Analysis script and documentation for Dynamic assessment of the effectiveness of digital game-based literacy training in beginning readers
Toivo Glatz1,2,3, Wim Tops1, Elisabeth Borleffs1,2, Ulla Richardson4, Natasha Maurits2,5, Annemie Desoete6, & Ben Maassen1,2,7;1Centre for Language and Cognition (CLCG),University of Groningen, The Netherlands, 2Behavioural and Cognitive Neuroscience (BCN), University of Groningen, The Netherlands, 3Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Public Health, 4Centre for Applied Language Studies, University of Jyväskylä, Finland, 5Department of Neurology, University of Groningen, the Netherlands, 6Department of Developmental Disorders, Ghent University, Belgium 7Department of Neuroscience, University Medical Center Groningen, University of Groningen, The Netherlands
Created on: June 21, 2021 - 10:45:18
1 Documentation & Analyses
1.1 Abstract
In this paper, we report on a study evaluating the effectiveness of a digital game-based learning (DGBL) tool for beginning readers of Dutch, employing active (math game) and passive (no game) control conditions. This classroom-level randomized control trial included 247 first graders from 16 classrooms in the Netherlands and the Dutch-speaking part of Belgium. The intervention consisted of 10 to 15 minutes of daily playing during school time for a period of 4 to 7 weeks. Our outcome measures included reading fluency, as well as purpose built in-game proficiency levels to measure written lexical decision and letter speech sound association. After an average of 28 playing sessions, the literacy game improved letter knowledge at a scale generalizable for all children in the classroom compared to the other two conditions. In addition to a small classroom wide benefit in terms of reading fluency, we furthermore discovered that children who scored high on phonological awareness prior to training were more fluent readers after extensive exposure to the reading game. This study is among the first to exploit game generated data for the evaluation of DGBL for literacy interventions.
Second revision submitted to PeerJ in May 2021.
1.2 R packages
The following R version and packages were used to generate this markdown:
sessionInfo()## R version 4.0.4 (2021-02-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
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## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] MuMIn_1.43.17 arsenal_3.6.2 Hmisc_4.5-0 ggplot2_3.3.3 Formula_1.2-4 survival_3.2-11 lattice_0.20-41 boot_1.3-26 psych_2.1.3 itsadug_2.4
## [11] plotfunctions_1.4 mgcv_1.8-35 nlme_3.1-152 data.table_1.14.0 effects_4.2-0 carData_3.0-4 lmerTest_3.1-3 lme4_1.1-26 Matrix_1.3-2
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## loaded via a namespace (and not attached):
## [1] splines_4.0.4 tmvnsim_1.0-2 assertthat_0.2.1 statmod_1.4.35 stats4_4.0.4 latticeExtra_0.6-29 yaml_2.2.1 numDeriv_2016.8-1.1 pillar_1.6.0
## [10] backports_1.2.1 glue_1.4.2 digest_0.6.27 RColorBrewer_1.1-2 checkmate_2.0.0 minqa_1.2.4 colorspace_2.0-1 htmltools_0.5.1.1 survey_4.0
## [19] pkgconfig_2.0.3 purrr_0.3.4 scales_1.1.1 jpeg_0.1-8.1 tibble_3.1.1 htmlTable_2.1.0 generics_0.1.0 ellipsis_0.3.2 withr_2.4.2
## [28] nnet_7.3-15 mnormt_2.0.2 magrittr_2.0.1 crayon_1.4.1 evaluate_0.14 fansi_0.4.2 MASS_7.3-54 foreign_0.8-81 tools_4.0.4
## [37] mitools_2.4 lifecycle_1.0.0 stringr_1.4.0 munsell_0.5.0 cluster_2.1.0 compiler_4.0.4 rlang_0.4.11 grid_4.0.4 nloptr_1.2.2.2
## [46] rstudioapi_0.13 htmlwidgets_1.5.3 base64enc_0.1-3 rmarkdown_2.8 gtable_0.3.0 DBI_1.1.1 R6_2.5.0 gridExtra_2.3 knitr_1.33
## [55] dplyr_1.0.6 utf8_1.2.1 insight_0.14.0 stringi_1.5.3 parallel_4.0.4 Rcpp_1.0.6 vctrs_0.3.8 rpart_4.1-15 png_0.1-7
## [64] tidyselect_1.1.1 xfun_0.221.3 Dataframes
Here we present the complete raw data of 286 children who initially consented to participate and who were subsequently allocated to a treatment condition via block randomization. Below are the reasons for exclusion, as stated in flowchart (Figure 1). ADD = attention deficit disorder, ADHD = attention deficit hyperactivity disorder, age = age above 8 years, consentGAM = parents only gave consent to play the game, but not to carry out psychometric testing at school, DCD = developmental coordination disorder, no = 247 participants included in the subsequent analyses, noT2 = lost to follow up, repeat = repeated first grade, sessions = played less than 20 gaming sessions.
summary(BehavDat$Exclude)## ADD ADHD age consentGAM DCD no noT2 repeat sessions
## 1 3 1 4 1 247 8 2 191.3.1 Offline assessments
Dataframe BehavDat contains the raw data based on questionnaires (e.g. familial risk for dyslexia) and psychometric test (e.g. CELF-IV-NL PA). Variable names and their descriptions are provided below.
1.3.1.1 Variable Names
The dataset consists of 286 rows and 40 columns with the following column names:
str(BehavDat)## 'data.frame': 286 obs. of 40 variables:
## $ Subj : Factor w/ 286 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ School : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Class : Factor w/ 16 levels "1","2","3","4",..: 11 11 11 11 11 11 11 11 11 11 ...
## $ T1AgeF : num 6.49 6.18 6.06 6.42 6.82 ...
## $ Gender : Factor w/ 2 levels "female","male": 2 2 2 2 2 1 1 2 2 2 ...
## $ Exclude : Factor w/ 9 levels "ADD","ADHD","age",..: 6 6 6 7 6 6 6 6 6 6 ...
## $ Cond : Factor w/ 3 levels "Math","Passive",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ HandScore : num 0.1847 0.0154 0.5232 0.3539 0.0154 ...
## $ FamRisk : Factor w/ 2 levels "no","yes": 1 1 1 1 2 1 1 1 1 1 ...
## $ Lang : Factor w/ 2 levels "mono","multi": 1 1 1 1 1 1 1 1 1 1 ...
## $ SON_1st : num 0.075 -1.959 -0.739 -2.366 -0.739 ...
## $ hoursPlayed : num 2.73 3.35 3.01 3.23 3.16 ...
## $ levelsPlayed : int 332 386 304 374 423 345 230 391 265 422 ...
## $ sessionsPlayed: int 25 26 25 24 26 27 27 26 25 26 ...
## $ MaxLevel : int 114 116 136 136 85 82 132 113 95 122 ...
## $ itemsSeen : int 5975 6849 5915 7119 7283 6413 4140 7010 5370 7581 ...
## $ responsesGiven: int 2406 2867 2370 2930 3197 2668 1559 2911 2020 3262 ...
## $ T2EMTaCor : num 11 9 9 NA 6 12 11 16 15 10 ...
## $ T2EMTbCor : num 9 15 10 NA 4 12 6 15 21 11 ...
## $ T1CF_Total : int 6 10 31 11 19 25 29 31 26 28 ...
## $ T1CF_TotalPc : num 5 9 50 16 16 37 50 50 37 50 ...
## $ T2CF_Total : int 29 13 31 NA 29 29 37 35 22 33 ...
## $ T2CF_TotalPc : num 37 16 50 NA 16 37 63 75 25 50 ...
## $ T1PF_Total : int 30 23 33 22 29 30 30 24 27 34 ...
## $ T1PF_TotalPc : num 37.5 12.5 55 10 32.5 37.5 37.5 12.5 22.5 55 ...
## $ T2PF_Total : int 34 30 31 NA 34 36 35 33 32 36 ...
## $ T2PF_TotalPc : num 55 37.5 42.5 NA 47.5 75 62.5 55 47.5 75 ...
## $ T1RANcT : int 73 105 70 80 73 100 65 81 104 57 ...
## $ T1RANoT : int 88 88 61 95 117 88 78 89 95 72 ...
## $ T2RANcT : int 56 62 50 NA 78 62 62 76 64 46 ...
## $ T2RANoT : int 71 80 44 NA 85 82 80 91 72 80 ...
## $ T2.LetKen : int NA NA NA NA NA NA 27 NA NA NA ...
## $ T2.LexDec : int NA NA NA NA NA NA 17 NA NA NA ...
## $ T1.LetKen : int 5 0 15 6 8 3 5 6 4 8 ...
## $ Country : Factor w/ 2 levels "B","NL": 1 1 1 1 1 1 1 1 1 1 ...
## $ EMT : num 10 12 9.5 NA 5 12 8.5 15.5 18 10.5 ...
## $ T1CF_TotalZ : num -1.971 -1.566 0.556 -1.465 -0.657 ...
## $ T1PF_TotalZ : num -0.148 -1.391 0.384 -1.568 -0.326 ...
## $ T1.LetKenZ : num -1.0362 -1.5995 0.0905 -0.9235 -0.6982 ...
## $ LK : int NA NA NA NA NA NA 22 NA NA NA ...summary(BehavDat)## Subj School Class T1AgeF Gender Exclude Cond HandScore FamRisk Lang SON_1st hoursPlayed levelsPlayed
## 1 : 1 6 :71 4 : 31 Min. :5.460 female:123 no :247 Math :117 Min. :-3.2006 no :241 mono :250 Min. :-2.3655 Min. :1.161 Min. : 87.0
## 2 : 1 4 :50 1 : 27 1st Qu.:5.989 male :163 sessions : 19 Passive: 58 1st Qu.: 0.1847 yes: 45 multi: 36 1st Qu.:-0.7385 1st Qu.:2.620 1st Qu.: 210.8
## 3 : 1 5 :37 10 : 25 Median :6.296 noT2 : 8 Read :111 Median : 0.3539 Median : 0.0750 Median :3.243 Median : 280.0
## 4 : 1 3 :32 9 : 24 Mean :6.271 consentGAM: 4 Mean : 0.0000 Mean : 0.0000 Mean :3.323 Mean : 308.8
## 5 : 1 7 :31 8 : 22 3rd Qu.:6.500 ADHD : 3 3rd Qu.: 0.5232 3rd Qu.: 0.4818 3rd Qu.:4.146 3rd Qu.: 363.8
## 6 : 1 1 :28 2 : 19 Max. :8.047 repeat : 2 Max. : 0.5232 Max. : 4.9561 Max. :5.236 Max. :1026.0
## (Other):280 (Other):37 (Other):138 (Other) : 3 NA's :4 NA's :58 NA's :58
## sessionsPlayed MaxLevel itemsSeen responsesGiven T2EMTaCor T2EMTbCor T1CF_Total T1CF_TotalPc T2CF_Total T2CF_TotalPc T1PF_Total T1PF_TotalPc
## Min. :11.00 Min. : 24.00 Min. : 2010 Min. : 628 Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.10 Min. : 2.0 Min. : 0.10 Min. : 0.00 Min. : 0.00
## 1st Qu.:24.00 1st Qu.: 89.25 1st Qu.: 5987 1st Qu.: 2079 1st Qu.: 8.00 1st Qu.: 7.00 1st Qu.:18.25 1st Qu.:25.00 1st Qu.:28.0 1st Qu.:37.00 1st Qu.:28.00 1st Qu.: 32.50
## Median :26.00 Median :132.00 Median : 8448 Median : 2850 Median :13.00 Median :13.00 Median :27.00 Median :37.00 Median :34.0 Median :63.00 Median :31.00 Median : 52.50
## Mean :26.17 Mean :126.37 Mean : 9431 Mean : 3113 Mean :15.29 Mean :14.53 Mean :25.50 Mean :44.41 Mean :32.5 Mean :58.24 Mean :30.84 Mean : 51.93
## 3rd Qu.:30.00 3rd Qu.:150.00 3rd Qu.:12516 3rd Qu.: 3586 3rd Qu.:19.00 3rd Qu.:18.25 3rd Qu.:33.00 3rd Qu.:63.00 3rd Qu.:38.0 3rd Qu.:75.00 3rd Qu.:35.00 3rd Qu.: 75.00
## Max. :37.00 Max. :265.00 Max. :22782 Max. :10877 Max. :45.00 Max. :45.00 Max. :44.00 Max. :99.00 Max. :44.0 Max. :99.00 Max. :40.00 Max. :100.00
## NA's :58 NA's :58 NA's :58 NA's :58 NA's :14 NA's :14 NA's :8 NA's :8 NA's :14 NA's :14 NA's :5 NA's :5
## T2PF_Total T2PF_TotalPc T1RANcT T1RANoT T2RANcT T2RANoT T2.LetKen T2.LexDec T1.LetKen Country EMT T1CF_TotalZ
## Min. :13.0 Min. : 0.00 Min. : 39.00 Min. : 40.00 Min. : 34.00 Min. : 39.00 Min. : 3.00 Min. : 8.00 Min. : 0.0 B :191 Min. : 0.000 Min. :-2.5770
## 1st Qu.:31.0 1st Qu.: 42.50 1st Qu.: 56.00 1st Qu.: 63.00 1st Qu.: 47.00 1st Qu.: 57.00 1st Qu.:19.00 1st Qu.:18.00 1st Qu.: 7.0 NL: 95 1st Qu.: 8.375 1st Qu.:-0.7327
## Median :34.0 Median : 55.00 Median : 65.00 Median : 72.00 Median : 55.00 Median : 66.00 Median :25.00 Median :21.00 Median :13.0 Median :12.500 Median : 0.1516
## Mean :33.7 Mean : 61.77 Mean : 67.78 Mean : 75.82 Mean : 56.52 Mean : 69.65 Mean :23.42 Mean :20.74 Mean :14.2 Mean :14.923 Mean : 0.0000
## 3rd Qu.:37.0 3rd Qu.: 90.00 3rd Qu.: 78.00 3rd Qu.: 85.50 3rd Qu.: 63.00 3rd Qu.: 79.00 3rd Qu.:29.00 3rd Qu.:24.00 3rd Qu.:23.0 3rd Qu.:18.500 3rd Qu.: 0.7579
## Max. :40.0 Max. :100.00 Max. :122.00 Max. :180.00 Max. :103.00 Max. :240.00 Max. :32.00 Max. :30.00 Max. :30.0 Max. :45.000 Max. : 1.8696
## NA's :13 NA's :13 NA's :5 NA's :7 NA's :13 NA's :16 NA's :79 NA's :81 NA's :1 NA's :14 NA's :8
## T1PF_TotalZ T1.LetKenZ LK
## Min. :-5.47241 Min. :-1.5995 Min. :-16.000
## 1st Qu.:-0.50335 1st Qu.:-0.8108 1st Qu.: 3.000
## Median : 0.02905 Median :-0.1348 Median : 7.000
## Mean : 0.00000 Mean : 0.0000 Mean : 7.908
## 3rd Qu.: 0.73892 3rd Qu.: 0.9919 3rd Qu.: 13.000
## Max. : 1.62625 Max. : 1.7806 Max. : 27.000
## NA's :5 NA's :1 NA's :801.3.1.2 Variable Description
- Subj - unique subject ID
- School - unique school ID
- Class - unique classroom ID
- Country - country: Netherlands (NL) or Belgium (B)
- T1AgeF - fractional age in years at pretest (T1)
- Gender - gender (male/female)
- Exclude - exclusion criteria (e.g. ADHD or not enough sessions). Participants with Exclude==“no” are used for analyses
- Cond - gaming condition (Passive, Math, Read)
- HandScore - handedness score based on average of 11 questions (z-transformed)
- FamRisk - self reported familal risk for dyslexia
- Lang - language spoken at home (monolingual/multilingual)
- SON_1st - analogies and categories subtest of SON-R 6-40 IQ test (z-transformed)
- hoursPlayed - time on task (read & math only)
- levelsPlayed - number of played levels (read & math only)
- sessionsPlayed - number of played sessions (read & math only)
- MaxLevel - highest level that was played (read & math only)
- MaxLevelS - highest level that was played (as above, but z-transformed)
- itemsSeen - total number of items seen during gameplay (read & math only)
- responsesGiven - number of responses give during gameplay (read & math only)
- T1CF_Total - CELF IV NL: total test score at T1 (raw scores 0-45)
- T1CF_TotalPc - CELF IV NL: total test score at T1 (percentile 0-100)
- T2CF_Total - CELF IV NL: total test score at T2 (raw scores 0-45)
- T2CF_TotalPc - CELF IV NL: total test score at T2 (percentiles 0-100)
- T1PF_Total - PREOF PA: total test score at T1 (raw scores 0-10)
- T1PF_TotalPc - PREOF PA: total test score at T1 (percentiles 0-100)
- T2PF_Total - PREOF PA: total test score at T2 (raw scores 0-10)
- T2PF_TotalPc - PREOF PA:total test score at T2 (percentiles 0-100)
- T1RANcT - Rapid Automatized Naming of colours at T1 (time in seconds)
- T1RANoT - Rapid Automatized Naming of objects at T1 (time in seconds)
- T2RANcT - Rapid Automatized Naming of colours at T2 (time in seconds)
- T2RANoT - Rapid Automatized Naming of objects at T2 (time in seconds)
- T2.LetKen - Computerized letter speech sound identification at T2 (raw scores 0-32)
- T1.LetKen - Computerized letter speech sound identification at T1 (raw scores 0-32)
- T2.LexDec - Computerized written lexical decision at T2 (raw scores 0-32)
- EMT - One minute reading fluency scores at T2 (raw scores in correctly read words per minute)
1.3.1.3 Reliability of offline measures
1.3.1.3.1 Reading fluency
readingdata <- droplevels(subset(BehavDat,EMT<100))
readingdata <- subset(readingdata, select=c(T2EMTaCor, T2EMTbCor))
(r = cor(readingdata$T2EMTaCor,readingdata$T2EMTbCor)) # 0.93 correlation between two lists## [1] 0.9281803((2 * r) / (1 + r)) # 0.96 split half reliability with Spearman Brown correction## [1] 0.9627526psych::alpha(readingdata) # 0.96 based on 272 children##
## Reliability analysis
## Call: psych::alpha(x = readingdata)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.93 0.93 26 0.0046 15 10 0.93
##
## lower alpha upper 95% confidence boundaries
## 0.95 0.96 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## T2EMTaCor 1.00 0.93 0.86 0.93 13 NA 0 0.93
## T2EMTbCor 0.86 0.93 0.86 0.93 13 NA 0 0.93
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## T2EMTaCor 272 0.98 0.98 0.95 0.93 15 10.5
## T2EMTbCor 272 0.98 0.98 0.95 0.93 15 9.81.3.2 Online assessments
Dataframe inGameDat contains the raw data from the in-game assessments that took place during the very first and the very last gaming session.
1.3.2.1 Variable Names
The dataset consists of 25134 rows and 57 columns with the following additional variables:
str(inGameDat[1:18])## 'data.frame': 25134 obs. of 18 variables:
## $ idLevel : int 3984112 3984112 3984112 3984112 4140137 4140137 4140137 4140137 4140137 4140137 ...
## $ stream : Factor w/ 2 levels "T1","T2": 1 1 1 1 2 2 2 2 2 2 ...
## $ lvl : Factor w/ 3 levels "LetKenDifficult",..: 1 1 1 1 2 2 2 2 2 2 ...
## $ selectionsCounts : num 10 10 10 10 10 10 10 10 10 10 ...
## $ selectionsCorrect : num 1 1 1 1 10 10 10 10 10 10 ...
## $ selectionsIncorrect: num 9 9 9 9 0 0 0 0 0 0 ...
## $ idTrial : int 21196635 21196632 21196633 21196634 22792572 22792573 22792574 22792575 22792576 22792577 ...
## $ result : Factor w/ 2 levels "Correct","null": 2 2 2 2 1 1 1 1 1 1 ...
## $ itemCountOnScreen : int 6 6 6 6 10 10 10 10 10 10 ...
## $ correctResponse : Factor w/ 33 levels "a","aa","b","correct",..: 5 31 33 24 5 6 25 12 19 16 ...
## $ playerResponse : Factor w/ 34 levels "a","aa","b","correct",..: 16 19 6 5 5 6 25 12 19 16 ...
## $ correctSelections : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 2 2 2 2 ...
## $ incorrectSelections: num 1 1 1 1 0 0 0 0 0 0 ...
## $ itemsOnScreen : chr "k;d;g;p;f;b" "h;n;u;e;v;f" "e;s;r;f;m;z" "p;d;h;b;t;w" ...
## $ trialRTs : num 3.591 1.467 0.284 4.676 1.962 ...
## $ TrialNum : int 4 1 2 3 1 2 3 4 5 6 ...
## $ prevRT : num 4.676 NA 1.467 0.284 NA ...
## $ Subj : Factor w/ 286 levels "1","2","3","4",..: 70 70 70 70 70 70 70 70 70 70 ...summary(inGameDat[1:18])## idLevel stream lvl selectionsCounts selectionsCorrect selectionsIncorrect idTrial result itemCountOnScreen correctResponse playerResponse
## Min. :3977791 T1:10562 LetKenDifficult: 4635 Min. : 1.00 Min. : 0.000 Min. : 0.00 Min. :21131607 Correct:15953 Min. : 3.000 correct: 6444 correct: 4270
## 1st Qu.:3985919 T2:14572 LetKenEasy :14055 1st Qu.:10.00 1st Qu.: 5.000 1st Qu.: 2.00 1st Qu.:21210963 null : 9181 1st Qu.: 3.000 g : 949 wrong : 2174
## Median :4082142 LexDec : 6444 Median :11.00 Median : 8.000 Median : 4.00 Median :22172822 Median :10.000 p : 949 z : 826
## Mean :4076320 Mean :11.52 Mean : 7.425 Mean : 4.09 Mean :22130302 Mean : 7.853 d : 938 g : 794
## 3rd Qu.:4117278 3rd Qu.:16.00 3rd Qu.:10.000 3rd Qu.: 6.00 3rd Qu.:22541745 3rd Qu.:11.000 z : 933 ij : 737
## Max. :4188568 Max. :16.00 Max. :16.000 Max. :11.00 Max. :23299834 Max. :11.000 ij : 927 d : 735
## (Other):13994 (Other):15598
## correctSelections incorrectSelections itemsOnScreen trialRTs TrialNum prevRT Subj
## 0: 9150 Min. :0.000 Length:25134 Min. : 0.001 Min. : 1.000 Min. : 0.001 30 : 116
## 1:15984 1st Qu.:0.000 Class :character 1st Qu.: 1.816 1st Qu.: 3.000 1st Qu.: 1.806 40 : 116
## Median :0.000 Mode :character Median : 2.901 Median : 6.000 Median : 2.867 48 : 116
## Mean :0.364 Mean : 3.735 Mean : 6.227 Mean : 3.649 54 : 116
## 3rd Qu.:1.000 3rd Qu.: 4.569 3rd Qu.: 9.000 3rd Qu.: 4.504 62 : 116
## Max. :1.000 Max. :44.408 Max. :16.000 Max. :44.408 76 : 116
## NA's :2407 (Other):244381.3.2.2 Variable Description
- idLevel - ingame level ID
- stream - pre- or post test measure (T1/T2)
- lvl - ingame level name
- selectionsCounts - number of targets/distractors selected in a given level ID
- selectionsCorrect - number of correctly selected targets in a given level ID
- selectionsIncorrect - number of mistakenly selected distractors in a given level ID
- idTirla - ingame trial ID
- result - outcome of a single trial/click (correct/null[=incorrect])
- itemCountOnScreen - sum of target+distractors displayed on screen in a givent rial
- correctResponse - the correct response to the task the child has to solve
- playerResponse - the actual response to the task by the child
- correctSelections - number of correctly selected targets in a given trial ID
- incorrectSelections - number of mistakenly selected distractors in a given trial ID
- itemsOnScreen - character representation of the order of graphemes on screen. Written lexical decision task additional encodes order of response buttons (correct/wrong)
- trialRTs - response time of a given trial ID (in seconds)
- TrialNum - sequential number of a given trial ID within a given level ID
- prevRT - response time to the previous trial ID
1.3.2.3 Reliability of online assessments
This section computes the reliability for the letter speech sound identification (LSSI) and written lexical decision (WLD) tasks based on all available data at pre-test. We present Cronbach’s alpha as a measure for internal consistency, as well as split-half reliability. Furthermore, we present values for item based reliability and aggregated by level. Although Henson (2001) suggested that speeded tests violate the assumption of measuring a single underlying construct, we can consider speed as part of the construct of interest (GPC automation and reading fluency).
