Abbie’s Dissertation Project
Sanshiroh Ogawa
2025-06-27
- 1 Prep
- 2 Fit Models
- 2.1 RQ1a:
- 2.1.1 RQ1a(i): Syntactic Complexity (Subordination)
- 2.1.2 RQ1a(ii): Syntactic Complexity (Coordination)
- 2.1.3 RQ1a(iii): Accuracy (Number)
- 2.1.4 RQ1a(iv): Accuracy (Gender)
- 2.1.5 RQ1a(v): Accuracy (Tense)
- 2.1.6 RQ1a(vi): Accuracy (Aspect)
- 2.1.7 RQ1a(vii): Lexical Complexity (Lexical Density)
- 2.1.8 RQ1a(viii): Lexical Complexity (Lexical Diversity)
- 2.1.9 RQ1a(ix): Fluency (Syllables per minute)
- 2.2 RQ1b:
- 2.2.1 RQ1b(i): To what extent do individual differences moderate the effect of task complexity and genre on subordination?
- 2.2.2 RQ1b(ii): To what extent do individual differences moderate the effect of task complexity and genre on coordination?
- 2.2.3 RQ1b(iii): To what extent do individual differences moderate the effect of task complexity and genre on accuracy in number?
- 2.2.4 RQ1b(iv): To what extent do individual differences moderate the effect of task complexity and genre on accuracy in gender?
- 2.2.5 RQ1b(v): To what extent do individual differences moderate the effect of task complexity and genre on accuracy in tense?
- 2.2.6 RQ1b(vi): To what extent do individual differences moderate the effect of task complexity and genre on accuracy in aspect?
- 2.2.7 RQ1b(vii): To what extent do individual differences moderate the effect of task complexity and genre on lexical density?
- 2.2.8 RQ1b(viii): To what extent do individual differences moderate the effect of task complexity and genre on lexical diversity?
- 2.2.9 RQ1b(ix): To what extent do individual differences moderate the effect of task complexity and genre on fluency?
- 2.3 RQ2a:
- 2.4 RQ2b:
- 2.4.1 Method 1: Buildmer
- 2.4.2 Method 2: Checking the
moderation of individual differences, one by one.
- 2.4.2.1 RQ2b(i): Effect of Task Complexity and Genre moderated by Achievement Engagement
- 2.4.2.2 RQ2a(ii): Effect of Task Complexity and Genre moderated by Motivation Engagement
- 2.4.2.3 RQ2a(iii): Effect of Task Complexity and Genre moderated by Stress Anxiety
- 2.4.2.4 RQ2b(iv): Effect of Task Complexity and Genre moderated by Cognitive Anxiety
- 2.4.2.5 RQ2b(vi): Effect of Task Complexity and Genre moderated by Working Memory
- 2.5 Buildmer …. code template
- 2.1 RQ1a:
1 Prep
Set Working Directory:
Load necessary packages:
library(readr)
library(dplyr)
library(psych)
library(lme4)
library(lmerTest)
library(ggplot2)
library(emmeans)
library(effects)
library(glmmTMB)
library(buildmer)
library(DHARMa)
library(HLMdiag)Import datasets:
dat <- read_csv("dataset_complete.csv", show_col_types = FALSE)
dat$Participant <- as.factor(dat$Participant)
is.factor(dat$Participant)## [1] TRUE## [1] TRUE## [1] TRUE## Participant MasteryGoal_motivation
## 0 0
## AvoidanceGoal_motivation PerformanceGoal_motivation
## 0 0
## SelfEfficacy_motivation TaskValue_motivation
## 0 0
## Attribution_motivation Somatic_Anxiety
## 0 0
## Avoidance_Anxiety Cognitive_Anxiety
## 0 0
## OSPANabsolute_WM OSPANtotal_WM
## 0 0
## OSPANmath_WM RSPANpartial_WM
## 0 0
## RSPANtotal_WM MotEng
## 0 0
## AchievEng StAnx
## 0 0
## CogAnx Genre
## 0 0
## Complexity Task
## 0 0
## Subordination Coordination
## 0 0
## AccuracyGender AccuracyNumber
## 0 0
## AccuracyTense AccuracyAspect
## 0 0
## LexicalDensity LexicalDiversity
## 0 0
## FluencySyllablesperMinute mentaleffort
## 0 0
## difficulty contentplanning
## 0 0
## linguisticchallenges PTD_composite
## 0 0## vars n mean sd median trimmed mad min
## Participant* 1 632 79.50 45.65 79.50 79.50 58.56 1.00
## MasteryGoal_motivation 2 632 22.64 5.77 22.00 22.62 5.93 7.00
## AvoidanceGoal_motivation 3 632 40.27 8.40 41.00 40.33 8.90 17.00
## PerformanceGoal_motivation 4 632 23.74 6.65 24.00 23.73 5.93 3.00
## SelfEfficacy_motivation 5 632 39.70 6.43 39.50 39.47 5.93 24.00
## TaskValue_motivation 6 632 27.80 7.95 27.00 27.56 7.41 7.00
## Attribution_motivation 7 632 19.41 3.29 20.00 19.51 2.97 10.00
## Somatic_Anxiety 8 632 22.22 6.64 22.00 22.11 7.41 10.00
## Avoidance_Anxiety 9 632 17.18 3.99 17.00 17.11 4.45 7.00
## Cognitive_Anxiety 10 632 33.90 5.10 35.00 34.09 5.93 19.00
## OSPANabsolute_WM 11 632 38.30 15.58 39.00 38.79 14.08 0.00
## OSPANtotal_WM 12 632 52.32 15.04 55.00 54.16 11.86 1.00
## OSPANmath_WM* 13 632 15.51 10.44 16.00 15.55 14.83 1.00
## RSPANpartial_WM 14 632 15.92 6.29 15.50 15.75 5.19 1.00
## RSPANtotal_WM 15 632 29.03 8.92 29.00 28.86 7.41 7.00
## MotEng 16 632 -0.01 1.00 -0.01 -0.02 0.83 -2.65
## AchievEng 17 632 0.00 1.00 -0.05 -0.02 0.92 -2.98
## StAnx 18 632 0.00 1.00 -0.06 -0.02 1.09 -2.06
## CogAnx 19 632 -0.01 1.00 0.11 0.02 1.02 -3.36
## Genre* 20 632 1.50 0.50 1.50 1.50 0.74 1.00
## Complexity* 21 632 1.50 0.50 1.50 1.50 0.74 1.00
## Task* 22 632 2.50 1.12 2.50 2.50 1.48 1.00
## Subordination 23 632 1.53 1.59 1.00 1.26 1.48 0.00
## Coordination 24 632 2.37 2.04 2.00 2.12 1.48 0.00
## AccuracyGender 25 632 2.20 1.91 2.00 1.97 1.48 0.00
## AccuracyNumber 26 632 0.73 1.01 0.00 0.57 0.00 0.00
## AccuracyTense 27 632 1.56 1.52 1.00 1.37 1.48 0.00
## AccuracyAspect 28 632 0.62 1.59 0.00 0.19 0.00 0.00
## LexicalDensity* 29 632 120.97 68.36 126.00 121.43 87.47 1.00
## LexicalDiversity 30 632 0.75 2.06 0.64 0.64 0.15 0.06
## FluencySyllablesperMinute* 31 632 136.51 85.04 142.00 136.70 114.16 1.00
## mentaleffort 32 632 6.07 1.63 6.00 6.14 1.48 1.00
## difficulty 33 632 5.94 1.73 6.00 6.00 1.48 1.00
## contentplanning 34 632 5.32 1.96 5.00 5.32 2.22 1.00
## linguisticchallenges 35 632 6.48 1.72 7.00 6.56 1.48 1.00
## PTD_composite 36 632 5.95 1.47 6.00 5.99 1.48 1.50
## max range skew kurtosis se
## Participant* 158.00 157.00 0.00 -1.21 1.82
## MasteryGoal_motivation 39.00 32.00 0.11 0.40 0.23
## AvoidanceGoal_motivation 59.00 42.00 -0.12 -0.45 0.33
## PerformanceGoal_motivation 40.00 37.00 -0.06 0.33 0.26
## SelfEfficacy_motivation 58.00 34.00 0.34 0.33 0.26
## TaskValue_motivation 50.00 43.00 0.23 0.18 0.32
## Attribution_motivation 30.00 20.00 -0.19 0.81 0.13
## Somatic_Anxiety 37.00 27.00 0.15 -0.82 0.26
## Avoidance_Anxiety 29.00 22.00 0.20 0.18 0.16
## Cognitive_Anxiety 44.00 25.00 -0.36 -0.60 0.20
## OSPANabsolute_WM 75.00 75.00 -0.28 0.04 0.62
## OSPANtotal_WM 78.00 77.00 -1.13 1.18 0.60
## OSPANmath_WM* 29.00 28.00 -0.16 -1.65 0.42
## RSPANpartial_WM 36.00 35.00 0.36 0.33 0.25
## RSPANtotal_WM 54.00 47.00 0.14 0.30 0.35
## MotEng 3.06 5.71 0.20 0.65 0.04
## AchievEng 2.73 5.70 0.06 0.50 0.04
## StAnx 2.36 4.42 0.22 -0.52 0.04
## CogAnx 2.23 5.59 -0.35 -0.05 0.04
## Genre* 2.00 1.00 0.00 -2.00 0.02
## Complexity* 2.00 1.00 0.00 -2.00 0.02
## Task* 4.00 3.00 0.00 -1.37 0.04
## Subordination 7.00 7.00 1.26 1.12 0.06
## Coordination 10.00 10.00 1.02 0.74 0.08
## AccuracyGender 10.00 10.00 1.08 1.21 0.08
## AccuracyNumber 7.00 7.00 1.50 2.89 0.04
## AccuracyTense 10.00 10.00 1.34 3.56 0.06
## AccuracyAspect 12.00 12.00 3.30 12.93 0.06
## LexicalDensity* 246.00 245.00 -0.10 -1.21 2.72
## LexicalDiversity 37.23 37.17 17.51 306.83 0.08
## FluencySyllablesperMinute* 273.00 272.00 -0.04 -1.41 3.38
## mentaleffort 9.00 8.00 -0.37 -0.10 0.06
## difficulty 9.00 8.00 -0.31 -0.38 0.07
## contentplanning 9.00 8.00 -0.04 -0.76 0.08
## linguisticchallenges 9.00 8.00 -0.44 -0.33 0.07
## PTD_composite 9.00 7.50 -0.24 -0.21 0.06CALF measures of interest:
- Syntactic Complexity
- Subordination
- Coordination
- Accuracy
- Number
- Gender
- Tense
- Aspect
- Lexical Complexity
- Lexical Density
- Lexical Diversity
- Fluency
- Syllables per minute
2 Fit Models
2.1 RQ1a:
How does altering task complexity and genre — specifically argumentative and narrative — influence the quality of adolescent L2 writing?
