Participants

In this analysis, children with >50% accuracy on non-cognate words in English CLT are excluded in the analysis.

1st grade/class	2nd grade/class	keep
48	40	Current Keepers
6	11	Children with >50% accuracy on non-cognate words in English CLT
NA	7	English CLT still needs to be coded
3	9	No questionnaire data
2	NA	Child has reported hearing problems
1	NA	Child has SLD
NA	1	Child has ADHD
1	NA	Child is suspected to have autism
3	NA	Child did not want to continue
1	NA	not enough trials per condition
2	NA	Questionnaire was started but not completed

Recognition: Cognate vs. Non-cognate words

Main Analysis

Cognate Status by Word Status

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10327.2  10370.4  -5157.6  10315.2     9934 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3192 -0.6227 -0.3569  0.7709  5.7957 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03509  0.1873  
##  subject_nr   (Intercept) 1.46764  1.2115  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -0.896138   0.133152  -6.730 1.69e-11 ***
## Word_Type1              -0.035044   0.050249  -0.697    0.486    
## Word_Status1             1.062822   0.051020  20.832  < 2e-16 ***
## Word_Type1:Word_Status1  0.007196   0.100499   0.072    0.943    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1
## Word_Type1   0.001              
## Word_Stats1  0.031 -0.002       
## Wrd_T1:W_S1  0.000  0.225  0.000

Individual Differences with Cognate Status by Word Status

Model below is built using buildmer, mostly only for fixed effects for now

# f <- detection ~ Word_Status*Word_Type + 
#   RAVENS_IQ.c + WM_BackDigitSpan.c + Ger_CLT_Prod.c + NWRT_total_correct.c + Phoneme_Manipulation.c + Onset_Rhyme.c + ENG_CLT_score.c +
#   (1+Word_Status*Word_Type | subject_nr) +
#   (1+Word_Status*Word_Type | Sentence_Num)
# 
# model_1 <- buildmer(f,
#                     family=binomial,
#                     data=Lseg_Htest,
#               buildmerControl=buildmerControl(direction=c('order', "backward")))
# 
# model_1 <- model_1@model
# 
# model_1_formula <- model_1@call
# 
# summary(model_1)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [‘glmerMod’] Family: binomial ( logit ) Formula: detection ~ 1 + Word_Status + (1 + Word_Status | subject_nr) + (1 + Word_Status | Sentence_Num) Data: Lseg_Htest

 AIC      BIC   logLik deviance df.resid

10117.3 10174.9 -5050.6 10101.3 9932

Scaled residuals: Min 1Q Median 3Q Max -2.0333 -0.6041 -0.3141 0.7600 6.3618

Random effects: Groups Name Variance Std.Dev. Corr Sentence_Num (Intercept) 0.04088 0.2022
Word_Status1 0.18360 0.4285 0.08 subject_nr (Intercept) 1.48437 1.2183
Word_Status1 0.90161 0.9495 -0.42 Number of obs: 9940, groups: Sentence_Num, 160; subject_nr, 88

Fixed effects: Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.9653 0.1344 -7.184 6.78e-13 Word_Status1 1.1797 0.1223 9.645 < 2e-16

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects: (Intr) Word_Stats1 -0.336

Role of Phonological Awareness, with control variables Ravens IQ, Working Memory, and German CLT

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * Phoneme_Manipulation.c +  
##     RAVENS_IQ + Ger_CLT_Prod.c + WM_BackDigitSpan.c + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##   9968.7  10061.8  -4971.4   9942.7     9466 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2400 -0.6366 -0.3524  0.7977  5.8656 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.04121  0.203   
##  subject_nr   (Intercept) 1.23848  1.113   
## Number of obs: 9479, groups:  Sentence_Num, 160; subject_nr, 84
## 
## Fixed effects:
##                                                 Estimate Std. Error z value
## (Intercept)                                     1.828419   1.109744   1.648
## Word_Type1                                     -0.034509   0.051221  -0.674
## Word_Status1                                    1.048195   0.051924  20.187
## Phoneme_Manipulation.c                          0.025563   0.041297   0.619
## RAVENS_IQ                                      -0.026544   0.010926  -2.429
## Ger_CLT_Prod.c                                 -0.145229   0.064007  -2.269
## WM_BackDigitSpan.c                             -0.145039   0.164306  -0.883
## Word_Type1:Word_Status1                         0.014473   0.102439   0.141
## Word_Type1:Phoneme_Manipulation.c              -0.022077   0.014956  -1.476
## Word_Status1:Phoneme_Manipulation.c             0.074854   0.015195   4.926
## Word_Type1:Word_Status1:Phoneme_Manipulation.c -0.005554   0.029919  -0.186
##                                                Pr(>|z|)    
## (Intercept)                                      0.0994 .  
## Word_Type1                                       0.5005    
## Word_Status1                                    < 2e-16 ***
## Phoneme_Manipulation.c                           0.5359    
## RAVENS_IQ                                        0.0151 *  
## Ger_CLT_Prod.c                                   0.0233 *  
## WM_BackDigitSpan.c                               0.3774    
## Word_Type1:Word_Status1                          0.8876    
## Word_Type1:Phoneme_Manipulation.c                0.1399    
## Word_Status1:Phoneme_Manipulation.c            8.39e-07 ***
## Word_Type1:Word_Status1:Phoneme_Manipulation.c   0.8527    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Phn_M. RAVENS G_CLT_ WM_BDS Wr_T1:W_S1 W_T1:P
## Word_Type1   0.000                                                            
## Word_Stats1  0.014 -0.003                                                     
## Phnm_Mnplt.  0.148  0.003 -0.006                                              
## RAVENS_IQ   -0.994  0.000 -0.010 -0.147                                       
## Gr_CLT_Prd. -0.084 -0.001 -0.008 -0.063  0.086                                
## WM_BckDgtS.  0.048  0.001 -0.002 -0.382 -0.051 -0.291                         
## Wrd_T1:W_S1  0.000  0.225  0.002 -0.001  0.000  0.001  0.000                  
## Wrd_T1:P_M.  0.000  0.002 -0.007  0.003  0.000  0.000 -0.001 -0.057           
## Wrd_S1:P_M. -0.001 -0.005  0.007  0.024  0.000  0.005  0.006  0.009     -0.013
## W_T1:W_S1:P  0.000 -0.057  0.010 -0.001  0.000  0.000  0.000  0.002      0.190
##             W_S1:P
## Word_Type1        
## Word_Stats1       
## Phnm_Mnplt.       
## RAVENS_IQ         
## Gr_CLT_Prd.       
## WM_BckDgtS.       
## Wrd_T1:W_S1       
## Wrd_T1:P_M.       
## Wrd_S1:P_M.       
## W_T1:W_S1:P  0.013
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)
## Model failed to converge with max|grad| = 0.0472149 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

d’ analyses

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Word_Type + Phoneme_Manipulation.c + RAVENS_IQ +  
##     Ger_CLT_Prod.c + WM_BackDigitSpan.c + (1 | subject_nr)
##    Data: d_calc
## 
## REML criterion at convergence: 302.2
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.94361 -0.53598 -0.01963  0.52970  2.10026 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.2000   0.4472  
##  Residual               0.1643   0.4053  
## Number of obs: 168, groups:  subject_nr, 84
## 
## Fixed effects:
##                         Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)            -0.113089   0.509562 79.000001  -0.222  0.82494   
## Word_Type1             -0.010236   0.062536 83.000000  -0.164  0.87038   
## Phoneme_Manipulation.c  0.014839   0.019193 79.000000   0.773  0.44172   
## RAVENS_IQ               0.007241   0.005011 79.000001   1.445  0.15240   
## Ger_CLT_Prod.c         -0.003508   0.029677 79.000000  -0.118  0.90621   
## WM_BackDigitSpan.c      0.203228   0.075800 79.000000   2.681  0.00893 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Phn_M. RAVENS G_CLT_
## Word_Type1   0.000                            
## Phnm_Mnplt.  0.144  0.000                     
## RAVENS_IQ   -0.994  0.000 -0.144              
## Gr_CLT_Prd. -0.067  0.000 -0.056  0.068       
## WM_BckDgtS.  0.043  0.000 -0.382 -0.045 -0.299

Experiment Task Variables

Trial

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Word_Type * Word_Status * Trial.c + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10329.1  10401.1  -5154.5  10309.1     9930 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4107 -0.6220 -0.3566  0.7704  6.1317 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03511  0.1874  
##  subject_nr   (Intercept) 1.46894  1.2120  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                     -0.8967235  0.1332218  -6.731 1.68e-11 ***
## Word_Type1                      -0.0342483  0.0502805  -0.681   0.4958    
## Word_Status1                     1.0623772  0.0510509  20.810  < 2e-16 ***
## Trial.c                         -0.0002858  0.0005470  -0.523   0.6013    
## Word_Type1:Word_Status1          0.0081480  0.1005630   0.081   0.9354    
## Word_Type1:Trial.c               0.0006620  0.0010953   0.604   0.5456    
## Word_Status1:Trial.c            -0.0025968  0.0010938  -2.374   0.0176 *  
## Word_Type1:Word_Status1:Trial.c -0.0005755  0.0021912  -0.263   0.7928    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Tril.c Wr_T1:W_S1 W_T1:T W_S1:T
## Word_Type1   0.001                                              
## Word_Stats1  0.031 -0.002                                       
## Trial.c      0.000 -0.003  0.014                                
## Wrd_T1:W_S1  0.000  0.226  0.000 -0.006                         
## Wrd_Typ1:T. -0.002 -0.001 -0.005  0.004  0.016                  
## Wrd_Stt1:T.  0.003 -0.006 -0.002  0.239 -0.003      0.001       
## W_T1:W_S1:T -0.002  0.016 -0.003  0.001  0.000      0.238  0.004
## optimizer (bobyqa) convergence code: 0 (OK)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

Counterbalance List

## # A tibble: 8 × 2
##   List  total_participants
##   <fct>              <int>
## 1 A                     13
## 2 B                     10
## 3 C                     12
## 4 D                      6
## 5 E                     13
## 6 F                     14
## 7 G                     10
## 8 H                     10

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * List + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10332.3  10577.3  -5132.2  10264.3     9906 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0936 -0.6201 -0.3563  0.7640  6.3663 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03534  0.188   
##  subject_nr   (Intercept) 1.33091  1.154   
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                   -1.02113    0.32853  -3.108 0.001882 ** 
## Word_Type1                    -0.28790    0.13735  -2.096 0.036076 *  
## Word_Status1                   0.86801    0.13922   6.235 4.53e-10 ***
## ListB                         -0.50339    0.49912  -1.009 0.313192    
## ListC                          0.70076    0.47403   1.478 0.139328    
## ListD                          0.65984    0.58049   1.137 0.255664    
## ListE                          0.33346    0.46340   0.720 0.471773    
## ListF                         -0.22551    0.45625  -0.494 0.621123    
## ListG                          0.34421    0.49636   0.693 0.488012    
## ListH                         -0.10970    0.49926  -0.220 0.826089    
## Word_Type1:Word_Status1       -0.34086    0.27397  -1.244 0.213446    
## Word_Type1:ListB               0.14843    0.21708   0.684 0.494138    
## Word_Type1:ListC               0.21917    0.19503   1.124 0.261109    
## Word_Type1:ListD               0.52241    0.22773   2.294 0.021789 *  
## Word_Type1:ListE               0.45424    0.19366   2.346 0.018997 *  
## Word_Type1:ListF               0.40482    0.19371   2.090 0.036631 *  
## Word_Type1:ListG               0.20740    0.20140   1.030 0.303110    
## Word_Type1:ListH               0.07709    0.20743   0.372 0.710166    
## Word_Status1:ListB             0.13256    0.22012   0.602 0.547011    
## Word_Status1:ListC             0.09327    0.20189   0.462 0.644112    
## Word_Status1:ListD             0.50370    0.23036   2.187 0.028778 *  
## Word_Status1:ListE             0.67548    0.18886   3.577 0.000348 ***
## Word_Status1:ListF             0.03849    0.19625   0.196 0.844512    
## Word_Status1:ListG             0.21772    0.20840   1.045 0.296158    
## Word_Status1:ListH            -0.08432    0.20967  -0.402 0.687580    
## Word_Type1:Word_Status1:ListB  0.14776    0.43330   0.341 0.733099    
## Word_Type1:Word_Status1:ListC  0.31916    0.38945   0.820 0.412493    
## Word_Type1:Word_Status1:ListD  0.30344    0.45472   0.667 0.504571    
## Word_Type1:Word_Status1:ListE  0.94946    0.38671   2.455 0.014080 *  
## Word_Type1:Word_Status1:ListF  0.75954    0.38689   1.963 0.049623 *  
## Word_Type1:Word_Status1:ListG  0.11277    0.40194   0.281 0.779045    
## Word_Type1:Word_Status1:ListH -0.08651    0.41404  -0.209 0.834501    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## maxfun < 10 * length(par)^2 is not recommended.

