpeg", width=8, height=4)## No summary function supplied, defaulting to `mean_se()`Model outputs using post-hoc exploratory orthogonal contrasts (recommended by Reviewer 3)
# incorrect lure responses
modelincorrectcontrasts <- data %>%
filter(Validity == "Inaccurate") %>%
dplyr::mutate(Difficulty = ifelse(Difficulty == "Easy",-.5,.5)) %>%
dplyr::mutate(Contrast1 = ifelse(JudgmentCondition == "Interest-Only",2,-1)) %>%
dplyr::mutate(Contrast2 = ifelse(JudgmentCondition == "Accuracy-Only",-1,ifelse(JudgmentCondition == "Mixed Judgment",1,0))) %>%
glmer(Error ~ Contrast1*Difficulty + Contrast2*Difficulty + (1 + Difficulty | ID) + (1 | Item), ., family = binomial, control = glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e5)))
summary(modelincorrectcontrasts)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Error ~ Contrast1 * Difficulty + Contrast2 * Difficulty + (1 +
## Difficulty | ID) + (1 | Item)
## Data: .
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 6535.7 6606.7 -3257.8 6515.7 8950
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9678 -0.4041 -0.2576 -0.1464 7.0909
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ID (Intercept) 0.7675 0.8761
## Difficulty 0.7921 0.8900 -0.14
## Item (Intercept) 0.8091 0.8995
## Number of obs: 8960, groups: ID, 224; Item, 80
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.360059 0.125841 -18.754 < 2e-16 ***
## Contrast1 0.196514 0.047860 4.106 4.03e-05 ***
## Difficulty 1.027573 0.229207 4.483 7.35e-06 ***
## Contrast2 0.028684 0.086912 0.330 0.741
## Contrast1:Difficulty -0.252381 0.063565 -3.970 7.17e-05 ***
## Difficulty:Contrast2 -0.003162 0.119344 -0.026 0.979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cntrs1 Dffclt Cntrs2 Cnt1:D
## Contrast1 -0.050
## Difficulty -0.077 0.034
## Contrast2 0.022 -0.023 -0.006
## Cntrst1:Dff 0.047 -0.172 -0.066 0.005
## Dffclty:Cn2 -0.009 0.005 0.020 -0.226 -0.022tab_model(modelincorrectcontrasts)| Error | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.09 | 0.07 – 0.12 | <0.001 |
| Contrast1 | 1.22 | 1.11 – 1.34 | <0.001 |
| Difficulty | 2.79 | 1.78 – 4.38 | <0.001 |
| Contrast2 | 1.03 | 0.87 – 1.22 | 0.741 |
| Contrast1 * Difficulty | 0.78 | 0.69 – 0.88 | <0.001 |
| Difficulty * Contrast2 | 1.00 | 0.79 – 1.26 | 0.979 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 ID | 0.77 | ||
| τ00 Item | 0.81 | ||
| τ11 ID.Difficulty | 0.79 | ||
| ρ01 ID | -0.14 | ||
| ICC | 0.35 | ||
| N ID | 224 | ||
| N Item | 80 | ||
| Observations | 8960 | ||
| Marginal R2 / Conditional R2 | 0.068 / 0.395 | ||
# correct answer responses
modelincorrectcontrasts <- data %>%
filter(Validity == "Inaccurate") %>%
dplyr::mutate(Difficulty = ifelse(Difficulty == "Easy",.5,-.5)) %>%
dplyr::mutate(Contrast1 = ifelse(JudgmentCondition == "Interest-Only",-2,1)) %>%
dplyr::mutate(Contrast2 = ifelse(JudgmentCondition == "Accuracy-Only",1,ifelse(JudgmentCondition == "Mixed Judgment",-1,0))) %>%
glmer(Correct ~ Contrast1*Difficulty + Contrast2*Difficulty + (1 + Difficulty | ID) + (1 | Item), ., family = binomial, control = glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e5)))
summary(modelincorrectcontrasts)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Correct ~ Contrast1 * Difficulty + Contrast2 * Difficulty + (1 +
## Difficulty | ID) + (1 | Item)
## Data: .
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 8807.7 8878.7 -4393.8 8787.7 8950
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1236 -0.5111 -0.0784 0.5137 4.8019
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ID (Intercept) 2.0106 1.418
## Difficulty 1.7872 1.337 -0.09
## Item (Intercept) 0.3981 0.631
## Number of obs: 8960, groups: ID, 224; Item, 80
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03812 0.12296 -0.310 0.756574
## Contrast1 0.14365 0.06984 2.057 0.039703 *
## Difficulty 2.55061 0.18014 14.159 < 2e-16 ***
## Contrast2 -0.01263 0.12335 -0.102 0.918420
## Contrast1:Difficulty 0.26811 0.07602 3.527 0.000421 ***
## Difficulty:Contrast2 0.06774 0.13569 0.499 0.617620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cntrs1 Dffclt Cntrs2 Cnt1:D
## Contrast1 0.024
## Difficulty -0.043 0.008
## Contrast2 -0.024 -0.021 0.000
## Cntrst1:Dff 0.011 -0.086 0.029 0.001
## Dffclty:Cn2 0.000 0.001 -0.020 -0.070 -0.024tab_model(modelincorrectcontrasts)| Correct | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.96 | 0.76 – 1.22 | 0.757 |
| Contrast1 | 1.15 | 1.01 – 1.32 | 0.040 |
| Difficulty | 12.81 | 9.00 – 18.24 | <0.001 |
| Contrast2 | 0.99 | 0.78 – 1.26 | 0.918 |
| Contrast1 * Difficulty | 1.31 | 1.13 – 1.52 | <0.001 |
| Difficulty * Contrast2 | 1.07 | 0.82 – 1.40 | 0.618 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 ID | 2.01 | ||
| τ00 Item | 0.40 | ||
| τ11 ID.Difficulty | 1.79 | ||
| ρ01 ID | -0.09 | ||
| ICC | 0.46 | ||
| N ID | 224 | ||
| N Item | 80 | ||
| Observations | 8960 | ||
| Marginal R2 / Conditional R2 | 0.216 / 0.581 | ||