2.1.1 RQ1a(i): Syntactic Complexity (Subordination)
How does altering task complexity and genre — specifically argumentative and narrative — influence the syntactic subordination?
First, check to see the distribution of “Subordination.”
## [1] 2 0 1 3 4 6 5 7## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 632 1.53 1.59 1 1.26 1.48 0 7 7 1.26 1.12 0.06
Things to note:
- Subordination includes 0.
- It is a count variable.
Therefore, I will fit a poisson regression.
Run the model:
mod1sub <- glmmTMB(Subordination ~ Genre * Complexity + (1|Participant), family = poisson, dat)
summary(mod1sub)## Family: poisson ( log )
## Formula: Subordination ~ Genre * Complexity + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 1883.0 1905.2 -936.5 1873.0 627
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4748 0.6891
## Number of obs: 632, groups: Participant, 158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.44979 0.08297 5.421 5.93e-08 ***
## GenreNAR -0.27393 0.08600 -3.185 0.001447 **
## ComplexitySIMPLE -0.09021 0.08180 -1.103 0.270108
## GenreNAR:ComplexitySIMPLE -0.51453 0.13633 -3.774 0.000161 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- Main effect of Genre significant
- Interaction between Genre and Complexity significant
Visualization
## model: Subordination ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 1.567988 1.4327306
## NAR 1.192279 0.6512435
Interpretation:
- Main effect of Genre: Subordination decreases for the Narrative task compared to the Argumentative task.
- Interaction effect of Genre x Complexity: When Genre is NAR and Complexity is SIMPLE, the subordination count decreases compared to ARG and COMPLEX.
2.1.2 RQ1a(ii): Syntactic Complexity (Coordination)
How does altering task complexity and genre — specifically argumentative and narrative — influence the syntactic coordination?
First, check to see the distribution of “Coordination.”
## [1] 1 3 2 6 8 4 7 5 0 10 9## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 632 2.37 2.04 2 2.12 1.48 0 10 10 1.02 0.74 0.08
Things to note:
- Coordination includes 0.
- It is a count variable.
Therefore, I will fit a poisson regression.
Run the model:
mod1coor <- glmmTMB(Coordination ~ Genre * Complexity + (1|Participant), family = poisson, dat)
summary(mod1coor)## Family: poisson ( log )
## Formula: Coordination ~ Genre * Complexity + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 2311.4 2333.6 -1150.7 2301.4 627
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4238 0.651
## Number of obs: 632, groups: Participant, 158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.57888 0.07732 7.487 7.07e-14 ***
## GenreNAR 0.10216 0.07438 1.374 0.170
## ComplexitySIMPLE 0.03989 0.07550 0.528 0.597
## GenreNAR:ComplexitySIMPLE 0.04799 0.10358 0.463 0.643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- Nothing is significant.
Visualization
## model: Coordination ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 1.784031 1.856640
## NAR 1.975920 2.157434
Interpretation:
- NAR seems to lead to more Coordination than ARG, but the effect is not statistically significant.
- When Genre is NAR and Complexity is SIMPLE, it seems like the Coordination increases, but it’s not statistically significant.
2.1.3 RQ1a(iii): Accuracy (Number)
How does altering task complexity and genre — specifically argumentative and narrative — influence the accuracy in number?
First, check to see the distribution of “AccuracyNumber.”
## [1] 0 1 2 5 3 7 4##
## 0 1 2 3 4 5 7
## 361 127 113 20 9 1 1## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 632 0.73 1.01 0 0.57 0 0 7 7 1.5 2.89 0.04
Things to note:
- AccuracyNumber includes 0.
- It is a count variable.
mod1num1 <- glmmTMB(AccuracyNumber ~ Genre * Complexity + (1|Participant), family = poisson, dat)
summary(mod1num1)## Family: poisson ( log )
## Formula: AccuracyNumber ~ Genre * Complexity + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 1381.1 1403.4 -685.6 1371.1 627
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9376 0.9683
## Number of obs: 632, groups: Participant, 158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.83913 0.13911 -6.032 1.62e-09 ***
## GenreNAR 0.29725 0.12945 2.296 0.0217 *
## ComplexitySIMPLE 0.06514 0.13647 0.477 0.6331
## GenreNAR:ComplexitySIMPLE -0.34334 0.18762 -1.830 0.0673 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Better model with zero-inflated generalized linear mixed model
mod1num2 <- glmmTMB(AccuracyNumber ~ Genre * Complexity + (1|Participant),
family = nbinom2, # negative binomial
ziformula = ~1, # zero-inflated generalized linear mixed model
dat)
summary(mod1num2)## Family: nbinom2 ( log )
## Formula: AccuracyNumber ~ Genre * Complexity + (1 | Participant)
## Zero inflation: ~1
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 1385.1 1416.3 -685.6 1371.1 625
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9376 0.9683
## Number of obs: 632, groups: Participant, 158
##
## Dispersion parameter for nbinom2 family (): 2.54e+07
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.83913 0.13911 -6.032 1.62e-09 ***
## GenreNAR 0.29725 0.12945 2.296 0.0217 *
## ComplexitySIMPLE 0.06514 0.13647 0.477 0.6331
## GenreNAR:ComplexitySIMPLE -0.34334 0.18762 -1.830 0.0673 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -16.88 770.49 -0.022 0.983Results:
- Main effect of Genre significant
Visualization
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect## model: AccuracyNumber ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.4320854 0.4611681
## NAR 0.5816535 0.4403948## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
Interpretation:
- Main effect of Genre: Changing Genre from ARG to NAR leads to higher accuracy in number.
2.1.4 RQ1a(iv): Accuracy (Gender)
How does altering task complexity and genre — specifically argumentative and narrative — influence the accuracy in gender?
First, check to see the distribution of “AccuracyGender.”
## [1] 1 0 3 2 5 4 7 6 10 8 9##
## 0 1 2 3 4 5 6 7 8 9 10
## 127 134 143 97 52 42 14 12 5 5 1## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 632 2.2 1.91 2 1.97 1.48 0 10 10 1.08 1.21 0.08
Things to note:
- AccuracyGender includes 0.
- It is a count variable.
mod1gen1 <- glmmTMB(AccuracyGender ~ Genre * Complexity + (1|Participant), family = poisson, dat)
summary(mod1gen1)## Family: poisson ( log )
## Formula: AccuracyGender ~ Genre * Complexity + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 2403.9 2426.1 -1196.9 2393.9 627
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.075 0.2739
## Number of obs: 632, groups: Participant, 158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.05924 0.05185 20.431 < 2e-16 ***
## GenreNAR -0.45198 0.07373 -6.130 8.79e-10 ***
## ComplexitySIMPLE -0.19038 0.06835 -2.785 0.00535 **
## GenreNAR:ComplexitySIMPLE -0.09178 0.11135 -0.824 0.40981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Better model with zero-inflated generalized linear mixed model
mod1gen2 <- glmmTMB(AccuracyGender ~ Genre * Complexity + (1|Participant),
family = nbinom2, # negative binomial
ziformula = ~1, # zero-inflated generalized linear mixed model
dat)
summary(mod1gen2)## Family: nbinom2 ( log )
## Formula: AccuracyGender ~ Genre * Complexity + (1 | Participant)
## Zero inflation: ~1
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 2372.7 2403.8 -1179.3 2358.7 625
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.01082 0.104
## Number of obs: 632, groups: Participant, 158
##
## Dispersion parameter for nbinom2 family (): 6.07
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.12296 0.06386 17.583 < 2e-16 ***
## GenreNAR -0.43587 0.09128 -4.775 1.8e-06 ***
## ComplexitySIMPLE -0.19057 0.08471 -2.250 0.0245 *
## GenreNAR:ComplexitySIMPLE -0.09992 0.13310 -0.751 0.4528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.2531 0.7999 -4.067 4.77e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- Main effect of Genre significant
- Main effect of Complexity significant
Visualization
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect## model: AccuracyGender ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 3.073943 2.540588
## NAR 1.987914 1.486768## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
Interpretation:
- Main effect of Genre: Changing Genre from ARG to NAR leads to fewer gender accuracy.
- Main effect of Complexity: Changing Complexity from COMPLEX to SIMPLE leads to fewer gender accuracy.
2.1.5 RQ1a(v): Accuracy (Tense)
How does altering task complexity and genre — specifically argumentative and narrative — influence the accuracy in tense?
First, check to see the distribution of “AccuracyTense.”
## [1] 1 0 3 4 2 5 6 10 7 8 9##
## 0 1 2 3 4 5 6 7 8 9 10
## 192 146 144 87 45 8 5 1 1 1 2## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 632 1.56 1.52 1 1.37 1.48 0 10 10 1.34 3.56 0.06
Things to note:
- AccuracyTense includes 0.
- It is a count variable.
mod1ten <- glmmTMB(AccuracyTense ~ Genre * Complexity + (1|Participant), family = poisson, dat)
summary(mod1ten)## Family: poisson ( log )
## Formula: AccuracyTense ~ Genre * Complexity + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 2031.8 2054.1 -1010.9 2021.8 627
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.1668 0.4084
## Number of obs: 632, groups: Participant, 158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06722 0.08213 0.819 0.413
## GenreNAR 0.51516 0.09317 5.529 3.22e-08 ***
## ComplexitySIMPLE -0.02756 0.10498 -0.262 0.793
## GenreNAR:ComplexitySIMPLE 0.05951 0.13196 0.451 0.652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- Main effect of Genre is significant.
Visualization
## model: AccuracyTense ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 1.069536 1.040467
## NAR 1.790303 1.848428
Interpretation:
- Changing Genre from ARG to NAR leads to higher accuracy in tense.
2.1.6 RQ1a(vi): Accuracy (Aspect)
How does altering task complexity and genre — specifically argumentative and narrative — influence the accuracy in aspect?
First, check to see the distribution of “AccuracyAspect.”
## [1] 0 1 3 4 2 5 6 8 10 12 9 7 11##
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 514 23 24 33 11 12 5 5 1 1 1 1 1## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 632 0.62 1.59 0 0.19 0 0 12 12 3.3 12.93 0.06
Things to note:
- AccuracyTense includes 0.