Position in Sentence

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * target_position + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##    10334    10406    -5157    10314     9930 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3634 -0.6228 -0.3562  0.7711  5.7823 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03478  0.1865  
##  subject_nr   (Intercept) 1.46794  1.2116  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                           Estimate Std. Error z value Pr(>|z|)
## (Intercept)                              -0.896378   0.133168  -6.731 1.68e-11
## Word_Type1                               -0.034984   0.050257  -0.696    0.486
## Word_Status1                              1.062534   0.051029  20.822  < 2e-16
## target_position1                          0.044182   0.058273   0.758    0.448
## Word_Type1:Word_Status1                   0.007207   0.100515   0.072    0.943
## Word_Type1:target_position1               0.047621   0.100601   0.473    0.636
## Word_Status1:target_position1             0.081159   0.100601   0.807    0.420
## Word_Type1:Word_Status1:target_position1  0.041758   0.201135   0.208    0.836
##                                             
## (Intercept)                              ***
## Word_Type1                                  
## Word_Status1                             ***
## target_position1                            
## Word_Type1:Word_Status1                     
## Word_Type1:target_position1                 
## Word_Status1:target_position1               
## Word_Type1:Word_Status1:target_position1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 trgt_1 Wr_T1:W_S1 W_T1:_ W_S1:_
## Word_Type1   0.001                                              
## Word_Stats1  0.031 -0.002                                       
## targt_pstn1 -0.002  0.000 -0.007                                
## Wrd_T1:W_S1  0.000  0.226  0.000  0.007                         
## Wrd_Typ1:_1  0.000 -0.008  0.007  0.002 -0.009                  
## Wrd_Stt1:_1 -0.003  0.007 -0.005  0.197  0.000      0.000       
## W_T1:W_S1:_  0.001 -0.009  0.001 -0.001 -0.008      0.225  0.000

Word Length (monosyllabic, bisyllabic)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Word_Type * Word_Status * Syllable + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10327.3  10399.3  -5153.6  10307.3     9930 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3986 -0.6204 -0.3541  0.7690  5.9544 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03453  0.1858  
##  subject_nr   (Intercept) 1.46919  1.2121  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                       -0.896606   0.133207  -6.731 1.69e-11 ***
## Word_Type1                        -0.034498   0.050276  -0.686   0.4926    
## Word_Status1                       1.064404   0.051047  20.852  < 2e-16 ***
## Syllable1                         -0.060545   0.058223  -1.040   0.2984    
## Word_Type1:Word_Status1            0.004493   0.100554   0.045   0.9644    
## Word_Type1:Syllable1               0.231923   0.100571   2.306   0.0211 *  
## Word_Status1:Syllable1            -0.020674   0.100550  -0.206   0.8371    
## Word_Type1:Word_Status1:Syllable1 -0.141480   0.201109  -0.703   0.4817    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Syllb1 Wr_T1:W_S1 W_T1:S W_S1:S
## Word_Type1   0.001                                              
## Word_Stats1  0.031 -0.001                                       
## Syllable1    0.001 -0.007 -0.009                                
## Wrd_T1:W_S1  0.000  0.225  0.000  0.011                         
## Wrd_Typ1:S1 -0.003  0.001  0.016  0.001 -0.009                  
## Wrd_Stt1:S1 -0.001  0.013  0.000  0.195 -0.008      0.000       
## W_T1:W_S1:S  0.003 -0.009 -0.009 -0.001  0.001      0.225 -0.001

ALINE Score

Here, ALINE score is based on the Probe word.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Status * ALINE + (1 | subject_nr) + (1 |  
##     Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10327.2  10370.5  -5157.6  10315.2     9934 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3258 -0.6228 -0.3565  0.7690  5.8223 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03521  0.1876  
##  subject_nr   (Intercept) 1.46788  1.2116  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        -0.91253    0.13958  -6.538 6.24e-11 ***
## Word_Status1        1.10511    0.09787  11.292  < 2e-16 ***
## ALINE               0.04038    0.10256   0.394    0.694    
## Word_Status1:ALINE -0.10426    0.20495  -0.509    0.611    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_S1 ALINE 
## Word_Stats1  0.071              
## ALINE       -0.300 -0.186       
## Wr_S1:ALINE -0.064 -0.853  0.215

Participant Variables: Predictors

Correlation Individual Differences

Take with a grain of salt, these scores are the “grob” ones coded during the experiment and for some participants there are missing values (for unsure codes), but this should be fairly close to what the final coded values will be.

Phonological Awareness

Phoneme Manipulation task score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * Phoneme_Manipulation.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10239.8  10311.7  -5109.9  10219.8     9815 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2448 -0.6261 -0.3426  0.7842  5.6066 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03797  0.1949  
##  subject_nr   (Intercept) 1.42745  1.1948  
## Number of obs: 9825, groups:  Sentence_Num, 160; subject_nr, 87
## 
## Fixed effects:
##                                                 Estimate Std. Error z value
## (Intercept)                                    -0.883652   0.132230  -6.683
## Word_Type1                                     -0.031303   0.050519  -0.620
## Word_Status1                                    1.062843   0.051247  20.740
## Phoneme_Manipulation.c                         -0.020927   0.039243  -0.533
## Word_Type1:Word_Status1                         0.018887   0.101042   0.187
## Word_Type1:Phoneme_Manipulation.c              -0.020523   0.014904  -1.377
## Word_Status1:Phoneme_Manipulation.c             0.080974   0.015160   5.341
## Word_Type1:Word_Status1:Phoneme_Manipulation.c -0.006331   0.029813  -0.212
##                                                Pr(>|z|)    
## (Intercept)                                    2.35e-11 ***
## Word_Type1                                        0.536    
## Word_Status1                                    < 2e-16 ***
## Phoneme_Manipulation.c                            0.594    
## Word_Type1:Word_Status1                           0.852    
## Word_Type1:Phoneme_Manipulation.c                 0.169    
## Word_Status1:Phoneme_Manipulation.c            9.24e-08 ***
## Word_Type1:Word_Status1:Phoneme_Manipulation.c    0.832    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Phn_M. Wr_T1:W_S1 W_T1:P W_S1:P
## Word_Type1   0.001                                              
## Word_Stats1  0.031 -0.003                                       
## Phnm_Mnplt.  0.001  0.003 -0.012                                
## Wrd_T1:W_S1 -0.001  0.220  0.002  0.000                         
## Wrd_T1:P_M.  0.003 -0.006 -0.006  0.002 -0.061                  
## Wrd_S1:P_M. -0.012 -0.004  0.000  0.028  0.008     -0.013       
## W_T1:W_S1:P  0.000 -0.060  0.009 -0.001 -0.006      0.187  0.012

Onset/Rhyme Score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * Onset_Rhyme.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10094.2  10166.0  -5037.1  10074.2     9701 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2348 -0.6121 -0.3255  0.7861  6.7126 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.04009  0.2002  
##  subject_nr   (Intercept) 1.42975  1.1957  
## Number of obs: 9711, groups:  Sentence_Num, 160; subject_nr, 86
## 
## Fixed effects:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           -0.889742   0.133177  -6.681 2.37e-11 ***
## Word_Type1                            -0.041233   0.050978  -0.809    0.419    
## Word_Status1                           1.057409   0.051701  20.452  < 2e-16 ***
## Onset_Rhyme.c                         -0.036197   0.031124  -1.163    0.245    
## Word_Type1:Word_Status1                0.008291   0.101952   0.081    0.935    
## Word_Type1:Onset_Rhyme.c               0.018786   0.012050   1.559    0.119    
## Word_Status1:Onset_Rhyme.c             0.098171   0.012282   7.993 1.32e-15 ***
## Word_Type1:Word_Status1:Onset_Rhyme.c  0.030921   0.024109   1.283    0.200    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 Ons_R. Wr_T1:W_S1 W_T1:O W_S1:O
## Word_Type1   0.001                                              
## Word_Stats1  0.030  0.000                                       
## Onst_Rhym.c  0.008 -0.001 -0.019                                
## Wrd_T1:W_S1  0.000  0.216  0.002 -0.001                         
## Wrd_T1:O_R. -0.001  0.021 -0.003  0.001 -0.072                  
## Wrd_S1:O_R. -0.019 -0.003  0.033  0.025 -0.001      0.001       
## W_T1:W_S1:O -0.002 -0.072  0.000  0.000  0.021      0.180  0.009

Grade

Approximation for now for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * DEMO_SchoolGrade +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10273.8  10345.8  -5126.9  10253.8     9930 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0250 -0.6070 -0.3504  0.7794  6.3496 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03709  0.1926  
##  subject_nr   (Intercept) 1.42915  1.1955  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                            Estimate Std. Error z value Pr(>|z|)
## (Intercept)                               -0.918744   0.132087  -6.956 3.51e-12
## Word_Type1                                -0.034010   0.050674  -0.671    0.502
## Word_Status1                               1.094046   0.051424  21.275  < 2e-16
## DEMO_SchoolGrade1                          0.234256   0.262193   0.893    0.372
## Word_Type1:Word_Status1                    0.009454   0.101354   0.093    0.926
## Word_Type1:DEMO_SchoolGrade1               0.012392   0.101326   0.122    0.903
## Word_Status1:DEMO_SchoolGrade1            -0.796123   0.102721  -7.750 9.17e-15
## Word_Type1:Word_Status1:DEMO_SchoolGrade1  0.058421   0.202621   0.288    0.773
##                                              
## (Intercept)                               ***
## Word_Type1                                   
## Word_Status1                              ***
## DEMO_SchoolGrade1                            
## Word_Type1:Word_Status1                      
## Word_Type1:DEMO_SchoolGrade1                 
## Word_Status1:DEMO_SchoolGrade1            ***
## Word_Type1:Word_Status1:DEMO_SchoolGrade1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 DEMO_S Wr_T1:W_S1 W_T1:D W_S1:D
## Word_Type1   0.001                                              
## Word_Stats1  0.026 -0.002                                       
## DEMO_SchlG1 -0.088 -0.001  0.014                                
## Wrd_T1:W_S1  0.000  0.199  0.001  0.000                         
## W_T1:DEMO_S -0.001 -0.089  0.000  0.001  0.059                  
## W_S1:DEMO_S  0.015 -0.001 -0.102  0.029 -0.005     -0.004       
## W_T1:W_S1:D  0.000  0.059 -0.006  0.000 -0.089      0.200  0.003