1.3.2.3.1 LSSI aggregated by level
# Letter speech sound identification. Aggregated by level
# Establish level variable to sort each trial into the level it belongs to
inGameDat$LSSI[inGameDat$itemsOnScreen=="k;d;e;oe;a;n;j;v;r;i"] <- "LK1"
inGameDat$LSSI[inGameDat$itemsOnScreen=="s;ee;b;aa;p;oo;u;w;g;ui;z"] <- "LK2"
inGameDat$LSSI[inGameDat$itemsOnScreen=="uu;m;l;f;h;t;ou;ie;ij;o;eu"] <- "LK3"
inGameDat$LSSI[inGameDat$lvl=="LetKenDifficult"] <- "LK4"
inGameDat$LSSI <- as.factor(inGameDat$LSSI)
inGameDat$correctSelections <- as.integer(inGameDat$correctSelections) - 1
# We have isolated cases of double pre-test assessments. Here we filter those out and only take the first.
assessments <- aggregate(correctSelections~idLevel+Subj+LSSI,subset(inGameDat,stream=="T1"), FUN=sum)
firstAssessed <- aggregate(idLevel~Subj+LSSI, assessments,FUN=min)
assessment <- subset(assessments, idLevel %in% firstAssessed$idLevel)
# internal consistency with cronbach's alpha
LSSI <- reshape(assessment, idvar = "Subj", timevar = "LSSI", direction = "wide")[,c(3,5,7,9)]
LSSIn <- LSSI[complete.cases(LSSI),]
#a = (4 / (4 - 1)) * (1 - (var(LSSIn$correctSelections.LK1) + var(LSSIn$correctSelections.LK2) + var(LSSIn$correctSelections.LK3)+ var(LSSIn$correctSelections.LK4)) / var (LSSIn$correctSelections.LK1 +LSSIn$correctSelections.LK2 + LSSIn$correctSelections.LK3 + LSSIn$correctSelections.LK4)) # compute alpha from formula
score_e <- rowMeans(LSSIn[, c(TRUE, FALSE)],na.rm=T) # with even items
score_o <- rowMeans(LSSIn[, c(FALSE, TRUE)],na.rm=T) # with odd items
r <- cor(score_e, score_o)
(2 * r) / (1 + r) # 0.72 split half reliability with Spearman Brown correction## [1] 0.9078268psych::alpha(LSSI[complete.cases(LSSI),]) # 0.68 Cronbach's alpha##
## Reliability analysis
## Call: psych::alpha(x = LSSI[complete.cases(LSSI), ])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.88 0.7 9.3 0.0096 4.8 2.7 0.69
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## correctSelections.LK1 0.87 0.87 0.82 0.70 6.9 0.013 0.00033 0.70
## correctSelections.LK2 0.86 0.86 0.81 0.68 6.3 0.015 0.00027 0.68
## correctSelections.LK3 0.87 0.88 0.83 0.70 7.1 0.013 0.00201 0.70
## correctSelections.LK4 0.88 0.89 0.84 0.72 7.7 0.012 0.00080 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## correctSelections.LK1 270 0.89 0.88 0.83 0.79 5.3 3.5
## correctSelections.LK2 270 0.90 0.90 0.86 0.82 5.4 3.3
## correctSelections.LK3 270 0.87 0.88 0.82 0.78 4.0 2.9
## correctSelections.LK4 270 0.85 0.86 0.79 0.75 4.6 2.81.3.2.3.2 LSSI by item
# Letter speech sound identification. Single trial data
itemanalysis <- subset(inGameDat, idLevel %in% firstAssessed$idLevel & stream=="T1")
items <- aggregate(correctSelections~correctResponse+Subj,itemanalysis,FUN=sum)
item <- reshape(items, idvar = "Subj", timevar = "correctResponse", direction = "wide")[,-1]
score_e <- rowMeans(item[, c(TRUE, FALSE)],na.rm=T) # with even items
score_o <- rowMeans(item[, c(FALSE, TRUE)],na.rm=T) # with odd items
r <- cor(score_e, score_o)
(2 * r) / (1 + r) # 0.87 split half reliability with Spearman Brown correction## [1] 0.9180898psych::alpha(item) # 0.9 Cronbach's alpha##
## Reliability analysis
## Call: psych::alpha(x = item)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.94 0.95 0.31 14 0.0054 0.63 0.34 0.31
##
## lower alpha upper 95% confidence boundaries
## 0.92 0.93 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## correctSelections.aa 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.b 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.d 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.e 0.93 0.93 0.94 0.30 14 0.0057 0.011 0.30
## correctSelections.ee 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.eu 0.93 0.93 0.94 0.31 14 0.0055 0.012 0.31
## correctSelections.f 0.93 0.93 0.94 0.31 14 0.0055 0.012 0.31
## correctSelections.g 0.93 0.93 0.94 0.30 13 0.0058 0.011 0.30
## correctSelections.h 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.i 0.93 0.93 0.94 0.31 14 0.0055 0.012 0.31
## correctSelections.ie 0.93 0.93 0.94 0.32 14 0.0054 0.012 0.31
## correctSelections.ij 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.j 0.93 0.93 0.94 0.30 13 0.0058 0.011 0.30
## correctSelections.k 0.93 0.93 0.94 0.31 14 0.0055 0.012 0.31
## correctSelections.l 0.93 0.93 0.94 0.32 14 0.0054 0.012 0.32
## correctSelections.m 0.93 0.93 0.94 0.31 14 0.0054 0.012 0.31
## correctSelections.n 0.93 0.93 0.94 0.31 14 0.0055 0.012 0.31
## correctSelections.o 0.93 0.93 0.95 0.32 14 0.0054 0.012 0.32
## correctSelections.oo 0.93 0.93 0.94 0.31 14 0.0055 0.012 0.31
## correctSelections.ou 0.93 0.93 0.94 0.31 14 0.0054 0.012 0.31
## correctSelections.p 0.93 0.93 0.94 0.32 14 0.0053 0.012 0.32
## correctSelections.r 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.s 0.93 0.94 0.95 0.32 14 0.0054 0.012 0.32
## correctSelections.t 0.93 0.93 0.94 0.30 13 0.0057 0.011 0.30
## correctSelections.u 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.ui 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.30
## correctSelections.uu 0.93 0.94 0.95 0.32 15 0.0054 0.011 0.32
## correctSelections.v 0.93 0.93 0.94 0.31 14 0.0057 0.012 0.30
## correctSelections.w 0.93 0.93 0.94 0.31 14 0.0056 0.012 0.31
## correctSelections.z 0.93 0.93 0.94 0.31 14 0.0056 0.013 0.30
## correctSelections.a 0.93 0.93 0.94 0.31 14 0.0055 0.012 0.31
## correctSelections.oe 0.93 0.93 0.94 0.31 14 0.0055 0.012 0.31
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## correctSelections.aa 265 0.64 0.64 0.63 0.60 0.58 0.49
## correctSelections.b 281 0.62 0.63 0.62 0.60 0.57 0.50
## correctSelections.d 283 0.65 0.62 0.61 0.59 0.89 0.77
## correctSelections.e 270 0.74 0.74 0.74 0.71 0.47 0.50
## correctSelections.ee 190 0.68 0.69 0.69 0.66 0.54 0.50
## correctSelections.eu 247 0.46 0.48 0.45 0.44 0.21 0.41
## correctSelections.f 259 0.52 0.55 0.53 0.50 0.36 0.48
## correctSelections.g 282 0.77 0.75 0.75 0.74 0.88 0.83
## correctSelections.h 280 0.65 0.65 0.63 0.62 0.40 0.49
## correctSelections.i 250 0.47 0.48 0.46 0.43 0.68 0.47
## correctSelections.ie 279 0.44 0.45 0.43 0.41 0.27 0.44
## correctSelections.ij 281 0.65 0.62 0.61 0.60 0.77 0.80
## correctSelections.j 274 0.76 0.76 0.76 0.74 0.91 0.83
## correctSelections.k 227 0.46 0.48 0.46 0.43 0.81 0.39
## correctSelections.l 282 0.44 0.45 0.42 0.39 0.57 0.50
## correctSelections.m 262 0.49 0.49 0.46 0.44 1.00 0.78
## correctSelections.n 242 0.61 0.60 0.59 0.57 0.67 0.47
## correctSelections.o 235 0.39 0.42 0.39 0.36 0.71 0.45
## correctSelections.oo 217 0.55 0.55 0.54 0.51 0.74 0.44
## correctSelections.ou 244 0.50 0.48 0.46 0.45 0.45 0.67
## correctSelections.p 282 0.47 0.44 0.41 0.40 0.83 0.82
## correctSelections.r 260 0.62 0.62 0.61 0.58 0.71 0.46
## correctSelections.s 273 0.38 0.41 0.38 0.35 0.84 0.36
## correctSelections.t 213 0.73 0.75 0.75 0.73 0.50 0.50
## correctSelections.u 265 0.64 0.63 0.62 0.61 0.70 0.72
## correctSelections.ui 239 0.64 0.63 0.62 0.59 0.54 0.50
## correctSelections.uu 274 0.32 0.33 0.29 0.28 0.23 0.42
## correctSelections.v 284 0.70 0.68 0.67 0.65 1.00 0.82
## correctSelections.w 275 0.63 0.64 0.63 0.61 0.37 0.48
## correctSelections.z 282 0.64 0.61 0.59 0.58 1.18 0.81
## correctSelections.a 215 0.58 0.60 0.58 0.55 0.59 0.49
## correctSelections.oe 179 0.56 0.56 0.54 0.52 0.64 0.48
##
## Non missing response frequency for each item
## 0 1 2 miss
## correctSelections.aa 0.42 0.58 0.00 0.07
## correctSelections.b 0.43 0.57 0.00 0.01
## correctSelections.d 0.36 0.40 0.25 0.01
## correctSelections.e 0.53 0.47 0.00 0.05
## correctSelections.ee 0.46 0.54 0.00 0.33
## correctSelections.eu 0.79 0.21 0.00 0.13
## correctSelections.f 0.64 0.36 0.00 0.09
## correctSelections.g 0.41 0.29 0.29 0.01
## correctSelections.h 0.60 0.40 0.00 0.02
## correctSelections.i 0.32 0.68 0.00 0.12
## correctSelections.ie 0.73 0.27 0.00 0.02
## correctSelections.ij 0.46 0.31 0.23 0.01
## correctSelections.j 0.40 0.30 0.30 0.04
## correctSelections.k 0.19 0.81 0.00 0.20
## correctSelections.l 0.43 0.57 0.00 0.01
## correctSelections.m 0.30 0.39 0.31 0.08
## correctSelections.n 0.33 0.67 0.00 0.15
## correctSelections.o 0.29 0.71 0.00 0.18
## correctSelections.oo 0.26 0.74 0.00 0.24
## correctSelections.ou 0.66 0.24 0.10 0.14
## correctSelections.p 0.43 0.31 0.26 0.01
## correctSelections.r 0.29 0.71 0.00 0.09
## correctSelections.s 0.16 0.84 0.00 0.04
## correctSelections.t 0.50 0.50 0.00 0.25
## correctSelections.u 0.45 0.39 0.15 0.07
## correctSelections.ui 0.46 0.54 0.00 0.16
## correctSelections.uu 0.77 0.23 0.00 0.04
## correctSelections.v 0.33 0.33 0.33 0.00
## correctSelections.w 0.63 0.37 0.00 0.04
## correctSelections.z 0.25 0.32 0.43 0.01
## correctSelections.a 0.41 0.59 0.00 0.25
## correctSelections.oe 0.36 0.64 0.00 0.371.3.2.3.3 Written Lexical Decision aggregated by level
# We have to subset data and remove bos and kos
WLDreliab <- droplevels(subset(inGameDat, lvl=="LexDec"))
WLDreliab <- subset(WLDreliab, itemsOnScreen != "bos;correct;wrong" & itemsOnScreen !="kos;wrong;correct")
# Check how many unique levelIDs per subject
WLDlevels <- aggregate(idLevel~Subj,WLDreliab,FUN=function(x) NROW(unique(x))) # only one subject has 3 times this level #StM105 - solution: remove trial 3991226
WLDreliab <- subset(WLDreliab, idLevel != "3991226") # only 2 levels per person!
# now split up in min and max level per person
WLDlevel <- aggregate(idLevel~Subj,WLDreliab,FUN=min)
WLDlevel$max <- aggregate(idLevel~Subj,WLDreliab,FUN=max)$idLevel
WLDlevel$diff <- WLDlevel$max - WLDlevel$idLevel
WLDinclude <- droplevels(subset(WLDlevel, diff>0)$Subj)
WLDrelia <- droplevels(subset(WLDreliab, Subj %in% WLDinclude))
WLDassess <- aggregate(correctSelections~Subj+idLevel,WLDrelia,FUN=sum)
WLDassess$lvl <- "2"
WLDassess$lvl[WLDassess$idLevel %in% WLDlevel$idLevel] <- "1"
WLDassess$lvl <- as.factor(WLDassess$lvl)
WLDassess$idLevel <- NULL
WLD <- reshape(WLDassess, idvar = "Subj", timevar = "lvl", direction = "wide")[,-1]
r <- cor(WLD$correctSelections.1,WLD$correctSelections.2)
((2 * r) / (1 + r)) # 0.65 split-half reliability with Spearman Brown correction## [1] 0.6394148psych::alpha(WLD) # 0.65 Cronbach's alpha##
## Reliability analysis
## Call: psych::alpha(x = WLD)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.64 0.64 0.47 0.47 1.8 0.051 9.6 2 0.47
##
## lower alpha upper 95% confidence boundaries
## 0.54 0.64 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## correctSelections.1 0.42 0.47 0.22 0.47 0.89 NA 0 0.47
## correctSelections.2 0.53 0.47 0.22 0.47 0.89 NA 0 0.47
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## correctSelections.1 199 0.84 0.86 0.59 0.47 8.8 2.2
## correctSelections.2 199 0.88 0.86 0.59 0.47 10.4 2.51.3.2.3.4 Written Lexical Decision by item
items <- aggregate(correctSelections~itemsOnScreen+Subj,WLDreliab,FUN=sum)
item <- reshape(items, idvar = "Subj", timevar = "itemsOnScreen", direction = "wide")[,-1]
score_e <- rowMeans(item[, c(TRUE, FALSE)],na.rm=T) # with even items
score_o <- rowMeans(item[, c(FALSE, TRUE)],na.rm=T) # with odd items
r <- cor(score_e, score_o)
(2 * r) / (1 + r) # 0.65 split half reliability with Spearman Brown correction## [1] 0.6522993psych::alpha(item,check.keys=TRUE) # Cronbach's alpha # 0.68##
## Reliability analysis
## Call: psych::alpha(x = item, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.68 0.68 0.74 0.066 2.1 0.031 0.64 0.14 0.067
##
## lower alpha upper 95% confidence boundaries
## 0.62 0.68 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## correctSelections.ag;wrong;correct 0.68 0.68 0.74 0.067 2.1 0.031 0.0103 0.067
## correctSelections.ak;wrong;correct 0.67 0.67 0.73 0.064 2.0 0.033 0.0097 0.065
## correctSelections.al;correct;wrong 0.68 0.68 0.74 0.067 2.1 0.031 0.0101 0.068
## correctSelections.as;correct;wrong- 0.68 0.67 0.74 0.066 2.1 0.032 0.0100 0.067
## correctSelections.bem;wrong;correct 0.67 0.66 0.73 0.064 2.0 0.033 0.0100 0.065
## correctSelections.daf;wrong;correct 0.67 0.67 0.74 0.065 2.0 0.032 0.0101 0.066
## correctSelections.dag;correct;wrong 0.68 0.67 0.74 0.066 2.1 0.032 0.0100 0.066
## correctSelections.de;correct;wrong 0.68 0.67 0.74 0.066 2.1 0.032 0.0100 0.068
## correctSelections.hat;wrong;correct- 0.69 0.69 0.75 0.070 2.2 0.030 0.0099 0.072
## correctSelections.hem;correct;wrong- 0.69 0.69 0.75 0.071 2.2 0.030 0.0096 0.073
## correctSelections.hend;wrong;correct 0.67 0.67 0.74 0.065 2.0 0.032 0.0099 0.066
## correctSelections.hond;correct;wrong 0.69 0.68 0.74 0.069 2.2 0.030 0.0095 0.068
## correctSelections.jal;wrong;correct 0.66 0.66 0.73 0.063 1.9 0.033 0.0098 0.065
## correctSelections.jas;correct;wrong 0.68 0.68 0.74 0.067 2.1 0.031 0.0101 0.068
## correctSelections.ke;wrong;correct 0.67 0.66 0.73 0.063 1.9 0.033 0.0099 0.062
## correctSelections.kels;wrong;correct 0.67 0.67 0.73 0.065 2.0 0.032 0.0098 0.067
## correctSelections.kerk;correct;wrong 0.68 0.68 0.74 0.068 2.1 0.031 0.0097 0.067
## correctSelections.mat;correct;wrong 0.68 0.68 0.74 0.069 2.1 0.031 0.0101 0.068
## correctSelections.neul;wrong;correct 0.66 0.66 0.73 0.062 1.9 0.033 0.0100 0.062
## correctSelections.neus;correct;wrong 0.68 0.67 0.73 0.066 2.0 0.032 0.0098 0.067
## correctSelections.pe;wrong;correct 0.67 0.66 0.73 0.062 1.9 0.033 0.0098 0.062
## correctSelections.te;correct;wrong- 0.68 0.68 0.74 0.068 2.1 0.031 0.0100 0.068
## correctSelections.tijd;correct;wrong- 0.69 0.69 0.75 0.071 2.2 0.030 0.0095 0.072
## correctSelections.toed;wrong;correct 0.67 0.66 0.73 0.063 2.0 0.033 0.0097 0.065
## correctSelections.ves;wrong;correct 0.66 0.65 0.72 0.061 1.9 0.034 0.0098 0.062
## correctSelections.vis;correct;wrong 0.68 0.67 0.74 0.066 2.1 0.032 0.0098 0.068
## correctSelections.zeep;correct;wrong 0.68 0.67 0.74 0.067 2.1 0.032 0.0098 0.068
## correctSelections.zeup;wrong;correct 0.65 0.65 0.72 0.060 1.9 0.034 0.0096 0.061
## correctSelections.zoof;wrong;correct 0.67 0.66 0.73 0.064 2.0 0.033 0.0101 0.063
## correctSelections.zoon;correct;wrong 0.69 0.69 0.75 0.070 2.2 0.031 0.0099 0.069
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## correctSelections.ag;wrong;correct 204 0.278 0.256 0.19277 0.15690 0.58 0.49
## correctSelections.ak;wrong;correct 204 0.398 0.389 0.36422 0.29914 0.74 0.44
## correctSelections.al;correct;wrong 204 0.270 0.260 0.21142 0.15394 0.48 0.50
## correctSelections.as;correct;wrong- 204 0.298 0.288 0.23713 0.19484 0.53 0.50
## correctSelections.bem;wrong;correct 197 0.405 0.392 0.35904 0.29989 0.55 0.50
## correctSelections.daf;wrong;correct 202 0.362 0.362 0.31545 0.26339 0.54 0.50
## correctSelections.dag;correct;wrong 203 0.283 0.292 0.24021 0.18496 0.82 0.38
## correctSelections.de;correct;wrong 199 0.271 0.300 0.25321 0.18152 0.81 0.39
## correctSelections.hat;wrong;correct- 203 0.117 0.113 0.02773 -0.00043 0.67 0.47
## correctSelections.hem;correct;wrong- 199 0.084 0.080 -0.00066 -0.01901 0.27 0.44
## correctSelections.hend;wrong;correct 199 0.349 0.332 0.28882 0.23706 0.49 0.50
## correctSelections.hond;correct;wrong 199 0.158 0.156 0.09002 0.04020 0.58 0.50
## correctSelections.jal;wrong;correct 199 0.479 0.452 0.43828 0.37906 0.49 0.50
## correctSelections.jas;correct;wrong 204 0.241 0.248 0.19339 0.14561 0.80 0.40
## correctSelections.ke;wrong;correct 199 0.442 0.441 0.41885 0.34877 0.72 0.45
## correctSelections.kels;wrong;correct 204 0.391 0.358 0.32933 0.28195 0.59 0.49
## correctSelections.kerk;correct;wrong 199 0.233 0.240 0.19247 0.12589 0.71 0.46
## correctSelections.mat;correct;wrong 199 0.152 0.188 0.11550 0.07572 0.88 0.32
## correctSelections.neul;wrong;correct 204 0.490 0.481 0.46748 0.39163 0.55 0.50
## correctSelections.neus;correct;wrong 204 0.313 0.318 0.28570 0.20894 0.71 0.46
## correctSelections.pe;wrong;correct 204 0.448 0.474 0.45946 0.38127 0.81 0.39
## correctSelections.te;correct;wrong- 199 0.234 0.234 0.17766 0.12518 0.68 0.47
## correctSelections.tijd;correct;wrong- 199 0.102 0.087 0.00346 -0.00971 0.34 0.48
## correctSelections.toed;wrong;correct 198 0.446 0.433 0.41241 0.34237 0.54 0.50
## correctSelections.ves;wrong;correct 199 0.529 0.516 0.51673 0.43206 0.52 0.50
## correctSelections.vis;correct;wrong 198 0.236 0.286 0.24482 0.17923 0.93 0.25
## correctSelections.zeep;correct;wrong 199 0.244 0.279 0.24111 0.17393 0.89 0.31
## correctSelections.zeup;wrong;correct 199 0.568 0.557 0.56551 0.48380 0.59 0.49
## correctSelections.zoof;wrong;correct 202 0.413 0.401 0.36866 0.30714 0.70 0.46
## correctSelections.zoon;correct;wrong 205 0.128 0.132 0.05033 0.02824 0.76 0.43
##
## Non missing response frequency for each item
## 0 1 miss
## correctSelections.ag;wrong;correct 0.42 0.58 0.00
## correctSelections.ak;wrong;correct 0.26 0.74 0.00
## correctSelections.al;correct;wrong 0.52 0.48 0.00
## correctSelections.as;correct;wrong 0.53 0.47 0.00
## correctSelections.bem;wrong;correct 0.45 0.55 0.04
## correctSelections.daf;wrong;correct 0.46 0.54 0.01
## correctSelections.dag;correct;wrong 0.18 0.82 0.01
## correctSelections.de;correct;wrong 0.19 0.81 0.03
## correctSelections.hat;wrong;correct 0.67 0.33 0.01
## correctSelections.hem;correct;wrong 0.27 0.73 0.03
## correctSelections.hend;wrong;correct 0.51 0.49 0.03
## correctSelections.hond;correct;wrong 0.42 0.58 0.03
## correctSelections.jal;wrong;correct 0.51 0.49 0.03
## correctSelections.jas;correct;wrong 0.20 0.80 0.00
## correctSelections.ke;wrong;correct 0.28 0.72 0.03
## correctSelections.kels;wrong;correct 0.41 0.59 0.00
## correctSelections.kerk;correct;wrong 0.29 0.71 0.03
## correctSelections.mat;correct;wrong 0.12 0.88 0.03
## correctSelections.neul;wrong;correct 0.45 0.55 0.00
## correctSelections.neus;correct;wrong 0.29 0.71 0.00
## correctSelections.pe;wrong;correct 0.19 0.81 0.00
## correctSelections.te;correct;wrong 0.68 0.32 0.03
## correctSelections.tijd;correct;wrong 0.34 0.66 0.03
## correctSelections.toed;wrong;correct 0.46 0.54 0.03
## correctSelections.ves;wrong;correct 0.48 0.52 0.03
## correctSelections.vis;correct;wrong 0.07 0.93 0.03
## correctSelections.zeep;correct;wrong 0.11 0.89 0.03
## correctSelections.zeup;wrong;correct 0.41 0.59 0.03
## correctSelections.zoof;wrong;correct 0.30 0.70 0.01
## correctSelections.zoon;correct;wrong 0.24 0.76 0.001.4 Analyses
1.4.1 Descriptives
Descriptives for the 247 children included in the analyses by Condition, Country, Gender and Familal Risk.