- It is a count variable.
mod1asp1 <- glmmTMB(AccuracyAspect ~ Genre * Complexity + (1|Participant), family = poisson, dat)
summary(mod1asp1)## Family: poisson ( log )
## Formula: AccuracyAspect ~ Genre * Complexity + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 965.0 987.3 -477.5 955.0 627
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5652 0.7518
## Number of obs: 632, groups: Participant, 158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7300 0.1818 -9.516 < 2e-16 ***
## GenreNAR 2.2326 0.1730 12.906 < 2e-16 ***
## ComplexitySIMPLE -2.9178 0.7260 -4.019 5.84e-05 ***
## GenreNAR:ComplexitySIMPLE -1.3163 0.8544 -1.541 0.123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Better model with zero-inflated generalized linear mixed model
mod1asp2 <- glmmTMB(AccuracyAspect ~ Genre * Complexity + (1|Participant),
family = nbinom1, # negative binomial (nbinom1; nbinom2 did not resolve issues)
ziformula = ~1, # zero-inflated generalized linear mixed model
dat)
summary(mod1asp2)## Family: nbinom1 ( log )
## Formula: AccuracyAspect ~ Genre * Complexity + (1 | Participant)
## Zero inflation: ~1
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 802.4 833.6 -394.2 788.4 625
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 2.227e-09 4.719e-05
## Number of obs: 632, groups: Participant, 158
##
## Dispersion parameter for nbinom1 family (): 1.62
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6362 0.4560 -5.781 7.41e-09 ***
## GenreNAR 3.6747 0.4595 7.997 1.27e-15 ***
## ComplexitySIMPLE -1.0193 0.8385 -1.216 0.22412
## GenreNAR:ComplexitySIMPLE -2.7585 0.9560 -2.886 0.00391 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5430 0.4692 -3.289 0.00101 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- Main effect of Genre is significant.
- Interaction between Genre and Complexity is significant
Visualization
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect## model: AccuracyAspect ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.0716303 0.02584830
## NAR 2.8249326 0.06461372## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
Interpretation:
- Main effect of Genre: Changing ARG to NAR seems to lead to higher accuracy in aspect.
- Interaction between Genre and Complexity: When Genre is NAR and Complexity is SIMPLE, the accuracy in aspect is lower.
2.1.7 RQ1a(vii): Lexical Complexity (Lexical Density)
How does altering task complexity and genre — specifically argumentative and narrative — influence the lexical density?
First, check to see the distribution of “LexicalDensity.”
unique(dat$LexicalDensity) # Participant 141 has "%" in the cell ... I will remove for this analysis. ## [1] "0.28129999999999999" "0.42859999999999998" "0.31709999999999999"
## [4] "0.47499999999999998" "0.3448" "0.25690000000000002"
## [7] "0.28789999999999999" "0.39219999999999999" "0.5585"
## [10] "0.52629999999999999" "0.58460000000000001" "0.64"
## [13] "0.71430000000000005" "0.66669999999999996" "0.7"
## [16] "0.6" "0.6522" "0.73109999999999997"
## [19] "0.67290000000000005" "0.84550000000000003" "0.43590000000000001"
## [22] "0.55000000000000004" "0.47370000000000001" "0.60709999999999997"
## [25] "0.63639999999999997" "0.60609999999999997" "0.6552"
## [28] "0.55559999999999998" "0.73909999999999998" "0.50819999999999999"
## [31] "0.52" "0.51470000000000005" "0.5"
## [34] "0.64710000000000001" "0.375" "0.4375"
## [37] "0.46150000000000002" "0.59" "0.62"
## [40] "0.61" "0.28570000000000001" "0.38300000000000001"
## [43] "0.32140000000000002" "0.44190000000000002" "0.57279999999999998"
## [46] "0.59460000000000002" "0.54730000000000001" "0.51910000000000001"
## [49] "0.53849999999999998" "0.60699999999999998" "0.34620000000000001"
## [52] "0.56979999999999997" "0.48780000000000001" "0.55320000000000003"
## [55] "0.35899999999999999" "0.36170000000000002" "0.51280000000000003"
## [58] "0.33329999999999999" "0.25" "0.35"
## [61] "0.35289999999999999" "0.56100000000000005" "0.59260000000000002"
## [64] "0.45350000000000001" "0.47470000000000001" "0.44969999999999999"
## [67] "0.46289999999999998" "0.57140000000000002" "0.59379999999999999"
## [70] "0.4783" "0.44440000000000002" "0.44740000000000002"
## [73] "0.64859999999999995" "0.59240000000000004" "0.61539999999999995"
## [76] "0.56189999999999996" "0.54210000000000003" "0.55210000000000004"
## [79] "0.27160000000000001" "0.22770000000000001" "0.27850000000000003"
## [82] "0.40739999999999998" "0.50849999999999995" "0.47170000000000001"
## [85] "0.56520000000000004" "0.54239999999999999" "0.6512"
## [88] "0.61899999999999999" "0.3226" "0.42"
## [91] "0.4" "0.71150000000000002" "0.69"
## [94] "0.51129999999999998" "0.65310000000000001" "0.63200000000000001"
## [97] "0.71299999999999997" "0.51849999999999996" "0.4178"
## [100] "0.70669999999999999" "0.48699999999999999" "0.50449999999999995"
## [103] "0.45" "0.37209999999999999" "0.26669999999999999"
## [106] "0.42499999999999999" "0.37309999999999999" "0.42549999999999999"
## [109] "0.37780000000000002" "0.58620000000000005" "0.625"
## [112] "0.22439999999999999" "0.5161" "0.3846"
## [115] "0.44" "0.54549999999999998" "0.41670000000000001"
## [118] "0.51719999999999999" "0.36840000000000001" "0.36"
## [121] "0.5333" "0.45829999999999999" "0.63"
## [124] "0.65" "0.73960000000000004" "0.60199999999999998"
## [127] "0.70069999999999999" "0.65380000000000005" "0.69230000000000003"
## [130] "0.71699999999999997" "0.77780000000000005" "0.75"
## [133] "0.42109999999999997" "0.45710000000000001" "0.62860000000000005"
## [136] "0.62919999999999998" "0.56000000000000005" "0.49"
## [139] "0.57999999999999996" "0.63829999999999998" "0.67"
## [142] "0.66" "0.67649999999999999" "0.61699999999999999"
## [145] "0.60529999999999995" "0.60980000000000001" "0.56920000000000004"
## [148] "0.70450000000000002" "0.61729999999999996" "0.48649999999999999"
## [151] "0.48" "0.3019" "0.25929999999999997"
## [154] "0.44119999999999998" "0.32790000000000002" "0.53129999999999999"
## [157] "0.5625" "0.34210000000000002" "0.55879999999999996"
## [160] "0.34338000000000002" "0.3548" "0.29549999999999998"
## [163] "0.3095" "0.6573" "0.72919999999999996"
## [166] "0.69389999999999996" "0.2581" "0.2571"
## [169] "0.3478" "0.61109999999999998" "0.72"
## [172] "0.3256" "0.52939999999999998" "0.3947"
## [175] "0.38240000000000002" "0.36670000000000003" "0.34379999999999999"
## [178] "0.35420000000000001" "0.41860000000000003" "0.83299999999999996"
## [181] "0.63009999999999999" "0.71050000000000002" "0.4667"
## [184] "0.52380000000000004" "0.47220000000000001" "0.32079999999999997"
## [187] "0.78569999999999995" "0.5111" "0.44829999999999998"
## [190] "0.58819999999999995" "0.40029999999999999" "0.61160000000000003"
## [193] "0.72729999999999995" "0.6875" "0.6714"
## [196] "0.68689999999999996" "0.59040000000000004" "0.27589999999999998"
## [199] "0.39290000000000003" "0.30769999999999997" "0.63270000000000004"
## [202] "0.67569999999999997" "0.69769999999999999" "0.63890000000000002"
## [205] "0.63160000000000005" "0.69569999999999999" "0.68420000000000003"
## [208] "0.56759999999999999" "0.64439999999999997" "0.59299999999999997"
## [211] "0.49280000000000002" "0.45610000000000001" "0.59570000000000001"
## [214] "0.4516" "0.42420000000000002" "0.78949999999999998"
## [217] "0.67349999999999999" "0.72340000000000004" "0.43330000000000002"
## [220] "0.2545" "0.37930000000000003" "0.46510000000000001"
## [223] "0.68630000000000002" "0.68" "0.70730000000000004"
## [226] "0.81479999999999997" "0.69830000000000003" "0.83160000000000001"
## [229] "0.39579999999999999" "0.3962" "0.62070000000000003"
## [232] "0.27779999999999999" "56`%" "0.37140000000000001"
## [235] "0.67859999999999998" "0.65849999999999997" "0.6774"
## [238] "0.62370000000000003" "0.66269999999999996" "0.66069999999999995"
## [241] "0.72640000000000005" "0.43240000000000001" "0.2424"
## [244] "0.64839999999999998" "0.56999999999999995" "0.30499999999999999"dat_density <- dat %>%
filter(Participant != "141")
dat_density$LexicalDensity <- as.numeric(dat_density$LexicalDensity) # The values are stored as characters, change to numeric
describe(dat$AccuracyAspect)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 632 0.62 1.59 0 0.19 0 0 12 12 3.3 12.93 0.06
Fit model:
mod1density <- lmer(LexicalDensity ~ Genre * Complexity + (1|Participant), dat_density)
summary(mod1density)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LexicalDensity ~ Genre * Complexity + (1 | Participant)
## Data: dat_density
##
## REML criterion at convergence: -1099.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0866 -0.5644 0.0439 0.4954 3.4928
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.013923 0.11799
## Residual 0.005275 0.07263
## Number of obs: 628, groups: Participant, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.524925 0.011058 242.055324 47.471 <2e-16 ***
## GenreNAR 0.012968 0.008197 468.000004 1.582 0.1143
## ComplexitySIMPLE -0.017729 0.008197 468.000004 -2.163 0.0311 *
## GenreNAR:ComplexitySIMPLE -0.012870 0.011592 468.000004 -1.110 0.2675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CSIMPL
## GenreNAR -0.371
## CmplxSIMPLE -0.371 0.500
## GNAR:CSIMPL 0.262 -0.707 -0.707Note:
- Some caution for interpretation necessary because the assumptions are not met.
Results:
- Main effect of Complexity is significant.