Age

Approximation for now for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * DEMO_AgeYears.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10011.1  10082.8  -4995.6   9991.1     9584 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9690 -0.6141 -0.3472  0.7807  6.0865 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.04152  0.2038  
##  subject_nr   (Intercept) 1.45477  1.2061  
## Number of obs: 9594, groups:  Sentence_Num, 160; subject_nr, 85
## 
## Fixed effects:
##                                         Estimate Std. Error z value Pr(>|z|)
## (Intercept)                             -0.87850    0.13505  -6.505 7.76e-11
## Word_Type1                              -0.03467    0.05115  -0.678    0.498
## Word_Status1                             1.03859    0.05181  20.048  < 2e-16
## DEMO_AgeYears.c                         -0.04834    0.17832  -0.271    0.786
## Word_Type1:Word_Status1                  0.01171    0.10230   0.114    0.909
## Word_Type1:DEMO_AgeYears.c              -0.04028    0.06786  -0.594    0.553
## Word_Status1:DEMO_AgeYears.c             0.50681    0.06871   7.376 1.63e-13
## Word_Type1:Word_Status1:DEMO_AgeYears.c -0.11947    0.13572  -0.880    0.379
##                                            
## (Intercept)                             ***
## Word_Type1                                 
## Word_Status1                            ***
## DEMO_AgeYears.c                            
## Word_Type1:Word_Status1                    
## Word_Type1:DEMO_AgeYears.c                 
## Word_Status1:DEMO_AgeYears.c            ***
## Word_Type1:Word_Status1:DEMO_AgeYears.c    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 DEMO_A Wr_T1:W_S1 W_T1:D W_S1:D
## Word_Type1   0.001                                              
## Word_Stats1  0.032 -0.002                                       
## DEMO_AgYrs.  0.004  0.002 -0.013                                
## Wrd_T1:W_S1 -0.001  0.222 -0.001  0.001                         
## W_T1:DEMO_A  0.002 -0.005  0.001  0.000 -0.064                  
## W_S1:DEMO_A -0.014  0.001  0.005  0.035  0.006     -0.007       
## W_T1:W_S1:D  0.001 -0.064  0.005 -0.001 -0.006      0.235 -0.004

German Production CLT

Another approximation for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * Ger_CLT_Prod.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10323.2  10395.2  -5151.6  10303.2     9930 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4223 -0.6184 -0.3571  0.7795  6.0769 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03539  0.1881  
##  subject_nr   (Intercept) 1.38696  1.1777  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                         Estimate Std. Error z value Pr(>|z|)
## (Intercept)                            -0.899666   0.129684  -6.937 3.99e-12
## Word_Type1                             -0.034517   0.050291  -0.686   0.4925
## Word_Status1                            1.064976   0.051046  20.863  < 2e-16
## Ger_CLT_Prod.c                         -0.137184   0.062581  -2.192   0.0284
## Word_Type1:Word_Status1                 0.007922   0.100578   0.079   0.9372
## Word_Type1:Ger_CLT_Prod.c              -0.035840   0.024021  -1.492   0.1357
## Word_Status1:Ger_CLT_Prod.c             0.045943   0.024312   1.890   0.0588
## Word_Type1:Word_Status1:Ger_CLT_Prod.c -0.069922   0.048053  -1.455   0.1456
##                                           
## (Intercept)                            ***
## Word_Type1                                
## Word_Status1                           ***
## Ger_CLT_Prod.c                         *  
## Word_Type1:Word_Status1                   
## Word_Type1:Ger_CLT_Prod.c                 
## Word_Status1:Ger_CLT_Prod.c            .  
## Word_Type1:Word_Status1:Ger_CLT_Prod.c    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 G_CLT_ Wr_T1:W_S1 W_T1:G W_S1:G
## Word_Type1   0.001                                              
## Word_Stats1  0.031  0.000                                       
## Gr_CLT_Prd.  0.010  0.001 -0.010                                
## Wrd_T1:W_S1  0.000  0.221  0.002  0.001                         
## W_T1:G_CLT_  0.001  0.021 -0.002  0.003 -0.028                  
## W_S1:G_CLT_ -0.009  0.000  0.025  0.049 -0.001      0.009       
## W_T1:W_S1:G  0.001 -0.028 -0.001  0.002  0.021      0.294  0.017

Participant Variables: Exploratory

Language Background (Monolingual, Bilingual)

## # A tibble: 2 × 2
##   LANG_Bilingual total_participants
##   <fct>                       <int>
## 1 monolingual                    70
## 2 bilingual                      18

## # A tibble: 1 × 8
##   .y.           group1      group2       n1    n2 statistic    df      p
## * <chr>         <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 DEMO_AgeYears monolingual bilingual    68    17      2.49  29.9 0.0187

## # A tibble: 1 × 8
##   .y.       group1      group2       n1    n2 statistic    df     p
## * <chr>     <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 RAVENS_IQ monolingual bilingual    68    17    -0.998  23.2 0.329

## # A tibble: 1 × 8
##   .y.              group1      group2       n1    n2 statistic    df     p
## * <chr>            <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 WM_BackDigitSpan monolingual bilingual    70    18      1.13  22.5 0.272

## # A tibble: 1 × 8
##   .y.          group1      group2       n1    n2 statistic    df      p
## * <chr>        <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 Ger_CLT_Prod monolingual bilingual    70    18      2.35  18.9 0.0297

## # A tibble: 1 × 8
##   .y.                group1      group2       n1    n2 statistic    df     p
## * <chr>              <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 NWRT_total_correct monolingual bilingual    70    18   -0.0658  26.4 0.948

## # A tibble: 1 × 8
##   .y.                  group1      group2       n1    n2 statistic    df     p
## * <chr>                <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 Phoneme_Manipulation monolingual bilingual    69    18      1.49  27.8 0.149

## # A tibble: 1 × 8
##   .y.         group1      group2       n1    n2 statistic    df     p
## * <chr>       <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 Onset_Rhyme monolingual bilingual    69    17    -0.196  27.4 0.846

## # A tibble: 1 × 8
##   .y.           group1      group2       n1    n2 statistic    df     p
## * <chr>         <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 ENG_CLT_score monolingual bilingual    69    18    -0.313  29.2 0.757

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * LANG_Bilingual + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10313.0  10385.0  -5146.5  10293.0     9930 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0981 -0.6267 -0.3495  0.7623  5.9750 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03529  0.1878  
##  subject_nr   (Intercept) 1.37779  1.1738  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                           Estimate Std. Error z value Pr(>|z|)
## (Intercept)                             -7.101e-01  1.602e-01  -4.434 9.25e-06
## Word_Type1                              -6.048e-05  6.163e-02  -0.001   0.9992
## Word_Status1                             9.189e-01  6.208e-02  14.802  < 2e-16
## LANG_Bilingual1                         -6.406e-01  3.189e-01  -2.009   0.0446
## Word_Type1:Word_Status1                  8.941e-02  1.232e-01   0.725   0.4682
## Word_Type1:LANG_Bilingual1              -1.133e-01  1.232e-01  -0.920   0.3578
## Word_Status1:LANG_Bilingual1             4.977e-01  1.239e-01   4.016 5.91e-05
## Word_Type1:Word_Status1:LANG_Bilingual1 -2.703e-01  2.463e-01  -1.097   0.2726
##                                            
## (Intercept)                             ***
## Word_Type1                                 
## Word_Status1                            ***
## LANG_Bilingual1                         *  
## Word_Type1:Word_Status1                    
## Word_Type1:LANG_Bilingual1                 
## Word_Status1:LANG_Bilingual1            ***
## Word_Type1:Word_Status1:LANG_Bilingual1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 LANG_B Wr_T1:W_S1 W_T1:L W_S1:L
## Word_Type1   0.000                                              
## Word_Stats1  0.049  0.000                                       
## LANG_Blngl1 -0.590  0.001 -0.040                                
## Wrd_T1:W_S1 -0.001  0.289  0.000  0.002                         
## W_T1:LANG_B  0.001 -0.578 -0.001  0.000 -0.209                  
## W_S1:LANG_B -0.039  0.000 -0.570  0.050  0.000     -0.001       
## W_T1:W_S1:L  0.001 -0.209  0.000 -0.001 -0.578      0.291  0.001

English CLT

Overall performance

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * ENG_CLT_score.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10219.3  10291.3  -5099.7  10199.3     9814 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2926 -0.6165 -0.3558  0.7877  5.6450 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03601  0.1898  
##  subject_nr   (Intercept) 1.47711  1.2154  
## Number of obs: 9824, groups:  Sentence_Num, 160; subject_nr, 87
## 
## Fixed effects:
##                                           Estimate Std. Error z value Pr(>|z|)
## (Intercept)                             -0.8939676  0.1343613  -6.653 2.86e-11
## Word_Type1                              -0.0285983  0.0505602  -0.566  0.57165
## Word_Status1                             1.0489382  0.0512987  20.448  < 2e-16
## ENG_CLT_score.c                          0.0006173  0.0132655   0.047  0.96288
## Word_Type1:Word_Status1                  0.0034640  0.1011194   0.034  0.97267
## Word_Type1:ENG_CLT_score.c               0.0019634  0.0049590   0.396  0.69216
## Word_Status1:ENG_CLT_score.c             0.0180442  0.0050257   3.590  0.00033
## Word_Type1:Word_Status1:ENG_CLT_score.c  0.0059911  0.0099191   0.604  0.54585
##                                            
## (Intercept)                             ***
## Word_Type1                                 
## Word_Status1                            ***
## ENG_CLT_score.c                            
## Word_Type1:Word_Status1                    
## Word_Type1:ENG_CLT_score.c                 
## Word_Status1:ENG_CLT_score.c            ***
## Word_Type1:Word_Status1:ENG_CLT_score.c    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 ENG_CL Wr_T1:W_S1 W_T1:E W_S1:E
## Word_Type1   0.001                                              
## Word_Stats1  0.031 -0.001                                       
## ENG_CLT_sc.  0.013  0.000 -0.005                                
## Wrd_T1:W_S1  0.000  0.226  0.000  0.001                         
## W_T1:ENG_CL  0.001 -0.010  0.005  0.000 -0.032                  
## W_S1:ENG_CL -0.006  0.006 -0.006  0.036  0.002     -0.005       
## W_T1:W_S1:E  0.001 -0.032  0.004  0.000 -0.010      0.237  0.002

Specific performance on cognate words

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * ENG_CLT_cognates.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10202.4  10274.3  -5091.2  10182.4     9814 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2709 -0.6181 -0.3528  0.7841  5.7182 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03637  0.1907  
##  subject_nr   (Intercept) 1.47997  1.2165  
## Number of obs: 9824, groups:  Sentence_Num, 160; subject_nr, 87
## 
## Fixed effects:
##                                              Estimate Std. Error z value
## (Intercept)                                -0.8963054  0.1344930  -6.664
## Word_Type1                                 -0.0270313  0.0506125  -0.534
## Word_Status1                                1.0519872  0.0513638  20.481
## ENG_CLT_cognates.c                          0.0001771  0.0083909   0.021
## Word_Type1:Word_Status1                     0.0071999  0.1012270   0.071
## Word_Type1:ENG_CLT_cognates.c              -0.0002743  0.0031021  -0.088
## Word_Status1:ENG_CLT_cognates.c             0.0172752  0.0031386   5.504
## Word_Type1:Word_Status1:ENG_CLT_cognates.c -0.0001662  0.0062047  -0.027
##                                            Pr(>|z|)    
## (Intercept)                                2.66e-11 ***
## Word_Type1                                    0.593    
## Word_Status1                                < 2e-16 ***
## ENG_CLT_cognates.c                            0.983    
## Word_Type1:Word_Status1                       0.943    
## Word_Type1:ENG_CLT_cognates.c                 0.930    
## Word_Status1:ENG_CLT_cognates.c            3.71e-08 ***
## Word_Type1:Word_Status1:ENG_CLT_cognates.c    0.979    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 ENG_CL Wr_T1:W_S1 W_T1:E W_S1:E
## Word_Type1   0.000                                              
## Word_Stats1  0.031 -0.002                                       
## ENG_CLT_cg.  0.008  0.001 -0.007                                
## Wrd_T1:W_S1  0.000  0.223 -0.001  0.001                         
## W_T1:ENG_CL  0.001  0.006  0.005  0.000 -0.039                  
## W_S1:ENG_CL -0.009  0.006  0.014  0.037  0.005     -0.011       
## W_T1:W_S1:E  0.001 -0.039  0.006 -0.001  0.006      0.246 -0.005