1.4.1.1 Tables by Condition, Country, Gender and status of familial risk
TableDat <- droplevels(subset(BehavDat, Exclude == "no"))
tab <- tableby(Cond ~ EMT + FamRisk + Country + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_TotalPc + T1PF_TotalPc + T1.LetKen + T1RANcT + T1RANoT, data = TableDat, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | Math (N=104) | Passive (N=52) | Read (N=91) | Total (N=247) |
## |:-----------------|:---------------------------:|:---------------------:|:----------------------------:|:---------------------------:|
## |EMT | | | | |
## |- Mean (SD) | 17.34 (11.34) | 10.05 (4.59) | 15.43 (9.30) | 15.08 (9.85) |
## |- Median (Range) | 14.75 (1.00, 45.00) | 10.50 (2.50, 22.00) | 13.50 (1.00, 45.00) | 12.50 (1.00, 45.00) |
## |FamRisk | | | | |
## |- no | 86 (82.7%) | 45 (86.5%) | 75 (82.4%) | 206 (83.4%) |
## |- yes | 18 (17.3%) | 7 (13.5%) | 16 (17.6%) | 41 (16.6%) |
## |Country | | | | |
## |- B | 56 (53.8%) | 52 (100.0%) | 53 (58.2%) | 161 (65.2%) |
## |- NL | 48 (46.2%) | 0 (0.0%) | 38 (41.8%) | 86 (34.8%) |
## |T1AgeF | | | | |
## |- Mean (SD) | 6.28 (0.31) | 6.20 (0.30) | 6.23 (0.34) | 6.25 (0.32) |
## |- Median (Range) | 6.32 (5.55, 7.07) | 6.17 (5.68, 6.80) | 6.23 (5.46, 7.39) | 6.26 (5.46, 7.39) |
## |HandScore | | | | |
## |- Mean (SD) | 0.09 (0.94) | -0.27 (1.27) | -0.01 (0.96) | -0.02 (1.03) |
## |- Median (Range) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) |
## |Gender | | | | |
## |- female | 44 (42.3%) | 21 (40.4%) | 47 (51.6%) | 112 (45.3%) |
## |- male | 60 (57.7%) | 31 (59.6%) | 44 (48.4%) | 135 (54.7%) |
## |Lang | | | | |
## |- mono | 89 (85.6%) | 46 (88.5%) | 87 (95.6%) | 222 (89.9%) |
## |- multi | 15 (14.4%) | 6 (11.5%) | 4 (4.4%) | 25 (10.1%) |
## |SON_1st | | | | |
## |- Mean (SD) | 0.11 (0.96) | -0.29 (0.99) | 0.17 (1.00) | 0.05 (0.99) |
## |- Median (Range) | 0.08 (-2.37, 3.33) | -0.54 (-2.37, 1.70) | 0.08 (-2.37, 4.96) | 0.08 (-2.37, 4.96) |
## |hoursPlayed | | | | |
## |- Mean (SD) | 3.38 (0.89) | NA | 3.59 (0.84) | 3.48 (0.87) |
## |- Median (Range) | 3.14 (1.96, 5.24) | NA | 3.61 (1.68, 5.23) | 3.40 (1.68, 5.24) |
## |levelsPlayed | | | | |
## |- Mean (SD) | 401.81 (153.29) | NA | 235.14 (61.40) | 324.03 (145.51) |
## |- Median (Range) | 358.50 (132.00, 1026.00) | NA | 232.00 (101.00, 377.00) | 289.00 (101.00, 1026.00) |
## |sessionsPlayed | | | | |
## |- Mean (SD) | 26.74 (3.96) | NA | 27.93 (4.94) | 27.30 (4.47) |
## |- Median (Range) | 26.00 (18.00, 35.00) | NA | 27.00 (16.00, 37.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | | |
## |- Mean (SD) | 136.42 (27.86) | NA | 125.19 (59.63) | 131.18 (45.75) |
## |- Median (Range) | 136.00 (39.00, 178.00) | NA | 109.00 (38.00, 265.00) | 135.00 (38.00, 265.00) |
## |itemsSeen | | | | |
## |- Mean (SD) | 8857.08 (4167.68) | NA | 11036.49 (3918.98) | 9874.14 (4187.51) |
## |- Median (Range) | 7666.00 (2827.00, 22782.00) | NA | 10838.00 (2983.00, 20113.00) | 8778.00 (2827.00, 22782.00) |
## |responsesGiven | | | | |
## |- Mean (SD) | 3817.48 (2040.07) | NA | 2671.35 (810.57) | 3282.62 (1686.15) |
## |- Median (Range) | 3249.00 (1019.00, 10877.00) | NA | 2626.00 (948.00, 4522.00) | 2943.00 (948.00, 10877.00) |
## |T1CF_TotalPc | | | | |
## |- Mean (SD) | 48.40 (24.21) | 41.14 (23.91) | 47.41 (25.05) | 46.53 (24.52) |
## |- Median (Range) | 50.00 (1.00, 91.00) | 37.00 (1.00, 95.00) | 37.00 (5.00, 99.00) | 50.00 (1.00, 99.00) |
## |T1PF_TotalPc | | | | |
## |- Mean (SD) | 54.52 (29.52) | 49.52 (25.94) | 56.07 (27.95) | 54.04 (28.22) |
## |- Median (Range) | 55.00 (0.00, 100.00) | 47.50 (0.00, 97.50) | 55.00 (0.00, 100.00) | 55.00 (0.00, 100.00) |
## |T1.LetKen | | | | |
## |- Mean (SD) | 16.35 (9.48) | 11.27 (5.30) | 14.08 (9.53) | 14.46 (8.97) |
## |- Median (Range) | 15.50 (0.00, 30.00) | 10.00 (0.00, 25.00) | 13.00 (0.00, 30.00) | 13.50 (0.00, 30.00) |
## |T1RANcT | | | | |
## |- Mean (SD) | 63.32 (14.50) | 70.88 (15.92) | 68.46 (17.23) | 66.81 (16.09) |
## |- Median (Range) | 61.50 (40.00, 107.00) | 67.50 (48.00, 115.00) | 66.00 (39.00, 122.00) | 65.00 (39.00, 122.00) |
## |T1RANoT | | | | |
## |- Mean (SD) | 73.55 (16.51) | 75.56 (17.49) | 75.05 (21.26) | 74.53 (18.55) |
## |- Median (Range) | 70.00 (40.00, 126.00) | 76.50 (46.00, 127.00) | 72.00 (43.00, 180.00) | 72.00 (40.00, 180.00) |tab <- tableby(Country ~ EMT + FamRisk + Cond + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_TotalPc + T1PF_TotalPc + T1.LetKen + T1RANcT + T1RANoT, data = TableDat, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | B (N=161) | NL (N=86) | Total (N=247) |
## |:-----------------|:----------------------------:|:---------------------------:|:---------------------------:|
## |EMT | | | |
## |- Mean (SD) | 11.58 (6.83) | 21.80 (11.23) | 15.08 (9.85) |
## |- Median (Range) | 10.50 (1.00, 41.50) | 18.00 (3.50, 45.00) | 12.50 (1.00, 45.00) |
## |FamRisk | | | |
## |- no | 137 (85.1%) | 69 (80.2%) | 206 (83.4%) |
## |- yes | 24 (14.9%) | 17 (19.8%) | 41 (16.6%) |
## |Cond | | | |
## |- Math | 56 (34.8%) | 48 (55.8%) | 104 (42.1%) |
## |- Passive | 52 (32.3%) | 0 (0.0%) | 52 (21.1%) |
## |- Read | 53 (32.9%) | 38 (44.2%) | 91 (36.8%) |
## |T1AgeF | | | |
## |- Mean (SD) | 6.26 (0.33) | 6.23 (0.31) | 6.25 (0.32) |
## |- Median (Range) | 6.26 (5.68, 7.39) | 6.26 (5.46, 6.83) | 6.26 (5.46, 7.39) |
## |HandScore | | | |
## |- Mean (SD) | -0.05 (1.03) | 0.03 (1.03) | -0.02 (1.03) |
## |- Median (Range) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) |
## |Gender | | | |
## |- female | 66 (41.0%) | 46 (53.5%) | 112 (45.3%) |
## |- male | 95 (59.0%) | 40 (46.5%) | 135 (54.7%) |
## |Lang | | | |
## |- mono | 150 (93.2%) | 72 (83.7%) | 222 (89.9%) |
## |- multi | 11 (6.8%) | 14 (16.3%) | 25 (10.1%) |
## |SON_1st | | | |
## |- Mean (SD) | -0.17 (0.97) | 0.44 (0.93) | 0.05 (0.99) |
## |- Median (Range) | -0.33 (-2.37, 3.33) | 0.48 (-1.55, 4.96) | 0.08 (-2.37, 4.96) |
## |hoursPlayed | | | |
## |- Mean (SD) | 3.90 (0.78) | 2.94 (0.66) | 3.48 (0.87) |
## |- Median (Range) | 4.07 (1.97, 5.24) | 2.93 (1.68, 4.95) | 3.40 (1.68, 5.24) |
## |levelsPlayed | | | |
## |- Mean (SD) | 355.40 (174.26) | 284.27 (82.96) | 324.03 (145.51) |
## |- Median (Range) | 309.00 (119.00, 1026.00) | 280.00 (101.00, 607.00) | 289.00 (101.00, 1026.00) |
## |sessionsPlayed | | | |
## |- Mean (SD) | 27.78 (5.21) | 26.69 (3.23) | 27.30 (4.47) |
## |- Median (Range) | 26.00 (16.00, 37.00) | 27.00 (20.00, 34.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | |
## |- Mean (SD) | 119.65 (43.14) | 145.79 (45.02) | 131.18 (45.75) |
## |- Median (Range) | 128.00 (38.00, 265.00) | 136.00 (62.00, 265.00) | 135.00 (38.00, 265.00) |
## |itemsSeen | | | |
## |- Mean (SD) | 10518.28 (4097.98) | 9057.73 (4180.63) | 9874.14 (4187.51) |
## |- Median (Range) | 10010.00 (2827.00, 22782.00) | 7844.00 (3304.00, 20113.00) | 8778.00 (2827.00, 22782.00) |
## |responsesGiven | | | |
## |- Mean (SD) | 3695.00 (2001.38) | 2759.95 (951.37) | 3282.62 (1686.15) |
## |- Median (Range) | 3090.00 (948.00, 10877.00) | 2686.00 (999.00, 6984.00) | 2943.00 (948.00, 10877.00) |
## |T1CF_TotalPc | | | |
## |- Mean (SD) | 38.31 (22.56) | 61.55 (20.63) | 46.53 (24.52) |
## |- Median (Range) | 37.00 (1.00, 95.00) | 63.00 (9.00, 99.00) | 50.00 (1.00, 99.00) |
## |T1PF_TotalPc | | | |
## |- Mean (SD) | 46.46 (26.89) | 68.23 (25.11) | 54.04 (28.22) |
## |- Median (Range) | 42.50 (0.00, 100.00) | 75.00 (7.50, 100.00) | 55.00 (0.00, 100.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 9.59 (6.27) | 23.50 (5.60) | 14.46 (8.97) |
## |- Median (Range) | 8.00 (0.00, 26.00) | 25.00 (2.00, 30.00) | 13.50 (0.00, 30.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 71.05 (15.97) | 58.86 (13.09) | 66.81 (16.09) |
## |- Median (Range) | 69.00 (40.00, 122.00) | 57.50 (39.00, 102.00) | 65.00 (39.00, 122.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 77.86 (19.83) | 68.35 (14.00) | 74.53 (18.55) |
## |- Median (Range) | 76.00 (46.00, 180.00) | 67.00 (40.00, 126.00) | 72.00 (40.00, 180.00) |tab <- tableby(Gender ~ EMT + FamRisk + Cond + T1AgeF + HandScore + Country + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_TotalPc + T1PF_TotalPc + T1.LetKen + T1RANcT + T1RANoT, data = TableDat, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | female (N=112) | male (N=135) | Total (N=247) |
## |:-----------------|:---------------------------:|:---------------------------:|:---------------------------:|
## |EMT | | | |
## |- Mean (SD) | 17.00 (10.60) | 13.52 (8.94) | 15.08 (9.85) |
## |- Median (Range) | 14.75 (1.50, 45.00) | 11.50 (1.00, 45.00) | 12.50 (1.00, 45.00) |
## |FamRisk | | | |
## |- no | 92 (82.1%) | 114 (84.4%) | 206 (83.4%) |
## |- yes | 20 (17.9%) | 21 (15.6%) | 41 (16.6%) |
## |Cond | | | |
## |- Math | 44 (39.3%) | 60 (44.4%) | 104 (42.1%) |
## |- Passive | 21 (18.8%) | 31 (23.0%) | 52 (21.1%) |
## |- Read | 47 (42.0%) | 44 (32.6%) | 91 (36.8%) |
## |T1AgeF | | | |
## |- Mean (SD) | 6.22 (0.33) | 6.26 (0.31) | 6.25 (0.32) |
## |- Median (Range) | 6.22 (5.46, 7.39) | 6.30 (5.55, 7.26) | 6.26 (5.46, 7.39) |
## |HandScore | | | |
## |- Mean (SD) | 0.05 (0.97) | -0.08 (1.08) | -0.02 (1.03) |
## |- Median (Range) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) |
## |Country | | | |
## |- B | 66 (58.9%) | 95 (70.4%) | 161 (65.2%) |
## |- NL | 46 (41.1%) | 40 (29.6%) | 86 (34.8%) |
## |Lang | | | |
## |- mono | 99 (88.4%) | 123 (91.1%) | 222 (89.9%) |
## |- multi | 13 (11.6%) | 12 (8.9%) | 25 (10.1%) |
## |SON_1st | | | |
## |- Mean (SD) | 0.19 (0.98) | -0.08 (0.99) | 0.05 (0.99) |
## |- Median (Range) | 0.08 (-2.37, 4.96) | 0.08 (-2.37, 2.52) | 0.08 (-2.37, 4.96) |
## |hoursPlayed | | | |
## |- Mean (SD) | 3.50 (0.90) | 3.46 (0.85) | 3.48 (0.87) |
## |- Median (Range) | 3.40 (1.68, 5.24) | 3.37 (1.97, 5.18) | 3.40 (1.68, 5.24) |
## |levelsPlayed | | | |
## |- Mean (SD) | 302.49 (120.42) | 342.88 (162.61) | 324.03 (145.51) |
## |- Median (Range) | 275.00 (101.00, 694.00) | 310.50 (119.00, 1026.00) | 289.00 (101.00, 1026.00) |
## |sessionsPlayed | | | |
## |- Mean (SD) | 27.40 (4.48) | 27.21 (4.48) | 27.30 (4.47) |
## |- Median (Range) | 27.00 (16.00, 37.00) | 26.00 (19.00, 37.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | |
## |- Mean (SD) | 133.13 (48.22) | 129.47 (43.65) | 131.18 (45.75) |
## |- Median (Range) | 135.00 (38.00, 265.00) | 135.50 (41.00, 265.00) | 135.00 (38.00, 265.00) |
## |itemsSeen | | | |
## |- Mean (SD) | 9722.09 (4018.76) | 10007.18 (4344.79) | 9874.14 (4187.51) |
## |- Median (Range) | 9286.00 (2827.00, 18528.00) | 8688.50 (2983.00, 22782.00) | 8778.00 (2827.00, 22782.00) |
## |responsesGiven | | | |
## |- Mean (SD) | 3059.71 (1416.44) | 3477.66 (1876.05) | 3282.62 (1686.15) |
## |- Median (Range) | 2859.00 (999.00, 8746.00) | 3074.00 (948.00, 10877.00) | 2943.00 (948.00, 10877.00) |
## |T1CF_TotalPc | | | |
## |- Mean (SD) | 52.00 (24.11) | 42.02 (24.03) | 46.53 (24.52) |
## |- Median (Range) | 50.00 (5.00, 99.00) | 37.00 (1.00, 95.00) | 50.00 (1.00, 99.00) |
## |T1PF_TotalPc | | | |
## |- Mean (SD) | 60.83 (28.01) | 48.41 (27.23) | 54.04 (28.22) |
## |- Median (Range) | 57.50 (2.50, 100.00) | 47.50 (0.00, 100.00) | 55.00 (0.00, 100.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 16.84 (9.24) | 12.50 (8.28) | 14.46 (8.97) |
## |- Median (Range) | 18.00 (1.00, 30.00) | 10.00 (0.00, 30.00) | 13.50 (0.00, 30.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 64.37 (15.01) | 68.83 (16.72) | 66.81 (16.09) |
## |- Median (Range) | 63.00 (39.00, 115.00) | 66.00 (40.00, 122.00) | 65.00 (39.00, 122.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 71.28 (17.44) | 77.21 (19.06) | 74.53 (18.55) |
## |- Median (Range) | 69.00 (43.00, 127.00) | 76.00 (40.00, 180.00) | 72.00 (40.00, 180.00) |tab <- tableby(FamRisk ~ EMT + Country + Cond + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_TotalPc + T1PF_TotalPc + T1.LetKen + T1RANcT + T1RANoT, data = TableDat, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | no (N=206) | yes (N=41) | Total (N=247) |
## |:-----------------|:---------------------------:|:---------------------------:|:---------------------------:|
## |EMT | | | |
## |- Mean (SD) | 15.15 (9.68) | 14.73 (10.80) | 15.08 (9.85) |
## |- Median (Range) | 12.50 (1.00, 45.00) | 12.50 (1.00, 45.00) | 12.50 (1.00, 45.00) |
## |Country | | | |
## |- B | 137 (66.5%) | 24 (58.5%) | 161 (65.2%) |
## |- NL | 69 (33.5%) | 17 (41.5%) | 86 (34.8%) |
## |Cond | | | |
## |- Math | 86 (41.7%) | 18 (43.9%) | 104 (42.1%) |
## |- Passive | 45 (21.8%) | 7 (17.1%) | 52 (21.1%) |
## |- Read | 75 (36.4%) | 16 (39.0%) | 91 (36.8%) |
## |T1AgeF | | | |
## |- Mean (SD) | 6.25 (0.32) | 6.23 (0.34) | 6.25 (0.32) |
## |- Median (Range) | 6.28 (5.46, 7.26) | 6.22 (5.76, 7.39) | 6.26 (5.46, 7.39) |
## |HandScore | | | |
## |- Mean (SD) | -0.02 (1.05) | -0.03 (0.94) | -0.02 (1.03) |
## |- Median (Range) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) |
## |Gender | | | |
## |- female | 92 (44.7%) | 20 (48.8%) | 112 (45.3%) |
## |- male | 114 (55.3%) | 21 (51.2%) | 135 (54.7%) |
## |Lang | | | |
## |- mono | 186 (90.3%) | 36 (87.8%) | 222 (89.9%) |
## |- multi | 20 (9.7%) | 5 (12.2%) | 25 (10.1%) |
## |SON_1st | | | |
## |- Mean (SD) | 0.03 (0.97) | 0.12 (1.11) | 0.05 (0.99) |
## |- Median (Range) | 0.08 (-2.37, 4.96) | 0.08 (-2.37, 3.33) | 0.08 (-2.37, 4.96) |
## |hoursPlayed | | | |
## |- Mean (SD) | 3.49 (0.89) | 3.42 (0.77) | 3.48 (0.87) |
## |- Median (Range) | 3.40 (1.68, 5.24) | 3.38 (1.97, 4.94) | 3.40 (1.68, 5.24) |
## |levelsPlayed | | | |
## |- Mean (SD) | 324.78 (146.11) | 320.50 (144.78) | 324.03 (145.51) |
## |- Median (Range) | 287.00 (101.00, 1026.00) | 314.50 (119.00, 722.00) | 289.00 (101.00, 1026.00) |
## |sessionsPlayed | | | |
## |- Mean (SD) | 27.48 (4.64) | 26.44 (3.52) | 27.30 (4.47) |
## |- Median (Range) | 27.00 (16.00, 37.00) | 25.00 (22.00, 35.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | |
## |- Mean (SD) | 132.84 (44.52) | 123.32 (51.18) | 131.18 (45.75) |
## |- Median (Range) | 135.00 (38.00, 265.00) | 135.50 (41.00, 265.00) | 135.00 (38.00, 265.00) |
## |itemsSeen | | | |
## |- Mean (SD) | 9868.60 (4065.96) | 9900.35 (4788.53) | 9874.14 (4187.51) |
## |- Median (Range) | 8842.00 (2827.00, 21452.00) | 8409.00 (2983.00, 22782.00) | 8778.00 (2827.00, 22782.00) |
## |responsesGiven | | | |
## |- Mean (SD) | 3263.51 (1595.57) | 3373.12 (2088.11) | 3282.62 (1686.15) |
## |- Median (Range) | 2969.00 (999.00, 9826.00) | 2853.00 (948.00, 10877.00) | 2943.00 (948.00, 10877.00) |
## |T1CF_TotalPc | | | |
## |- Mean (SD) | 47.31 (24.28) | 42.71 (25.68) | 46.53 (24.52) |
## |- Median (Range) | 50.00 (1.00, 99.00) | 37.00 (1.00, 91.00) | 50.00 (1.00, 99.00) |
## |T1PF_TotalPc | | | |
## |- Mean (SD) | 54.61 (27.93) | 51.16 (29.79) | 54.04 (28.22) |
## |- Median (Range) | 55.00 (0.00, 100.00) | 47.50 (0.00, 100.00) | 55.00 (0.00, 100.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 14.32 (8.95) | 15.15 (9.19) | 14.46 (8.97) |
## |- Median (Range) | 13.00 (0.00, 30.00) | 14.00 (1.00, 30.00) | 13.50 (0.00, 30.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 66.00 (16.50) | 70.83 (13.31) | 66.81 (16.09) |
## |- Median (Range) | 63.00 (39.00, 122.00) | 70.00 (43.00, 106.00) | 65.00 (39.00, 122.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 73.26 (18.08) | 80.88 (19.75) | 74.53 (18.55) |
## |- Median (Range) | 71.00 (40.00, 180.00) | 75.00 (56.00, 127.00) | 72.00 (40.00, 180.00) |TableDat$CountrCond <- interaction(TableDat$Country, TableDat$Cond)
tab <- tableby(CountrCond ~ EMT + FamRisk + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_TotalPc + T1PF_TotalPc + T1.LetKen + T1RANcT + T1RANoT, data = TableDat, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | B.Math (N=56) | NL.Math (N=48) | B.Passive (N=52) | B.Read (N=53) | NL.Read (N=38) | Total (N=247) |
## |:-----------------|:---------------------------:|:---------------------------:|:---------------------:|:----------------------------:|:----------------------------:|:---------------------------:|
## |EMT | | | | | | |
## |- Mean (SD) | 12.02 (7.79) | 23.83 (11.67) | 10.05 (4.59) | 12.62 (7.43) | 19.34 (10.29) | 15.08 (9.85) |
## |- Median (Range) | 10.00 (1.00, 41.50) | 19.50 (3.50, 45.00) | 10.50 (2.50, 22.00) | 12.50 (1.00, 32.50) | 16.50 (8.00, 45.00) | 12.50 (1.00, 45.00) |
## |FamRisk | | | | | | |
## |- no | 49 (87.5%) | 37 (77.1%) | 45 (86.5%) | 43 (81.1%) | 32 (84.2%) | 206 (83.4%) |
## |- yes | 7 (12.5%) | 11 (22.9%) | 7 (13.5%) | 10 (18.9%) | 6 (15.8%) | 41 (16.6%) |
## |T1AgeF | | | | | | |
## |- Mean (SD) | 6.31 (0.31) | 6.25 (0.32) | 6.20 (0.30) | 6.26 (0.37) | 6.19 (0.29) | 6.25 (0.32) |
## |- Median (Range) | 6.31 (5.70, 7.07) | 6.32 (5.55, 6.83) | 6.17 (5.68, 6.80) | 6.25 (5.71, 7.39) | 6.20 (5.46, 6.69) | 6.26 (5.46, 7.39) |
## |HandScore | | | | | | |
## |- Mean (SD) | 0.07 (1.00) | 0.12 (0.87) | -0.27 (1.27) | 0.05 (0.76) | -0.10 (1.20) | -0.02 (1.03) |
## |- Median (Range) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.44 (-3.20, 0.52) | 0.35 (-3.20, 0.52) |
## |Gender | | | | | | |
## |- female | 21 (37.5%) | 23 (47.9%) | 21 (40.4%) | 24 (45.3%) | 23 (60.5%) | 112 (45.3%) |
## |- male | 35 (62.5%) | 25 (52.1%) | 31 (59.6%) | 29 (54.7%) | 15 (39.5%) | 135 (54.7%) |
## |Lang | | | | | | |
## |- mono | 53 (94.6%) | 36 (75.0%) | 46 (88.5%) | 51 (96.2%) | 36 (94.7%) | 222 (89.9%) |
## |- multi | 3 (5.4%) | 12 (25.0%) | 6 (11.5%) | 2 (3.8%) | 2 (5.3%) | 25 (10.1%) |
## |SON_1st | | | | | | |
## |- Mean (SD) | -0.15 (1.02) | 0.41 (0.81) | -0.29 (0.99) | -0.06 (0.88) | 0.48 (1.07) | 0.05 (0.99) |
## |- Median (Range) | 0.08 (-2.37, 3.33) | 0.48 (-1.55, 2.52) | -0.54 (-2.37, 1.70) | 0.08 (-2.37, 1.30) | 0.48 (-1.55, 4.96) | 0.08 (-2.37, 4.96) |
## |hoursPlayed | | | | | | |
## |- Mean (SD) | 3.96 (0.76) | 2.70 (0.43) | NA | 3.83 (0.80) | 3.26 (0.77) | 3.48 (0.87) |
## |- Median (Range) | 4.17 (2.12, 5.24) | 2.72 (1.96, 3.80) | NA | 3.98 (1.97, 5.23) | 3.24 (1.68, 4.95) | 3.40 (1.68, 5.24) |
## |levelsPlayed | | | | | | |
## |- Mean (SD) | 473.43 (165.94) | 318.25 (77.64) | NA | 230.70 (55.57) | 241.34 (69.02) | 324.03 (145.51) |
## |- Median (Range) | 460.00 (132.00, 1026.00) | 317.50 (191.00, 607.00) | NA | 228.00 (119.00, 369.00) | 237.50 (101.00, 377.00) | 289.00 (101.00, 1026.00) |
## |sessionsPlayed | | | | | | |
## |- Mean (SD) | 27.82 (4.82) | 25.48 (2.02) | NA | 27.74 (5.64) | 28.21 (3.81) | 27.30 (4.47) |
## |- Median (Range) | 27.00 (18.00, 35.00) | 25.00 (20.00, 30.00) | NA | 26.00 (16.00, 37.00) | 29.00 (20.00, 34.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | | | | |
## |- Mean (SD) | 136.84 (31.04) | 135.94 (23.95) | NA | 101.49 (46.82) | 158.24 (60.35) | 131.18 (45.75) |
## |- Median (Range) | 137.50 (39.00, 178.00) | 135.50 (62.00, 178.00) | NA | 87.00 (38.00, 265.00) | 164.50 (71.00, 265.00) | 135.00 (38.00, 265.00) |
## |itemsSeen | | | | | | |
## |- Mean (SD) | 10747.04 (4563.03) | 6652.12 (2116.28) | NA | 10276.57 (3569.36) | 12096.39 (4179.95) | 9874.14 (4187.51) |
## |- Median (Range) | 9805.50 (2827.00, 22782.00) | 6722.50 (3304.00, 15832.00) | NA | 10145.00 (2983.00, 18124.00) | 11825.50 (4191.00, 20113.00) | 8778.00 (2827.00, 22782.00) |
## |responsesGiven | | | | | | |
## |- Mean (SD) | 4729.16 (2259.21) | 2753.85 (1001.08) | NA | 2602.30 (742.90) | 2767.66 (897.87) | 3282.62 (1686.15) |
## |- Median (Range) | 4261.00 (1019.00, 10877.00) | 2668.50 (1197.00, 6984.00) | NA | 2603.00 (948.00, 4072.00) | 2767.00 (999.00, 4522.00) | 2943.00 (948.00, 10877.00) |
## |T1CF_TotalPc | | | | | | |
## |- Mean (SD) | 38.22 (23.68) | 59.85 (19.35) | 41.14 (23.91) | 35.74 (20.05) | 63.68 (22.22) | 46.53 (24.52) |
## |- Median (Range) | 37.00 (1.00, 91.00) | 63.00 (9.00, 91.00) | 37.00 (1.00, 95.00) | 37.00 (5.00, 84.00) | 56.50 (25.00, 99.00) | 50.00 (1.00, 99.00) |
## |T1PF_TotalPc | | | | | | |
## |- Mean (SD) | 45.67 (29.28) | 64.84 (26.55) | 49.52 (25.94) | 44.29 (25.37) | 72.50 (22.80) | 54.04 (28.22) |
## |- Median (Range) | 45.00 (0.00, 100.00) | 66.25 (7.50, 100.00) | 47.50 (0.00, 97.50) | 42.50 (0.00, 97.50) | 75.00 (22.50, 100.00) | 55.00 (0.00, 100.00) |
## |T1.LetKen | | | | | | |
## |- Mean (SD) | 9.36 (6.42) | 24.50 (4.74) | 11.27 (5.30) | 8.23 (6.70) | 22.24 (6.36) | 14.46 (8.97) |
## |- Median (Range) | 8.00 (0.00, 26.00) | 26.00 (13.00, 30.00) | 10.00 (0.00, 25.00) | 6.00 (0.00, 25.00) | 24.00 (2.00, 30.00) | 13.50 (0.00, 30.00) |
## |T1RANcT | | | | | | |
## |- Mean (SD) | 67.50 (15.37) | 58.44 (11.80) | 70.88 (15.92) | 74.96 (16.02) | 59.39 (14.71) | 66.81 (16.09) |
## |- Median (Range) | 67.00 (40.00, 107.00) | 57.50 (41.00, 96.00) | 67.50 (48.00, 115.00) | 70.00 (49.00, 122.00) | 57.50 (39.00, 102.00) | 65.00 (39.00, 122.00) |
## |T1RANoT | | | | | | |
## |- Mean (SD) | 76.25 (17.33) | 70.46 (15.11) | 75.56 (17.49) | 81.77 (23.82) | 65.68 (12.13) | 74.53 (18.55) |
## |- Median (Range) | 72.00 (50.00, 120.00) | 68.00 (40.00, 126.00) | 76.50 (46.00, 127.00) | 76.00 (49.00, 180.00) | 65.50 (43.00, 90.00) | 72.00 (40.00, 180.00) |TableDat$CountrFamRisk <- interaction(TableDat$Country, TableDat$FamRisk)
tab <- tableby(CountrFamRisk ~ EMT + Cond + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_TotalPc + T1PF_TotalPc + T1.LetKen + T1RANcT + T1RANoT, data = TableDat, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | B.no (N=137) | NL.no (N=69) | B.yes (N=24) | NL.yes (N=17) | Total (N=247) |