Visualization
## model: LexicalDensity ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.5249255 0.5071968
## NAR 0.5378936 0.5072954
Interpretation:
- Main effect of Complexity: Changing Complexity from SIMPLE to COMPLEX increases lexical density.
2.1.8 RQ1a(viii): Lexical Complexity (Lexical Diversity)
How does altering task complexity and genre — specifically argumentative and narrative — influence the lexical diversity?
First, check to see the distribution of “LexicalDiversity.”
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 632 0.75 2.06 0.64 0.64 0.15 0.06 37.23 37.17 17.51 306.83 0.08
unique(dat$LexicalDiversity) # Participants 157 and 158 have extreme values ... remove for this analysis. ## [1] 0.5938 0.8095 0.5610 0.9000 0.4500 0.4900 0.4242 0.6078 0.3900
## [10] 0.4700 0.6494 0.6797 0.6923 0.7400 0.8571 0.9167 0.4957 0.6134
## [19] 0.5701 0.6182 0.4872 0.4103 0.5263 0.6429 0.6970 0.7333 0.7879
## [28] 0.6500 0.6207 0.6296 0.7391 0.4426 0.4800 0.4559 0.4231 0.8235
## [37] 0.2810 0.6875 0.9231 0.6900 0.6800 0.7100 0.7000 0.4400 0.6809
## [46] 0.5000 0.8140 0.4660 0.5405 0.4737 0.7692 0.7140 0.5556 0.4615
## [55] 0.6977 0.6585 0.6170 0.6316 0.3617 0.5897 0.6667 0.8500 0.7059
## [64] 0.8049 0.8000 0.7407 0.8519 0.6047 0.5886 0.6190 0.5943 0.5873
## [73] 0.6406 0.8261 0.8333 0.6053 0.6061 0.6486 0.6417 0.5091 0.4381
## [82] 0.6636 0.6250 0.5172 0.3761 0.2469 0.2079 0.2532 0.3529 0.5741
## [91] 0.6102 0.6226 0.6304 0.7627 0.6111 0.7619 0.8700 0.4839 0.7083
## [100] 0.5882 0.5100 0.7500 0.7600 0.4962 0.6224 0.5600 0.6731 0.6364
## [109] 0.7877 0.8533 0.7739 0.7928 0.6000 0.8182 0.8667 0.4250 0.3731
## [118] 0.4681 0.5102 0.6897 0.6222 0.7097 0.7308 0.4000 0.7300 0.6333
## [127] 0.8367 0.6087 0.7200 0.5500 0.5816 0.5839 0.7576 0.8462 0.7143
## [136] 0.6857 0.6538 0.5714 0.7547 0.8889 0.8750 0.6579 0.6571 0.8330
## [145] 0.7857 0.6067 0.5900 0.5278 0.8056 0.6892 0.7674 0.7667 0.7053
## [154] 0.7241 0.9091 0.7660 0.8185 0.5231 0.6591 0.4595 0.5309 0.5946
## [163] 0.9032 0.8400 0.9048 0.5660 0.4815 0.5385 0.5410 0.4490 0.7813
## [172] 0.7900 0.7105 0.6471 0.6563 0.5806 0.7045 0.6842 0.7381 0.4196
## [181] 0.7917 0.6774 0.6944 0.8900 0.6512 0.8250 0.7353 0.4688 0.6042
## [190] 0.8372 0.6905 0.6400 0.4658 0.5300 0.5640 0.5116 0.4468 0.8600
## [199] 0.9400 0.4038 0.7222 0.5094 0.4889 0.0986 0.5526 0.6164 0.6600
## [208] 0.6984 0.3429 0.3243 0.7647 0.5571 0.5812 0.7349 0.7544 0.7632
## [217] 0.6786 0.7234 0.7551 0.7368 0.6860 0.6377 0.7281 0.6383 0.4082
## [226] 0.5390 0.5143 0.7250 0.6458 0.5814 0.7561 0.7531 0.7895 0.5849
## [235] 0.8460 0.3700 0.6349 0.6118 0.7714 0.6154 0.7273 0.7317 0.7419
## [244] 0.5161 0.5904 0.5446 0.5283 0.5192 0.4697 0.0604 0.4018 37.2300dat_diversity <- dat %>%
filter(Participant != "157") %>%
filter(Participant != "158")
hist(dat_diversity$LexicalDiversity)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 624 0.63 0.14 0.64 0.64 0.15 0.06 0.94 0.88 -0.31 0.12 0.01Fit model:
mod1diversity <- lmer(LexicalDiversity ~ Genre * Complexity + (1|Participant), dat_diversity)
summary(mod1diversity)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LexicalDiversity ~ Genre * Complexity + (1 | Participant)
## Data: dat_diversity
##
## REML criterion at convergence: -872.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2675 -0.4809 0.0282 0.4831 3.3926
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.011743 0.10837
## Residual 0.008734 0.09346
## Number of obs: 624, groups: Participant, 156
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.62803 0.01146 312.08985 54.816 < 2e-16 ***
## GenreNAR 0.03443 0.01058 465.00000 3.254 0.00122 **
## ComplexitySIMPLE -0.01341 0.01058 465.00000 -1.267 0.20567
## GenreNAR:ComplexitySIMPLE -0.01646 0.01497 465.00000 -1.100 0.27198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CSIMPL
## GenreNAR -0.462
## CmplxSIMPLE -0.462 0.500
## GNAR:CSIMPL 0.327 -0.707 -0.707Note:
Some caution for interpretation necessary because the assumptions are not met.
Results:
- Main effect of Genre is significant.
Visualization
## model: LexicalDiversity ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.6280340 0.6146231
## NAR 0.6624673 0.6325974
Interpretation:
- Main effect of Genre: Changing Genre from ARG to NAR increases lexical diversity.
2.1.9 RQ1a(ix): Fluency (Syllables per minute)
How does altering task complexity and genre — specifically argumentative and narrative — influence the fluency?
## [1] "4.45" "9.14" "7.75"
## [4] "9" "10.33" "15"
## [7] "10" "14" "12"
## [10] "16" "11" "17"
## [13] "7.17" "13.2" "9.33"
## [16] "9.5" "14.64" "11.22"
## [19] "3.38" "3.67" "3.29"
## [22] "3.5" "32.39" "36"
## [25] "31.71" "43.2" "9.2899999999999991"
## [28] "6.86" "12.8" "1.97"
## [31] "10.57" "18.75" "3.64"
## [34] "6.25" "9.6" "10.86"
## [37] "9.8000000000000007" "13.67" "5.8"
## [40] "3.4" "5.33" "8.3000000000000007"
## [43] "8.98" "13.55" "10.42"
## [46] "13" "5.92" "5.54"
## [49] "8.1300000000000008" "6.75" "8.1999999999999993"
## [52] "10.36" "6.71" "6"
## [55] "4.0999999999999996" "9.0500000000000007" "19.440000000000001"
## [58] "11.64" "7.29" "9.7100000000000009"
## [61] "10.31" "6.63" "10.14"
## [64] "6.4" "5.4" "12.67"
## [67] "10.5" "11.7" "10.8"
## [70] "11.4" "8.57" "8.85"
## [73] "4.8" "4.58" "8.25"
## [76] "8.6300000000000008" "8.33" "3.17"
## [79] "5.88" "11.25" "18.89"
## [82] "19.64" "15.42" "9.75"
## [85] "14.82" "15.7" "11.8"
## [88] "12.6" "9.7799999999999994" "11.13"
## [91] "14.83" "13.5" "7.73"
## [94] "7.1" "10.6" "2.64"
## [97] "4.42" "2.8" "5"
## [100] "4.9400000000000004" "9.89" "5.07"
## [103] "7.36" "10.38" "17.329999999999998"
## [106] "15.5" "13.6" "14.67"
## [109] "7.63" "14.9" "15.4"
## [112] "18.329999999999998" "14.85" "2.83"
## [115] "5.9" "6.29" "3"
## [118] "1.86" "4" "2.11"
## [121] "6.22" "16.43" "7.71"
## [124] "5.5" "7.38" "5.25"
## [127] "8.8000000000000007" "4.7" "2.59"
## [130] "3.86" "6.38" "8.5"
## [133] "6.83" "2.93" "7.31"
## [136] "13.36" "10.83" "12.4"
## [139] "7" "8" "16.100000000000001"
## [142] "10.73" "14.13" "7.86"
## [145] "6.6" "8.8800000000000008" "11.17"
## [148] "3.27" "4.93" "3.63"
## [151] "7.2" "5.6" "2.86"
## [154] "5.75" "13.15" "23.22"
## [157] "15.79" "14.46" "4.3099999999999996"
## [160] "14.5" "6.78" "7.3"
## [163] "4.9000000000000004" "7.3." "9.1999999999999993"
## [166] "12.63" "12.43" "18.2"
## [169] "10.96" "10.85" "7.8"
## [172] "5.56" "6.49" "6.5"
## [175] "3.92" "4.88" "5.73"
## [178] "11.88" "7.13" "4.6399999999999997"
## [181] "3.1" "10.43" "11.3"
## [184] "15.55" "11.86" "9.43"
## [187] "12.5" "8.14" "27"
## [190] "5.29" "5.57" "6.92"
## [193] "6.67" "8.3800000000000008" "5.0999999999999996"
## [196] "6.64" "5.86" "7.33"
## [199] "1.9" "2" "1.2"
## [202] "8.9" "5.38" "18.38"
## [205] "16.8" "10.1" "4.71"
## [208] "2.89" "4.43" "9.4"
## [211] "11.71" "3.55" "4.5"
## [214] "7.88" "19.29" "12.25"
## [217] "4.13" "7.79" "16.5"
## [220] "15.56" "13.7" "7.9"
## [223] "9.44" "12.3" "12.9"
## [226] "17.43" "10.09" "9.25"
## [229] "4.4000000000000004" "7.14" "8.4"
## [232] "5.82" "15.2" "16.170000000000002"
## [235] "13.44" "14.17" "7.56"
## [238] "10.17" "11.57" "21.46"
## [241] "22.25" "8.67" "12.29"
## [244] "22.5" "7.67" "4.75"
## [247] "9.85" "16.600000000000001" "14.21"
## [250] "4.87" "7.5" "4.2"
## [253] "4.57" "6.98" "1.08"
## [256] "2.5" "3.6" "8.7100000000000009"
## [259] "11.2" "15.17" "5.17"
## [262] "4.55" "5.67" "9.4499999999999993"
## [265] "14.89" "14.77" "9.86"
## [268] "7.6" "21.33" "4.5999999999999996"
## [271] "8.56" "10.89" "30.5"dat_fluency <- dat %>%
filter(Participant != "72")
dat_fluency$FluencySyllablesperMinute <- as.numeric(dat_fluency$FluencySyllablesperMinute) # Store values as numeric
is.numeric(dat_fluency$FluencySyllablesperMinute)## [1] TRUE
Fit the model.