Difference score (Prop cognates correct – prop noncognates correct )

difference score where 0 is similar performance between cognates and noncognates

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * English_CLT_diffscore.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10203.5  10275.5  -5091.8  10183.5     9814 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2837 -0.6199 -0.3530  0.7741  6.1635 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03612  0.19    
##  subject_nr   (Intercept) 1.48806  1.22    
## Number of obs: 9824, groups:  Sentence_Num, 160; subject_nr, 87
## 
## Fixed effects:
##                                                   Estimate Std. Error z value
## (Intercept)                                     -0.8959018  0.1348332  -6.645
## Word_Type1                                      -0.0274872  0.0506218  -0.543
## Word_Status1                                     1.0567042  0.0514092  20.555
## English_CLT_diffscore.c                         -0.0001377  0.0080864  -0.017
## Word_Type1:Word_Status1                          0.0076190  0.1012488   0.075
## Word_Type1:English_CLT_diffscore.c              -0.0016187  0.0030197  -0.536
## Word_Status1:English_CLT_diffscore.c             0.0163357  0.0030488   5.358
## Word_Type1:Word_Status1:English_CLT_diffscore.c -0.0039578  0.0060406  -0.655
##                                                 Pr(>|z|)    
## (Intercept)                                     3.04e-11 ***
## Word_Type1                                         0.587    
## Word_Status1                                     < 2e-16 ***
## English_CLT_diffscore.c                            0.986    
## Word_Type1:Word_Status1                            0.940    
## Word_Type1:English_CLT_diffscore.c                 0.592    
## Word_Status1:English_CLT_diffscore.c            8.41e-08 ***
## Word_Type1:Word_Status1:English_CLT_diffscore.c    0.512    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 E_CLT_ Wr_T1:W_S1 W_T1:E W_S1:E
## Word_Type1   0.000                                              
## Word_Stats1  0.030 -0.002                                       
## Engls_CLT_.  0.000  0.001 -0.007                                
## Wrd_T1:W_S1  0.000  0.223 -0.001  0.002                         
## W_T1:E_CLT_  0.001  0.021  0.003  0.000 -0.033                  
## W_S1:E_CLT_ -0.009  0.004  0.032  0.036  0.006     -0.012       
## W_T1:W_S1:E  0.002 -0.033  0.005 -0.003  0.021      0.243 -0.008

Ravens Matricies IQ score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * RAVENS_IQ.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10036.5  10108.2  -5008.3  10016.5     9584 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4038 -0.6348 -0.3425  0.8046  8.0687 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.04092  0.2023  
##  subject_nr   (Intercept) 1.39783  1.1823  
## Number of obs: 9594, groups:  Sentence_Num, 160; subject_nr, 85
## 
## Fixed effects:
##                                      Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                         -0.881104   0.132572  -6.646 3.01e-11 ***
## Word_Type1                          -0.036864   0.051219  -0.720   0.4717    
## Word_Status1                         1.065852   0.052024  20.488  < 2e-16 ***
## RAVENS_IQ.c                         -0.025624   0.011365  -2.255   0.0242 *  
## Word_Type1:Word_Status1              0.001772   0.102436   0.017   0.9862    
## Word_Type1:RAVENS_IQ.c              -0.000699   0.004714  -0.148   0.8821    
## Word_Status1:RAVENS_IQ.c             0.024093   0.004846   4.971 6.65e-07 ***
## Word_Type1:Word_Status1:RAVENS_IQ.c -0.003196   0.009427  -0.339   0.7345    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 RAVENS Wr_T1:W_S1 W_T1:R W_S1:R
## Word_Type1   0.001                                              
## Word_Stats1  0.028 -0.001                                       
## RAVENS_IQ.c  0.017  0.001 -0.021                                
## Wrd_T1:W_S1  0.000  0.213  0.000  0.001                         
## W_T1:RAVENS  0.001  0.077  0.003  0.003 -0.054                  
## W_S1:RAVENS -0.021  0.002  0.092  0.003  0.002      0.002       
## W_T1:W_S1:R  0.000 -0.054  0.003  0.003  0.077      0.150  0.010

Backwards Digit Span

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * WM_BackDigitSpan.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10281.7  10353.8  -5130.9  10261.7     9930 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3111 -0.6111 -0.3448  0.7938  5.7900 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.03583  0.1893  
##  subject_nr   (Intercept) 1.39768  1.1822  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                            Estimate Std. Error z value Pr(>|z|)
## (Intercept)                                -0.90415    0.13017  -6.946 3.76e-12
## Word_Type1                                 -0.02742    0.05042  -0.544  0.58651
## Word_Status1                                1.04996    0.05112  20.541  < 2e-16
## WM_BackDigitSpan.c                         -0.20445    0.14712  -1.390  0.16462
## Word_Type1:Word_Status1                     0.03131    0.10085   0.310  0.75621
## Word_Type1:WM_BackDigitSpan.c              -0.17410    0.05833  -2.985  0.00284
## Word_Status1:WM_BackDigitSpan.c             0.37714    0.05898   6.394 1.61e-10
## Word_Type1:Word_Status1:WM_BackDigitSpan.c -0.19829    0.11659  -1.701  0.08897
##                                               
## (Intercept)                                ***
## Word_Type1                                    
## Word_Status1                               ***
## WM_BackDigitSpan.c                            
## Word_Type1:Word_Status1                       
## Word_Type1:WM_BackDigitSpan.c              ** 
## Word_Status1:WM_BackDigitSpan.c            ***
## Word_Type1:Word_Status1:WM_BackDigitSpan.c .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 WM_BDS Wr_T1:W_S1 W_T1:WM W_S1:W
## Word_Type1   0.001                                               
## Word_Stats1  0.031 -0.004                                        
## WM_BckDgtS.  0.001  0.002 -0.012                                 
## Wrd_T1:W_S1 -0.001  0.219  0.002 -0.001                          
## W_T1:WM_BDS  0.002 -0.028 -0.008  0.003 -0.063                   
## W_S1:WM_BDS -0.012 -0.006 -0.021  0.043  0.008     -0.014        
## W_T1:W_S1:W  0.000 -0.063  0.008 -0.001 -0.028      0.244  -0.005

NonWord Repetition Task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Word_Status * NWRT_total_correct.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10308.5  10380.5  -5144.2  10288.5     9930 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5463 -0.6250 -0.3464  0.7760  5.6118 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0357   0.1889  
##  subject_nr   (Intercept) 1.4584   1.2076  
## Number of obs: 9940, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                               Estimate Std. Error z value
## (Intercept)                                  -0.900517   0.132782  -6.782
## Word_Type1                                   -0.035335   0.050335  -0.702
## Word_Status1                                  1.059150   0.051114  20.721
## NWRT_total_correct.c                         -0.011452   0.063648  -0.180
## Word_Type1:Word_Status1                       0.010962   0.100668   0.109
## Word_Type1:NWRT_total_correct.c              -0.018349   0.024103  -0.761
## Word_Status1:NWRT_total_correct.c             0.124450   0.024352   5.110
## Word_Type1:Word_Status1:NWRT_total_correct.c -0.004784   0.048208  -0.099
##                                              Pr(>|z|)    
## (Intercept)                                  1.19e-11 ***
## Word_Type1                                      0.483    
## Word_Status1                                  < 2e-16 ***
## NWRT_total_correct.c                            0.857    
## Word_Type1:Word_Status1                         0.913    
## Word_Type1:NWRT_total_correct.c                 0.446    
## Word_Status1:NWRT_total_correct.c            3.21e-07 ***
## Word_Type1:Word_Status1:NWRT_total_correct.c    0.921    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_T1 Wrd_S1 NWRT__ Wr_T1:W_S1 W_T1:N W_S1:N
## Word_Type1   0.001                                              
## Word_Stats1  0.031 -0.003                                       
## NWRT_ttl_c.  0.005  0.001 -0.009                                
## Wrd_T1:W_S1 -0.001  0.223  0.001  0.000                         
## W_T1:NWRT__  0.001 -0.010 -0.006  0.000 -0.036                  
## W_S1:NWRT__ -0.009 -0.005 -0.001  0.037  0.004      0.001       
## W_T1:W_S1:N  0.000 -0.036  0.005  0.000 -0.010      0.241 -0.002

Recognition: Cognate vs. Non-cognate Following words

Main Analysis: Utterance Type by Word Status

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10450.9  10494.1  -5219.4  10438.9     9950 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9360 -0.6105 -0.3469  0.7547  6.8415 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.0617   0.2484  
##  subject_nr   (Intercept) 1.4615   1.2089  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                  -0.78872    0.13344  -5.911 3.41e-09 ***
## Utterance_Type1              -0.05284    0.04989  -1.059    0.290    
## Word_Status1                  1.27723    0.05102  25.035  < 2e-16 ***
## Utterance_Type1:Word_Status1 -0.10475    0.09984  -1.049    0.294    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1
## Uttrnc_Typ1  0.001              
## Word_Stats1  0.028 -0.002       
## Utt_T1:W_S1  0.001  0.219 -0.003

Role of Phonological Awareness, with control variables Ravens IQ, Working Memory, and German CLT

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Utterance_Type * Word_Status * Phoneme_Manipulation.c +  
##     RAVENS_IQ + Ger_CLT_Prod.c + WM_BackDigitSpan.c + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10020.7  10113.7  -4997.3   9994.7     9481 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7917 -0.6296 -0.3408  0.7775  6.3986 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06233  0.2497  
##  subject_nr   (Intercept) 1.24293  1.1149  
## Number of obs: 9494, groups:  Sentence_Num, 160; subject_nr, 84
## 
## Fixed effects:
##                                                     Estimate Std. Error z value
## (Intercept)                                          2.12267    1.10993   1.912
## Utterance_Type1                                     -0.05090    0.05108  -0.996
## Word_Status1                                         1.23565    0.05209  23.722
## Phoneme_Manipulation.c                               0.04790    0.04134   1.159
## RAVENS_IQ                                           -0.02856    0.01093  -2.614
## Ger_CLT_Prod.c                                      -0.14365    0.06405  -2.243
## WM_BackDigitSpan.c                                  -0.13518    0.16441  -0.822
## Utterance_Type1:Word_Status1                        -0.10266    0.10218  -1.005
## Utterance_Type1:Phoneme_Manipulation.c               0.01346    0.01497   0.900
## Word_Status1:Phoneme_Manipulation.c                  0.11188    0.01526   7.333
## Utterance_Type1:Word_Status1:Phoneme_Manipulation.c -0.05826    0.02991  -1.948
##                                                     Pr(>|z|)    
## (Intercept)                                          0.05582 .  
## Utterance_Type1                                      0.31908    
## Word_Status1                                         < 2e-16 ***
## Phoneme_Manipulation.c                               0.24658    
## RAVENS_IQ                                            0.00894 ** 
## Ger_CLT_Prod.c                                       0.02492 *  
## WM_BackDigitSpan.c                                   0.41098    
## Utterance_Type1:Word_Status1                         0.31505    
## Utterance_Type1:Phoneme_Manipulation.c               0.36837    
## Word_Status1:Phoneme_Manipulation.c                 2.24e-13 ***
## Utterance_Type1:Word_Status1:Phoneme_Manipulation.c  0.05147 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Phn_M. RAVENS G_CLT_ WM_BDS Ut_T1:W_S1 U_T1:P
## Uttrnc_Typ1 -0.001                                                            
## Word_Stats1  0.015  0.001                                                     
## Phnm_Mnplt.  0.148 -0.004 -0.005                                              
## RAVENS_IQ   -0.993  0.001 -0.011 -0.148                                       
## Gr_CLT_Prd. -0.081  0.001 -0.010 -0.064  0.083                                
## WM_BckDgtS.  0.045  0.000 -0.003 -0.382 -0.048 -0.290                         
## Utt_T1:W_S1 -0.001  0.221 -0.007  0.002  0.001 -0.001 -0.001                  
## Utt_T1:P_M.  0.000  0.004  0.010 -0.005  0.000  0.000  0.000 -0.055           
## Wrd_S1:P_M.  0.000  0.009  0.012  0.024 -0.001  0.006  0.007 -0.019      0.007
## U_T1:W_S1:P  0.000 -0.057 -0.019  0.001  0.001  0.000  0.001  0.004      0.182
##             W_S1:P
## Uttrnc_Typ1       
## Word_Stats1       
## Phnm_Mnplt.       
## RAVENS_IQ         
## Gr_CLT_Prd.       
## WM_BckDgtS.       
## Utt_T1:W_S1       
## Utt_T1:P_M.       
## Wrd_S1:P_M.       
## U_T1:W_S1:P -0.031
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)
## Model failed to converge with max|grad| = 0.00298108 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