## |:-----------------|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:---------------------------:|
## |EMT | | | | | |
## |- Mean (SD) | 11.83 (6.98) | 21.96 (10.87) | 10.17 (5.88) | 21.18 (12.90) | 15.08 (9.85) |
## |- Median (Range) | 10.50 (1.00, 41.50) | 18.00 (8.00, 45.00) | 9.50 (1.00, 21.00) | 17.50 (3.50, 45.00) | 12.50 (1.00, 45.00) |
## |Cond | | | | | |
## |- Math | 49 (35.8%) | 37 (53.6%) | 7 (29.2%) | 11 (64.7%) | 104 (42.1%) |
## |- Passive | 45 (32.8%) | 0 (0.0%) | 7 (29.2%) | 0 (0.0%) | 52 (21.1%) |
## |- Read | 43 (31.4%) | 32 (46.4%) | 10 (41.7%) | 6 (35.3%) | 91 (36.8%) |
## |T1AgeF | | | | | |
## |- Mean (SD) | 6.26 (0.31) | 6.23 (0.33) | 6.25 (0.40) | 6.20 (0.23) | 6.25 (0.32) |
## |- Median (Range) | 6.29 (5.68, 7.26) | 6.27 (5.46, 6.83) | 6.22 (5.76, 7.39) | 6.22 (5.82, 6.56) | 6.26 (5.46, 7.39) |
## |HandScore | | | | | |
## |- Mean (SD) | -0.04 (1.04) | 0.01 (1.07) | -0.11 (1.00) | 0.08 (0.88) | -0.02 (1.03) |
## |- Median (Range) | 0.35 (-3.20, 0.52) | 0.52 (-3.20, 0.52) | 0.35 (-3.03, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) |
## |Gender | | | | | |
## |- female | 55 (40.1%) | 37 (53.6%) | 11 (45.8%) | 9 (52.9%) | 112 (45.3%) |
## |- male | 82 (59.9%) | 32 (46.4%) | 13 (54.2%) | 8 (47.1%) | 135 (54.7%) |
## |Lang | | | | | |
## |- mono | 128 (93.4%) | 58 (84.1%) | 22 (91.7%) | 14 (82.4%) | 222 (89.9%) |
## |- multi | 9 (6.6%) | 11 (15.9%) | 2 (8.3%) | 3 (17.6%) | 25 (10.1%) |
## |SON_1st | | | | | |
## |- Mean (SD) | -0.16 (0.92) | 0.41 (0.97) | -0.20 (1.23) | 0.58 (0.71) | 0.05 (0.99) |
## |- Median (Range) | -0.33 (-2.37, 1.70) | 0.48 (-1.55, 4.96) | 0.08 (-2.37, 3.33) | 0.48 (-0.33, 2.52) | 0.08 (-2.37, 4.96) |
## |hoursPlayed | | | | | |
## |- Mean (SD) | 3.91 (0.80) | 2.92 (0.67) | 3.81 (0.67) | 3.02 (0.66) | 3.48 (0.87) |
## |- Median (Range) | 4.11 (2.12, 5.24) | 2.94 (1.68, 4.95) | 3.89 (1.97, 4.68) | 2.82 (2.23, 4.94) | 3.40 (1.68, 5.24) |
## |levelsPlayed | | | | | |
## |- Mean (SD) | 356.64 (172.20) | 282.29 (85.66) | 348.71 (190.40) | 292.29 (72.74) | 324.03 (145.51) |
## |- Median (Range) | 314.00 (132.00, 1026.00) | 275.00 (101.00, 607.00) | 282.00 (119.00, 722.00) | 316.00 (152.00, 414.00) | 289.00 (101.00, 1026.00) |
## |sessionsPlayed | | | | | |
## |- Mean (SD) | 27.90 (5.38) | 26.91 (3.36) | 27.12 (4.26) | 25.76 (2.54) | 27.30 (4.47) |
## |- Median (Range) | 26.00 (16.00, 37.00) | 27.00 (20.00, 34.00) | 26.00 (22.00, 35.00) | 25.00 (23.00, 33.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | | | |
## |- Mean (SD) | 122.01 (40.98) | 147.28 (45.24) | 106.88 (52.98) | 139.76 (44.97) | 131.18 (45.75) |
## |- Median (Range) | 130.50 (38.00, 241.00) | 136.00 (71.00, 265.00) | 94.00 (41.00, 265.00) | 136.00 (62.00, 241.00) | 135.00 (38.00, 265.00) |
## |itemsSeen | | | | | |
## |- Mean (SD) | 10390.43 (3860.55) | 9172.83 (4253.74) | 11210.12 (5290.32) | 8590.59 (3957.31) | 9874.14 (4187.51) |
## |- Median (Range) | 10018.50 (2827.00, 21452.00) | 8025.00 (3304.00, 20113.00) | 10010.00 (2983.00, 22782.00) | 7664.00 (5069.00, 18594.00) | 8778.00 (2827.00, 22782.00) |
## |responsesGiven | | | | | |
## |- Mean (SD) | 3638.86 (1840.26) | 2763.04 (1006.70) | 3998.82 (2767.65) | 2747.41 (707.86) | 3282.62 (1686.15) |
## |- Median (Range) | 3106.50 (1019.00, 9826.00) | 2842.00 (999.00, 6984.00) | 3090.00 (948.00, 10877.00) | 2485.00 (1882.00, 4212.00) | 2943.00 (948.00, 10877.00) |
## |T1CF_TotalPc | | | | | |
## |- Mean (SD) | 39.02 (22.14) | 63.30 (19.91) | 34.42 (24.90) | 54.41 (22.54) | 46.53 (24.52) |
## |- Median (Range) | 37.00 (1.00, 95.00) | 63.00 (16.00, 99.00) | 31.00 (1.00, 84.00) | 50.00 (9.00, 91.00) | 50.00 (1.00, 99.00) |
## |T1PF_TotalPc | | | | | |
## |- Mean (SD) | 47.41 (26.62) | 68.91 (24.97) | 41.04 (28.38) | 65.44 (26.28) | 54.04 (28.22) |
## |- Median (Range) | 47.50 (0.00, 100.00) | 75.00 (7.50, 100.00) | 32.50 (0.00, 97.50) | 75.00 (20.00, 100.00) | 55.00 (0.00, 100.00) |
## |T1.LetKen | | | | | |
## |- Mean (SD) | 9.72 (6.40) | 23.38 (5.78) | 8.88 (5.57) | 24.00 (4.92) | 14.46 (8.97) |
## |- Median (Range) | 8.00 (0.00, 26.00) | 25.00 (2.00, 30.00) | 7.50 (1.00, 24.00) | 25.00 (15.00, 30.00) | 13.50 (0.00, 30.00) |
## |T1RANcT | | | | | |
## |- Mean (SD) | 70.80 (16.33) | 56.48 (12.19) | 72.46 (13.92) | 68.53 (12.45) | 66.81 (16.09) |
## |- Median (Range) | 68.00 (40.00, 122.00) | 56.00 (39.00, 102.00) | 72.00 (43.00, 106.00) | 68.00 (45.00, 96.00) | 65.00 (39.00, 122.00) |
## |T1RANoT | | | | | |
## |- Mean (SD) | 76.78 (19.42) | 66.33 (12.59) | 83.96 (21.46) | 76.53 (16.70) | 74.53 (18.55) |
## |- Median (Range) | 76.00 (46.00, 180.00) | 66.00 (40.00, 94.00) | 76.50 (58.00, 127.00) | 72.00 (56.00, 126.00) | 72.00 (40.00, 180.00) |1.4.1.2 Normality at T1
None of the variables were normally distributed at T1.
datComb <- droplevels(subset(BehavDat, Exclude=="no"))
shapiro.test(datComb$T1AgeF)##
## Shapiro-Wilk normality test
##
## data: datComb$T1AgeF
## W = 0.97958, p-value = 0.001267shapiro.test(datComb$T1.LetKen)##
## Shapiro-Wilk normality test
##
## data: datComb$T1.LetKen
## W = 0.93044, p-value = 2.314e-09shapiro.test(datComb$T1CF_TotalPc)##
## Shapiro-Wilk normality test
##
## data: datComb$T1CF_TotalPc
## W = 0.95943, p-value = 2.34e-06shapiro.test(datComb$T1PF_TotalPc)##
## Shapiro-Wilk normality test
##
## data: datComb$T1PF_TotalPc
## W = 0.95598, p-value = 7.716e-07shapiro.test(datComb$T1RANoT)##
## Shapiro-Wilk normality test
##
## data: datComb$T1RANoT
## W = 0.92143, p-value = 4.04e-10shapiro.test(datComb$T1RANcT)##
## Shapiro-Wilk normality test
##
## data: datComb$T1RANcT
## W = 0.95561, p-value = 6.996e-07shapiro.test(datComb$SON_1st)##
## Shapiro-Wilk normality test
##
## data: datComb$SON_1st
## W = 0.95947, p-value = 1.978e-061.4.1.3 Differences by country and condition
Chi-squared/Fisher’s exact test to compare counts of Gender, Multilingualism, Handedness and familial risk of dyslexia in the combined sample. No significant differences were observed, although there is a trend towards more multilinguals in the math condition.
1.4.1.3.1 Chi-square test Gender x Condition
chisq.test(matrix(c(21,31,44,60,47,44),ncol=3,dimnames=list(c("female","male"), c("Passive","Math","Read"))))##
## Pearson's Chi-squared test
##
## data: matrix(c(21, 31, 44, 60, 47, 44), ncol = 3, dimnames = list(c("female", "male"), c("Passive", "Math", "Read")))
## X-squared = 2.3623, df = 2, p-value = 0.30691.4.1.3.2 Fisher’s exact test Multilingualism x Condition
fisher.test(matrix(c(6,46,15,89,4,87),ncol=3,dimnames=list(c("multilingual","monolingual"), c("Passive","Math","Read"))))##
## Fisher's Exact Test for Count Data
##
## data: matrix(c(6, 46, 15, 89, 4, 87), ncol = 3, dimnames = list(c("multilingual", "monolingual"), c("Passive", "Math", "Read")))
## p-value = 0.05332
## alternative hypothesis: two.sided1.4.1.3.3 Chi-square test Handedness x Condition
chisq.test(matrix(c(9,43,8,96,10,81),ncol=3,dimnames=list(c("lefthander","righthander"), c("Passive","Math","Read"))))##
## Pearson's Chi-squared test
##
## data: matrix(c(9, 43, 8, 96, 10, 81), ncol = 3, dimnames = list(c("lefthander", "righthander"), c("Passive", "Math", "Read")))
## X-squared = 3.2924, df = 2, p-value = 0.19281.4.1.3.4 Chi-square test Familial Risk x Condition
chisq.test(matrix(c(7,45,18,86,16,75), ncol=3, dimnames=list(c("familial risk", "no familial risk"), c("Passive","Math","Read"))))##
## Pearson's Chi-squared test
##
## data: matrix(c(7, 45, 18, 86, 16, 75), ncol = 3, dimnames = list(c("familial risk", "no familial risk"), c("Passive", "Math", "Read")))
## X-squared = 0.47105, df = 2, p-value = 0.79021.4.1.3.5 ANOVA for variables on interval scales
ANOVAs testing for a Condition x Country interaction for variables Age, LK, CELF PA, PROEF PA, RAN objects, RAN colours, and abstract reasoning. We found main effect of country for LK, CELF PA, PROEF PA, RAN objects, and RAN colours. In addition, there were main effects of condition for LK and RAN colours. For LK all groups differed from knowing most letters (math) via passive to least letters (read). Further, the math group was faster in RAN colours than both other groups. We further observed a Condition x Country interaction for RAN objects in that the Math group did not differ between the two countries, but the Dutch reading group outperformed their Belgian reading group peers.
boxplot(T1AgeF~Cond+Country,data=datComb, main="Age at T1", ylab="Age in years")
summary(aov(T1AgeF~Cond*Country,data=datComb))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 0.278 0.13892 1.362 0.258
## Country 1 0.174 0.17399 1.706 0.193
## Cond:Country 1 0.001 0.00134 0.013 0.909
## Residuals 242 24.683 0.10199boxplot(T1.LetKen~Cond+Country,data=datComb, main="LK at T1", ylab="Letter knowledge")
summary(aov(T1.LetKen~Cond*Country,data=datComb))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 901 450 12.686 5.78e-06 ***
## Country 1 10256 10256 288.837 < 2e-16 ***
## Cond:Country 1 15 15 0.431 0.512
## Residuals 241 8557 36
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnessTukeyHSD(aov(T1.LetKen~Cond*Country,data=datComb))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = T1.LetKen ~ Cond * Country, data = datComb)
##
## $Cond
## diff lwr upr p adj
## Passive-Math -5.071644 -7.4738881 -2.669400 0.0000036
## Read-Math -2.269231 -4.2863565 -0.252105 0.0230612
## Read-Passive 2.802413 0.3443593 5.260467 0.0208848
##
## $Country
## diff lwr upr p adj
## NL-B 12.54066 10.97121 14.11012 0
##
## $`Cond:Country`
## diff lwr upr p adj
## Passive:B-Math:B 1.917367 -1.396032 5.230765 0.5580253
## Read:B-Math:B -1.130728 -4.411244 2.149788 0.9207562
## Math:NL-Math:B 15.142857 11.775704 18.510011 0.0000000
## Passive:NL-Math:B NA NA NA NA
## Read:NL-Math:B 12.879699 9.281883 16.477516 0.0000000
## Read:B-Passive:B -3.048095 -6.405892 0.309703 0.0992899
## Math:NL-Passive:B 13.225490 9.782999 16.667981 0.0000000
## Passive:NL-Passive:B NA NA NA NA
## Read:NL-Passive:B 10.962332 7.293913 14.630752 0.0000000
## Math:NL-Read:B 16.273585 12.862732 19.684438 0.0000000
## Passive:NL-Read:B NA NA NA NA
## Read:NL-Read:B 14.010427 10.371680 17.649174 0.0000000
## Passive:NL-Math:NL NA NA NA NA
## Read:NL-Math:NL -2.263158 -5.980202 1.453886 0.5007696
## Read:NL-Passive:NL NA NA NA NAboxplot(T1CF_TotalPc~Cond+Country,data=datComb, main="CELF PA at T1", ylab="CELF PA (percentile)")
summary(aov(T1CF_TotalPc~Cond*Country,data=datComb))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 1880 940 1.954 0.144
## Country 1 28707 28707 59.676 3.11e-13 ***
## Cond:Country 1 472 472 0.981 0.323
## Residuals 238 114488 481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingnessboxplot(T1PF_TotalPc~Cond+Country,data=datComb, main="PREOF PA at T1", ylab="PROEF PA (percentile)")
summary(aov(T1PF_TotalPc~Cond*Country,data=datComb))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 1462 731 1.058 0.349
## Country 1 26139 26139 37.812 3.19e-09 ***
## Cond:Country 1 973 973 1.407 0.237
## Residuals 242 167291 691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1boxplot(T1RANoT~Cond+Country,data=datComb, main="RAN Objects at T1", ylab="Time in seconds")
summary(aov(T1RANoT~Cond*Country,data=datComb))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 178 89 0.277 0.758
## Country 1 5332 5332 16.582 6.32e-05 ***
## Cond:Country 1 1258 1258 3.913 0.049 *
## Residuals 241 77495 322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnessTukeyHSD(aov(T1RANoT~Cond*Country,data=datComb))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = T1RANoT ~ Cond * Country, data = datComb)
##
## $Cond
## diff lwr upr p adj
## Passive-Math 2.0042942 -5.189691 9.198280 0.7885541
## Read-Math 1.5015470 -4.582402 7.585496 0.8298643
## Read-Passive -0.5027473 -7.854140 6.848646 0.9857635
##
## $Country
## diff lwr upr p adj
## NL-B -9.022104 -13.74513 -4.299079 0.000211
##
## $`Cond:Country`
## diff lwr upr p adj
## Passive:B-Math:B -0.6968531 -10.661014 9.2673075 0.9999545
## Read:B-Math:B 5.5190395 -4.396685 15.4347639 0.6000329
## Math:NL-Math:B -5.7962121 -15.971542 4.3791178 0.5752397
## Passive:NL-Math:B NA NA NA NA
## Read:NL-Math:B -10.5703349 -21.437098 0.2964277 0.0617692
## Read:B-Passive:B 6.2158926 -3.839219 16.2710041 0.4832867
## Math:NL-Passive:B -5.0993590 -15.410567 5.2118493 0.7144224
## Passive:NL-Passive:B NA NA NA NA
## Read:NL-Passive:B -9.8734818 -20.867581 1.1206170 0.1062016
## Math:NL-Read:B -11.3152516 -21.579661 -1.0508418 0.0212809
## Passive:NL-Read:B NA NA NA NA
## Read:NL-Read:B -16.0893744 -27.039594 -5.1391552 0.0004894
## Passive:NL-Math:NL NA NA NA NA
## Read:NL-Math:NL -4.7741228 -15.959964 6.4117186 0.8237344
## Read:NL-Passive:NL NA NA NA NAboxplot(T1RANcT~Cond+Country,data=datComb, main="RAN Colours at T1", ylab="Time in seconds")
summary(aov(T1RANcT~Cond*Country,data=datComb))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 2380 1190 5.352 0.00532 **
## Country 1 6982 6982 31.397 5.71e-08 ***
## Cond:Country 1 505 505 2.269 0.13331
## Residuals 242 53814 222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(T1RANcT~Cond*Country,data=datComb))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = T1RANcT ~ Cond * Country, data = datComb)
##
## $Cond
## diff lwr upr p adj
## Passive-Math 7.567308 1.59461955 13.539996 0.0086730
## Read-Math 5.144231 0.09638797 10.192074 0.0446632
## Read-Passive -2.423077 -8.53631814 3.690164 0.6188702
##
## $Country
## diff lwr upr p adj
## NL-B -10.32471 -14.24802 -6.401408 5e-07
##
## $`Cond:Country`
## diff lwr upr p adj
## Passive:B-Math:B 3.3846154 -4.8652450 11.6344758 0.8467470
## Read:B-Math:B 7.4622642 -0.7471413 15.6716696 0.0984482
## Math:NL-Math:B -9.0625000 -17.4887132 -0.6362868 0.0268696
## Passive:NL-Math:B NA NA NA NA
## Read:NL-Math:B -8.1052632 -17.1087041 0.8981778 0.1046278
## Read:B-Passive:B 4.0776488 -4.2838754 12.4391729 0.7264811
## Math:NL-Passive:B -12.4471154 -21.0216018 -3.8726290 0.0006003
## Passive:NL-Passive:B NA NA NA NA
## Read:NL-Passive:B -11.4898785 -20.6322358 -2.3475212 0.0049453
## Math:NL-Read:B -16.5247642 -25.0603344 -7.9891939 0.0000011
## Passive:NL-Read:B NA NA NA NA
## Read:NL-Read:B -15.5675273 -24.6733957 -6.4616589 0.0000245
## Passive:NL-Math:NL NA NA NA NA
## Read:NL-Math:NL 0.9572368 -8.3445678 10.2590415 0.9996944
## Read:NL-Passive:NL NA NA NA NAboxplot(SON_1st~Cond+Country,data=datComb, main="Abstract reasoning at T1", ylab ="Abstract reasoning (z-score)")
summary(aov(SON_1st~Cond*Country,data=datComb))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 7.71 3.857 4.226 0.0157 *
## Country 1 14.36 14.363 15.735 9.6e-05 ***
## Cond:Country 1 0.00 0.004 0.004 0.9469
## Residuals 242 220.91 0.913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 11.4.2 ANALYSIS 1 - Belgian sample
This is the analysis for the Belgium sample (N = 161) which features passive (non-playing) and active (math) control conditions.
1.4.2.1 Descriptives
Here, we show results of Chi-squared/Fisher’s exact test for Gender, Lang, Handedness and FR in the Belgian sample. The sample is balanced in that regard.
1.4.2.1.1 Chi-square test Gender x Condition
chisq.test(matrix(c(21,31,21,35,24,29),ncol=3,dimnames=list(c("female","male"), c("Passive","Math","Read"))))##
## Pearson's Chi-squared test
##
## data: matrix(c(21, 31, 21, 35, 24, 29), ncol = 3, dimnames = list(c("female", "male"), c("Passive", "Math", "Read")))
## X-squared = 0.69368, df = 2, p-value = 0.70691.4.2.1.2 Fisher’s exact test Multilingualism x Condition
fisher.test(matrix(c(6,46,3,53,2,51),ncol=3,dimnames=list(c("multilingual","monolingual"), c("Passive","Math","Read"))))##
## Fisher's Exact Test for Count Data
##
## data: matrix(c(6, 46, 3, 53, 2, 51), ncol = 3, dimnames = list(c("multilingual", "monolingual"), c("Passive", "Math", "Read")))
## p-value = 0.2808
## alternative hypothesis: two.sided1.4.2.1.3 Fisher’s exact test Handedness x Condition
fisher.test(matrix(c(9,43,5,51,4,49),ncol=3,dimnames=list(c("lefthander","righthander"), c("Passive","Math","Read"))))##
## Fisher's Exact Test for Count Data
##
## data: matrix(c(9, 43, 5, 51, 4, 49), ncol = 3, dimnames = list(c("lefthander", "righthander"), c("Passive", "Math", "Read")))
## p-value = 0.2677
## alternative hypothesis: two.sided1.4.2.1.4 Chi-square test Familial Risk x Condition
chisq.test(matrix(c(7,45,7,49,10,43),ncol=3,dimnames=list(c("namilial risk","no familial risk"), c("Passive","Math","Read"))))##
## Pearson's Chi-squared test
##
## data: matrix(c(7, 45, 7, 49, 10, 43), ncol = 3, dimnames = list(c("namilial risk", "no familial risk"), c("Passive", "Math", "Read")))
## X-squared = 0.99695, df = 2, p-value = 0.6075ANOVAs checking group differences at T1 revealed effects of condition for LK and RANcT. Subsequeny Tukey HSD tests revealed that the reading group knew less letters than the passive group, and that the math group was faster at RAN colours than the reading group. In both cases the math and passive groups did not differ from one another.
summary(aov(T1AgeF~Cond,data=datB))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 0.322 0.1612 1.53 0.22
## Residuals 158 16.639 0.1053summary(aov(T1.LetKen~Cond,data=datB))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 246 123.15 3.22 0.0426 *
## Residuals 157 6004 38.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnessTukeyHSD(aov(T1.LetKen~Cond,data=datB))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = T1.LetKen ~ Cond, data = datB)
##
## $Cond
## diff lwr upr p adj
## Passive-Math 1.917367 -0.9149175 4.749651 0.2478947
## Read-Math -1.130728 -3.9349044 1.673449 0.6069370
## Read-Passive -3.048095 -5.9183315 -0.177858 0.0345137summary(aov(T1CF_TotalPc~Cond,data=datB))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 752 376.0 0.736 0.48
## Residuals 154 78630 510.6
## 4 observations deleted due to missingnesssummary(aov(T1PF_TotalPc~Cond,data=datB))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 771 385.3 0.53 0.59
## Residuals 158 114924 727.4summary(aov(T1RANoT~Cond,data=datB))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 1229 614.6 1.574 0.211
## Residuals 157 61319 390.6
## 1 observation deleted due to missingnesssummary(aov(T1RANcT~Cond,data=datB))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 1518 759.2 3.055 0.0499 *
## Residuals 158 39265 248.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(T1RANcT~Cond,data=datB))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = T1RANcT ~ Cond, data = datB)
##
## $Cond
## diff lwr upr p adj
## Passive-Math 3.384615 -3.7984242 10.56765 0.5063501
## Read-Math 7.462264 0.3144481 14.61008 0.0385081
## Read-Passive 4.077649 -3.2026149 11.35791 0.3833327summary(aov(SON_1st~Cond,data=datB))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 1.6 0.8008 0.799 0.452
## Residuals 158 158.4 1.0025A summary of the Belgian data with number of observations, means, range and standard deviation per variable and condition.