mod1flu1 <- lmer(FluencySyllablesperMinute ~ Genre * Complexity + (1|Participant), dat_fluency)
summary(mod1flu1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FluencySyllablesperMinute ~ Genre * Complexity + (1 | Participant)
## Data: dat_fluency
##
## REML criterion at convergence: 3396.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1261 -0.5627 -0.0839 0.5210 5.9449
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 13.726 3.705
## Residual 7.779 2.789
## Number of obs: 628, groups: Participant, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.4454 0.3701 280.8003 25.521 < 2e-16 ***
## GenreNAR 0.3648 0.3148 468.0000 1.159 0.247123
## ComplexitySIMPLE -1.0564 0.3148 468.0000 -3.356 0.000856 ***
## GenreNAR:ComplexitySIMPLE 1.8588 0.4452 468.0000 4.175 3.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CSIMPL
## GenreNAR -0.425
## CmplxSIMPLE -0.425 0.500
## GNAR:CSIMPL 0.301 -0.707 -0.707How about if I log-transform “FluencySyllablsperMinute”? Does it improve the residual distribution?
dat_fluency$FluencySyllablesperMinuteLog10 <- log10(dat_fluency$FluencySyllablesperMinute)
hist(dat_fluency$FluencySyllablesperMinuteLog10) 
mod1flu2 <- lmer(FluencySyllablesperMinuteLog10 ~ Genre * Complexity + (1|Participant), dat_fluency)
summary(mod1flu2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FluencySyllablesperMinuteLog10 ~ Genre * Complexity + (1 | Participant)
## Data: dat_fluency
##
## REML criterion at convergence: -393.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7437 -0.4719 0.0520 0.5380 3.1832
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.02798 0.1673
## Residual 0.01851 0.1360
## Number of obs: 628, groups: Participant, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.92116 0.01721 298.99637 53.531 < 2e-16 ***
## GenreNAR 0.02275 0.01535 468.00000 1.482 0.13903
## ComplexitySIMPLE -0.04041 0.01535 468.00000 -2.632 0.00877 **
## GenreNAR:ComplexitySIMPLE 0.07086 0.02171 468.00000 3.263 0.00118 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CSIMPL
## GenreNAR -0.446
## CmplxSIMPLE -0.446 0.500
## GNAR:CSIMPL 0.315 -0.707 -0.707… Doesn’t really improve the residual distribution.
Results:
- Main effect of Complexity significant.
- Interaction between Genre and Complexity significant.
Visualization
## model: FluencySyllablesperMinute ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 9.445414 8.389045
## NAR 9.810191 10.612611
Interpretation:
- Main effect of Complexity significant: Changing Complexity from COMPLEX to SIMPLE reduces Fluency.
- Interaction between Genre and Complexity significant: When Genre is NAR and Complexity is SIMPLE, the fluency is higher compared to when Genre is ARG and Complexity is COMPLEX.
2.2 RQ1b:
To what extent do individual differences moderate the effect of task complexity and genre on writing quality?
2.2.1 RQ1b(i): To what extent do individual differences moderate the effect of task complexity and genre on subordination?
## Family: nbinom2 ( log )
## Formula:
## Subordination ~ 1 + Genre + Complexity + Genre:Complexity + StAnx +
## MotEng + Complexity:MotEng + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 1884.9 1925.0 -933.5 1866.9 623
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4535 0.6734
## Number of obs: 632, groups: Participant, 158
##
## Dispersion parameter for nbinom2 family (): 8.49e+07
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.44860 0.08217 5.459 4.79e-08 ***
## GenreNAR -0.27393 0.08600 -3.185 0.001447 **
## ComplexitySIMPLE -0.08765 0.08186 -1.071 0.284251
## StAnx -0.13551 0.06468 -2.095 0.036162 *
## MotEng 0.06554 0.07143 0.918 0.358866
## GenreNAR:ComplexitySIMPLE -0.51452 0.13633 -3.774 0.000161 ***
## ComplexitySIMPLE:MotEng -0.07113 0.06636 -1.072 0.283778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- Subordination ~ 1 + Genre + Complexity + Genre:Complexity + StAnx + (1 | Participant) is the best fitting model.
- Main effect of Genre significant
- Main effect of Stress Anxiety (StAnx) significant
- Interaction between Genre and Complexity significant
Visualization
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect## model: Subordination ~ 1 + Genre + Complexity + Genre:Complexity + StAnx +
## MotEng + Complexity:MotEng
##
## StAnx effect
## StAnx
## -2 -1 0.2 1 2
## 1.506783 1.315823 1.118339 1.003438 0.876269
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 1.565124 1.4345492
## NAR 1.190093 0.6520681
##
## Complexity*MotEng effect
## MotEng
## Complexity -3 -1 0.2 2 3
## COMPLEX 1.1217347 1.2788431 1.383482 1.556711 1.6621549
## SIMPLE 0.9834796 0.9725508 0.966052 0.956385 0.9510563## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
Interpretation:
- Main effect of Genre significant: When Genre is NAR, Subordination is lower compared to when Genre is ARG.
- Main effect of Stress Anxiety (StAnx) significant: The higher the stress anxiety, the fewer the subordination
- Interaction between Genre and Complexity significant: When Genre is NAR and Complexity is SIMPLE, Subordination is lower compared to when Genre is ARG and Complexity is COMPLEX.
2.2.2 RQ1b(ii): To what extent do individual differences moderate the effect of task complexity and genre on coordination?
## Family: poisson ( log )
## Formula:
## Coordination ~ 1 + Genre + Complexity + Genre:Complexity + (1 |
## Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 2311.4 2333.6 -1150.7 2301.4 627
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4238 0.651
## Number of obs: 632, groups: Participant, 158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.57888 0.07732 7.487 7.07e-14 ***
## GenreNAR 0.10216 0.07438 1.374 0.170
## ComplexitySIMPLE 0.03989 0.07550 0.528 0.597
## GenreNAR:ComplexitySIMPLE 0.04799 0.10358 0.463 0.643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- Coordination ~ 1 + Genre + Complexity + Genre:Complexity + (1|Participant) is the best fitting model.
- Nothing is significant
Visualization
## model: Coordination ~ 1 + Genre + Complexity + Genre:Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 1.784031 1.856640
## NAR 1.975920 2.157434
Interpretation:
- NAR seems to lead to more Coordination than ARG, but the effect is not statistically significant.
- When Genre is NAR and Complexity is SIMPLE, it seems like the Coordination increases, but it’s not statistically significant.
2.2.3 RQ1b(iii): To what extent do individual differences moderate the effect of task complexity and genre on accuracy in number?
## Family: nbinom2 ( log )
## Formula: AccuracyNumber ~ 1 + Genre + Complexity + Genre:Complexity +
## (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 1383.1 1409.8 -685.6 1371.1 626
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9376 0.9683
## Number of obs: 632, groups: Participant, 158
##
## Dispersion parameter for nbinom2 family (): 1.91e+07
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.83913 0.13911 -6.032 1.62e-09 ***
## GenreNAR 0.29725 0.12945 2.296 0.0217 *
## ComplexitySIMPLE 0.06514 0.13647 0.477 0.6331
## GenreNAR:ComplexitySIMPLE -0.34334 0.18762 -1.830 0.0673 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- AccuracyNumber ~ 1 + Genre + Complexity + Genre:Complexity + (1|Participant) is the best fitting model.
- Main effect of Genre is significant.
Visualization
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect## model: AccuracyNumber ~ 1 + Genre + Complexity + Genre:Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.4320854 0.4611681
## NAR 0.5816534 0.4403947## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
Interpretation:
- Main effect of Genre: Changing Genre from ARG to NAR leads to higher accuracy in number.
2.2.4 RQ1b(iv): To what extent do individual differences moderate the effect of task complexity and genre on accuracy in gender?
## Family: nbinom2 ( log )
## Formula: AccuracyGender ~ 1 + Genre + Complexity + Genre:Complexity +
## CogAnx + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 2370.0 2401.2 -1178.0 2356.0 625
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.0122 0.1104
## Number of obs: 632, groups: Participant, 158
##
## Dispersion parameter for nbinom2 family (): 4.7
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.09068 0.06071 17.966 < 2e-16 ***
## GenreNAR -0.45531 0.09042 -5.036 4.77e-07 ***
## ComplexitySIMPLE -0.19521 0.08608 -2.268 0.0233 *
## CogAnx -0.06905 0.03377 -2.045 0.0409 *
## GenreNAR:ComplexitySIMPLE -0.08789 0.13370 -0.657 0.5109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- AccuracyGender ~ 1 + Genre + Complexity + Genre:Complexity + CogAnx + (1|Participant) is the best fitting model.
- Main effect of Genre is significant.
- Main effect of Complexity is significant.
- Main effect of Cognitive Anxiety (CogAnx) is significant.
Visualization
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect## model: AccuracyGender ~ 1 + Genre + Complexity + Genre:Complexity +
## CogAnx
##
## CogAnx effect
## CogAnx
## -3 -2 -0.6 0.8 2
## 2.587304 2.414667 2.192155 1.990147 1.831880
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 2.977639 2.449588
## NAR 1.888580 1.422937## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
Interpretation:
- Main effect of Genre: Changing Genre from ARG to NAR leads to fewer gender accuracy.
- Main effect of Complexity: Changing Complexity from COMPLEX to SIMPLE leads to fewer gender accuracy.
- Main effect of Cognitive Anxiety (CogAnx) significant: The higher the cognitive anxiety, the less accurate in gender.
2.2.5 RQ1b(v): To what extent do individual differences moderate the effect of task complexity and genre on accuracy in tense?