d’ analyses

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Utterance_Type + Phoneme_Manipulation.c + RAVENS_IQ +  
##     Ger_CLT_Prod.c + WM_BackDigitSpan.c + (1 | subject_nr)
##    Data: d_calc
## 
## REML criterion at convergence: 331.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.36404 -0.43821  0.03075  0.47053  2.20277 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.2994   0.5472  
##  Residual               0.1727   0.4155  
## Number of obs: 168, groups:  subject_nr, 84
## 
## Fixed effects:
##                         Estimate Std. Error        df t value Pr(>|t|)  
## (Intercept)             0.141659   0.595871 78.999999   0.238   0.8127  
## Utterance_Type1        -0.090698   0.064119 83.000002  -1.415   0.1610  
## Phoneme_Manipulation.c  0.040156   0.022443 78.999997   1.789   0.0774 .
## RAVENS_IQ               0.005835   0.005860 78.999999   0.996   0.3224  
## Ger_CLT_Prod.c         -0.009049   0.034703 78.999997  -0.261   0.7950  
## WM_BackDigitSpan.c      0.221888   0.088638 78.999997   2.503   0.0144 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Phn_M. RAVENS G_CLT_
## Uttrnc_Typ1  0.000                            
## Phnm_Mnplt.  0.144  0.000                     
## RAVENS_IQ   -0.994  0.000 -0.144              
## Gr_CLT_Prd. -0.067  0.000 -0.056  0.068       
## WM_BckDgtS.  0.043  0.000 -0.382 -0.045 -0.299

Experiment Task Variables

Trial

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * Trial.c + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10447.6  10519.6  -5213.8  10427.6     9946 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1242 -0.6093 -0.3452  0.7504  7.2636 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06182  0.2486  
##  subject_nr   (Intercept) 1.46940  1.2122  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                          -0.7883747  0.1337799  -5.893 3.79e-09 ***
## Utterance_Type1                      -0.0567260  0.0499570  -1.135  0.25617    
## Word_Status1                          1.2805790  0.0510888  25.066  < 2e-16 ***
## Trial.c                              -0.0004885  0.0005415  -0.902  0.36702    
## Utterance_Type1:Word_Status1         -0.1104766  0.0999658  -1.105  0.26910    
## Utterance_Type1:Trial.c               0.0016726  0.0010845   1.542  0.12301    
## Word_Status1:Trial.c                 -0.0031407  0.0010833  -2.899  0.00374 ** 
## Utterance_Type1:Word_Status1:Trial.c -0.0009374  0.0021726  -0.431  0.66611    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Tril.c Ut_T1:W_S1 U_T1:T W_S1:T
## Uttrnc_Typ1  0.001                                              
## Word_Stats1  0.028 -0.003                                       
## Trial.c     -0.001  0.015 -0.004                                
## Utt_T1:W_S1  0.001  0.220 -0.003  0.025                         
## Uttrn_T1:T.  0.002 -0.014  0.028 -0.002 -0.002                  
## Wrd_Stt1:T.  0.002  0.026 -0.020  0.222  0.015     -0.013       
## U_T1:W_S1:T  0.005 -0.002  0.013 -0.013 -0.014      0.221  0.000
## optimizer (bobyqa) convergence code: 0 (OK)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

Counterbalance List

## # A tibble: 8 × 2
##   List  total_participants
##   <fct>              <int>
## 1 A                     13
## 2 B                     10
## 3 C                     12
## 4 D                      6
## 5 E                     13
## 6 F                     14
## 7 G                     10
## 8 H                     10

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Utterance_Type * Word_Status * List + (1 | subject_nr) +  
##     (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10455.0  10700.0  -5193.5  10387.0     9922 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3518 -0.6025 -0.3497  0.7567  7.4729 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05675  0.2382  
##  subject_nr   (Intercept) 1.32154  1.1496  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                        -0.82927    0.32701  -2.536  0.01122 *  
## Utterance_Type1                    -0.30432    0.13739  -2.215  0.02676 *  
## Word_Status1                        1.23277    0.13867   8.890  < 2e-16 ***
## ListB                              -0.52066    0.49619  -1.049  0.29404    
## ListC                               0.63672    0.47197   1.349  0.17731    
## ListD                               0.73297    0.57888   1.266  0.20545    
## ListE                               0.11983    0.46211   0.259  0.79539    
## ListF                              -0.33635    0.45413  -0.741  0.45891    
## ListG                               0.17625    0.49416   0.357  0.72135    
## ListH                              -0.15716    0.49599  -0.317  0.75135    
## Utterance_Type1:Word_Status1       -0.18978    0.27265  -0.696  0.48640    
## Utterance_Type1:ListB               0.15864    0.20999   0.755  0.44996    
## Utterance_Type1:ListC               0.16292    0.19701   0.827  0.40827    
## Utterance_Type1:ListD               0.54297    0.23955   2.267  0.02341 *  
## Utterance_Type1:ListE               0.24730    0.20440   1.210  0.22632    
## Utterance_Type1:ListF               0.39805    0.19944   1.996  0.04595 *  
## Utterance_Type1:ListG               0.23688    0.20278   1.168  0.24274    
## Utterance_Type1:ListH               0.45601    0.19972   2.283  0.02242 *  
## Word_Status1:ListB                  0.04954    0.21865   0.227  0.82074    
## Word_Status1:ListC                 -0.01231    0.20504  -0.060  0.95212    
## Word_Status1:ListD                  0.72105    0.24012   3.003  0.00268 ** 
## Word_Status1:ListE                  0.38865    0.18752   2.073  0.03821 *  
## Word_Status1:ListF                 -0.16023    0.19649  -0.815  0.41482    
## Word_Status1:ListG                 -0.15693    0.21056  -0.745  0.45610    
## Word_Status1:ListH                 -0.22879    0.20711  -1.105  0.26930    
## Utterance_Type1:Word_Status1:ListB -0.26564    0.42902  -0.619  0.53580    
## Utterance_Type1:Word_Status1:ListC  0.70481    0.39164   1.800  0.07192 .  
## Utterance_Type1:Word_Status1:ListD  0.11233    0.46618   0.241  0.80959    
## Utterance_Type1:Word_Status1:ListE  0.11121    0.40451   0.275  0.78337    
## Utterance_Type1:Word_Status1:ListF -0.07295    0.38364  -0.190  0.84918    
## Utterance_Type1:Word_Status1:ListG  0.05425    0.40348   0.134  0.89305    
## Utterance_Type1:Word_Status1:ListH -0.03125    0.40881  -0.076  0.93908    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## maxfun < 10 * length(par)^2 is not recommended.

Position in Sentence

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * target_position +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10428.0  10500.1  -5204.0  10408.0     9946 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8742 -0.6128 -0.3443  0.7499  7.0906 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05414  0.2327  
##  subject_nr   (Intercept) 1.47617  1.2150  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                               Estimate Std. Error z value
## (Intercept)                                   -0.78955    0.13390  -5.897
## Utterance_Type1                               -0.05391    0.05001  -1.078
## Word_Status1                                   1.27881    0.05114  25.008
## target_position1                               0.22025    0.06215   3.544
## Utterance_Type1:Word_Status1                  -0.10673    0.10006  -1.067
## Utterance_Type1:target_position1              -0.21056    0.10006  -2.104
## Word_Status1:target_position1                  0.43320    0.10037   4.316
## Utterance_Type1:Word_Status1:target_position1 -0.17254    0.20023  -0.862
##                                               Pr(>|z|)    
## (Intercept)                                   3.71e-09 ***
## Utterance_Type1                               0.280998    
## Word_Status1                                   < 2e-16 ***
## target_position1                              0.000395 ***
## Utterance_Type1:Word_Status1                  0.286159    
## Utterance_Type1:target_position1              0.035348 *  
## Word_Status1:target_position1                 1.59e-05 ***
## Utterance_Type1:Word_Status1:target_position1 0.388825    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 trgt_1 Ut_T1:W_S1 U_T1:_ W_S1:_
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.028 -0.004                                       
## targt_pstn1 -0.002  0.001  0.009                                
## Utt_T1:W_S1  0.001  0.222 -0.005 -0.008                         
## Uttrn_T1:_1  0.002  0.004 -0.013 -0.004  0.001                  
## Wrd_Stt1:_1 -0.004 -0.011  0.014  0.181  0.002     -0.003       
## U_T1:W_S1:_ -0.001  0.001 -0.001 -0.002  0.004      0.222 -0.005

Word Length (monosyllabic, bisyllabic)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * Syllable + (1 |  
##     subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10450.1  10522.2  -5215.1  10430.1     9946 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8028 -0.6113 -0.3456  0.7564  7.0571 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.05814  0.2411  
##  subject_nr   (Intercept) 1.46376  1.2099  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                            -0.78912    0.13345  -5.913 3.36e-09 ***
## Utterance_Type1                        -0.05315    0.04992  -1.065   0.2870    
## Word_Status1                            1.27735    0.05104  25.027  < 2e-16 ***
## Syllable1                              -0.13591    0.06284  -2.163   0.0306 *  
## Utterance_Type1:Word_Status1           -0.10298    0.09989  -1.031   0.3026    
## Utterance_Type1:Syllable1              -0.09908    0.09982  -0.993   0.3209    
## Word_Status1:Syllable1                 -0.16465    0.09997  -1.647   0.0996 .  
## Utterance_Type1:Word_Status1:Syllable1  0.19795    0.19976   0.991   0.3217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Syllb1 Ut_T1:W_S1 U_T1:S W_S1:S
## Uttrnc_Typ1  0.001                                              
## Word_Stats1  0.028 -0.002                                       
## Syllable1    0.003  0.006 -0.007                                
## Utt_T1:W_S1  0.001  0.219 -0.002 -0.007                         
## Uttrn_T1:S1  0.002  0.006 -0.010 -0.001 -0.004                  
## Wrd_Stt1:S1  0.001 -0.009  0.002  0.174  0.007      0.001       
## U_T1:W_S1:S -0.002 -0.004  0.008  0.000  0.006      0.219 -0.001

ALINE Score

skipping ALINE score analysis for now, need to check stimuli file

Ultimately, will want to calculate based on ALINE value of cognate/noncognate in the sentence.