tab <- tableby(Gender ~ EMT + Cond + T1AgeF + HandScore + FamRisk + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_Total + T1PF_Total + T1.LetKen + T1RANcT + T1RANoT, data = datB, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | female (N=66) | male (N=95) | Total (N=161) |
## |:-----------------|:---------------------------:|:----------------------------:|:----------------------------:|
## |EMT | | | |
## |- Mean (SD) | 0.14 (1.05) | -0.10 (0.95) | -0.00 (1.00) |
## |- Median (Range) | 0.06 (-1.48, 3.57) | -0.23 (-1.55, 4.38) | -0.16 (-1.55, 4.38) |
## |Cond | | | |
## |- Math | 21 (31.8%) | 35 (36.8%) | 56 (34.8%) |
## |- Passive | 21 (31.8%) | 31 (32.6%) | 52 (32.3%) |
## |- Read | 24 (36.4%) | 29 (30.5%) | 53 (32.9%) |
## |T1AgeF | | | |
## |- Mean (SD) | 6.23 (0.34) | 6.28 (0.31) | 6.26 (0.33) |
## |- Median (Range) | 6.22 (5.70, 7.39) | 6.31 (5.68, 7.26) | 6.26 (5.68, 7.39) |
## |HandScore | | | |
## |- Mean (SD) | 0.14 (0.83) | -0.10 (1.10) | 0.00 (1.00) |
## |- Median (Range) | 0.39 (-2.88, 0.55) | 0.39 (-3.05, 0.55) | 0.39 (-3.05, 0.55) |
## |FamRisk | | | |
## |- no | 55 (83.3%) | 82 (86.3%) | 137 (85.1%) |
## |- yes | 11 (16.7%) | 13 (13.7%) | 24 (14.9%) |
## |Lang | | | |
## |- mono | 62 (93.9%) | 88 (92.6%) | 150 (93.2%) |
## |- multi | 4 (6.1%) | 7 (7.4%) | 11 (6.8%) |
## |SON_1st | | | |
## |- Mean (SD) | 0.23 (1.01) | -0.16 (0.96) | -0.00 (1.00) |
## |- Median (Range) | 0.25 (-2.28, 3.62) | -0.17 (-2.28, 1.93) | -0.17 (-2.28, 3.62) |
## |hoursPlayed | | | |
## |- Mean (SD) | 4.06 (0.75) | 3.78 (0.78) | 3.90 (0.78) |
## |- Median (Range) | 4.15 (2.12, 5.24) | 3.99 (1.97, 5.18) | 4.07 (1.97, 5.24) |
## |levelsPlayed | | | |
## |- Mean (SD) | 324.18 (140.67) | 377.36 (192.48) | 355.40 (174.26) |
## |- Median (Range) | 287.00 (132.00, 694.00) | 323.50 (119.00, 1026.00) | 309.00 (119.00, 1026.00) |
## |sessionsPlayed | | | |
## |- Mean (SD) | 28.13 (5.56) | 27.53 (4.98) | 27.78 (5.21) |
## |- Median (Range) | 27.00 (16.00, 37.00) | 26.00 (19.00, 37.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | |
## |- Mean (SD) | 121.33 (49.05) | 118.47 (38.82) | 119.65 (43.14) |
## |- Median (Range) | 132.00 (38.00, 265.00) | 122.50 (41.00, 201.00) | 128.00 (38.00, 265.00) |
## |itemsSeen | | | |
## |- Mean (SD) | 10238.60 (3739.57) | 10714.92 (4350.49) | 10518.28 (4097.98) |
## |- Median (Range) | 9859.00 (2827.00, 18528.00) | 10349.00 (2983.00, 22782.00) | 10010.00 (2827.00, 22782.00) |
## |responsesGiven | | | |
## |- Mean (SD) | 3397.51 (1647.90) | 3904.17 (2205.07) | 3695.00 (2001.38) |
## |- Median (Range) | 3039.00 (1019.00, 8746.00) | 3147.00 (948.00, 10877.00) | 3090.00 (948.00, 10877.00) |
## |T1CF_Total | | | |
## |- Mean (SD) | 24.72 (8.23) | 21.75 (9.72) | 22.96 (9.23) |
## |- Median (Range) | 26.00 (9.00, 40.00) | 22.00 (1.00, 42.00) | 24.00 (1.00, 42.00) |
## |T1PF_Total | | | |
## |- Mean (SD) | 31.11 (4.98) | 28.87 (5.81) | 29.79 (5.58) |
## |- Median (Range) | 31.50 (18.00, 40.00) | 29.00 (0.00, 39.00) | 30.00 (0.00, 40.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 10.92 (6.97) | 8.68 (5.60) | 9.59 (6.27) |
## |- Median (Range) | 10.00 (1.00, 26.00) | 8.00 (0.00, 25.00) | 8.00 (0.00, 26.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 69.48 (15.16) | 72.14 (16.49) | 71.05 (15.97) |
## |- Median (Range) | 66.50 (42.00, 115.00) | 70.00 (40.00, 122.00) | 69.00 (40.00, 122.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 75.08 (18.77) | 79.76 (20.41) | 77.86 (19.83) |
## |- Median (Range) | 72.00 (46.00, 127.00) | 77.00 (48.00, 180.00) | 76.00 (46.00, 180.00) |tab <- tableby(FamRisk ~ EMT + Cond + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_Total + T1PF_Total + T1.LetKen + T1RANcT + T1RANoT, data = datB, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | no (N=137) | yes (N=24) | Total (N=161) |
## |:-----------------|:----------------------------:|:----------------------------:|:----------------------------:|
## |EMT | | | |
## |- Mean (SD) | 0.04 (1.02) | -0.21 (0.86) | -0.00 (1.00) |
## |- Median (Range) | -0.16 (-1.55, 4.38) | -0.30 (-1.55, 1.38) | -0.16 (-1.55, 4.38) |
## |Cond | | | |
## |- Math | 49 (35.8%) | 7 (29.2%) | 56 (34.8%) |
## |- Passive | 45 (32.8%) | 7 (29.2%) | 52 (32.3%) |
## |- Read | 43 (31.4%) | 10 (41.7%) | 53 (32.9%) |
## |T1AgeF | | | |
## |- Mean (SD) | 6.26 (0.31) | 6.25 (0.40) | 6.26 (0.33) |
## |- Median (Range) | 6.29 (5.68, 7.26) | 6.22 (5.76, 7.39) | 6.26 (5.68, 7.39) |
## |HandScore | | | |
## |- Mean (SD) | 0.01 (1.01) | -0.06 (0.96) | 0.00 (1.00) |
## |- Median (Range) | 0.39 (-3.05, 0.55) | 0.39 (-2.88, 0.55) | 0.39 (-3.05, 0.55) |
## |Gender | | | |
## |- female | 55 (40.1%) | 11 (45.8%) | 66 (41.0%) |
## |- male | 82 (59.9%) | 13 (54.2%) | 95 (59.0%) |
## |Lang | | | |
## |- mono | 128 (93.4%) | 22 (91.7%) | 150 (93.2%) |
## |- multi | 9 (6.6%) | 2 (8.3%) | 11 (6.8%) |
## |SON_1st | | | |
## |- Mean (SD) | 0.01 (0.95) | -0.03 (1.28) | -0.00 (1.00) |
## |- Median (Range) | -0.17 (-2.28, 1.93) | 0.25 (-2.28, 3.62) | -0.17 (-2.28, 3.62) |
## |hoursPlayed | | | |
## |- Mean (SD) | 3.91 (0.80) | 3.81 (0.67) | 3.90 (0.78) |
## |- Median (Range) | 4.11 (2.12, 5.24) | 3.89 (1.97, 4.68) | 4.07 (1.97, 5.24) |
## |levelsPlayed | | | |
## |- Mean (SD) | 356.64 (172.20) | 348.71 (190.40) | 355.40 (174.26) |
## |- Median (Range) | 314.00 (132.00, 1026.00) | 282.00 (119.00, 722.00) | 309.00 (119.00, 1026.00) |
## |sessionsPlayed | | | |
## |- Mean (SD) | 27.90 (5.38) | 27.12 (4.26) | 27.78 (5.21) |
## |- Median (Range) | 26.00 (16.00, 37.00) | 26.00 (22.00, 35.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | |
## |- Mean (SD) | 122.01 (40.98) | 106.88 (52.98) | 119.65 (43.14) |
## |- Median (Range) | 130.50 (38.00, 241.00) | 94.00 (41.00, 265.00) | 128.00 (38.00, 265.00) |
## |itemsSeen | | | |
## |- Mean (SD) | 10390.43 (3860.55) | 11210.12 (5290.32) | 10518.28 (4097.98) |
## |- Median (Range) | 10018.50 (2827.00, 21452.00) | 10010.00 (2983.00, 22782.00) | 10010.00 (2827.00, 22782.00) |
## |responsesGiven | | | |
## |- Mean (SD) | 3638.86 (1840.26) | 3998.82 (2767.65) | 3695.00 (2001.38) |
## |- Median (Range) | 3106.50 (1019.00, 9826.00) | 3090.00 (948.00, 10877.00) | 3090.00 (948.00, 10877.00) |
## |T1CF_Total | | | |
## |- Mean (SD) | 23.34 (9.02) | 20.88 (10.31) | 22.96 (9.23) |
## |- Median (Range) | 24.00 (1.00, 42.00) | 20.00 (1.00, 39.00) | 24.00 (1.00, 42.00) |
## |T1PF_Total | | | |
## |- Mean (SD) | 30.00 (5.49) | 28.58 (6.01) | 29.79 (5.58) |
## |- Median (Range) | 30.00 (0.00, 40.00) | 29.00 (11.00, 37.00) | 30.00 (0.00, 40.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 9.72 (6.40) | 8.88 (5.57) | 9.59 (6.27) |
## |- Median (Range) | 8.00 (0.00, 26.00) | 7.50 (1.00, 24.00) | 8.00 (0.00, 26.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 70.80 (16.33) | 72.46 (13.92) | 71.05 (15.97) |
## |- Median (Range) | 68.00 (40.00, 122.00) | 72.00 (43.00, 106.00) | 69.00 (40.00, 122.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 76.78 (19.42) | 83.96 (21.46) | 77.86 (19.83) |
## |- Median (Range) | 76.00 (46.00, 180.00) | 76.50 (58.00, 127.00) | 76.00 (46.00, 180.00) |tab <- tableby(Cond ~ EMT + FamRisk + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_Total + T1PF_Total + T1.LetKen + T1RANcT + T1RANoT, data = datB, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | Math (N=56) | Passive (N=52) | Read (N=53) | Total (N=161) |
## |:-----------------|:---------------------------:|:---------------------:|:----------------------------:|:----------------------------:|
## |EMT | | | | |
## |- Mean (SD) | 0.06 (1.14) | -0.22 (0.67) | 0.15 (1.09) | -0.00 (1.00) |
## |- Median (Range) | -0.23 (-1.55, 4.38) | -0.16 (-1.33, 1.53) | 0.13 (-1.55, 3.06) | -0.16 (-1.55, 4.38) |
## |FamRisk | | | | |
## |- no | 49 (87.5%) | 45 (86.5%) | 43 (81.1%) | 137 (85.1%) |
## |- yes | 7 (12.5%) | 7 (13.5%) | 10 (18.9%) | 24 (14.9%) |
## |T1AgeF | | | | |
## |- Mean (SD) | 6.31 (0.31) | 6.20 (0.30) | 6.26 (0.37) | 6.26 (0.33) |
## |- Median (Range) | 6.31 (5.70, 7.07) | 6.17 (5.68, 6.80) | 6.25 (5.71, 7.39) | 6.26 (5.68, 7.39) |
## |HandScore | | | | |
## |- Mean (SD) | 0.11 (0.97) | -0.21 (1.23) | 0.09 (0.73) | 0.00 (1.00) |
## |- Median (Range) | 0.39 (-3.05, 0.55) | 0.39 (-3.05, 0.55) | 0.39 (-3.05, 0.55) | 0.39 (-3.05, 0.55) |
## |Gender | | | | |
## |- female | 21 (37.5%) | 21 (40.4%) | 24 (45.3%) | 66 (41.0%) |
## |- male | 35 (62.5%) | 31 (59.6%) | 29 (54.7%) | 95 (59.0%) |
## |Lang | | | | |
## |- mono | 53 (94.6%) | 46 (88.5%) | 51 (96.2%) | 150 (93.2%) |
## |- multi | 3 (5.4%) | 6 (11.5%) | 2 (3.8%) | 11 (6.8%) |
## |SON_1st | | | | |
## |- Mean (SD) | 0.02 (1.06) | -0.13 (1.03) | 0.11 (0.91) | -0.00 (1.00) |
## |- Median (Range) | 0.25 (-2.28, 3.62) | -0.38 (-2.28, 1.93) | 0.25 (-2.28, 1.51) | -0.17 (-2.28, 3.62) |
## |hoursPlayed | | | | |
## |- Mean (SD) | 3.96 (0.76) | NA | 3.83 (0.80) | 3.90 (0.78) |
## |- Median (Range) | 4.17 (2.12, 5.24) | NA | 3.98 (1.97, 5.23) | 4.07 (1.97, 5.24) |
## |levelsPlayed | | | | |
## |- Mean (SD) | 473.43 (165.94) | NA | 230.70 (55.57) | 355.40 (174.26) |
## |- Median (Range) | 460.00 (132.00, 1026.00) | NA | 228.00 (119.00, 369.00) | 309.00 (119.00, 1026.00) |
## |sessionsPlayed | | | | |
## |- Mean (SD) | 27.82 (4.82) | NA | 27.74 (5.64) | 27.78 (5.21) |
## |- Median (Range) | 27.00 (18.00, 35.00) | NA | 26.00 (16.00, 37.00) | 26.00 (16.00, 37.00) |
## |MaxLevel | | | | |
## |- Mean (SD) | 136.84 (31.04) | NA | 101.49 (46.82) | 119.65 (43.14) |
## |- Median (Range) | 137.50 (39.00, 178.00) | NA | 87.00 (38.00, 265.00) | 128.00 (38.00, 265.00) |
## |itemsSeen | | | | |
## |- Mean (SD) | 10747.04 (4563.03) | NA | 10276.57 (3569.36) | 10518.28 (4097.98) |
## |- Median (Range) | 9805.50 (2827.00, 22782.00) | NA | 10145.00 (2983.00, 18124.00) | 10010.00 (2827.00, 22782.00) |
## |responsesGiven | | | | |
## |- Mean (SD) | 4729.16 (2259.21) | NA | 2602.30 (742.90) | 3695.00 (2001.38) |
## |- Median (Range) | 4261.00 (1019.00, 10877.00) | NA | 2603.00 (948.00, 4072.00) | 3090.00 (948.00, 10877.00) |
## |T1CF_Total | | | | |
## |- Mean (SD) | 23.44 (9.64) | 23.38 (9.84) | 22.08 (8.27) | 22.96 (9.23) |
## |- Median (Range) | 26.00 (1.00, 40.00) | 24.50 (1.00, 42.00) | 23.00 (2.00, 39.00) | 24.00 (1.00, 42.00) |
## |T1PF_Total | | | | |
## |- Mean (SD) | 29.93 (5.66) | 30.21 (5.33) | 29.23 (5.79) | 29.79 (5.58) |
## |- Median (Range) | 30.00 (16.00, 40.00) | 31.00 (11.00, 38.00) | 30.00 (0.00, 37.00) | 30.00 (0.00, 40.00) |
## |T1.LetKen | | | | |
## |- Mean (SD) | 9.36 (6.42) | 11.27 (5.30) | 8.23 (6.70) | 9.59 (6.27) |
## |- Median (Range) | 8.00 (0.00, 26.00) | 10.00 (0.00, 25.00) | 6.00 (0.00, 25.00) | 8.00 (0.00, 26.00) |
## |T1RANcT | | | | |
## |- Mean (SD) | 67.50 (15.37) | 70.88 (15.92) | 74.96 (16.02) | 71.05 (15.97) |
## |- Median (Range) | 67.00 (40.00, 107.00) | 67.50 (48.00, 115.00) | 70.00 (49.00, 122.00) | 69.00 (40.00, 122.00) |
## |T1RANoT | | | | |
## |- Mean (SD) | 76.25 (17.33) | 75.56 (17.49) | 81.77 (23.82) | 77.86 (19.83) |
## |- Median (Range) | 72.00 (50.00, 120.00) | 76.50 (46.00, 127.00) | 76.00 (49.00, 180.00) | 76.00 (46.00, 180.00) |1.4.2.2 Correlations
Here, we show the correlation matrices of the main outcome variables and covariates at T1 and T2: EMT (one minute reading), CF_Total (CELF PA raw scores), PF_Total (PROEF PA raw scores), RAN (rapid automatized naming) of objects (o) or colors (c), as well as LK (in-game letter knowledge) and WLD (written lexical decision). The asterisk indicates significance at p < .05
corB <- subset(datB,select=c(EMT, T1CF_Total,T2CF_Total, T1PF_Total,T2PF_Total, T1RANcT, T2RANcT, T1RANoT, T2RANoT, T1.LetKen, T2.LetKen, T2.LexDec))
names(corB) <- c("T2EMT","T1CF","T2CF","T1PF","T2PF","T1RANc","T2RANc","T1RANo","T2RANo","T1LK","T2LK","T2WLD")
corBP <- subset(datB,Cond=="Passive",select=c(EMT, T1CF_Total,T2CF_Total, T1PF_Total,T2PF_Total, T1RANcT, T2RANcT, T1RANoT, T2RANoT, T1.LetKen, T2.LetKen, T2.LexDec))
names(corBP) <- c("T2EMT","T1CF","T2CF","T1PF","T2PF","T1RANc","T2RANc","T1RANo","T2RANo","T1LK","T2LK","T2WLD")
corBM <- subset(datB,Cond=="Math",select=c(EMT, T1CF_Total,T2CF_Total, T1PF_Total,T2PF_Total, T1RANcT, T2RANcT, T1RANoT, T2RANoT, T1.LetKen, T2.LetKen, T2.LexDec))
names(corBM) <- c("T2EMT","T1CF","T2CF","T1PF","T2PF","T1RANc","T2RANc","T1RANo","T2RANo","T1LK","T2LK","T2WLD")
corBR <- subset(datB,Cond=="Read",select=c(EMT, T1CF_Total,T2CF_Total, T1PF_Total,T2PF_Total, T1RANcT, T2RANcT, T1RANoT, T2RANoT, T1.LetKen, T2.LetKen, T2.LexDec))
names(corBR) <- c("T2EMT","T1CF","T2CF","T1PF","T2PF","T1RANc","T2RANc","T1RANo","T2RANo","T1LK","T2LK","T2WLD")1.4.2.2.1 Entire Belgian sample
## T2EMT T1CF T2CF T1PF T2PF T1RANc T2RANc T1RANo T2RANo T1LK T2LK
## T2EMT
## T1CF 0.49*
## T2CF 0.33* 0.64*
## T1PF 0.18* 0.53* 0.57*
## T2PF 0.15 0.36* 0.65* 0.57*
## T1RANc -0.36* -0.35* -0.29* -0.28* -0.22*
## T2RANc -0.38* -0.29* -0.23* -0.24* -0.19* 0.62*
## T1RANo -0.31* -0.35* -0.28* -0.40* -0.25* 0.56* 0.54*
## T2RANo -0.34* -0.34* -0.32* -0.38* -0.25* 0.53* 0.65* 0.72*
## T1LK 0.32* 0.46* 0.29* 0.27* 0.15 -0.31* -0.33* -0.28* -0.29*
## T2LK 0.23* 0.36* 0.32* 0.21* 0.17 -0.17 -0.31* -0.21* -0.29* 0.37*
## T2WLD 0.37* 0.49* 0.33* 0.23* 0.15 -0.22* -0.22* -0.14 -0.31* 0.40* 0.55*1.4.2.2.2 Belgian Passive group
## T2EMT T1CF T2CF T1PF T2PF T1RANc T2RANc T1RANo T2RANo T1LK T2LK
## T2EMT
## T1CF 0.58*
## T2CF 0.43* 0.71*
## T1PF 0.30* 0.68* 0.68*
## T2PF 0.35* 0.53* 0.75* 0.66*
## T1RANc -0.34* -0.27 -0.11 -0.25 -0.18
## T2RANc -0.39* -0.20 -0.09 -0.18 -0.14 0.53*
## T1RANo -0.34* -0.31* -0.22 -0.29* -0.25 0.69* 0.56*
## T2RANo -0.41* -0.32* -0.18 -0.23 -0.18 0.51* 0.76* 0.66*
## T1LK 0.31* 0.56* 0.40* 0.50* 0.26 -0.22 -0.32* -0.41* -0.36*
## T2LK 0.40* 0.42* 0.43* 0.21 0.22 -0.18 -0.30* -0.28* -0.30* 0.44*
## T2WLD 0.37* 0.55* 0.42* 0.43* 0.33* -0.21 -0.18 -0.15 -0.29* 0.39* 0.51*1.4.2.2.3 Belgian Math group
## T2EMT T1CF T2CF T1PF T2PF T1RANc T2RANc T1RANo T2RANo T1LK T2LK
## T2EMT
## T1CF 0.50*
## T2CF 0.41* 0.68*
## T1PF 0.32* 0.60* 0.60*
## T2PF 0.25 0.24 0.52* 0.59*
## T1RANc -0.46* -0.36* -0.46* -0.29* -0.05
## T2RANc -0.52* -0.47* -0.40* -0.24 -0.05 0.67*
## T1RANo -0.41* -0.36* -0.28* -0.36* -0.04 0.49* 0.59*
## T2RANo -0.45* -0.41* -0.43* -0.31* -0.03 0.58* 0.57* 0.66*
## T1LK 0.39* 0.53* 0.38* 0.31* 0.11 -0.34* -0.41* -0.26 -0.34*
## T2LK -0.21 0.19 0.09 -0.05 -0.09 0.02 -0.23 0.29 -0.05 0.30
## T2WLD 0.31 0.42 0.26 -0.07 -0.04 -0.39 -0.43* 0.16 -0.46* 0.25 0.57*1.4.2.2.4 Belgian Read group
## T2EMT T1CF T2CF T1PF T2PF T1RANc T2RANc T1RANo T2RANo T1LK T2LK
## T2EMT
## T1CF 0.52*
## T2CF 0.19 0.50*
## T1PF -0.01 0.30* 0.45*
## T2PF -0.05 0.27 0.66* 0.46*
## T1RANc -0.34* -0.40* -0.31* -0.27 -0.34*
## T2RANc -0.29* -0.21 -0.22 -0.28* -0.32* 0.66*
## T1RANo -0.29* -0.39* -0.39* -0.49* -0.39* 0.51* 0.49*
## T2RANo -0.26 -0.25 -0.41* -0.56* -0.54* 0.50* 0.63* 0.79*
## T1LK 0.37* 0.32* 0.18 0.06 0.10 -0.33* -0.29* -0.19 -0.16
## T2LK 0.46* 0.49* 0.40* 0.37* 0.34 -0.35* -0.44* -0.40* -0.48* 0.43*
## T2WLD 0.49* 0.51* 0.28 0.18 0.07 -0.17 -0.16 -0.28 -0.21 0.59* 0.61*1.4.2.3 Reading Fluency
Single word reading fluency at T2 as measures by two custom lists with a time limit of one minute each.
## quartz_off_screen
## 2## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: EMT ~ log(T1RANcT) + T1CF_TotalZ + T1.LetKenZ + Cond + (1 | School)
## Data: datBemt.2
##
## REML criterion at convergence: 369.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9137 -0.6467 -0.1702 0.4866 4.5527
##
## Random effects:
## Groups Name Variance Std.Dev.
## School (Intercept) 0.08639 0.2939
## Residual 0.62233 0.7889
## Number of obs: 150, groups: School, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.79126 1.39908 143.91640 3.425 0.000802 ***
## log(T1RANcT) -1.13122 0.33013 143.01952 -3.427 0.000798 ***
## T1CF_TotalZ 0.26393 0.07987 143.58942 3.305 0.001201 **
## T1.LetKenZ 0.20405 0.07889 143.41895 2.587 0.010687 *
## CondPassive -0.27338 0.16820 141.78417 -1.625 0.106311
## CondRead 0.27244 0.15977 141.49981 1.705 0.090351 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) l(T1RA T1CF_T T1.LKZ CndPss
## lg(T1RANcT) -0.992
## T1CF_TotalZ -0.235 0.230
## T1.LetKenZ -0.140 0.147 -0.455
## CondPassive 0.058 -0.117 0.038 -0.144
## CondRead 0.116 -0.171 0.004 -0.002 0.452## Explained variance## Class Family Link Marginal Conditional AIC
## 1 lmerModLmerTest gaussian identity 0.3014201 0.3865766 371.9283## Sample size by condition## Math Passive Read
## 52 48 50## Trimmed observations:## [1] 7## Trimmed in percent:## [1] 4.347826## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `geom_smooth()` using formula 'y ~ x'
## Model summary for comparison Passive vs Read## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: EMT ~ log(T1RANcT) + T1CF_TotalZ + T1.LetKenZ + Cond + (1 | School)
## Data: datBemt.2
##
## REML criterion at convergence: 369.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9137 -0.6467 -0.1702 0.4866 4.5527
##
## Random effects:
## Groups Name Variance Std.Dev.
## School (Intercept) 0.08639 0.2939
## Residual 0.62233 0.7889
## Number of obs: 150, groups: School, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.51788 1.41880 143.99618 3.184 0.001779 **
## log(T1RANcT) -1.13122 0.33013 143.01951 -3.427 0.000798 ***
## T1CF_TotalZ 0.26393 0.07987 143.58942 3.305 0.001201 **
## T1.LetKenZ 0.20405 0.07889 143.41895 2.587 0.010687 *
## CondMath 0.27338 0.16820 141.78417 1.625 0.106311
## CondRead 0.54582 0.17178 141.96222 3.177 0.001823 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) l(T1RA T1CF_T T1.LKZ CndMth
## lg(T1RANcT) -0.992
## T1CF_TotalZ -0.228 0.230
## T1.LetKenZ -0.155 0.147 -0.455
## CondMath -0.176 0.117 -0.038 0.144
## CondRead -0.015 -0.045 -0.033 0.139 0.558## Computing bootstrap confidence intervals ...##
## 102 message(s): boundary (singular) fit: see ?isSingular## 2.5 % 97.5 %
## .sig01 0.00000000 0.5604682
## .sigma 0.69568260 0.8750023
## (Intercept) 1.89213982 7.5270843
## log(T1RANcT) -1.76792106 -0.4506568
## T1CF_TotalZ 0.10769399 0.4363178
## T1.LetKenZ 0.04361214 0.3570556
## CondPassive -0.59817112 0.0651892
## CondRead -0.04605775 0.6038840## Computing bootstrap confidence intervals ...##
## 84 message(s): boundary (singular) fit: see ?isSingular## 2.5 % 97.5 %
## .sig01 0.00000000 0.5612927
## .sigma 0.69123477 0.8786516
## (Intercept) 1.76481853 7.4670811
## log(T1RANcT) -1.83266666 -0.4714886
## T1CF_TotalZ 0.11412112 0.4225151
## T1.LetKenZ 0.05033333 0.3555478
## CondMath -0.06478391 0.6242061
## CondRead 0.20327189 0.86395251.4.2.4 PA
Phonological awareness at T2 as measured by the CELF-IV-NL and the PROEF.