## Family: poisson ( log )
## Formula:
## AccuracyTense ~ 1 + Genre + Complexity + Genre:Complexity + AchievEng +
## RSPANpartial_WM + Complexity:RSPANpartial_WM + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 2025.5 2061.0 -1004.7 2009.5 624
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.1537 0.392
## Number of obs: 632, groups: Participant, 158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.058328 0.162961 -0.358 0.72040
## GenreNAR 0.515165 0.093175 5.529 3.22e-08 ***
## ComplexitySIMPLE 0.405024 0.190899 2.122 0.03387 *
## AchievEng 0.097013 0.044903 2.161 0.03073 *
## RSPANpartial_WM 0.007891 0.008731 0.904 0.36611
## GenreNAR:ComplexitySIMPLE 0.059501 0.131958 0.451 0.65205
## ComplexitySIMPLE:RSPANpartial_WM -0.027510 0.010137 -2.714 0.00665 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- AccuracyTense ~ 1 + Genre + Complexity + Genre:Complexity + AchievEng + RSPANpartial_WM + Complexity:RSPANpartial_WM + (1|Participant) is the best fitting model.
- Main effect of Genre is significant.
- Main effect of Complexity is significant.
- Main effect of Achievement Engagement (AchievEng) is significant.
- Interaction between Complexity and RSPANpartial_WM is significant
Visualization
## model: AccuracyTense ~ 1 + Genre + Complexity + Genre:Complexity + AchievEng +
## RSPANpartial_WM + Complexity:RSPANpartial_WM
##
## AchievEng effect
## AchievEng
## -3 -2 -0.1 1 3
## 1.032751 1.137962 1.368292 1.522384 1.848368
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 1.069620 1.034855
## NAR 1.790453 1.838458
##
## Complexity*RSPANpartial_WM effect
## RSPANpartial_WM
## Complexity 1 10 20 30 40
## COMPLEX 1.230132 1.320671 1.429106 1.546445 1.6734178
## SIMPLE 1.848521 1.549321 1.273322 1.046490 0.8600662
Interpretation:
- Main effect of Genre significant: Changing Genre from ARG to NAR increases the accuracy in tense.
- Main effect of Complexity significant: Changing Complexity from COMPLEX to SIMPLE increases the accuracy in tense
- Main effect of AchievEng significant: Higher Achievement Engagement is associated with higher accuracy in tense
- Interaction between Complexity and RSPANpartial_WM is significant: When Complexity is SIMPLE, the higher the working memory, the less accurate in tense.
2.2.6 RQ1b(vi): To what extent do individual differences moderate the effect of task complexity and genre on accuracy in aspect?
## Family: nbinom1 ( log )
## Formula: AccuracyAspect ~ 1 + Genre + Complexity + Genre:Complexity +
## RSPANpartial_WM + Genre:RSPANpartial_WM + (1 | Participant)
## Data: dat
##
## AIC BIC logLik -2*log(L) df.resid
## 801.5 837.1 -392.8 785.5 624
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 3.392e-09 5.824e-05
## Number of obs: 632, groups: Participant, 158
##
## Dispersion parameter for nbinom1 family (): 2.29
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.47424 0.88253 -0.537 0.59101
## GenreNAR 1.15366 0.91145 1.266 0.20560
## ComplexitySIMPLE -1.07390 0.83695 -1.283 0.19946
## RSPANpartial_WM -0.16776 0.07020 -2.390 0.01685 *
## GenreNAR:ComplexitySIMPLE -2.56157 0.95276 -2.689 0.00718 **
## GenreNAR:RSPANpartial_WM 0.17744 0.07136 2.487 0.01290 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Results:
- AccuracyAspect ~ 1 + Genre + Complexity + Genre:Complexity + RSPANpartial_WM + Genre:RSPANpartial_WM + (1|Participant) is the best fitting model.
- Main effect of RSPANpartial_WM is significant.
- Interaction between Genre and Complexity is significant
- Interaction between Genre and RSPANpartial_WM is significant
Visualization
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect## model: AccuracyAspect ~ 1 + Genre + Complexity + Genre:Complexity +
## RSPANpartial_WM + Genre:RSPANpartial_WM
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.04303864 0.01470518
## NAR 2.30142106 0.06069220
##
## Genre*RSPANpartial_WM effect
## RSPANpartial_WM
## Genre 1 10 20 30 40
## ARG 0.3076006 0.06796314 0.01269704 0.002372093 0.0004431604
## NAR 0.3234733 0.35291133 0.38877268 0.428278106 0.4717979054## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
## Warning in Effect.glmmTMB(predictors, mod, vcov. = vcov., ...): overriding
## variance function for effects/dev.resids: computed variances may be incorrect
Interpretation:
- Main effect of RSPANpartial_WM: The higher the WM, the less accurate in aspect.
- Interaction between Genre and Complexity: When Genre is NAR and Complexity is SIMPLE, the accuracy in aspect is lower compared to ARG and COMPLEX
- Interaction between Genre and RSPANpartial_WM: When Genre is NAR, higher WM is associated with higher accuracy in aspect.
2.2.7 RQ1b(vii): To what extent do individual differences moderate the effect of task complexity and genre on lexical density?
## Linear mixed model fit by maximum likelihood (p-values based on Wald z-scores)
## [lmerMod]
## Formula: LexicalDensity ~ 1 + Genre + Complexity + Genre:Complexity +
## (1 | Participant)
## Data: dat_density
##
## AIC BIC logLik deviance df.resid
## -1119.3 -1092.7 565.7 -1131.3 622
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0965 -0.5662 0.0441 0.4970 3.5040
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.013834 0.1176
## Residual 0.005241 0.0724
## Number of obs: 628, groups: Participant, 157
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.524925 0.011023 47.623 <2e-16 ***
## GenreNAR 0.012968 0.008171 1.587 0.112
## ComplexitySIMPLE -0.017729 0.008171 -2.170 0.030 *
## GenreNAR:ComplexitySIMPLE -0.012870 0.011556 -1.114 0.265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CSIMPL
## GenreNAR -0.371
## CmplxSIMPLE -0.371 0.500
## GNAR:CSIMPL 0.262 -0.707 -0.707Note:
- Some caution for interpretation necessary because the assumptions are not met.
Results:
- LexicalDensity ~ 1 + Genre + Complexity + Genre:Complexity + (1|Participant) is the best-fitting model
- Main effect of Complexity is significant.
Visualization
## model: LexicalDensity ~ 1 + Genre + Complexity + Genre:Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.5249255 0.5071968
## NAR 0.5378936 0.5072954
Interpretation:
- Main effect of Complexity: When Complexity is SIMPLE, lexical density is lower compared to COMPLEX.
2.2.8 RQ1b(viii): To what extent do individual differences moderate the effect of task complexity and genre on lexical diversity?
## Linear mixed model fit by maximum likelihood (p-values based on Wald z-scores)
## [lmerMod]
## Formula: LexicalDiversity ~ 1 + Genre + Complexity + Genre:Complexity +
## (1 | Participant)
## Data: dat_diversity
##
## AIC BIC logLik deviance df.resid
## -890.2 -863.6 451.1 -902.2 618
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2812 -0.4824 0.0283 0.4847 3.4035
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.011668 0.10802
## Residual 0.008678 0.09316
## Number of obs: 624, groups: Participant, 156
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.62803 0.01142 54.992 <2e-16 ***
## GenreNAR 0.03443 0.01055 3.264 0.0011 **
## ComplexitySIMPLE -0.01341 0.01055 -1.271 0.2036
## GenreNAR:ComplexitySIMPLE -0.01646 0.01492 -1.103 0.2699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CSIMPL
## GenreNAR -0.462
## CmplxSIMPLE -0.462 0.500
## GNAR:CSIMPL 0.327 -0.707 -0.707Note:
Some caution for interpretation necessary because the assumptions are not met.
Results:
- LexicalDiversity ~ 1 + Genre + Complexity + Genre:Complexity + (1|Participant) is the best-fitting model
- Main effect of Genre is significant.
Visualization
## model: LexicalDiversity ~ 1 + Genre + Complexity + Genre:Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.6280340 0.6146231
## NAR 0.6624673 0.6325974
Interpretation:
- Main effect of Genre: When Genre is NAR, lexical diversity is higher compared to when Genre is ARG.
2.2.9 RQ1b(ix): To what extent do individual differences moderate the effect of task complexity and genre on fluency?
## Linear mixed model fit by maximum likelihood (p-values based on Wald z-scores)
## [lmerMod]
## Formula:
## FluencySyllablesperMinuteLog10 ~ 1 + Genre + Complexity + Genre:Complexity +
## (1 | Participant)
## Data: dat_fluency
##
## AIC BIC logLik deviance df.resid
## -408.7 -382.1 210.4 -420.7 622
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7589 -0.4734 0.0521 0.5397 3.1934
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.02781 0.1668
## Residual 0.01839 0.1356
## Number of obs: 628, groups: Participant, 157
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.92116 0.01715 53.702 < 2e-16 ***
## GenreNAR 0.02275 0.01531 1.487 0.13710
## ComplexitySIMPLE -0.04041 0.01531 -2.641 0.00828 **
## GenreNAR:ComplexitySIMPLE 0.07086 0.02164 3.274 0.00106 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CSIMPL
## GenreNAR -0.446
## CmplxSIMPLE -0.446 0.500
## GNAR:CSIMPL 0.315 -0.707 -0.707Note:
Some caution for interpretation necessary because the assumptions are not met.
Results:
- FluencySyllablesperMinuteLog10 ~ 1 + Genre + Complexity + Genre:Complexity + (1|Participant) is the best fitting model.
- Main effect of Complexity is significant.
- Interaction between Genre and Complexity is significant.
Visualization
## model: FluencySyllablesperMinuteLog10 ~ 1 + Genre + Complexity + Genre:Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 0.9211617 0.8807485
## NAR 0.9439152 0.9743591
Interpretation:
- Main effect of Complexity: When Complexity is SIMPLE, fluency is lower compared to when Complexity is COMPLEX.
- Interaction between Genre and Complexity: When Genre is NAR and Complexity is SIMPLE, the fluency is higher compared to when Genre is ARG and Complexity is COMPLEX.
2.3 RQ2a:
How does altering task complexity and genre — specifically argumentative and narrative — influence the perceived difficulty of a task in L2 writing?
Fit the model.
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PTD_composite ~ Genre * Complexity + (1 | Participant)
## Data: dat
##
## REML criterion at convergence: 2074.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7069 -0.5831 0.0497 0.6775 3.3679
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9409 0.970
## Residual 1.0545 1.027
## Number of obs: 632, groups: Participant, 158
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.5016 0.1124 376.7217 48.955 < 2e-16 ***
## GenreNAR 1.1630 0.1155 471.0000 10.066 < 2e-16 ***
## ComplexitySIMPLE 0.2911 0.1155 471.0000 2.520 0.0121 *
## GenreNAR:ComplexitySIMPLE -1.1108 0.1634 471.0000 -6.798 3.22e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CSIMPL
## GenreNAR -0.514
## CmplxSIMPLE -0.514 0.500
## GNAR:CSIMPL 0.363 -0.707 -0.707Note:
Some caution for interpretation necessary because the assumptions are not met.