Participant Variables: Predictors

Correlation Individual Differences

Phonological Awareness

Phoneme Manipulation task score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Utterance_Type * Word_Status * Phoneme_Manipulation.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10311.7  10383.7  -5145.9  10291.7     9830 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8839 -0.6179 -0.3378  0.7657  6.6637 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06148  0.2479  
##  subject_nr   (Intercept) 1.42727  1.1947  
## Number of obs: 9840, groups:  Sentence_Num, 160; subject_nr, 87
## 
## Fixed effects:
##                                                      Estimate Std. Error
## (Intercept)                                         -0.782367   0.132728
## Utterance_Type1                                     -0.052341   0.050333
## Word_Status1                                         1.266811   0.051385
## Phoneme_Manipulation.c                               0.001494   0.039231
## Utterance_Type1:Word_Status1                        -0.118274   0.100698
## Utterance_Type1:Phoneme_Manipulation.c               0.013051   0.014939
## Word_Status1:Phoneme_Manipulation.c                  0.119064   0.015259
## Utterance_Type1:Word_Status1:Phoneme_Manipulation.c -0.058931   0.029862
##                                                     z value Pr(>|z|)    
## (Intercept)                                          -5.895 3.76e-09 ***
## Utterance_Type1                                      -1.040   0.2984    
## Word_Status1                                         24.653  < 2e-16 ***
## Phoneme_Manipulation.c                                0.038   0.9696    
## Utterance_Type1:Word_Status1                         -1.175   0.2402    
## Utterance_Type1:Phoneme_Manipulation.c                0.874   0.3823    
## Word_Status1:Phoneme_Manipulation.c                   7.803 6.04e-15 ***
## Utterance_Type1:Word_Status1:Phoneme_Manipulation.c  -1.973   0.0484 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Phn_M. Ut_T1:W_S1 U_T1:P W_S1:P
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.029  0.002                                       
## Phnm_Mnplt.  0.000 -0.004 -0.011                                
## Utt_T1:W_S1  0.001  0.215 -0.008  0.002                         
## Utt_T1:P_M. -0.004 -0.002  0.009 -0.006 -0.057                  
## Wrd_S1:P_M. -0.011  0.009  0.007  0.028 -0.020      0.007       
## U_T1:W_S1:P  0.002 -0.059 -0.020  0.002 -0.002      0.180 -0.033

Onset/Rhyme Score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * Onset_Rhyme.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10182.2  10254.1  -5081.1  10162.2     9717 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7524 -0.6092 -0.3282  0.7495  7.7074 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06146  0.2479  
##  subject_nr   (Intercept) 1.43381  1.1974  
## Number of obs: 9727, groups:  Sentence_Num, 160; subject_nr, 86
## 
## Fixed effects:
##                                             Estimate Std. Error z value
## (Intercept)                                -0.791002   0.133800  -5.912
## Utterance_Type1                            -0.039819   0.050738  -0.785
## Word_Status1                                1.255987   0.051717  24.286
## Onset_Rhyme.c                              -0.031116   0.031157  -0.999
## Utterance_Type1:Word_Status1               -0.103405   0.101498  -1.019
## Utterance_Type1:Onset_Rhyme.c               0.009517   0.012032   0.791
## Word_Status1:Onset_Rhyme.c                  0.109916   0.012255   8.969
## Utterance_Type1:Word_Status1:Onset_Rhyme.c -0.058023   0.024041  -2.413
##                                            Pr(>|z|)    
## (Intercept)                                3.38e-09 ***
## Utterance_Type1                              0.4326    
## Word_Status1                                < 2e-16 ***
## Onset_Rhyme.c                                0.3179    
## Utterance_Type1:Word_Status1                 0.3083    
## Utterance_Type1:Onset_Rhyme.c                0.4290    
## Word_Status1:Onset_Rhyme.c                  < 2e-16 ***
## Utterance_Type1:Word_Status1:Onset_Rhyme.c   0.0158 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 Ons_R. Ut_T1:W_S1 U_T1:O W_S1:O
## Uttrnc_Typ1 -0.001                                              
## Word_Stats1  0.028  0.003                                       
## Onst_Rhym.c  0.008 -0.003 -0.018                                
## Utt_T1:W_S1  0.002  0.211 -0.011  0.002                         
## Utt_T1:O_R. -0.003  0.027  0.008 -0.004 -0.069                  
## Wrd_S1:O_R. -0.018  0.009  0.034  0.023 -0.018      0.008       
## U_T1:W_S1:O  0.003 -0.071 -0.018  0.002  0.026      0.170 -0.022

Grade

Approximation for now for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Word_Type * Utterance_Type * DEMO_SchoolGrade +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10374.4  10475.3  -5173.2  10346.4     9942 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4078 -0.5977 -0.3447  0.7680  6.4081 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06332  0.2516  
##  subject_nr   (Intercept) 1.42019  1.1917  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                                                     Estimate
## (Intercept)                                                         -1.19126
## Word_TypeFollowing                                                   0.86151
## Word_TypeNoncognate                                                  0.04782
## Utterance_Type1                                                     -0.19857
## DEMO_SchoolGrade2nd grade/class                                     -0.57900
## Word_TypeFollowing:Utterance_Type1                                   0.19438
## Word_TypeNoncognate:Utterance_Type1                                  0.25198
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                   0.93017
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                 -0.01760
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                      0.43754
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class  -0.63998
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class -0.52749
##                                                                     Std. Error
## (Intercept)                                                            0.18419
## Word_TypeFollowing                                                     0.08028
## Word_TypeNoncognate                                                    0.08215
## Utterance_Type1                                                        0.11611
## DEMO_SchoolGrade2nd grade/class                                        0.27259
## Word_TypeFollowing:Utterance_Type1                                     0.15967
## Word_TypeNoncognate:Utterance_Type1                                    0.16473
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                     0.12113
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                    0.12812
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                        0.18205
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class     0.23997
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class    0.25726
##                                                                     z value
## (Intercept)                                                          -6.467
## Word_TypeFollowing                                                   10.731
## Word_TypeNoncognate                                                   0.582
## Utterance_Type1                                                      -1.710
## DEMO_SchoolGrade2nd grade/class                                      -2.124
## Word_TypeFollowing:Utterance_Type1                                    1.217
## Word_TypeNoncognate:Utterance_Type1                                   1.530
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                    7.679
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                  -0.137
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                       2.403
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class   -2.667
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class  -2.050
##                                                                     Pr(>|z|)
## (Intercept)                                                         9.97e-11
## Word_TypeFollowing                                                   < 2e-16
## Word_TypeNoncognate                                                  0.56048
## Utterance_Type1                                                      0.08723
## DEMO_SchoolGrade2nd grade/class                                      0.03366
## Word_TypeFollowing:Utterance_Type1                                   0.22347
## Word_TypeNoncognate:Utterance_Type1                                  0.12611
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                  1.60e-14
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                  0.89070
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                      0.01624
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class   0.00765
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class  0.04032
##                                                                        
## (Intercept)                                                         ***
## Word_TypeFollowing                                                  ***
## Word_TypeNoncognate                                                    
## Utterance_Type1                                                     .  
## DEMO_SchoolGrade2nd grade/class                                     *  
## Word_TypeFollowing:Utterance_Type1                                     
## Word_TypeNoncognate:Utterance_Type1                                    
## Word_TypeFollowing:DEMO_SchoolGrade2nd grade/class                  ***
## Word_TypeNoncognate:DEMO_SchoolGrade2nd grade/class                    
## Utterance_Type1:DEMO_SchoolGrade2nd grade/class                     *  
## Word_TypeFollowing:Utterance_Type1:DEMO_SchoolGrade2nd grade/class  ** 
## Word_TypeNoncognate:Utterance_Type1:DEMO_SchoolGrade2nd grade/class *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Wrd_TF Wrd_TN Utt_T1 DEMO_g W_TF:U W_TN:U W_TF:Dg W_TN:Dg
## Wrd_TypFllw -0.239                                                          
## Wrd_TypNncg -0.223  0.512                                                   
## Uttrnc_Typ1  0.007 -0.016 -0.013                                            
## DEMO_ScG2g/ -0.667  0.160  0.151 -0.004                                     
## Wrd_TF:U_T1 -0.005  0.011  0.009 -0.730  0.003                              
## Wrd_TN:U_T1 -0.005  0.012  0.004 -0.707  0.003  0.516                       
## W_TF:DEMO_g  0.157 -0.660 -0.338  0.010 -0.263 -0.007 -0.007                
## W_TN:DEMO_g  0.143 -0.325 -0.640  0.008 -0.237 -0.006 -0.003  0.532         
## U_T1:DEMO_g -0.005  0.010  0.008 -0.640 -0.008  0.467  0.454  0.019   0.018 
## W_TF:U_T1:g  0.004 -0.008 -0.006  0.488  0.007 -0.667 -0.346 -0.017  -0.014 
## W_TN:U_T1:g  0.004 -0.008 -0.003  0.455  0.006 -0.332 -0.644 -0.013  -0.013 
##             U_T1:g W_TF:Ug
## Wrd_TypFllw               
## Wrd_TypNncg               
## Uttrnc_Typ1               
## DEMO_ScG2g/               
## Wrd_TF:U_T1               
## Wrd_TN:U_T1               
## W_TF:DEMO_g               
## W_TN:DEMO_g               
## U_T1:DEMO_g               
## W_TF:U_T1:g -0.760        
## W_TN:U_T1:g -0.710  0.540

Age

Approximation for now for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * DEMO_AgeYears.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10095.9  10167.6  -5037.9  10075.9     9600 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2456 -0.6074 -0.3478  0.7727  6.3687 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06455  0.2541  
##  subject_nr   (Intercept) 1.45469  1.2061  
## Number of obs: 9610, groups:  Sentence_Num, 160; subject_nr, 85
## 
## Fixed effects:
##                                              Estimate Std. Error z value
## (Intercept)                                  -0.78263    0.13553  -5.774
## Utterance_Type1                              -0.04761    0.05092  -0.935
## Word_Status1                                  1.22868    0.05184  23.702
## DEMO_AgeYears.c                               0.01013    0.17812   0.057
## Utterance_Type1:Word_Status1                 -0.08879    0.10190  -0.871
## Utterance_Type1:DEMO_AgeYears.c              -0.00292    0.06745  -0.043
## Word_Status1:DEMO_AgeYears.c                  0.56828    0.06841   8.307
## Utterance_Type1:Word_Status1:DEMO_AgeYears.c -0.21967    0.13498  -1.627
##                                              Pr(>|z|)    
## (Intercept)                                  7.72e-09 ***
## Utterance_Type1                                 0.350    
## Word_Status1                                  < 2e-16 ***
## DEMO_AgeYears.c                                 0.955    
## Utterance_Type1:Word_Status1                    0.384    
## Utterance_Type1:DEMO_AgeYears.c                 0.965    
## Word_Status1:DEMO_AgeYears.c                  < 2e-16 ***
## Utterance_Type1:Word_Status1:DEMO_AgeYears.c    0.104    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 DEMO_A Ut_T1:W_S1 U_T1:D W_S1:D
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.030  0.000                                       
## DEMO_AgYrs.  0.003 -0.002 -0.012                                
## Utt_T1:W_S1  0.001  0.217 -0.005  0.000                         
## U_T1:DEMO_A -0.001 -0.009 -0.002 -0.001 -0.068                  
## W_S1:DEMO_A -0.013 -0.002  0.000  0.034 -0.011      0.003       
## U_T1:W_S1:D  0.000 -0.068 -0.010  0.001 -0.013      0.228 -0.006