1.4.2.4.1 CELF-IV-NL Phonological Awareness
## quartz_off_screen
## 2## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: T2CF_TotalZ ~ SON_1st + T1PF_TotalZ + T1CF_TotalZ + Cond + (1 + T1PF_TotalZ | School)
## Data: datBcf.2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## REML criterion at convergence: 314.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00062 -0.59917 0.05576 0.76240 2.21515
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## School (Intercept) 0.05801 0.2409
## T1PF_TotalZ 0.05490 0.2343 -0.89
## Residual 0.39822 0.6310
## Number of obs: 152, groups: School, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.03990 0.14486 5.83354 0.275 0.79246
## SON_1st 0.16690 0.06305 123.44667 2.647 0.00918 **
## T1PF_TotalZ 0.31602 0.12853 4.39290 2.459 0.06418 .
## T1CF_TotalZ 0.43985 0.06831 141.91567 6.439 1.74e-09 ***
## CondPassive -0.31207 0.13210 131.24033 -2.362 0.01963 *
## CondRead -0.10431 0.12541 141.89643 -0.832 0.40698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SON_1s T1PF_T T1CF_T CndPss
## SON_1st 0.016
## T1PF_TotalZ -0.574 -0.140
## T1CF_TotalZ -0.037 -0.361 -0.195
## CondPassive -0.455 0.051 -0.024 0.004
## CondRead -0.429 -0.114 0.015 0.094 0.459## Explained variance## boundary (singular) fit: see ?isSingular## Class Family Link Marginal Conditional AIC
## 1 lmerModLmerTest gaussian identity 0.5420864 0.6406924 316.5835## Sample size by condition## Math Passive Read
## 52 48 52## Trimmed observations:## [1] 5## Trimmed in percent:## [1] 3.10559## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `geom_smooth()` using formula 'y ~ x'
## Model summary for comparison Passive vs Read## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: T2CF_TotalZ ~ SON_1st + T1PF_TotalZ + T1CF_TotalZ + Cond + (1 + T1PF_TotalZ | School)
## Data: datBcf.2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## REML criterion at convergence: 314.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00062 -0.59917 0.05576 0.76240 2.21515
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## School (Intercept) 0.05801 0.2409
## T1PF_TotalZ 0.05490 0.2343 -0.89
## Residual 0.39822 0.6310
## Number of obs: 152, groups: School, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.27217 0.14495 6.33391 -1.878 0.10693
## SON_1st 0.16690 0.06305 123.44666 2.647 0.00918 **
## T1PF_TotalZ 0.31602 0.12853 4.39290 2.459 0.06418 .
## T1CF_TotalZ 0.43985 0.06831 141.91566 6.439 1.74e-09 ***
## CondMath 0.31207 0.13210 131.24033 2.362 0.01963 *
## CondRead 0.20776 0.13399 131.06617 1.551 0.12342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SON_1s T1PF_T T1CF_T CndMth
## SON_1st 0.062
## T1PF_TotalZ -0.596 -0.140
## T1CF_TotalZ -0.033 -0.361 -0.195
## CondMath -0.456 -0.051 0.024 -0.004
## CondRead -0.460 -0.157 0.038 0.084 0.556## Computing bootstrap confidence intervals ...##
## 711 message(s): boundary (singular) fit: see ?isSingular
## 36 warning(s): NA values in sdcor matrix converted to 0## 2.5 % 97.5 %
## .sig01 0.000000000 0.45369374
## .sig02 -1.000000000 1.00000000
## .sig03 0.005644197 0.44941338
## .sigma 0.554702518 0.69969505
## (Intercept) -0.239423581 0.32319262
## SON_1st 0.044165615 0.29130676
## T1PF_TotalZ 0.060609227 0.57339020
## T1CF_TotalZ 0.298514714 0.57251358
## CondPassive -0.563894302 -0.04320932
## CondRead -0.344376944 0.14333415## Computing bootstrap confidence intervals ...##
## 693 message(s): boundary (singular) fit: see ?isSingular
## 33 warning(s): NA values in sdcor matrix converted to 0## 2.5 % 97.5 %
## .sig01 0.000000000 0.461607244
## .sig02 -1.000000000 1.000000000
## .sig03 0.007060223 0.454370049
## .sigma 0.550815859 0.706469612
## (Intercept) -0.562552031 0.003501017
## SON_1st 0.033879098 0.292656778
## T1PF_TotalZ 0.080863485 0.564718194
## T1CF_TotalZ 0.304782446 0.588459296
## CondMath 0.047141815 0.572305155
## CondRead -0.063046567 0.4731838761.4.2.4.2 PROEF Phonological Awareness
## quartz_off_screen
## 2## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: T2PF_TotalZ ~ T1CF_TotalPc + Cond + FamRisk + T1PF_TotalZ + (1 + T1PF_TotalZ | School)
## Data: datBpf.2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## REML criterion at convergence: 350.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6718 -0.4615 -0.0136 0.5862 2.4022
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## School (Intercept) 0.05968 0.2443
## T1PF_TotalZ 0.11580 0.3403 0.34
## Residual 0.49597 0.7043
## Number of obs: 150, groups: School, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.066667 0.197777 17.695726 -0.337 0.7400
## T1CF_TotalPc 0.006442 0.003029 142.806595 2.127 0.0352 *
## CondPassive -0.210174 0.150662 139.097203 -1.395 0.1652
## CondRead -0.199270 0.140761 138.159598 -1.416 0.1591
## FamRiskyes -0.234602 0.175181 140.521442 -1.339 0.1827
## T1PF_TotalZ 0.405484 0.174501 4.158058 2.324 0.0783 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) T1CF_T CndPss CondRd FmRsky
## T1CF_TotlPc -0.618
## CondPassive -0.362 -0.011
## CondRead -0.351 0.018 0.453
## FamRiskyes -0.174 0.066 0.004 -0.070
## T1PF_TotalZ 0.288 -0.199 -0.042 -0.013 0.037## Explained variance## Class Family Link Marginal Conditional AIC
## 1 lmerModLmerTest gaussian identity 0.2854256 0.472845 350.0313## Sample size by condition## Math Passive Read
## 52 47 51## Trimmed observations:## [1] 7## Trimmed in percent:## [1] 4.347826## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `geom_smooth()` using formula 'y ~ x'
## Model summary for comparison Passive vs Read## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: T2PF_TotalZ ~ T1CF_TotalPc + Cond + FamRisk + T1PF_TotalZ + (1 + T1PF_TotalZ | School)
## Data: datBpf.2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## REML criterion at convergence: 350.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6718 -0.4615 -0.0136 0.5862 2.4022
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## School (Intercept) 0.05968 0.2443
## T1PF_TotalZ 0.11580 0.3403 0.34
## Residual 0.49597 0.7043
## Number of obs: 150, groups: School, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.276841 0.200536 20.678106 -1.381 0.1822
## T1CF_TotalPc 0.006442 0.003029 142.806595 2.127 0.0352 *
## CondMath 0.210174 0.150662 139.097199 1.395 0.1652
## CondRead 0.010904 0.152675 136.232318 0.071 0.9432
## FamRiskyes -0.234602 0.175181 140.521441 -1.339 0.1827
## T1PF_TotalZ 0.405484 0.174501 4.158060 2.324 0.0783 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) T1CF_T CndMth CondRd FmRsky
## T1CF_TotlPc -0.618
## CondMath -0.394 0.011
## CondRead -0.394 0.027 0.569
## FamRiskyes -0.169 0.066 -0.004 -0.068
## T1PF_TotalZ 0.253 -0.199 0.042 0.029 0.037## Computing bootstrap confidence intervals ...##
## 433 message(s): boundary (singular) fit: see ?isSingular
## 43 warning(s): NA values in sdcor matrix converted to 0## 2.5 % 97.5 %
## .sig01 0.0000000000 0.48269234
## .sig02 -1.0000000000 1.00000000
## .sig03 0.0199435963 0.60276719
## .sigma 0.6154907740 0.78432781
## (Intercept) -0.4776526349 0.34247497
## T1CF_TotalPc 0.0003218559 0.01250006
## CondPassive -0.5259308555 0.08974560
## CondRead -0.4833103421 0.08202462
## FamRiskyes -0.5754436678 0.10933238
## T1PF_TotalZ 0.0752537922 0.74123204## Computing bootstrap confidence intervals ...##
## 480 message(s): boundary (singular) fit: see ?isSingular
## 44 warning(s): NA values in sdcor matrix converted to 0## 2.5 % 97.5 %
## .sig01 0.0000000000 0.45937205
## .sig02 -1.0000000000 1.00000000
## .sig03 0.0262184040 0.64838874
## .sigma 0.6197543955 0.78553786
## (Intercept) -0.6785631756 0.12283451
## T1CF_TotalPc 0.0004110071 0.01231995
## CondMath -0.0871827590 0.52842200
## CondRead -0.2985500670 0.30758592
## FamRiskyes -0.6167136423 0.09877992
## T1PF_TotalZ 0.0518092141 0.756051381.4.2.5 RAN
Rapid Automatized Naming of objects and colours at T2. Both of these models are problematic as they show a high multicollinearity of the Cond*PreTest interaction - indicating that there is not much change from pre to post which can be attributed to condition.
1.4.2.5.1 RAN colours
##
## Call:
## lm(formula = T2RANcT ~ Cond * T1RANcT, data = datBrC.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.5929 -5.7267 0.3601 4.6996 19.2800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.48504 4.89891 4.590 9.58e-06 ***
## CondPassive 1.27480 7.54872 0.169 0.866
## CondRead -5.44453 7.35907 -0.740 0.461
## T1RANcT 0.48943 0.07087 6.906 1.48e-10 ***
## CondPassive:T1RANcT -0.01589 0.10812 -0.147 0.883
## CondRead:T1RANcT 0.05551 0.10063 0.552 0.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.069 on 144 degrees of freedom
## Multiple R-squared: 0.4964, Adjusted R-squared: 0.4789
## F-statistic: 28.39 on 5 and 144 DF, p-value: < 2.2e-16## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## 2.5 % 97.5 %
## (Intercept) 12.8019680 32.1681040
## CondPassive -13.6458088 16.1954113
## CondRead -19.9902946 9.1012287
## T1RANcT 0.3493538 0.6295094
## CondPassive:T1RANcT -0.2295942 0.1978137
## CondRead:T1RANcT -0.1433869 0.25440551.4.2.5.2 RAN objects
##
## Call:
## lm(formula = T2RANcT ~ Cond * T1RANcT, data = datBrO.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.5222 -6.3315 0.3091 5.4736 24.1091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.45397 5.94122 3.948 0.000126 ***
## CondPassive 5.50818 8.92403 0.617 0.538108
## CondRead -8.99419 9.16830 -0.981 0.328317
## T1RANcT 0.47995 0.08758 5.480 1.98e-07 ***
## CondPassive:T1RANcT -0.09689 0.12980 -0.746 0.456647
## CondRead:T1RANcT 0.12044 0.12743 0.945 0.346249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.099 on 137 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4051, Adjusted R-squared: 0.3834
## F-statistic: 18.66 on 5 and 137 DF, p-value: 4.148e-14## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## 2.5 % 97.5 %
## (Intercept) 11.7056169 35.2023281
## CondPassive -12.1384819 23.1548350
## CondRead -27.1238727 9.1354980
## T1RANcT 0.3067638 0.6531395
## CondPassive:T1RANcT -0.3535560 0.1597709
## CondRead:T1RANcT -0.1315465 0.37242981.4.2.6 In-Game
Apart from the offline pencil and paper tests in 4.1.3 and 4.1.4 we also did in-game assessments in form of letter-speech-sound-identification (LSSI) and written lexical decision (WLD). Both of these feature seperate analyses for accuracy and response times.
1.4.2.6.1 LSSI Accuracy
accLKB <-glmer(correctSelections ~ lvl + trialRTs + T1CF_TotalZ + stream * Cond + (1+trialRTs|correctResponse) + (0+T1CF_TotalZ|correctResponse) + (1|Subj), family=binomial, data=LKaccB, glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=50000) ))
LKaccB.2 = LKaccB[abs(scale(resid(accLKB))) < 2 , ]
accLKB.2 <-glmer(correctSelections ~ lvl + trialRTs + T1CF_TotalZ + stream * Cond + (1+trialRTs|correctResponse) + (0+T1CF_TotalZ|correctResponse) + (1|Subj), family=binomial, data=LKaccB.2, glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=50000) ))
LKaccB.2$Cond <- relevel(LKaccB.2$Cond,ref="Passive") # refit the model to get passive as baseline for plotting
accLKB.3 <-glmer(correctSelections ~ lvl + trialRTs + T1CF_TotalZ + stream * Cond + (1+trialRTs|correctResponse) + (0+T1CF_TotalZ|correctResponse) + (1|Subj), family=binomial, data=LKaccB.2, glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=50000) ))
plot(effect("stream*Cond",accLKB.3), ylab="probability of correct response", main="Letter Knowledge Accuracy",
multiline=TRUE, ci.style="bars", xlab="Session",lines=list(col=c("black"),lty=c(1,2,3)),x.var="stream",
lattice=list(key.args=list(title="Condition",x=.05, y=.95,cex=1.2,cex.title=1.2)))
pdf("Ch3_B_LSSIacc.pdf", width=6,height=4)
plot(effect("stream*Cond",accLKB.3), ylab="probability of correct response", main="",
multiline=TRUE, ci.style="bars", xlab="Session",lines=list(col=c("black"),lty=c(1,2,3)),x.var="stream",
lattice=list(key.args=list(title="Condition",x=.05, y=.95,cex=1.2,cex.title=1.2)))
dev.off()## quartz_off_screen
## 2summary(accLKB.2)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( logit )
## Formula: correctSelections ~ lvl + trialRTs + T1CF_TotalZ + stream * Cond + (1 + trialRTs | correctResponse) + (0 + T1CF_TotalZ | correctResponse) + (1 | Subj)
## Data: LKaccB.2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## AIC BIC logLik deviance df.resid
## 7061.5 7157.0 -3516.8 7033.5 6742
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.5020 -0.6442 0.2467 0.5983 6.5378
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subj (Intercept) 0.534012 0.73076
## correctResponse T1CF_TotalZ 0.028902 0.17001
## correctResponse.1 (Intercept) 1.476235 1.21500
## trialRTs 0.002834 0.05324 -0.80
## Number of obs: 6756, groups: Subj, 101; correctResponse, 32
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.32991 0.29537 -1.117 0.26403
## lvlLetKenEasy 0.16466 0.08721 1.888 0.05902 .
## trialRTs -0.07127 0.01357 -5.252 1.51e-07 ***
## T1CF_TotalZ 0.69394 0.08991 7.719 1.18e-14 ***
## streamT2 1.77871 0.14021 12.686 < 2e-16 ***
## CondPassive 0.52582 0.22209 2.368 0.01790 *
## CondRead 0.02034 0.24006 0.085 0.93246
## streamT2:CondPassive -0.49154 0.16298 -3.016 0.00256 **
## streamT2:CondRead 0.33767 0.17975 1.878 0.06031 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lvlLKE trlRTs T1CF_T strmT2 CndPss CondRd sT2:CP
## lvlLetKnEsy -0.227
## trialRTs -0.499 -0.068
## T1CF_TotalZ 0.010 0.004 -0.018
## streamT2 -0.218 -0.033 0.049 0.047
## CondPassive -0.519 -0.005 -0.004 0.058 0.289
## CondRead -0.482 0.004 -0.002 0.113 0.265 0.649
## strmT2:CndP 0.182 0.025 -0.023 -0.020 -0.845 -0.343 -0.227
## strmT2:CndR 0.168 0.008 -0.023 -0.002 -0.758 -0.221 -0.355 0.654writeLines('Explained variance')## Explained variancersquared.glmm(list(accLKB.2))## Class Family Link Marginal Conditional AIC
## 1 glmerMod binomial logit 0.206102 0.4748549 7061.514writeLines('Sample size by condition')## Sample size by conditionsummary(aggregate(Cond~Subj,data=accLKB.2@frame,FUN=unique)$Cond)## Math Passive Read
## 21 47 33writeLines('Trimmed observations:')## Trimmed observations:nrow(LKaccB[abs(scale(resid(accLKB))) > 2 , ])## [1] 132writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(LKaccB[abs(scale(resid(accLKB))) < 2 , ]) / nrow(LKaccB)) * 100## [1] 1.844606(accLKB.2bs <- confint(accLKB.2, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 0.58851243 0.85120574
## .sig02 0.03246917 0.25017349
## .sig03 0.89060569 1.54219520
## .sig04 -1.00000000 -0.46767888
## .sig05 0.02427070 0.07600698
## (Intercept) -0.88411879 0.32543645
## lvlLetKenEasy -0.01145247 0.33758540
## trialRTs -0.09758046 -0.04390026
## T1CF_TotalZ 0.51717649 0.88225595
## streamT2 1.51287569 2.06082365
## CondPassive 0.07702440 0.95293074
## CondRead -0.44700271 0.48952336
## streamT2:CondPassive -0.83244064 -0.18793447
## streamT2:CondRead -0.01930842 0.67953986summary(accLKB.3)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( logit )
## Formula: correctSelections ~ lvl + trialRTs + T1CF_TotalZ + stream * Cond + (1 + trialRTs | correctResponse) + (0 + T1CF_TotalZ | correctResponse) + (1 | Subj)
## Data: LKaccB.2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## AIC BIC logLik deviance df.resid
## 7061.5 7157.0 -3516.8 7033.5 6742
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.5020 -0.6442 0.2467 0.5983 6.5378
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subj (Intercept) 0.534014 0.73076
## correctResponse T1CF_TotalZ 0.028901 0.17000
## correctResponse.1 (Intercept) 1.476270 1.21502
## trialRTs 0.002834 0.05324 -0.80
## Number of obs: 6756, groups: Subj, 101; correctResponse, 32
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.19591 0.26160 0.749 0.45392
## lvlLetKenEasy 0.16466 0.08721 1.888 0.05902 .
## trialRTs -0.07127 0.01357 -5.252 1.51e-07 ***
## T1CF_TotalZ 0.69394 0.08991 7.719 1.18e-14 ***
## streamT2 1.28717 0.08707 14.783 < 2e-16 ***
## CondMath -0.52581 0.22210 -2.367 0.01791 *
## CondRead -0.50547 0.19439 -2.600 0.00931 **
## streamT2:CondMath 0.49155 0.16299 3.016 0.00256 **
## streamT2:CondRead 0.82920 0.14329 5.787 7.17e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lvlLKE trlRTs T1CF_T strmT2 CndMth CondRd sT2:CM
## lvlLetKnEsy -0.260
## trialRTs -0.567 -0.068
## T1CF_TotalZ 0.060 0.004 -0.018
## streamT2 -0.160 -0.007 0.035 0.037
## CondMath -0.263 0.005 0.004 -0.058 0.176
## CondRead -0.292 0.010 0.002 0.074 0.203 0.341
## strmT2:CndM 0.085 -0.025 0.023 0.021 -0.510 -0.343 -0.111
## strmT2:CndR 0.100 -0.018 -0.002 0.021 -0.575 -0.113 -0.360 0.317(accLKB.3bs <- confint(accLKB.3, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 0.59560388 0.83133318
## .sig02 0.02222055 0.25507792
## .sig03 0.85457745 1.51701000
## .sig04 -1.00000000 -0.45365277
## .sig05 0.02338274 0.07390020
## (Intercept) -0.30318317 0.73641901
## lvlLetKenEasy -0.01075068 0.35034616
## trialRTs -0.09971771 -0.04560826
## T1CF_TotalZ 0.52250826 0.87967190
## streamT2 1.11587513 1.46490254
## CondMath -0.99941934 -0.09489142
## CondRead -0.86550616 -0.12942681
## streamT2:CondMath 0.17570747 0.82409193
## streamT2:CondRead 0.54412934 1.117799431.4.2.6.2 LSSI Response Time
bc <- MASS::boxcox(lm(trialRTs ~ lvl + log(T1RANcT) + T1.LetKenZ + TrialNum + prevRT + stream * Cond, data=LKrtB),plotit=FALSE)
lambda <- bc$x[which.max(bc$y)]
LKrtB$RTinv = LKrtB$trialRTs^lambda
LKrtB$prevRTinv = LKrtB$prevRT^lambda
rtLKB = lmer(RTinv ~ lvl + log(T1RANcT) + T1.LetKenZ + TrialNum + prevRTinv + stream * Cond + (1|itemsOnScreen) + (0+stream|Subj) + (1|correctResponse) + (1|Class), data=LKrtB)
LKrtB.2 = LKrtB[abs(scale(resid(rtLKB))) < 2 , ]
rtLKB.2 = lmer(RTinv ~ lvl + log(T1RANcT) + T1.LetKenZ + TrialNum + prevRTinv + stream * Cond + (1|itemsOnScreen) + (0+stream|Subj) + (1|correctResponse) + (1|Class), data=LKrtB.2 )
LKrtB.2$Cond <- relevel(LKrtB.2$Cond,ref="Passive") # refit the model to get passive as baseline for plotting
rtLKB.3 = lmer(RTinv ~ lvl + log(T1RANcT) + T1.LetKenZ + TrialNum + prevRTinv + stream * Cond + (1|itemsOnScreen) + (0+stream|Subj) + (1|correctResponse) + (1|Class), data=LKrtB.2)
LKrtef = effect("stream*Cond",rtLKB.3)
LKrtef$fit = LKrtef$fit^(1/lambda)
LKrtef$upper = LKrtef$upper^(1/lambda)
LKrtef$lower = LKrtef$lower^(1/lambda)
plot(LKrtef, ylab="Median response time (seconds)", main="Letter Knowledge Response Times", multiline=TRUE,
confint=list(style="bars"), xlab="Session", lines=list(col=c("black"), lty=c(1,2,3)),x.var="stream",
lattice=list(key.args=list(title="Condition",x=.62, y=.95,cex=1.2,cex.title=1.2)))
summary(rtLKB.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: RTinv ~ lvl + log(T1RANcT) + T1.LetKenZ + TrialNum + prevRTinv + stream * Cond + (1 | itemsOnScreen) + (0 + stream | Subj) + (1 | correctResponse) + (1 | Class)
## Data: LKrtB.2
##
## REML criterion at convergence: -12039.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3888 -0.6410 0.0046 0.6614 3.8514
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## itemsOnScreen (Intercept) 8.017e-05 0.008954
## Subj streamT1 1.852e-04 0.013610
## streamT2 1.350e-04 0.011618 0.47
## correctResponse (Intercept) 1.745e-04 0.013211
## Class (Intercept) 2.191e-05 0.004681
## Residual 1.306e-03 0.036136
## Number of obs: 3279, groups: itemsOnScreen, 741; Subj, 105; correctResponse, 30; Class, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.373e-01 2.800e-02 1.135e+02 33.482 < 2e-16 ***