Results:
- Main effect of Genre significant.
- Main effect of Complexity significant.
- Interaction between Genre and Complexity significant.
Visualization
## model: PTD_composite ~ Genre * Complexity
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 5.501582 5.792722
## NAR 6.664557 5.844937
Interpretation:
- Main effect of Genre: When Genre is NAR, PTD is higher compared to when Genre is ARG.
- Main effect of Complexity: When Complexity is SIMPLE, PTD is higher compared to when Complexity is COMPLEX.
- Interaction between Genre and Complexity: When Genre is NAR and Complexity is SIMPLE, PTD is lower compared to when Genre is ARG and Complexity is Complex.
2.4 RQ2b:
How do individual learner differences, such as working memory, motivation, and anxiety, moderate the relationship between task complexity, genre, and perceived task difficulty??
2.4.1 Method 1: Buildmer
## Linear mixed model fit by maximum likelihood (p-values based on Wald z-scores)
## [lmerMod]
## Formula: PTD_composite ~ 1 + Genre + Complexity + Genre:Complexity + CogAnx +
## Complexity:CogAnx + (1 | Participant)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 2073.3 2108.9 -1028.6 2057.3 624
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7445 -0.5777 0.0543 0.6690 3.3456
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9366 0.9678
## Residual 1.0354 1.0175
## Number of obs: 632, groups: Participant, 158
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.50120 0.11172 49.241 < 2e-16 ***
## GenreNAR 1.16297 0.11448 10.159 < 2e-16 ***
## ComplexitySIMPLE 0.29239 0.11448 2.554 0.0106 *
## CogAnx -0.05840 0.09598 -0.608 0.5429
## GenreNAR:ComplexitySIMPLE -1.11076 0.16190 -6.861 6.86e-12 ***
## ComplexitySIMPLE:CogAnx 0.19342 0.08099 2.388 0.0169 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GnrNAR CmSIMPLE CogAnx GNAR:C
## GenreNAR -0.512
## CmplxSIMPLE -0.512 0.500
## CogAnx 0.006 0.000 -0.002
## GNAR:CSIMPL 0.362 -0.707 -0.707 0.000
## CmSIMPLE:CA -0.002 0.000 0.005 -0.422 0.000Results:
- PTD_composite ~ 1 + Genre + Complexity + Genre:Complexity + CogAnx + Complexity:CogAnx + (1|Participant) is the best-fitting model.
- Main effect of Genre is significant
- Main effect of Complexity is significant
- Interaction between Genre and Complexity is significant
- Interaction between Complexity and Cognitive Anxiety (CogAnx) is significant
Visualization
## model: PTD_composite ~ 1 + Genre + Complexity + Genre:Complexity + CogAnx +
## Complexity:CogAnx
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 5.501582 5.792722
## NAR 6.664557 5.844937
##
## Complexity*CogAnx effect
## CogAnx
## Complexity -3 -2 -0.6 0.8 2
## COMPLEX 6.257898 6.199496 6.117733 6.035969 5.965886
## SIMPLE 5.414661 5.549675 5.738696 5.927716 6.089733
Interpretation:
- Main effect of Genre: Changing Genre to NAR leads to an increase in PTD by 1.16.
- Main effect of Complexity: Changing Complexity to Simple leads to an increase in PTD by 0.29.
- Interaction between Genre and Complexity is significant: When Genre is NAR and Complexity is SIMPLE, there is a decrease in PTD by -1.11 compared to when Genre is ARG and Complexity is COMPLEX.
- Interaction between Complexity and Cognitive Anxiety (CogAnx): When Complexity is SIMPLE, a one-SD increase in CogAnx is associated with an increase in PTD by 0.19.
2.4.2 Method 2: Checking the moderation of individual differences, one by one.
2.4.2.1 RQ2b(i): Effect of Task Complexity and Genre moderated by Achievement Engagement
How does the effect of altering task complexity and genre — specifically argumentative and narrative — influence on the perceived difficulty of a task in L2 writing, moderated by Achievement Engagement?
Fit the model.
mod2eng <- lmer(PTD_composite ~ AchievEng * Genre + AchievEng * Complexity + Genre * Complexity + (1|Participant), dat)
summary(mod2eng)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PTD_composite ~ AchievEng * Genre + AchievEng * Complexity +
## Genre * Complexity + (1 | Participant)
## Data: dat
##
## REML criterion at convergence: 2083.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6987 -0.5918 0.0383 0.6744 3.3741
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9477 0.9735
## Residual 1.0582 1.0287
## Number of obs: 632, groups: Participant, 158
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.50159 0.11268 373.84119 48.827 < 2e-16 ***
## AchievEng 0.03466 0.10469 302.71935 0.331 0.7408
## GenreNAR 1.16297 0.11574 469.00000 10.048 < 2e-16 ***
## ComplexitySIMPLE 0.29113 0.11574 469.00000 2.515 0.0122 *
## AchievEng:GenreNAR -0.01565 0.08161 469.00000 -0.192 0.8481
## AchievEng:ComplexitySIMPLE -0.04570 0.08161 469.00000 -0.560 0.5758
## GenreNAR:ComplexitySIMPLE -1.11076 0.16368 469.00000 -6.786 3.49e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AchvEn GnrNAR CSIMPL AE:GNA AE:CSI
## AchievEng 0.000
## GenreNAR -0.514 0.000
## CmplxSIMPLE -0.514 0.000 0.500
## AchvEn:GNAR 0.000 -0.390 0.000 0.000
## AcE:CSIMPLE 0.000 -0.390 0.000 0.000 0.000
## GNAR:CSIMPL 0.363 0.000 -0.707 -0.707 0.000 0.000Results:
- Main effect of Genre significant.
- Main effect of Complexity significant.
- Interaction between Genre and Complexity significant.
Visualization
## model: PTD_composite ~ AchievEng * Genre + AchievEng * Complexity +
## Genre * Complexity
##
## AchievEng*Genre effect
## Genre
## AchievEng ARG NAR
## -3 5.611713 6.266240
## -2 5.623527 6.262408
## -0.1 5.645973 6.255129
## 1 5.658969 6.250915
## 3 5.682597 6.243252
##
## AchievEng*Complexity effect
## Complexity
## AchievEng COMPLEX SIMPLE
## -3 6.002556 5.875396
## -2 6.029396 5.856539
## -0.1 6.080392 5.820710
## 1 6.109916 5.799967
## 3 6.163596 5.762252
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 5.501582 5.792722
## NAR 6.664557 5.844937
2.4.2.2 RQ2a(ii): Effect of Task Complexity and Genre moderated by Motivation Engagement
How does the effect of altering task complexity and genre — specifically argumentative and narrative — influence on the perceived difficulty of a task in L2 writing, moderated by Motivation Engagement?
Fit the model.
mod2mot <- lmer(PTD_composite ~ MotEng * Genre + MotEng * Complexity + Genre * Complexity + (1|Participant), dat)
summary(mod2mot)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PTD_composite ~ MotEng * Genre + MotEng * Complexity + Genre *
## Complexity + (1 | Participant)
## Data: dat
##
## REML criterion at convergence: 2078.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7270 -0.5800 0.0550 0.6482 3.2352
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9382 0.9686
## Residual 1.0507 1.0250
## Number of obs: 632, groups: Participant, 158
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.501621 0.112200 374.297362 49.034 < 2e-16 ***
## MotEng 0.005166 0.104724 303.055389 0.049 0.9607
## GenreNAR 1.162124 0.115328 469.000001 10.077 < 2e-16 ***
## ComplexitySIMPLE 0.290300 0.115328 469.000001 2.517 0.0122 *
## MotEng:GenreNAR -0.112047 0.081703 469.000001 -1.371 0.1709
## MotEng:ComplexitySIMPLE -0.110539 0.081703 469.000001 -1.353 0.1767
## GenreNAR:ComplexitySIMPLE -1.110759 0.163096 469.000001 -6.810 2.99e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) MotEng GnrNAR CSIMPL ME:GNA ME:CSI
## MotEng 0.007
## GenreNAR -0.514 -0.002
## CmplxSIMPLE -0.514 -0.002 0.500
## MtEng:GnNAR -0.003 -0.390 0.005 0.000
## MtE:CSIMPLE -0.003 -0.390 0.000 0.005 0.000
## GNAR:CSIMPL 0.363 0.000 -0.707 -0.707 0.000 0.000Results:
- Main effect of Genre significant.
- Main effect of Complexity significant.
- Interaction between Genre and Complexity significant.
Visualization
## model: PTD_composite ~ MotEng * Genre + MotEng * Complexity + Genre *
## Complexity
##
## MotEng*Genre effect
## Genre
## MotEng ARG NAR
## -3 5.797082 6.739966
## -1 5.696875 6.415666
## 0.2 5.636751 6.221086
## 2 5.546565 5.929216
## 3 5.496462 5.767067
##
## MotEng*Complexity effect
## Complexity
## MotEng COMPLEX SIMPLE
## -3 6.235255 6.301793
## -1 6.133541 5.979001
## 0.2 6.072512 5.785325
## 2 5.980969 5.494812
## 3 5.930112 5.333416
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 5.501582 5.792722
## NAR 6.664557 5.844937
2.4.2.3 RQ2a(iii): Effect of Task Complexity and Genre moderated by Stress Anxiety
How does the effect of altering task complexity and genre — specifically argumentative and narrative — influence on the perceived difficulty of a task in L2 writing, moderated by Stress Anxiety?