German Production CLT

Another approximation for L1 knowledge

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * Ger_CLT_Prod.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10444.8  10516.9  -5212.4  10424.8     9946 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0710 -0.6093 -0.3470  0.7643  6.8730 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06135  0.2477  
##  subject_nr   (Intercept) 1.38401  1.1764  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                             Estimate Std. Error z value
## (Intercept)                                 -0.79260    0.13011  -6.092
## Utterance_Type1                             -0.05514    0.04994  -1.104
## Word_Status1                                 1.28019    0.05104  25.081
## Ger_CLT_Prod.c                              -0.13100    0.06245  -2.098
## Utterance_Type1:Word_Status1                -0.10014    0.09991  -1.002
## Utterance_Type1:Ger_CLT_Prod.c              -0.03847    0.02375  -1.620
## Word_Status1:Ger_CLT_Prod.c                  0.06171    0.02415   2.555
## Utterance_Type1:Word_Status1:Ger_CLT_Prod.c -0.02688    0.04757  -0.565
##                                             Pr(>|z|)    
## (Intercept)                                 1.12e-09 ***
## Utterance_Type1                               0.2695    
## Word_Status1                                 < 2e-16 ***
## Ger_CLT_Prod.c                                0.0359 *  
## Utterance_Type1:Word_Status1                  0.3162    
## Utterance_Type1:Ger_CLT_Prod.c                0.1053    
## Word_Status1:Ger_CLT_Prod.c                   0.0106 *  
## Utterance_Type1:Word_Status1:Ger_CLT_Prod.c   0.5721    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 G_CLT_ Ut_T1:W_S1 U_T1:G W_S1:G
## Uttrnc_Typ1  0.001                                              
## Word_Stats1  0.028 -0.002                                       
## Gr_CLT_Prd.  0.009  0.000 -0.012                                
## Utt_T1:W_S1  0.001  0.214 -0.001 -0.002                         
## U_T1:G_CLT_  0.000  0.018 -0.014  0.003 -0.030                  
## W_S1:G_CLT_ -0.009 -0.014  0.022  0.046 -0.003      0.032       
## U_T1:W_S1:G -0.002 -0.032 -0.003  0.006  0.016      0.279  0.013

Participant Variables: Exploratory

Language Background (Monolingual, Bilingual)

## # A tibble: 2 × 2
##   LANG_Bilingual total_participants
##   <fct>                       <int>
## 1 monolingual                    70
## 2 bilingual                      18

## # A tibble: 1 × 8
##   .y.           group1      group2       n1    n2 statistic    df      p
## * <chr>         <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 DEMO_AgeYears monolingual bilingual    68    17      2.49  29.9 0.0187

## # A tibble: 1 × 8
##   .y.       group1      group2       n1    n2 statistic    df     p
## * <chr>     <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 RAVENS_IQ monolingual bilingual    68    17    -0.998  23.2 0.329

## # A tibble: 1 × 8
##   .y.              group1      group2       n1    n2 statistic    df     p
## * <chr>            <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 WM_BackDigitSpan monolingual bilingual    70    18      1.13  22.5 0.272

## # A tibble: 1 × 8
##   .y.          group1      group2       n1    n2 statistic    df      p
## * <chr>        <chr>       <chr>     <int> <int>     <dbl> <dbl>  <dbl>
## 1 Ger_CLT_Prod monolingual bilingual    70    18      2.35  18.9 0.0297

## # A tibble: 1 × 8
##   .y.                group1      group2       n1    n2 statistic    df     p
## * <chr>              <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 NWRT_total_correct monolingual bilingual    70    18   -0.0658  26.4 0.948

## # A tibble: 1 × 8
##   .y.                  group1      group2       n1    n2 statistic    df     p
## * <chr>                <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 Phoneme_Manipulation monolingual bilingual    69    18      1.49  27.8 0.149

## # A tibble: 1 × 8
##   .y.         group1      group2       n1    n2 statistic    df     p
## * <chr>       <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 Onset_Rhyme monolingual bilingual    69    17    -0.196  27.4 0.846

## # A tibble: 1 × 8
##   .y.           group1      group2       n1    n2 statistic    df     p
## * <chr>         <chr>       <chr>     <int> <int>     <dbl> <dbl> <dbl>
## 1 ENG_CLT_score monolingual bilingual    69    18    -0.313  29.2 0.757

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * LANG_Bilingual +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10440.1  10512.2  -5210.1  10420.1     9946 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4998 -0.6111 -0.3425  0.7535  6.9524 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06136  0.2477  
##  subject_nr   (Intercept) 1.36158  1.1669  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                              Estimate Std. Error z value
## (Intercept)                                  -0.59142    0.15975  -3.702
## Utterance_Type1                              -0.01452    0.06172  -0.235
## Word_Status1                                  1.15892    0.06265  18.498
## LANG_Bilingual1                              -0.67405    0.31706  -2.126
## Utterance_Type1:Word_Status1                  0.02143    0.12368   0.173
## Utterance_Type1:LANG_Bilingual1              -0.11978    0.12371  -0.968
## Word_Status1:LANG_Bilingual1                  0.40387    0.12484   3.235
## Utterance_Type1:Word_Status1:LANG_Bilingual1 -0.42665    0.24736  -1.725
##                                              Pr(>|z|)    
## (Intercept)                                  0.000214 ***
## Utterance_Type1                              0.814055    
## Word_Status1                                  < 2e-16 ***
## LANG_Bilingual1                              0.033508 *  
## Utterance_Type1:Word_Status1                 0.862430    
## Utterance_Type1:LANG_Bilingual1              0.332912    
## Word_Status1:LANG_Bilingual1                 0.001216 ** 
## Utterance_Type1:Word_Status1:LANG_Bilingual1 0.084563 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 LANG_B Ut_T1:W_S1 U_T1:L W_S1:L
## Uttrnc_Typ1  0.004                                              
## Word_Stats1  0.048  0.019                                       
## LANG_Blngl1 -0.589 -0.004 -0.041                                
## Utt_T1:W_S1  0.004  0.290  0.021 -0.003                         
## U_T1:LANG_B -0.004 -0.587 -0.021  0.004 -0.211                  
## W_S1:LANG_B -0.040 -0.022 -0.581  0.050 -0.025      0.020       
## U_T1:W_S1:L -0.003 -0.214 -0.025  0.004 -0.589      0.287  0.021

English CLT

Overall performance

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * ENG_CLT_score.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10294.9  10366.9  -5137.5  10274.9     9833 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9095 -0.6008 -0.3499  0.7715  6.5179 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06386  0.2527  
##  subject_nr   (Intercept) 1.46365  1.2098  
## Number of obs: 9843, groups:  Sentence_Num, 160; subject_nr, 87
## 
## Fixed effects:
##                                               Estimate Std. Error z value
## (Intercept)                                  -0.790822   0.134377  -5.885
## Utterance_Type1                              -0.064948   0.050421  -1.288
## Word_Status1                                  1.249295   0.051488  24.264
## ENG_CLT_score.c                               0.011085   0.013210   0.839
## Utterance_Type1:Word_Status1                 -0.131980   0.100852  -1.309
## Utterance_Type1:ENG_CLT_score.c               0.007837   0.005001   1.567
## Word_Status1:ENG_CLT_score.c                  0.037184   0.005093   7.300
## Utterance_Type1:Word_Status1:ENG_CLT_score.c  0.015736   0.010015   1.571
##                                              Pr(>|z|)    
## (Intercept)                                  3.98e-09 ***
## Utterance_Type1                                 0.198    
## Word_Status1                                  < 2e-16 ***
## ENG_CLT_score.c                                 0.401    
## Utterance_Type1:Word_Status1                    0.191    
## Utterance_Type1:ENG_CLT_score.c                 0.117    
## Word_Status1:ENG_CLT_score.c                 2.87e-13 ***
## Utterance_Type1:Word_Status1:ENG_CLT_score.c    0.116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 ENG_CL Ut_T1:W_S1 U_T1:E W_S1:E
## Uttrnc_Typ1  0.001                                              
## Word_Stats1  0.030  0.001                                       
## ENG_CLT_sc.  0.011 -0.001 -0.006                                
## Utt_T1:W_S1  0.002  0.223  0.000 -0.001                         
## U_T1:ENG_CL -0.001 -0.017  0.002  0.001 -0.036                  
## W_S1:ENG_CL -0.007  0.001 -0.001  0.036  0.005      0.015       
## U_T1:W_S1:E -0.001 -0.031  0.004  0.003 -0.015      0.232  0.002

Specific performance on cognate words

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * ENG_CLT_cognates.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10288.1  10360.1  -5134.1  10268.1     9833 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8933 -0.5996 -0.3452  0.7643  7.0540 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06384  0.2527  
##  subject_nr   (Intercept) 1.47393  1.2141  
## Number of obs: 9843, groups:  Sentence_Num, 160; subject_nr, 87
## 
## Fixed effects:
##                                                  Estimate Std. Error z value
## (Intercept)                                     -0.794258   0.134818  -5.891
## Utterance_Type1                                 -0.063013   0.050432  -1.249
## Word_Status1                                     1.254711   0.051505  24.361
## ENG_CLT_cognates.c                               0.004479   0.008373   0.535
## Utterance_Type1:Word_Status1                    -0.135226   0.100873  -1.341
## Utterance_Type1:ENG_CLT_cognates.c               0.005462   0.003122   1.750
## Word_Status1:ENG_CLT_cognates.c                  0.024872   0.003173   7.838
## Utterance_Type1:Word_Status1:ENG_CLT_cognates.c  0.005397   0.006248   0.864
##                                                 Pr(>|z|)    
## (Intercept)                                     3.83e-09 ***
## Utterance_Type1                                   0.2115    
## Word_Status1                                     < 2e-16 ***
## ENG_CLT_cognates.c                                0.5927    
## Utterance_Type1:Word_Status1                      0.1801    
## Utterance_Type1:ENG_CLT_cognates.c                0.0802 .  
## Word_Status1:ENG_CLT_cognates.c                 4.58e-15 ***
## Utterance_Type1:Word_Status1:ENG_CLT_cognates.c   0.3877    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 ENG_CL Ut_T1:W_S1 U_T1:E W_S1:E
## Uttrnc_Typ1  0.001                                              
## Word_Stats1  0.029  0.002                                       
## ENG_CLT_cg.  0.008 -0.002 -0.008                                
## Utt_T1:W_S1  0.002  0.219 -0.002 -0.001                         
## U_T1:ENG_CL -0.002 -0.001  0.004  0.002 -0.047                  
## W_S1:ENG_CL -0.009  0.003  0.013  0.038 -0.003      0.014       
## U_T1:W_S1:E -0.001 -0.043 -0.003  0.003  0.000      0.244  0.006

Difference score (Prop cognates correct – prop noncognates correct )

difference score where 0 is similar performance between cognates and noncognates

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## detection ~ 1 + Utterance_Type * Word_Status * English_CLT_diffscore.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10322.7  10394.7  -5151.4  10302.7     9833 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9225 -0.6075 -0.3392  0.7546  7.4107 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06332  0.2516  
##  subject_nr   (Intercept) 1.48183  1.2173  
## Number of obs: 9843, groups:  Sentence_Num, 160; subject_nr, 87
## 
## Fixed effects:
##                                                        Estimate Std. Error
## (Intercept)                                          -7.930e-01  1.351e-01
## Utterance_Type1                                      -5.999e-02  5.030e-02
## Word_Status1                                          1.263e+00  5.139e-02
## English_CLT_diffscore.c                              -2.914e-05  8.063e-03
## Utterance_Type1:Word_Status1                         -1.365e-01  1.006e-01
## Utterance_Type1:English_CLT_diffscore.c               3.622e-03  3.011e-03
## Word_Status1:English_CLT_diffscore.c                  1.632e-02  3.057e-03
## Utterance_Type1:Word_Status1:English_CLT_diffscore.c -1.514e-03  6.034e-03
##                                                      z value Pr(>|z|)    
## (Intercept)                                           -5.869 4.38e-09 ***
## Utterance_Type1                                       -1.193    0.233    
## Word_Status1                                          24.565  < 2e-16 ***
## English_CLT_diffscore.c                               -0.004    0.997    
## Utterance_Type1:Word_Status1                          -1.357    0.175    
## Utterance_Type1:English_CLT_diffscore.c                1.203    0.229    
## Word_Status1:English_CLT_diffscore.c                   5.339 9.36e-08 ***
## Utterance_Type1:Word_Status1:English_CLT_diffscore.c  -0.251    0.802    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 E_CLT_ Ut_T1:W_S1 U_T1:E W_S1:E
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.028  0.001                                       
## Engls_CLT_.  0.002 -0.002 -0.007                                
## Utt_T1:W_S1  0.001  0.216 -0.005  0.000                         
## U_T1:E_CLT_ -0.002  0.020  0.005  0.001 -0.038                  
## W_S1:E_CLT_ -0.008  0.005  0.030  0.035 -0.008      0.004       
## U_T1:W_S1:E  0.000 -0.036 -0.008  0.002  0.018      0.243 -0.001