## lvlLetKenEasy -1.496e-02 5.610e-03 2.828e+00 -2.667 0.080853 .
## log(T1RANcT) -1.500e-02 6.460e-03 9.404e+01 -2.322 0.022380 *
## T1.LetKenZ 6.401e-03 1.814e-03 8.313e+01 3.528 0.000685 ***
## TrialNum -1.765e-03 4.612e-04 5.220e+02 -3.827 0.000146 ***
## prevRTinv 3.195e-02 7.090e-03 3.184e+03 4.507 6.82e-06 ***
## streamT2 2.254e-02 4.184e-03 7.336e+01 5.389 8.23e-07 ***
## CondPassive -1.017e-03 5.802e-03 1.065e+01 -0.175 0.864104
## CondRead 1.413e-03 6.481e-03 1.161e+01 0.218 0.831148
## streamT2:CondPassive -5.658e-03 4.980e-03 7.054e+01 -1.136 0.259813
## streamT2:CondRead -2.250e-03 5.552e-03 7.536e+01 -0.405 0.686474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lvlLKE l(T1RA T1.LKZ TrilNm prvRTn strmT2 CndPss CondRd sT2:CP
## lvlLetKnEsy -0.031
## lg(T1RANcT) -0.949 0.005
## T1.LetKenZ -0.214 -0.005 0.258
## TrialNum -0.071 -0.068 -0.009 -0.037
## prevRTinv -0.247 0.040 0.018 -0.032 -0.027
## streamT2 -0.092 -0.017 0.009 0.052 -0.027 -0.046
## CondPassive -0.018 -0.007 -0.115 -0.047 0.014 -0.014 0.479
## CondRead 0.005 -0.002 -0.123 -0.006 -0.013 -0.011 0.430 0.572
## strmT2:CndP 0.086 0.013 -0.010 -0.029 -0.015 0.026 -0.836 -0.568 -0.360
## strmT2:CndR 0.080 0.002 -0.007 0.012 0.019 -0.004 -0.746 -0.361 -0.586 0.627writeLines('Explained variance')## Explained variancer.squaredGLMM(rtLKB.2) ## R2m R2c
## [1,] 0.09523256 0.3187728writeLines('Sample size by condition')## Sample size by conditionsummary(aggregate(Cond~Subj,data=rtLKB.2@frame,FUN=unique)$Cond)## Math Passive Read
## 23 49 33writeLines('Trimmed observations:')## Trimmed observations:nrow(LKrtB[abs(scale(resid(rtLKB))) > 2 , ])## [1] 166writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(LKrtB[abs(scale(resid(rtLKB))) < 2 , ]) / nrow(LKrtB))*100## [1] 4.262969performance::check_model(rtLKB.2)
acf(resid(rtLKB.2),main="autocorrelation of observations")
(LKrtB.2bs <- confint(rtLKB.2, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 3.056149e-10 0.0152181009
## .sig02 9.778588e-03 0.0171548371
## .sig03 1.617789e-01 0.7492298826
## .sig04 8.914382e-03 0.0140634091
## .sig05 9.397917e-03 0.0172127545
## .sig06 0.000000e+00 0.0087633184
## .sigma 3.518221e-02 0.0370543626
## (Intercept) 8.812632e-01 0.9921513987
## lvlLetKenEasy -2.628612e-02 -0.0038535528
## log(T1RANcT) -2.763350e-02 -0.0020181413
## T1.LetKenZ 2.887642e-03 0.0097897250
## TrialNum -2.630172e-03 -0.0008140065
## prevRTinv 1.755197e-02 0.0459319950
## streamT2 1.446415e-02 0.0305706938
## CondPassive -1.249804e-02 0.0101464817
## CondRead -1.186895e-02 0.0135634652
## streamT2:CondPassive -1.530936e-02 0.0043331545
## streamT2:CondRead -1.358167e-02 0.0088660316summary(rtLKB.3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: RTinv ~ lvl + log(T1RANcT) + T1.LetKenZ + TrialNum + prevRTinv + stream * Cond + (1 | itemsOnScreen) + (0 + stream | Subj) + (1 | correctResponse) + (1 | Class)
## Data: LKrtB.2
##
## REML criterion at convergence: -12039.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3888 -0.6410 0.0046 0.6614 3.8514
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## itemsOnScreen (Intercept) 8.017e-05 0.008954
## Subj streamT1 1.852e-04 0.013610
## streamT2 1.350e-04 0.011618 0.47
## correctResponse (Intercept) 1.745e-04 0.013211
## Class (Intercept) 2.191e-05 0.004681
## Residual 1.306e-03 0.036136
## Number of obs: 3279, groups: itemsOnScreen, 741; Subj, 105; correctResponse, 30; Class, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.363e-01 2.849e-02 1.107e+02 32.865 < 2e-16 ***
## lvlLetKenEasy -1.496e-02 5.610e-03 2.828e+00 -2.667 0.080853 .
## log(T1RANcT) -1.500e-02 6.460e-03 9.404e+01 -2.322 0.022380 *
## T1.LetKenZ 6.401e-03 1.814e-03 8.313e+01 3.528 0.000685 ***
## TrialNum -1.765e-03 4.612e-04 5.220e+02 -3.827 0.000146 ***
## prevRTinv 3.195e-02 7.090e-03 3.184e+03 4.507 6.82e-06 ***
## streamT2 1.689e-02 2.730e-03 6.703e+01 6.185 4.21e-08 ***
## CondMath 1.017e-03 5.802e-03 1.065e+01 0.175 0.864104
## CondRead 2.431e-03 5.715e-03 7.387e+00 0.425 0.682739
## streamT2:CondMath 5.658e-03 4.980e-03 7.054e+01 1.136 0.259813
## streamT2:CondRead 3.408e-03 4.580e-03 7.498e+01 0.744 0.459138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lvlLKE l(T1RA T1.LKZ TrilNm prvRTn strmT2 CndMth CondRd sT2:CM
## lvlLetKnEsy -0.031
## lg(T1RANcT) -0.956 0.005
## T1.LetKenZ -0.220 -0.005 0.258
## TrialNum -0.067 -0.068 -0.009 -0.037
## prevRTinv -0.246 0.040 0.018 -0.032 -0.027
## streamT2 -0.046 -0.002 -0.005 0.028 -0.068 -0.023
## CondMath -0.186 0.007 0.115 0.047 -0.014 0.014 0.303
## CondRead -0.051 0.005 -0.022 0.041 -0.028 0.002 0.310 0.367
## strmT2:CndM 0.031 -0.013 0.010 0.029 0.015 -0.026 -0.543 -0.568 -0.169
## strmT2:CndR 0.040 -0.012 0.003 0.046 0.039 -0.034 -0.590 -0.181 -0.546 0.328(LKrtB.3bs <- confint(rtLKB.3, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 0.000000000 0.014776699
## .sig02 0.009875536 0.016936384
## .sig03 0.138955789 0.739840615
## .sig04 0.008887174 0.014125669
## .sig05 0.009191420 0.016975512
## .sig06 0.000000000 0.008720795
## .sigma 0.035154125 0.037148426
## (Intercept) 0.878857444 0.991797355
## lvlLetKenEasy -0.026111591 -0.004250092
## log(T1RANcT) -0.027156052 -0.001907220
## T1.LetKenZ 0.003288355 0.009821642
## TrialNum -0.002668453 -0.000871387
## prevRTinv 0.017646804 0.045927019
## streamT2 0.012061735 0.022270030
## CondMath -0.010175827 0.013044194
## CondRead -0.008377829 0.013844676
## streamT2:CondMath -0.003909305 0.015519873
## streamT2:CondRead -0.005760380 0.0125860551.4.2.6.3 WLD Accuracy
accLDB <- glmer(correctSelections ~ Cond + targettype + T1CF_TotalZ + (1|target) + (1|Subj), family=binomial, data=LDaccB, glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=50000) ))
LDaccB.2 = LDaccB[abs(scale(resid(accLDB))) < 2 , ]
accLDB.2 <- glmer(correctSelections ~ Cond + targettype + T1CF_TotalZ + (1|target) + (1|Subj), family=binomial, data=LDaccB.2, glmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=50000) ))
LDaccB.2$Cond <- relevel(LDaccB.2$Cond,ref="Passive") # refit the model to get passive as baseline for plotting
accLDB.3 <- glmer(correctSelections ~ Cond + targettype + T1CF_TotalZ + (1|target) + (1|Subj), family=binomial, data=LDaccB.2, glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=50000) ))
plot(effect("Cond",accLDB.3), ylab="probability of correct response", main="Written Lexical Decision Accuracy",multiline=TRUE, ci.style="bars", xlab="",lines=list(col=c("black"),lty=0))
summary(accLDB.2)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( logit )
## Formula: correctSelections ~ Cond + targettype + T1CF_TotalZ + (1 | target) + (1 | Subj)
## Data: LDaccB.2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## AIC BIC logLik deviance df.resid
## 3229.8 3270.8 -1607.9 3215.8 2553
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0708 -0.9462 0.4744 0.8218 2.0182
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subj (Intercept) 0.006573 0.08107
## target (Intercept) 0.510651 0.71460
## Number of obs: 2560, groups: Subj, 101; target, 30
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.888e-01 2.173e-01 3.631 0.000283 ***
## CondPassive -5.331e-05 1.208e-01 0.000 0.999648
## CondRead -1.952e-02 1.280e-01 -0.153 0.878787
## targettypewrong -5.911e-01 2.755e-01 -2.146 0.031913 *
## T1CF_TotalZ 2.674e-01 4.669e-02 5.726 1.03e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CndPss CondRd trgtty
## CondPassive -0.402
## CondRead -0.378 0.691
## trgttypwrng -0.641 -0.003 -0.004
## T1CF_TotalZ 0.030 0.035 0.114 -0.015writeLines('Explained variance')## Explained variancersquared.glmm(list(accLDB.2))## Class Family Link Marginal Conditional AIC
## 1 glmerMod binomial logit 0.03794057 0.168644 3229.818writeLines('Sample size by condition')## Sample size by conditionsummary(aggregate(Cond~Subj,data=accLDB.2@frame,FUN=unique)$Cond)## Math Passive Read
## 20 48 33writeLines('Trimmed observations:')## Trimmed observations:nrow(LDaccB[abs(scale(resid(accLDB))) > 2 , ])## [1] 1writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(LDaccB[abs(scale(resid(accLDB))) < 2 , ]) / nrow(LDaccB)) * 100## [1] 0.03745318(accLDB.2bs <- confint(accLDB.2, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 0.0000000 0.23260199
## .sig02 0.4758814 0.90127453
## (Intercept) 0.3776357 1.25051816
## CondPassive -0.2537051 0.23896910
## CondRead -0.2795643 0.23850201
## targettypewrong -1.0978086 -0.07098937
## T1CF_TotalZ 0.1759053 0.358605141.4.2.6.4 WLD Response Time
bc <- MASS::boxcox(lm(trialRTs ~ T1.LetKenZ + T1CF_TotalZ + targettype + T1AgeF + prevRT + Cond, data=LDrtB),plotit=FALSE)
lambda <- bc$x[which.max(bc$y)]
LDrtB$RTinv = LDrtB$trialRTs^lambda
LDrtB$prevRTinv = LDrtB$prevRT^lambda
rtLDB = lmer(RTinv ~ T1.LetKenZ + T1CF_TotalZ + targettype + T1AgeF + prevRTinv + Cond + (1|target) + (1+prevRTinv|Subj),data=LDrtB)
LDrtB.2 = LDrtB[abs(scale(resid(rtLDB))) < 2 , ]
rtLDB.2 = lmer(RTinv ~ T1.LetKenZ + T1CF_TotalZ + targettype + T1AgeF + prevRTinv + Cond + (1|target) + (1+prevRTinv|Subj),data=LDrtB.2)
LDrtB.2$Cond <- relevel(LDrtB.2$Cond,ref="Passive") # refit the model to get passive as baseline for plotting
rtLDB.3 = lmer(RTinv ~ T1.LetKenZ + T1CF_TotalZ + targettype + T1AgeF + prevRTinv + Cond + (1|target) + (1+prevRTinv|Subj),data=LDrtB.2)
LKrtef = effect("Cond",rtLDB.3)
LKrtef$fit = LKrtef$fit^(1/lambda)
LKrtef$upper = LKrtef$upper^(1/lambda)
LKrtef$lower = LKrtef$lower^(1/lambda)
plot(LKrtef, ylab="Median response time (seconds)", main="Written Lexical Decision Response Times", multiline=TRUE, ci.style="bars", xlab="", lines=list(col=c("black"), lty=0))
# compute effect size for age
summary(rtLDB.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: RTinv ~ T1.LetKenZ + T1CF_TotalZ + targettype + T1AgeF + prevRTinv + Cond + (1 | target) + (1 + prevRTinv | Subj)
## Data: LDrtB.2
##
## REML criterion at convergence: 747.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2168 -0.6345 -0.0459 0.5976 3.3212
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subj (Intercept) 0.066306 0.25750
## prevRTinv 0.004995 0.07068 -0.81
## target (Intercept) 0.008723 0.09339
## Residual 0.084286 0.29032
## Number of obs: 1313, groups: Subj, 100; target, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.69467 0.39484 87.54257 6.825 1.1e-09 ***
## T1.LetKenZ -0.04831 0.03261 79.18842 -1.481 0.142479
## T1CF_TotalZ 0.06623 0.02360 84.01687 2.807 0.006221 **
## targettypewrong 0.12262 0.03882 28.04879 3.159 0.003772 **
## T1AgeF -0.19504 0.06153 84.83820 -3.170 0.002123 **
## prevRTinv 0.06794 0.01786 73.66440 3.805 0.000291 ***
## CondPassive 0.05352 0.05281 86.99414 1.013 0.313636
## CondRead 0.06282 0.05615 86.58906 1.119 0.266329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) T1.LKZ T1CF_T trgtty T1AgeF prvRTn CndPss
## T1.LetKenZ 0.306
## T1CF_TotalZ -0.007 -0.483
## trgttypwrng -0.052 -0.010 -0.011
## T1AgeF -0.988 -0.284 -0.003 0.006
## prevRTinv -0.124 0.035 -0.057 -0.012 0.049
## CondPassive -0.170 -0.108 0.086 -0.002 0.077 -0.027
## CondRead 0.023 0.060 0.104 -0.009 -0.105 -0.075 0.654writeLines('Explained variance')## Explained variancersquared.glmm(list(rtLDB.2))## Class Family Link Marginal Conditional AIC
## 1 lmerModLmerTest gaussian identity 0.1057042 0.4023973 733.3377writeLines('Sample size by condition')## Sample size by conditionsummary(aggregate(Cond~Subj,data=rtLDB.2@frame,FUN=unique))## Subj Cond
## 7 : 1 Math :20
## 12 : 1 Passive:47
## 57 : 1 Read :33
## 60 : 1
## 66 : 1
## 69 : 1
## (Other):94writeLines('Trimmed observations:')## Trimmed observations:nrow(LDrtB[abs(scale(resid(rtLDB))) > 2 , ])## [1] 68writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(LDrtB[abs(scale(resid(rtLDB))) < 2 , ]) / nrow(LDrtB)) * 100## [1] 4.579125performance::check_model(rtLDB)
acf(resid(rtLDB.2),main="autocorrelation of observations")
(rtLDB.2bs <- confint(rtLDB.2, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 0.18744544 0.32846060
## .sig02 -1.00000000 -0.54088864
## .sig03 0.01400177 0.12030901
## .sig04 0.05932034 0.12605630
## .sigma 0.27748255 0.30081130
## (Intercept) 1.88026530 3.41832904
## T1.LetKenZ -0.11371788 0.01435871
## T1CF_TotalZ 0.02021556 0.11698591
## targettypewrong 0.04355840 0.20156252
## T1AgeF -0.30489316 -0.06822864
## prevRTinv 0.03113782 0.10195543
## CondPassive -0.04830069 0.15973385
## CondRead -0.05305058 0.177614671.4.3 ANALYSIS 2 - Dutch Sample
This is the analysis for the Dutch sample (N = 86) which only features an active (math) control condition.
1.4.3.1 Descriptives
Here, we show results of Chi-squared/Fisher’s exact test for Gender, Language, Handedness and Familial risk in Dutch sample There are more multilingual children in the Dutch math cohort.
1.4.3.1.1 Chi-square test Gender x Condition
chisq.test(matrix(c(23,25,23,15),ncol=2,dimnames=list(c("female","male"), c("Math","Read"))))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: matrix(c(23, 25, 23, 15), ncol = 2, dimnames = list(c("female", "male"), c("Math", "Read")))
## X-squared = 0.89606, df = 1, p-value = 0.34381.4.3.1.2 Fisher’s exact test Multilingualism x Condition
fisher.test(matrix(c(12,36,2,36),ncol=2,dimnames=list(c("multilingual","monolingual"), c("Math","Read"))))##
## Fisher's Exact Test for Count Data
##
## data: matrix(c(12, 36, 2, 36), ncol = 2, dimnames = list(c("multilingual", "monolingual"), c("Math", "Read")))
## p-value = 0.01783
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.18392 57.86893
## sample estimates:
## odds ratio
## 5.8917571.4.3.1.3 Fisher’s exact test Handedness x Condition
fisher.test(matrix(c(3,45,6,32),ncol=2,dimnames=list(c("lefthander","righthander"), c("Math","Read"))))##
## Fisher's Exact Test for Count Data
##
## data: matrix(c(3, 45, 6, 32), ncol = 2, dimnames = list(c("lefthander", "righthander"), c("Math", "Read")))
## p-value = 0.1751
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.05421993 1.83485472
## sample estimates:
## odds ratio
## 0.35988731.4.3.1.4 Chi-square test Familial Risk x Condition
chisq.test(matrix(c(11,37,6,32),ncol=2,dimnames=list(c("namilial risk","no familial risk"), c("Math","Read"))))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: matrix(c(11, 37, 6, 32), ncol = 2, dimnames = list(c("namilial risk", "no familial risk"), c("Math", "Read")))
## X-squared = 0.30424, df = 1, p-value = 0.5812ANOVAs comparing the remaining T1 measures revealed that the reading group knew marginally less letters at T1.
summary(aov(T1AgeF~Cond,data=datNL))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 1 0.079 0.07905 0.825 0.366
## Residuals 84 8.044 0.09576summary(aov(T1.LetKen~Cond,data=datNL))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 1 108.6 108.63 3.574 0.0621 .
## Residuals 84 2552.9 30.39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(T1.LetKen~Cond,data=datNL))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = T1.LetKen ~ Cond, data = datNL)
##
## $Cond
## diff lwr upr p adj
## Read-Math -2.263158 -4.643618 0.1173023 0.0621234summary(aov(T1CF_TotalPc~Cond,data=datNL))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 1 311 311.1 0.729 0.396
## Residuals 84 35858 426.9summary(aov(T1PF_TotalPc~Cond,data=datNL))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 1 1243 1243.3 1.994 0.162
## Residuals 84 52368 623.4summary(aov(T1RANoT~Cond,data=datNL))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 1 483 483.4 2.51 0.117
## Residuals 84 16176 192.6summary(aov(T1RANcT~Cond,data=datNL))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 1 19 19.43 0.112 0.738
## Residuals 84 14549 173.20summary(aov(SON_1st~Cond,data=datNL))## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 1 0.14 0.1434 0.142 0.707
## Residuals 84 84.86 1.0102Summaries of the Dutch data with number of observations, means, range and standard deviation per variable and gaming condition.
tab <- tableby(Gender ~ EMT + Cond + T1AgeF + HandScore + FamRisk + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_Total + T1PF_Total + T1.LetKen + T1RANcT + T1RANoT, data = datNL, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | female (N=46) | male (N=40) | Total (N=86) |
## |:-----------------|:---------------------------:|:---------------------------:|:---------------------------:|
## |EMT | | | |
## |- Mean (SD) | 0.17 (1.02) | -0.19 (0.96) | 0.00 (1.00) |
## |- Median (Range) | -0.20 (-1.23, 2.07) | -0.43 (-1.63, 2.07) | -0.34 (-1.63, 2.07) |
## |Cond | | | |
## |- Math | 23 (50.0%) | 25 (62.5%) | 48 (55.8%) |
## |- Read | 23 (50.0%) | 15 (37.5%) | 38 (44.2%) |
## |T1AgeF | | | |
## |- Mean (SD) | 6.21 (0.33) | 6.24 (0.29) | 6.23 (0.31) |
## |- Median (Range) | 6.24 (5.46, 6.72) | 6.28 (5.55, 6.83) | 6.26 (5.46, 6.83) |
## |HandScore | | | |
## |- Mean (SD) | -0.04 (1.08) | 0.04 (0.92) | -0.00 (1.00) |
## |- Median (Range) | 0.32 (-3.14, 0.48) | 0.40 (-3.14, 0.48) | 0.32 (-3.14, 0.48) |
## |FamRisk | | | |
## |- no | 37 (80.4%) | 32 (80.0%) | 69 (80.2%) |
## |- yes | 9 (19.6%) | 8 (20.0%) | 17 (19.8%) |
## |Lang | | | |
## |- mono | 37 (80.4%) | 35 (87.5%) | 72 (83.7%) |
## |- multi | 9 (19.6%) | 5 (12.5%) | 14 (16.3%) |
## |SON_1st | | | |
## |- Mean (SD) | -0.05 (1.04) | 0.06 (0.96) | 0.00 (1.00) |
## |- Median (Range) | 0.05 (-2.15, 4.87) | 0.05 (-2.15, 2.24) | 0.05 (-2.15, 4.87) |
## |hoursPlayed | | | |
## |- Mean (SD) | 2.94 (0.66) | 2.94 (0.67) | 2.94 (0.66) |
## |- Median (Range) | 2.94 (1.68, 4.95) | 2.78 (1.98, 4.94) | 2.93 (1.68, 4.95) |
## |levelsPlayed | | | |
## |- Mean (SD) | 281.28 (93.38) | 287.70 (70.10) | 284.27 (82.96) |
## |- Median (Range) | 268.50 (101.00, 607.00) | 295.00 (134.00, 432.00) | 280.00 (101.00, 607.00) |
## |sessionsPlayed | | | |
## |- Mean (SD) | 26.67 (2.98) | 26.70 (3.54) | 26.69 (3.23) |
## |- Median (Range) | 27.00 (20.00, 32.00) | 26.00 (20.00, 34.00) | 27.00 (20.00, 34.00) |
## |MaxLevel | | | |
## |- Mean (SD) | 144.67 (44.97) | 147.07 (45.61) | 145.79 (45.02) |
## |- Median (Range) | 136.00 (71.00, 265.00) | 136.00 (62.00, 265.00) | 136.00 (62.00, 265.00) |
## |itemsSeen | | | |
## |- Mean (SD) | 9216.80 (4254.36) | 8874.80 (4140.55) | 9057.73 (4180.63) |
## |- Median (Range) | 8229.00 (3304.00, 18509.00) | 7696.00 (3814.00, 20113.00) | 7844.00 (3304.00, 20113.00) |
## |responsesGiven | | | |
## |- Mean (SD) | 2729.26 (1063.78) | 2795.25 (815.21) | 2759.95 (951.37) |
## |- Median (Range) | 2602.00 (999.00, 6984.00) | 2928.00 (1295.00, 4522.00) | 2686.00 (999.00, 6984.00) |
## |T1CF_Total | | | |
## |- Mean (SD) | 33.07 (6.24) | 30.73 (6.97) | 31.98 (6.66) |
## |- Median (Range) | 34.00 (17.00, 44.00) | 31.50 (8.00, 40.00) | 32.00 (8.00, 44.00) |
## |T1PF_Total | | | |
## |- Mean (SD) | 34.30 (4.06) | 33.38 (3.94) | 33.87 (4.01) |
## |- Median (Range) | 35.50 (23.00, 40.00) | 33.50 (21.00, 40.00) | 35.00 (21.00, 40.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 25.20 (4.18) | 21.55 (6.38) | 23.50 (5.60) |
## |- Median (Range) | 27.00 (12.00, 30.00) | 23.00 (2.00, 30.00) | 25.00 (2.00, 30.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 57.02 (11.41) | 60.98 (14.66) | 58.86 (13.09) |
## |- Median (Range) | 57.00 (39.00, 82.00) | 58.00 (43.00, 102.00) | 57.50 (39.00, 102.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 65.91 (13.85) | 71.15 (13.81) | 68.35 (14.00) |
## |- Median (Range) | 67.00 (43.00, 126.00) | 66.50 (40.00, 97.00) | 67.00 (40.00, 126.00) |tab <- tableby(FamRisk ~ EMT + Cond + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_Total + T1PF_Total + T1.LetKen + T1RANcT + T1RANoT, data = datNL, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | no (N=69) | yes (N=17) | Total (N=86) |
## |:-----------------|:---------------------------:|:---------------------------:|:---------------------------:|
## |EMT | | | |
## |- Mean (SD) | 0.01 (0.97) | -0.06 (1.15) | 0.00 (1.00) |
## |- Median (Range) | -0.34 (-1.23, 2.07) | -0.38 (-1.63, 2.07) | -0.34 (-1.63, 2.07) |
## |Cond | | | |
## |- Math | 37 (53.6%) | 11 (64.7%) | 48 (55.8%) |
## |- Read | 32 (46.4%) | 6 (35.3%) | 38 (44.2%) |
## |T1AgeF | | | |
## |- Mean (SD) | 6.23 (0.33) | 6.20 (0.23) | 6.23 (0.31) |
## |- Median (Range) | 6.27 (5.46, 6.83) | 6.22 (5.82, 6.56) | 6.26 (5.46, 6.83) |
## |HandScore | | | |
## |- Mean (SD) | -0.01 (1.04) | 0.05 (0.85) | -0.00 (1.00) |
## |- Median (Range) | 0.48 (-3.14, 0.48) | 0.32 (-3.14, 0.48) | 0.32 (-3.14, 0.48) |
## |Gender | | | |
## |- female | 37 (53.6%) | 9 (52.9%) | 46 (53.5%) |
## |- male | 32 (46.4%) | 8 (47.1%) | 40 (46.5%) |
## |Lang | | | |
## |- mono | 58 (84.1%) | 14 (82.4%) | 72 (83.7%) |
## |- multi | 11 (15.9%) | 3 (17.6%) | 14 (16.3%) |
## |SON_1st | | | |
## |- Mean (SD) | -0.04 (1.05) | 0.15 (0.77) | 0.00 (1.00) |
## |- Median (Range) | 0.05 (-2.15, 4.87) | 0.05 (-0.83, 2.24) | 0.05 (-2.15, 4.87) |
## |hoursPlayed | | | |
## |- Mean (SD) | 2.92 (0.67) | 3.02 (0.66) | 2.94 (0.66) |
## |- Median (Range) | 2.94 (1.68, 4.95) | 2.82 (2.23, 4.94) | 2.93 (1.68, 4.95) |
## |levelsPlayed | | | |
## |- Mean (SD) | 282.29 (85.66) | 292.29 (72.74) | 284.27 (82.96) |
## |- Median (Range) | 275.00 (101.00, 607.00) | 316.00 (152.00, 414.00) | 280.00 (101.00, 607.00) |
## |sessionsPlayed | | | |
## |- Mean (SD) | 26.91 (3.36) | 25.76 (2.54) | 26.69 (3.23) |
## |- Median (Range) | 27.00 (20.00, 34.00) | 25.00 (23.00, 33.00) | 27.00 (20.00, 34.00) |
## |MaxLevel | | | |
## |- Mean (SD) | 147.28 (45.24) | 139.76 (44.97) | 145.79 (45.02) |
## |- Median (Range) | 136.00 (71.00, 265.00) | 136.00 (62.00, 241.00) | 136.00 (62.00, 265.00) |
## |itemsSeen | | | |
## |- Mean (SD) | 9172.83 (4253.74) | 8590.59 (3957.31) | 9057.73 (4180.63) |
## |- Median (Range) | 8025.00 (3304.00, 20113.00) | 7664.00 (5069.00, 18594.00) | 7844.00 (3304.00, 20113.00) |
## |responsesGiven | | | |
## |- Mean (SD) | 2763.04 (1006.70) | 2747.41 (707.86) | 2759.95 (951.37) |
## |- Median (Range) | 2842.00 (999.00, 6984.00) | 2485.00 (1882.00, 4212.00) | 2686.00 (999.00, 6984.00) |
## |T1CF_Total | | | |
## |- Mean (SD) | 32.64 (5.98) | 29.29 (8.62) | 31.98 (6.66) |
## |- Median (Range) | 33.00 (19.00, 44.00) | 28.00 (8.00, 41.00) | 32.00 (8.00, 44.00) |
## |T1PF_Total | | | |
## |- Mean (SD) | 34.04 (3.95) | 33.18 (4.29) | 33.87 (4.01) |
## |- Median (Range) | 35.00 (21.00, 40.00) | 32.00 (23.00, 40.00) | 35.00 (21.00, 40.