Fit the model.
mod2stanx <- lmer(PTD_composite ~ StAnx * Genre * Complexity + (1|Participant), dat)
summary(mod2stanx)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PTD_composite ~ StAnx * Genre * Complexity + (1 | Participant)
## Data: dat
##
## REML criterion at convergence: 2084
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6879 -0.5868 0.0479 0.6780 3.2630
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9381 0.9686
## Residual 1.0606 1.0299
## Number of obs: 632, groups: Participant, 158
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.50152 0.11247 375.70097 48.914 < 2e-16
## StAnx 0.06586 0.11215 375.70097 0.587 0.5574
## GenreNAR 1.16295 0.11587 468.00000 10.037 < 2e-16
## ComplexitySIMPLE 0.29111 0.11587 468.00000 2.512 0.0123
## StAnx:GenreNAR 0.02532 0.11554 468.00000 0.219 0.8266
## StAnx:ComplexitySIMPLE 0.02413 0.11554 468.00000 0.209 0.8347
## GenreNAR:ComplexitySIMPLE -1.11077 0.16386 468.00000 -6.779 3.66e-11
## StAnx:GenreNAR:ComplexitySIMPLE 0.01301 0.16339 468.00000 0.080 0.9366
##
## (Intercept) ***
## StAnx
## GenreNAR ***
## ComplexitySIMPLE *
## StAnx:GenreNAR
## StAnx:ComplexitySIMPLE
## GenreNAR:ComplexitySIMPLE ***
## StAnx:GenreNAR:ComplexitySIMPLE
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) StAnx GnrNAR CSIMPL StA:GNAR SA:CSI GNAR:C
## StAnx -0.001
## GenreNAR -0.515 0.001
## CmplxSIMPLE -0.515 0.001 0.500
## StAnx:GnNAR 0.001 -0.515 -0.001 -0.001
## StA:CSIMPLE 0.001 -0.515 -0.001 -0.001 0.500
## GNAR:CSIMPL 0.364 0.000 -0.707 -0.707 0.001 0.001
## SA:GNAR:CSI 0.000 0.364 0.001 0.001 -0.707 -0.707 -0.001Results:
- Main effect of Genre significant.
- Main effect of Complexity significant.
- Interaction between Genre and Complexity significant.
Visualization
## model: PTD_composite ~ StAnx * Genre * Complexity
##
## StAnx*Genre*Complexity effect
## , , Complexity = COMPLEX
##
## Genre
## StAnx ARG NAR
## -2 5.369787 6.482092
## -1 5.435652 6.573278
## 0.2 5.514689 6.682702
## 1 5.567380 6.755651
## 2 5.633244 6.846838
##
## , , Complexity = SIMPLE
##
## Genre
## StAnx ARG NAR
## -2 5.612645 5.588154
## -1 5.702638 5.716481
## 0.2 5.810629 5.870472
## 1 5.882623 5.973133
## 2 5.972616 6.101460
2.4.2.4 RQ2b(iv): Effect of Task Complexity and Genre moderated by Cognitive Anxiety
How does the effect of altering task complexity and genre — specifically argumentative and narrative — influence on the perceived difficulty of a task in L2 writing, moderated by Cognitive Anxiety?
Fit the model.
mod2coganx <- lmer(PTD_composite ~ CogAnx * Genre + CogAnx * Complexity + Genre * Complexity + (1|Participant), dat)
summary(mod2coganx)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PTD_composite ~ CogAnx * Genre + CogAnx * Complexity + Genre *
## Complexity + (1 | Participant)
## Data: dat
##
## REML criterion at convergence: 2077.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7338 -0.5508 0.0559 0.6591 3.3172
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9494 0.9743
## Residual 1.0457 1.0226
## Number of obs: 632, groups: Participant, 158
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.50105 0.11237 371.67356 48.954 < 2e-16 ***
## CogAnx -0.08208 0.10479 301.14097 -0.783 0.4341
## GenreNAR 1.16328 0.11505 469.00000 10.111 < 2e-16 ***
## ComplexitySIMPLE 0.29239 0.11505 469.00000 2.541 0.0114 *
## CogAnx:GenreNAR 0.04735 0.08139 469.00000 0.582 0.5610
## CogAnx:ComplexitySIMPLE 0.19342 0.08139 469.00000 2.377 0.0179 *
## GenreNAR:ComplexitySIMPLE -1.11076 0.16271 469.00000 -6.827 2.7e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CogAnx GnrNAR CSIMPL CA:GNA CA:CSI
## CogAnx 0.006
## GenreNAR -0.512 -0.002
## CmplxSIMPLE -0.512 -0.002 0.500
## CgAnx:GnNAR -0.002 -0.388 0.005 0.000
## CgA:CSIMPLE -0.002 -0.388 0.000 0.005 0.000
## GNAR:CSIMPL 0.362 0.000 -0.707 -0.707 0.000 0.000Results:
- Main effect of Genre significant.
- Main effect of Complexity significant.
- Interaction between Cognitive Anxiety and Complexity significant.
- Interaction between Genre and Complexity significant.
Visualization
## model: PTD_composite ~ CogAnx * Genre + CogAnx * Complexity + Genre *
## Complexity
##
## CogAnx*Genre effect
## Genre
## CogAnx ARG NAR
## -3 5.603356 6.069203
## -2 5.617987 6.131185
## -0.6 5.638469 6.217959
## 0.8 5.658951 6.304734
## 2 5.676507 6.379112
##
## CogAnx*Complexity effect
## Complexity
## CogAnx COMPLEX SIMPLE
## -3 6.257898 5.414661
## -2 6.199496 5.549675
## -0.6 6.117733 5.738696
## 0.8 6.035969 5.927716
## 2 5.965886 6.089733
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 5.501582 5.792722
## NAR 6.664557 5.844937
2.4.2.5 RQ2b(vi): Effect of Task Complexity and Genre moderated by Working Memory
How does the effect of altering task complexity and genre — specifically argumentative and narrative — influence on the perceived difficulty of a task in L2 writing, moderated by Reading Span Working Memory?
Fit the model.
mod2wm <- lmer(PTD_composite ~ RSPANpartial_WM * Genre + RSPANpartial_WM * Complexity + Genre * Complexity + (1|Participant), dat)
summary(mod2wm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PTD_composite ~ RSPANpartial_WM * Genre + RSPANpartial_WM * Complexity +
## Genre * Complexity + (1 | Participant)
## Data: dat
##
## REML criterion at convergence: 2091.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7094 -0.5719 0.0289 0.6720 3.3715
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.9477 0.9735
## Residual 1.0528 1.0260
## Number of obs: 632, groups: Participant, 158
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.615343 0.288526 312.009939 19.462
## RSPANpartial_WM -0.007144 0.016684 302.117094 -0.428
## GenreNAR 0.898472 0.236855 469.000002 3.793
## ComplexitySIMPLE 0.513236 0.236855 469.000002 2.167
## RSPANpartial_WM:GenreNAR 0.016610 0.012988 469.000002 1.279
## RSPANpartial_WM:ComplexitySIMPLE -0.013947 0.012988 469.000002 -1.074
## GenreNAR:ComplexitySIMPLE -1.110759 0.163256 469.000002 -6.804
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## RSPANpartial_WM 0.668819
## GenreNAR 0.000168 ***
## ComplexitySIMPLE 0.030747 *
## RSPANpartial_WM:GenreNAR 0.201561
## RSPANpartial_WM:ComplexitySIMPLE 0.283434
## GenreNAR:ComplexitySIMPLE 3.12e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RSPANp_WM GnrNAR CSIMPL RSPAN_WM:G RSPAN_WM:C
## RSPANprt_WM -0.921
## GenreNAR -0.410 0.340
## CmplxSIMPLE -0.410 0.340 0.119
## RSPAN_WM:GN 0.358 -0.389 -0.873 0.000
## RSPAN_WM:CS 0.358 -0.389 0.000 -0.873 0.000
## GNAR:CSIMPL 0.141 0.000 -0.345 -0.345 0.000 0.000Results:
- Main effect of Genre significant.
- Interaction between Genre and Complexity significant.
Visualization
## model: PTD_composite ~ RSPANpartial_WM * Genre + RSPANpartial_WM * Complexity +
## Genre * Complexity
##
## RSPANpartial_WM*Genre effect
## Genre
## RSPANpartial_WM ARG NAR
## 1 5.857844 6.217546
## 10 5.730785 6.239980
## 20 5.589609 6.264907
## 30 5.448433 6.289834
## 40 5.307257 6.314760
##
## RSPANpartial_WM*Complexity effect
## Complexity
## RSPANpartial_WM COMPLEX SIMPLE
## 1 6.065741 6.009649
## 10 6.076191 5.894574
## 20 6.087802 5.766714
## 30 6.099414 5.638853
## 40 6.111025 5.510992
##
## Genre*Complexity effect
## Complexity
## Genre COMPLEX SIMPLE
## ARG 5.501582 5.792722
## NAR 6.664557 5.844937
2.5 Buildmer …. code template
# mod1sub_trunc_nbinom_BM <- buildglmmTMB(formula = Subordination ~ Genre * Complexity +
# MotEng * Genre +
# MotEng * Complexity +
# AchievEng * Genre +
# AchievEng * Complexity +
# StAnx * Genre +
# CogAnx * Complexity +
# RSPANpartial_WM * Genre +
# RSPANpartial_WM * Complexity +
# (1|Participant),
# data = dat,
# family = truncated_nbinom2,
# buildmerControl(cl = 6,
# include = ~ Genre * Complexity + (1|Participant)))
# summary(mod1sub_trunc_nbinom_BM)
# allEffects(mod1sub_trunc_nbinom_BM@model)
# allEffects(mod1sub_trunc_nbinom_BM@model) %>% plot(multiline=TRUE)
#
#
# mod1coor_trunc_nbinom_BM <- buildglmmTMB(formula = Coordination ~ Genre * Complexity +
# MotEng * Genre +
# MotEng * Complexity +
# AchievEng * Genre +
# AchievEng * Complexity +
# StAnx * Genre +
# CogAnx * Complexity +
# RSPANpartial_WM * Genre +
# RSPANpartial_WM * Complexity +
# (1|Participant),
# data = dat,
# family = ,
# buildmerControl(cl = 6,
# include = ~ Genre * Complexity + (1|Participant)))
# summary(mod1sub_trunc_nbinom_BM)
# allEffects(mod1sub_trunc_nbinom_BM@model)
# allEffects(mod1sub_trunc_nbinom_BM@model) %>% plot(multiline=TRUE)
#
# mod1sub_trunc_poisson <- glmmTMB(Subordination ~ Genre * Complexity + (1|Participant), family = truncated_poisson, dat)
# mod1sub_trunc_nbinom <- glmmTMB(Subordination ~ Genre * Complexity + (1|Participant), family = truncated_nbinom2, dat)
#summary(mod1sub_trunc_poisson)
#summary(mod1sub_trunc_nbinom)
# allEffects(mod1sub_trunc_poisson)
# allEffects(mod1sub_trunc_poisson) %>% plot(multiline=TRUE)
#
# allEffects(mod1sub_trunc_nbinom)
# allEffects(mod1sub_trunc_nbinom) %>% plot(multiline=TRUE)