Ravens Matricies IQ score

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * RAVENS_IQ.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10135.3  10207.0  -5057.6  10115.3     9600 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9256 -0.6171 -0.3497  0.7625  7.4229 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06326  0.2515  
##  subject_nr   (Intercept) 1.39978  1.1831  
## Number of obs: 9610, groups:  Sentence_Num, 160; subject_nr, 85
## 
## Fixed effects:
##                                           Estimate Std. Error z value Pr(>|z|)
## (Intercept)                              -0.783336   0.133110  -5.885 3.98e-09
## Utterance_Type1                          -0.049263   0.050924  -0.967   0.3334
## Word_Status1                              1.262638   0.052070  24.249  < 2e-16
## RAVENS_IQ.c                              -0.026704   0.011336  -2.356   0.0185
## Utterance_Type1:Word_Status1             -0.082707   0.101941  -0.811   0.4172
## Utterance_Type1:RAVENS_IQ.c               0.007023   0.004665   1.506   0.1322
## Word_Status1:RAVENS_IQ.c                  0.022927   0.004806   4.770 1.84e-06
## Utterance_Type1:Word_Status1:RAVENS_IQ.c  0.014103   0.009292   1.518   0.1291
##                                             
## (Intercept)                              ***
## Utterance_Type1                             
## Word_Status1                             ***
## RAVENS_IQ.c                              *  
## Utterance_Type1:Word_Status1                
## Utterance_Type1:RAVENS_IQ.c                 
## Word_Status1:RAVENS_IQ.c                 ***
## Utterance_Type1:Word_Status1:RAVENS_IQ.c    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 RAVENS Ut_T1:W_S1 U_T1:R W_S1:R
## Uttrnc_Typ1  0.000                                              
## Word_Stats1  0.026 -0.002                                       
## RAVENS_IQ.c  0.013 -0.002 -0.020                                
## Utt_T1:W_S1  0.000  0.210 -0.002 -0.002                         
## U_T1:RAVENS -0.003  0.079 -0.001 -0.004 -0.045                  
## W_S1:RAVENS -0.019 -0.003  0.098  0.002 -0.001     -0.001       
## U_T1:W_S1:R -0.002 -0.042 -0.002 -0.004  0.085      0.153  0.000

Backwards Digit Span

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * WM_BackDigitSpan.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10400.6  10472.7  -5190.3  10380.6     9946 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9109 -0.6070 -0.3450  0.7561  6.7423 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06287  0.2507  
##  subject_nr   (Intercept) 1.38283  1.1759  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                                 Estimate Std. Error z value
## (Intercept)                                     -0.79676    0.13009  -6.125
## Utterance_Type1                                 -0.05301    0.05008  -1.058
## Word_Status1                                     1.25894    0.05109  24.643
## WM_BackDigitSpan.c                              -0.17297    0.14625  -1.183
## Utterance_Type1:Word_Status1                    -0.11099    0.10021  -1.108
## Utterance_Type1:WM_BackDigitSpan.c               0.04785    0.05774   0.829
## Word_Status1:WM_BackDigitSpan.c                  0.42717    0.05843   7.310
## Utterance_Type1:Word_Status1:WM_BackDigitSpan.c -0.13368    0.11549  -1.157
##                                                 Pr(>|z|)    
## (Intercept)                                     9.08e-10 ***
## Utterance_Type1                                    0.290    
## Word_Status1                                     < 2e-16 ***
## WM_BackDigitSpan.c                                 0.237    
## Utterance_Type1:Word_Status1                       0.268    
## Utterance_Type1:WM_BackDigitSpan.c                 0.407    
## Word_Status1:WM_BackDigitSpan.c                 2.67e-13 ***
## Utterance_Type1:Word_Status1:WM_BackDigitSpan.c    0.247    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 WM_BDS Ut_T1:W_S1 U_T1:WM W_S1:W
## Uttrnc_Typ1  0.000                                               
## Word_Stats1  0.028  0.002                                        
## WM_BckDgtS.  0.001 -0.002 -0.012                                 
## Utt_T1:W_S1  0.002  0.212 -0.004 -0.001                          
## U_T1:WM_BDS -0.003 -0.031 -0.002  0.000 -0.060                   
## W_S1:WM_BDS -0.010 -0.004 -0.022  0.042 -0.016      0.029        
## U_T1:W_S1:W -0.001 -0.062 -0.016  0.006 -0.029      0.234   0.007

NonWord Repetition Task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: detection ~ 1 + Utterance_Type * Word_Status * NWRT_total_correct.c +  
##     (1 | subject_nr) + (1 | Sentence_Num)
##    Data: Lseg_Htest
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  10410.8  10482.9  -5195.4  10390.8     9946 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2372 -0.6061 -0.3402  0.7548  7.0614 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Sentence_Num (Intercept) 0.06306  0.2511  
##  subject_nr   (Intercept) 1.45287  1.2053  
## Number of obs: 9956, groups:  Sentence_Num, 160; subject_nr, 88
## 
## Fixed effects:
##                                                    Estimate Std. Error z value
## (Intercept)                                       -0.793499   0.133124  -5.961
## Utterance_Type1                                   -0.055407   0.050053  -1.107
## Word_Status1                                       1.271373   0.051199  24.832
## NWRT_total_correct.c                               0.009459   0.063525   0.149
## Utterance_Type1:Word_Status1                      -0.113270   0.100160  -1.131
## Utterance_Type1:NWRT_total_correct.c               0.040896   0.024213   1.689
## Word_Status1:NWRT_total_correct.c                  0.165619   0.024540   6.749
## Utterance_Type1:Word_Status1:NWRT_total_correct.c  0.018502   0.048535   0.381
##                                                   Pr(>|z|)    
## (Intercept)                                       2.51e-09 ***
## Utterance_Type1                                     0.2683    
## Word_Status1                                       < 2e-16 ***
## NWRT_total_correct.c                                0.8816    
## Utterance_Type1:Word_Status1                        0.2581    
## Utterance_Type1:NWRT_total_correct.c                0.0912 .  
## Word_Status1:NWRT_total_correct.c                 1.49e-11 ***
## Utterance_Type1:Word_Status1:NWRT_total_correct.c   0.7031    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_T1 Wrd_S1 NWRT__ Ut_T1:W_S1 U_T1:N W_S1:N
## Uttrnc_Typ1  0.001                                              
## Word_Stats1  0.029  0.001                                       
## NWRT_ttl_c.  0.004 -0.003 -0.008                                
## Utt_T1:W_S1  0.002  0.218 -0.002 -0.001                         
## U_T1:NWRT__ -0.003 -0.006  0.001  0.003 -0.029                  
## W_S1:NWRT__ -0.008 -0.001  0.007  0.039 -0.010      0.012       
## U_T1:W_S1:N -0.001 -0.030 -0.007  0.003 -0.006      0.255  0.012

Correspondance between Cognate/Noncognate target and Cognate/Noncognate following

d’ for Cognate/Noncognate target

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Utterance_Type + (1 | subject_nr)
##    Data: Lseg_target
## 
## REML criterion at convergence: 323.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.85751 -0.58695 -0.00492  0.60245  2.13122 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.2651   0.5149  
##  Residual               0.1797   0.4239  
## Number of obs: 176, groups:  subject_nr, 88
## 
## Fixed effects:
##                 Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)      0.59514    0.06351 87.00000   9.371 7.82e-15 ***
## Utterance_Type1 -0.02008    0.06391 87.00000  -0.314    0.754    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## Uttrnc_Typ1 0.000

d’ for Cognate/Noncognate following

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Utterance_Type + (1 | subject_nr)
##    Data: Lseg_following
## 
## REML criterion at convergence: 341.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.23365 -0.45542 -0.00666  0.49038  2.34696 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.3743   0.6118  
##  Residual               0.1702   0.4126  
## Number of obs: 176, groups:  subject_nr, 88
## 
## Fixed effects:
##                 Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)      0.74513    0.07225 87.00000  10.313   <2e-16 ***
## Utterance_Type1 -0.09863    0.06220 87.00000  -1.586    0.116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## Uttrnc_Typ1 0.000

Comparison in performance

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dprime ~ 1 + Utterance_Type * Target_Type + (1 | subject_nr)
##    Data: d_comp
## 
## REML criterion at convergence: 580.2
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.73897 -0.61388 -0.01708  0.61990  3.06240 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_nr (Intercept) 0.3218   0.5673  
##  Residual               0.1728   0.4157  
## Number of obs: 352, groups:  subject_nr, 88
## 
## Fixed effects:
##                                             Estimate Std. Error        df
## (Intercept)                                  0.69581    0.07497 153.30686
## Utterance_TypeNoncognate                     0.09863    0.06267 261.00000
## Target_TypeTarget                           -0.11072    0.06267 261.00000
## Utterance_TypeNoncognate:Target_TypeTarget  -0.07854    0.08863 261.00000
##                                            t value Pr(>|t|)    
## (Intercept)                                  9.281   <2e-16 ***
## Utterance_TypeNoncognate                     1.574   0.1168    
## Target_TypeTarget                           -1.767   0.0785 .  
## Utterance_TypeNoncognate:Target_TypeTarget  -0.886   0.3763    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Utt_TN Trg_TT
## Uttrnc_TypN -0.418              
## Trgt_TypTrg -0.418  0.500       
## Utt_TN:T_TT  0.296 -0.707 -0.707

Exploratory Analyses Lexical Transparency

Katie Von Holzen

2023-06-16

Participants

Recognition: Cognate vs. Non-cognate words

Main Analysis

Cognate Status by Word Status

Individual Differences with Cognate Status by Word Status

Role of Phonological Awareness, with control variables Ravens IQ, Working Memory, and German CLT

d’ analyses

Experiment Task Variables

Trial

Counterbalance List

Position in Sentence

Word Length (monosyllabic, bisyllabic)

ALINE Score

Participant Variables: Predictors

Correlation Individual Differences

Phonological Awareness

Phoneme Manipulation task score

Onset/Rhyme Score

Grade

Age

German Production CLT

Participant Variables: Exploratory

Language Background (Monolingual, Bilingual)

English CLT

Overall performance

Specific performance on cognate words

Difference score (Prop cognates correct – prop noncognates correct )

Ravens Matricies IQ score

Backwards Digit Span

NonWord Repetition Task

Recognition: Cognate vs. Non-cognate Following words

Main Analysis: Utterance Type by Word Status

Role of Phonological Awareness, with control variables Ravens IQ, Working Memory, and German CLT

d’ analyses

Experiment Task Variables

Trial

Counterbalance List

Position in Sentence

Word Length (monosyllabic, bisyllabic)

ALINE Score

Participant Variables: Predictors

Correlation Individual Differences

Phonological Awareness

Phoneme Manipulation task score

Onset/Rhyme Score

Grade

Age

German Production CLT

Participant Variables: Exploratory

Language Background (Monolingual, Bilingual)

English CLT

Overall performance

Specific performance on cognate words

Difference score (Prop cognates correct – prop noncognates correct )

Ravens Matricies IQ score

Backwards Digit Span

NonWord Repetition Task

Correspondance between Cognate/Noncognate target and Cognate/Noncognate following

d’ for Cognate/Noncognate target

d’ for Cognate/Noncognate following

Comparison in performance

Correlation in performance