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 23.38 (5.78) | 24.00 (4.92) | 23.50 (5.60) |
## |- Median (Range) | 25.00 (2.00, 30.00) | 25.00 (15.00, 30.00) | 25.00 (2.00, 30.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 56.48 (12.19) | 68.53 (12.45) | 58.86 (13.09) |
## |- Median (Range) | 56.00 (39.00, 102.00) | 68.00 (45.00, 96.00) | 57.50 (39.00, 102.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 66.33 (12.59) | 76.53 (16.70) | 68.35 (14.00) |
## |- Median (Range) | 66.00 (40.00, 94.00) | 72.00 (56.00, 126.00) | 67.00 (40.00, 126.00) |tab <- tableby(Cond ~ EMT + FamRisk + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_Total + T1PF_Total + T1.LetKen + T1RANcT + T1RANoT, data = datNL, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | Math (N=48) | Read (N=38) | Total (N=86) |
## |:-----------------|:---------------------------:|:----------------------------:|:---------------------------:|
## |EMT | | | |
## |- Mean (SD) | 0.18 (1.04) | -0.22 (0.92) | 0.00 (1.00) |
## |- Median (Range) | -0.20 (-1.63, 2.07) | -0.47 (-1.23, 2.07) | -0.34 (-1.63, 2.07) |
## |FamRisk | | | |
## |- no | 37 (77.1%) | 32 (84.2%) | 69 (80.2%) |
## |- yes | 11 (22.9%) | 6 (15.8%) | 17 (19.8%) |
## |T1AgeF | | | |
## |- Mean (SD) | 6.25 (0.32) | 6.19 (0.29) | 6.23 (0.31) |
## |- Median (Range) | 6.32 (5.55, 6.83) | 6.20 (5.46, 6.69) | 6.26 (5.46, 6.83) |
## |HandScore | | | |
## |- Mean (SD) | 0.10 (0.85) | -0.12 (1.17) | -0.00 (1.00) |
## |- Median (Range) | 0.32 (-3.14, 0.48) | 0.40 (-3.14, 0.48) | 0.32 (-3.14, 0.48) |
## |Gender | | | |
## |- female | 23 (47.9%) | 23 (60.5%) | 46 (53.5%) |
## |- male | 25 (52.1%) | 15 (39.5%) | 40 (46.5%) |
## |Lang | | | |
## |- mono | 36 (75.0%) | 36 (94.7%) | 72 (83.7%) |
## |- multi | 12 (25.0%) | 2 (5.3%) | 14 (16.3%) |
## |SON_1st | | | |
## |- Mean (SD) | -0.04 (0.87) | 0.05 (1.15) | 0.00 (1.00) |
## |- Median (Range) | 0.05 (-2.15, 2.24) | 0.05 (-2.15, 4.87) | 0.05 (-2.15, 4.87) |
## |hoursPlayed | | | |
## |- Mean (SD) | 2.70 (0.43) | 3.26 (0.77) | 2.94 (0.66) |
## |- Median (Range) | 2.72 (1.96, 3.80) | 3.24 (1.68, 4.95) | 2.93 (1.68, 4.95) |
## |levelsPlayed | | | |
## |- Mean (SD) | 318.25 (77.64) | 241.34 (69.02) | 284.27 (82.96) |
## |- Median (Range) | 317.50 (191.00, 607.00) | 237.50 (101.00, 377.00) | 280.00 (101.00, 607.00) |
## |sessionsPlayed | | | |
## |- Mean (SD) | 25.48 (2.02) | 28.21 (3.81) | 26.69 (3.23) |
## |- Median (Range) | 25.00 (20.00, 30.00) | 29.00 (20.00, 34.00) | 27.00 (20.00, 34.00) |
## |MaxLevel | | | |
## |- Mean (SD) | 135.94 (23.95) | 158.24 (60.35) | 145.79 (45.02) |
## |- Median (Range) | 135.50 (62.00, 178.00) | 164.50 (71.00, 265.00) | 136.00 (62.00, 265.00) |
## |itemsSeen | | | |
## |- Mean (SD) | 6652.12 (2116.28) | 12096.39 (4179.95) | 9057.73 (4180.63) |
## |- Median (Range) | 6722.50 (3304.00, 15832.00) | 11825.50 (4191.00, 20113.00) | 7844.00 (3304.00, 20113.00) |
## |responsesGiven | | | |
## |- Mean (SD) | 2753.85 (1001.08) | 2767.66 (897.87) | 2759.95 (951.37) |
## |- Median (Range) | 2668.50 (1197.00, 6984.00) | 2767.00 (999.00, 4522.00) | 2686.00 (999.00, 6984.00) |
## |T1CF_Total | | | |
## |- Mean (SD) | 31.54 (6.71) | 32.53 (6.64) | 31.98 (6.66) |
## |- Median (Range) | 32.00 (8.00, 41.00) | 32.50 (19.00, 44.00) | 32.00 (8.00, 44.00) |
## |T1PF_Total | | | |
## |- Mean (SD) | 33.17 (4.26) | 34.76 (3.51) | 33.87 (4.01) |
## |- Median (Range) | 33.50 (21.00, 40.00) | 35.00 (27.00, 40.00) | 35.00 (21.00, 40.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 24.50 (4.74) | 22.24 (6.36) | 23.50 (5.60) |
## |- Median (Range) | 26.00 (13.00, 30.00) | 24.00 (2.00, 30.00) | 25.00 (2.00, 30.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 58.44 (11.80) | 59.39 (14.71) | 58.86 (13.09) |
## |- Median (Range) | 57.50 (41.00, 96.00) | 57.50 (39.00, 102.00) | 57.50 (39.00, 102.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 70.46 (15.11) | 65.68 (12.13) | 68.35 (14.00) |
## |- Median (Range) | 68.00 (40.00, 126.00) | 65.50 (43.00, 90.00) | 67.00 (40.00, 126.00) |1.4.3.2 Correlations
Here, we show the correlation matrices of the main outcome variables and covariates at T1 and T2: EMT (one minute reading), CF_Total (CELF PA raw scores), PF_Total (PROEF PA raw scores), RAN (rapid automatized naming) of objects (o) or colors (c), as well as LK (in-game letter knowledge) and WLD (written lexical decision). An asterisk indicates significance at p < .05
corNL <- subset(datNL,select=c(EMT, T1CF_Total,T2CF_Total, T1PF_Total,T2PF_Total, T1RANcT, T2RANcT, T1RANoT, T2RANoT, T1.LetKen, T2.LetKen, T2.LexDec))
names(corNL) <- c("T2EMT","T1CF","T2CF","T1PF","T2PF","T1RANc","T2RANc","T1RANo","T2RANo","T1LK","T2LK","T2WLD")
corNLM <- subset(datNL,Cond=="Math",select=c(EMT, T1CF_Total,T2CF_Total, T1PF_Total,T2PF_Total, T1RANcT, T2RANcT, T1RANoT, T2RANoT, T1.LetKen, T2.LetKen, T2.LexDec))
names(corNLM) <- c("T2EMT","T1CF","T2CF","T1PF","T2PF","T1RANc","T2RANc","T1RANo","T2RANo","T1LK","T2LK","T2WLD")
corNLR <- subset(datNL,Cond=="Read",select=c(EMT, T1CF_Total,T2CF_Total, T1PF_Total,T2PF_Total, T1RANcT, T2RANcT, T1RANoT, T2RANoT, T1.LetKen, T2.LetKen, T2.LexDec))
names(corNLR) <- c("T2EMT","T1CF","T2CF","T1PF","T2PF","T1RANc","T2RANc","T1RANo","T2RANo","T1LK","T2LK","T2WLD")1.4.3.2.1 Entire Dutch Sample
## T2EMT T1CF T2CF T1PF T2PF T1RANc T2RANc T1RANo T2RANo T1LK T2LK
## T2EMT
## T1CF 0.38*
## T2CF 0.34* 0.55*
## T1PF 0.31* 0.47* 0.17
## T2PF 0.37* 0.57* 0.48* 0.43*
## T1RANc -0.32* -0.41* -0.33* -0.13 -0.26*
## T2RANc -0.33* -0.40* -0.39* -0.17 -0.20 0.73*
## T1RANo -0.30* -0.32* -0.14 -0.20 -0.11 0.59* 0.44*
## T2RANo -0.34* -0.27* -0.19 -0.22* -0.21 0.49* 0.49* 0.66*
## T1LK 0.45* 0.47* 0.45* 0.19 0.36* -0.40* -0.46* -0.34* -0.33*
## T2LK 0.32* 0.19 0.25* 0.17 0.26* -0.26* -0.25* -0.28* -0.18 0.37*
## T2WLD 0.41* 0.41* 0.29* 0.18 0.34* -0.20 -0.17 -0.17 -0.13 0.41* 0.34*1.4.3.2.2 Dutch Math Group
## T2EMT T1CF T2CF T1PF T2PF T1RANc T2RANc T1RANo T2RANo T1LK T2LK
## T2EMT
## T1CF 0.35*
## T2CF 0.16 0.70*
## T1PF 0.35* 0.49* 0.23
## T2PF 0.34* 0.68* 0.58* 0.58*
## T1RANc -0.34* -0.43* -0.21 -0.17 -0.18
## T2RANc -0.30* -0.36* -0.23 -0.09 -0.09 0.69*
## T1RANo -0.35* -0.33* -0.09 -0.14 -0.14 0.55* 0.36*
## T2RANo -0.36* -0.32* -0.12 -0.21 -0.23 0.51* 0.43* 0.67*
## T1LK 0.35* 0.51* 0.51* 0.16 0.40* -0.37* -0.26 -0.36* -0.27
## T2LK 0.15 0.27 0.15 0.29* 0.16 -0.03 0.01 -0.27 -0.12 0.31*
## T2WLD 0.34* 0.47* 0.36* 0.26 0.38* -0.31* -0.18 -0.25 -0.13 0.49* 0.171.4.3.2.3 Dutch Read Group
## T2EMT T1CF T2CF T1PF T2PF T1RANc T2RANc T1RANo T2RANo T1LK T2LK
## T2EMT
## T1CF 0.47*
## T2CF 0.61* 0.37*
## T1PF 0.41* 0.44* 0.10
## T2PF 0.44* 0.47* 0.38* 0.28
## T1RANc -0.32 -0.40* -0.44* -0.12 -0.33*
## T2RANc -0.37* -0.46* -0.55* -0.30 -0.30 0.77*
## T1RANo -0.33* -0.28 -0.23 -0.24 -0.07 0.71* 0.60*
## T2RANo -0.35* -0.18 -0.29 -0.20 -0.19 0.49* 0.59* 0.65*
## T1LK 0.53* 0.50* 0.42* 0.36* 0.35* -0.42* -0.64* -0.45* -0.48*
## T2LK 0.43* 0.17 0.35 0.16 0.34 -0.42* -0.46* -0.56* -0.48* 0.32
## T2WLD 0.51* 0.34 0.17 0.07 0.29 -0.06 -0.12 -0.11 -0.22 0.27 0.47*1.4.3.3 Reading Fluency
Single word reading fluency at T2 as measures by two custom lists with a time limit of one minute each.
## boundary (singular) fit: see ?isSingular
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: EMT ~ HandScore + T1PF_TotalZ + T1.LetKenZ + Cond + (1 | Class)
## Data: datNLemt.2
##
## REML criterion at convergence: 171.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9108 -0.6936 -0.1244 0.6708 2.6002
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.01109 0.1053
## Residual 0.46464 0.6816
## Number of obs: 78, groups: Class, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.06727 0.13056 1.39930 0.515 0.675933
## HandScore -0.32140 0.08810 71.68593 -3.648 0.000497 ***
## T1PF_TotalZ 0.27146 0.08424 72.69679 3.222 0.001904 **
## T1.LetKenZ 0.24912 0.08090 71.59112 3.079 0.002940 **
## CondRead -0.30637 0.19348 1.59783 -1.583 0.283579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) HndScr T1PF_T T1.LKZ
## HandScore -0.091
## T1PF_TotalZ 0.072 0.245
## T1.LetKenZ -0.089 0.062 -0.248
## CondRead -0.691 0.079 -0.128 0.183## Explained variance## boundary (singular) fit: see ?isSingular## Class Family Link Marginal Conditional AIC
## 1 lmerModLmerTest gaussian identity 0.4138466 0.4275107 171.2494## Sample size by condition## Math Read
## 42 36## Trimmed observations:## [1] 6## Trimmed in percent:## [1] 7.142857## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `geom_smooth()` using formula 'y ~ x'
## Computing bootstrap confidence intervals ...##
## 483 message(s): boundary (singular) fit: see ?isSingular## 2.5 % 97.5 %
## .sig01 0.00000000 0.34621077
## .sigma 0.57004910 0.79366215
## (Intercept) -0.18228291 0.32647355
## HandScore -0.49755926 -0.15494947
## T1PF_TotalZ 0.11243107 0.44528462
## T1.LetKenZ 0.08747288 0.40652492
## CondRead -0.66142906 0.068699931.4.3.4 PA
Phonological awareness at T2 as measured by the CELF-IV-NL and the PROEF.
1.4.3.4.1 CELF-IV-NL Phonological Awareness
##
## Call:
## lm(formula = T2CF_TotalZ ~ T1.LetKenZ + T1CF_TotalZ + Cond, data = datNLcf.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5770 -0.5057 0.1039 0.4939 1.2189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06284 0.10215 0.615 0.5402
## T1.LetKenZ 0.19089 0.09018 2.117 0.0374 *
## T1CF_TotalZ 0.52802 0.09143 5.775 1.45e-07 ***
## CondRead 0.03626 0.15742 0.230 0.8184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6895 on 79 degrees of freedom
## Multiple R-squared: 0.4679, Adjusted R-squared: 0.4477
## F-statistic: 23.16 on 3 and 79 DF, p-value: 7.449e-11## Sample size by condition## Math Read
## 47 36## Trimmed observations:## [1] 2## Trimmed in percent:## [1] 2.352941
## 2.5 % 97.5 %
## (Intercept) -0.14048226 0.2661717
## T1.LetKenZ 0.01138896 0.3703962
## T1CF_TotalZ 0.34602735 0.7100189
## CondRead -0.27708420 0.34959641.4.3.4.2 PROEF Phonological Awareness
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: T2PF_TotalZ ~ T1PF_TotalZ + T1CF_TotalZ + Cond + (1 | Class)
## Data: datNLpf.2
##
## REML criterion at convergence: 166.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5931 -0.6577 0.0413 0.7821 1.9531
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.02245 0.1498
## Residual 0.40672 0.6377
## Number of obs: 81, groups: Class, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.02227 0.14207 1.87792 0.157 0.891
## T1PF_TotalZ 0.12135 0.08178 76.96299 1.484 0.142
## T1CF_TotalZ 0.57917 0.08223 76.93570 7.044 6.9e-10 ***
## CondRead 0.06635 0.21173 2.27398 0.313 0.780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) T1PF_T T1CF_T
## T1PF_TotalZ 0.074
## T1CF_TotalZ -0.013 -0.475
## CondRead -0.679 -0.144 0.023## Explained variance## Class Family Link Marginal Conditional AIC
## 1 lmerModLmerTest gaussian identity 0.4958194 0.5221933 167.1584## Sample size by condition## Math Read
## 47 34## Trimmed observations:## [1] 5## Trimmed in percent:## [1] 5.813953## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `geom_smooth()` using formula 'y ~ x'
## Computing bootstrap confidence intervals ...##
## 384 message(s): boundary (singular) fit: see ?isSingular## 2.5 % 97.5 %
## .sig01 0.00000000 0.4093421
## .sigma 0.53173459 0.7371162
## (Intercept) -0.25788475 0.3067094
## T1PF_TotalZ -0.04396725 0.3011417
## T1CF_TotalZ 0.42764211 0.7538871
## CondRead -0.34385814 0.51824381.4.3.5 RAN
Rapid Automatized Naming of objects and colours at T2. Both of these models are problematic as they show a high multicollinearity of the Cond*PreTest interaction - indicating that there is not much change from pre to post which can be attributed to condition.
1.4.3.5.1 RAN Colours
##
## Call:
## lm(formula = T2RANcT ~ Cond * T1RANcT, data = datNLrC.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.9925 -3.9795 0.6341 3.5747 12.8613
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.39457 4.52363 3.403 0.00106 **
## CondRead 1.07781 6.09448 0.177 0.86009
## T1RANcT 0.62663 0.07676 8.163 4.93e-12 ***
## CondRead:T1RANcT -0.02757 0.10185 -0.271 0.78732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.974 on 77 degrees of freedom
## Multiple R-squared: 0.6559, Adjusted R-squared: 0.6425
## F-statistic: 48.93 on 3 and 77 DF, p-value: < 2.2e-16## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## 2.5 % 97.5 %
## (Intercept) 6.3868735 24.4022681
## CondRead -11.0578429 13.2134596
## T1RANcT 0.4737814 0.7794834
## CondRead:T1RANcT -0.2303855 0.17523741.4.3.5.2 RAN Objects
##
## Call:
## lm(formula = T2RANcT ~ Cond * T1RANcT, data = datNLrO.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.4005 -4.9093 0.5972 3.1293 21.6014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.2436 5.9252 1.898 0.0619 .
## CondRead 5.1870 7.8607 0.660 0.5115
## T1RANcT 0.7193 0.1025 7.020 1.15e-09 ***
## CondRead:T1RANcT -0.1044 0.1328 -0.786 0.4343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.348 on 70 degrees of freedom
## Multiple R-squared: 0.5943, Adjusted R-squared: 0.5769
## F-statistic: 34.18 on 3 and 70 DF, p-value: 1.019e-13## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## 2.5 % 97.5 %
## (Intercept) -0.5737568 23.0609439
## CondRead -10.4906007 20.8646639
## T1RANcT 0.5149382 0.9236265
## CondRead:T1RANcT -0.3691707 0.16038971.4.3.6 In-Game
Apart from the offline pencil and paper tests in 4.2.3 and 4.2.4 we also did in-game assessments in form of letter-speech-sound-identification (LSSI) and written lexical decision (WLD). Both of these feature seperate analyses for accuracy and response times.
1.4.3.6.1 LSSI Accuracy
accLKNL <-glmer(correctSelections ~ T1CF_TotalZ + Cond * stream + Gender + lvl + trialRTs + (1+trialRTs|correctResponse) + (1+trialRTs|Subj), family=binomial, data=LKaccNL, glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=50000) ))
LKaccNL.2 = LKaccNL[abs(scale(resid(accLKNL))) < 2 , ]
accLKNL.2 <-glmer(correctSelections ~ T1CF_TotalZ + Cond * stream + Gender + lvl + trialRTs + (1+trialRTs|correctResponse) + (1+trialRTs|Subj), family=binomial, data=LKaccNL.2, glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=50000) ))
plot(effect("Cond*stream",accLKNL.2), ylab="probability of correct response", main="Letter Knowledge Accuracy",
multiline=TRUE, ci.style="bars", xlab="", lines=list(col=c("black"), lty=c(2,3)),x.var="stream",
lattice=list(key.args=list(title="Condition",x=.05, y=.95,cex=1.2,cex.title=1.2)))
summary(accLKNL.2)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( logit )
## Formula: correctSelections ~ T1CF_TotalZ + Cond * stream + Gender + lvl + trialRTs + (1 + trialRTs | correctResponse) + (1 + trialRTs | Subj)
## Data: LKaccNL.2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## AIC BIC logLik deviance df.resid
## 1638.1 1728.9 -805.0 1610.1 4849
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7842 0.0134 0.0436 0.1332 5.0556
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subj (Intercept) 3.49852 1.8704
## trialRTs 0.04529 0.2128 -0.76
## correctResponse (Intercept) 8.29981 2.8809
## trialRTs 0.09177 0.3029 -0.54
## Number of obs: 4863, groups: Subj, 75; correctResponse, 32
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.92166 0.72330 6.804 1.01e-11 ***
## T1CF_TotalZ 0.82912 0.25532 3.247 0.00116 **
## CondRead -1.11106 0.36549 -3.040 0.00237 **
## streamT2 2.40742 0.24199 9.948 < 2e-16 ***
## Gendermale -1.61921 0.35255 -4.593 4.37e-06 ***
## lvlLetKenEasy 1.52361 0.22147 6.879 6.01e-12 ***
## trialRTs -0.32342 0.08026 -4.030 5.59e-05 ***
## CondRead:streamT2 -0.40637 0.33620 -1.209 0.22677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) T1CF_T CondRd strmT2 Gndrml lvlLKE trlRTs
## T1CF_TotalZ -0.223
## CondRead -0.202 -0.066
## streamT2 0.007 0.062 0.085
## Gendermale -0.335 0.152 0.084 -0.086
## lvlLetKnEsy -0.134 0.006 -0.019 0.083 -0.060
## trialRTs -0.540 0.021 -0.023 -0.021 0.029 -0.093
## CndRd:strT2 0.049 0.000 -0.226 -0.637 0.017 -0.038 -0.008writeLines('Explained variance')## Explained variancersquared.glmm(list(accLKNL.2))## Class Family Link Marginal Conditional AIC
## 1 glmerMod binomial logit 0.2658415 0.8068688 1638.054writeLines('Sample size by condition')## Sample size by conditionsummary(aggregate(Cond~Subj,data=accLKNL.2@frame,FUN=unique)$Cond)## Math Read
## 48 27writeLines('Trimmed observations:')## Trimmed observations:nrow(LKaccNL[abs(scale(resid(accLKNL))) > 2 , ])## [1] 470writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(LKaccNL[abs(scale(resid(accLKNL))) < 2 , ]) / nrow(LKaccNL)) * 100## [1] 8.813051(accLKNL.2bs <- confint(accLKNL.2, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 1.19234340 2.39135156
## .sig02 -0.95278169 -0.40591361
## .sig03 0.09770797 0.29889081
## .sig04 1.83756145 3.80710361
## .sig05 -0.79989811 -0.06947328
## .sig06 0.17859374 0.43548408
## (Intercept) 3.49035662 6.64625965
## T1CF_TotalZ 0.35344520 1.40175247
## CondRead -1.90491248 -0.43822198
## streamT2 1.91238089 2.98846661
## Gendermale -2.40844192 -0.90218306
## lvlLetKenEasy 1.03363459 1.97653552
## trialRTs -0.47084218 -0.15539969
## CondRead:streamT2 -1.11882773 0.292191181.4.3.6.2 LSSI Response Time
bc <- MASS::boxcox(lm(trialRTs ~ T1PF_TotalZ + T1AgeF + prevRT + TrialNum + stream * Cond, data=LKrtNL),plotit=FALSE)
lambda <- bc$x[which.max(bc$y)]
LKrtNL$RTinv = LKrtNL$trialRTs^lambda
LKrtNL$prevRTinv = LKrtNL$prevRT^lambda
rtLKNL = lmer(RTinv ~ T1PF_TotalZ + T1AgeF + prevRTinv + TrialNum + stream * Cond + (1+T1.LetKenZ|itemsOnScreen) + (0+stream|Subj) + (0+stream|correctResponse) + (1|Class), data=LKrtNL, control=lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun=50000) ))
LKrtNL.2 = LKrtNL[abs(scale(resid(rtLKNL))) < 2 , ]
rtLKNL.2 = lmer(RTinv ~ T1PF_TotalZ + T1AgeF + prevRTinv + TrialNum + stream * Cond + (1+T1.LetKenZ|itemsOnScreen) + (0+stream|Subj) + (0+stream|correctResponse) + (1|Class), data=LKrtNL.2, control=lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun=50000) ))
LKrtef = effect("stream*Cond",rtLKNL.2)
LKrtef$fit = LKrtef$fit^(1/lambda)
LKrtef$upper = LKrtef$upper^(1/lambda)
LKrtef$lower = LKrtef$lower^(1/lambda)
plot(LKrtef, ylab="Median response time (seconds)", main="Letter Knowledge Response Times", multiline=TRUE,
ci.style="bars", xlab="Session", lines=list(col=c("black"), lty=c(2,3)),x.var="stream",
lattice=list(key.args=list(title="Condition",x=.62, y=.95,cex=1.2,cex.title=1.2)))
pdf("Ch3_NL_LSSIrt.pdf", width=6,height=4)
plot(LKrtef, ylab="Median response time (seconds)", main="", multiline=TRUE,
ci.style="bars", xlab="Session", lines=list(col=c("black"), lty=c(2,3)),x.var="stream",
lattice=list(key.args=list(title="Condition",x=.62, y=.95,cex=1.2,cex.title=1.2)))
dev.off()## quartz_off_screen
## 2pdf("Ch3_NL_LSSIrtGG.pdf", width=6,height=4)
ggplot(as.data.frame(LKrtef), aes(Cond, fit, color=stream)) + geom_point() + geom_errorbar(aes(ymin=lower, ymax=upper), width=0.3) + theme_bw(base_size=12) + labs(x = "Condition", y = "Median response time (seconds)",colour="Session") + scale_colour_grey() + theme(panel.grid.major = element_blank(), axis.line = element_line(colour = "black"))
dev.off()## quartz_off_screen
## 2# compute effect size for age
summary(rtLKNL.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: RTinv ~ T1PF_TotalZ + T1AgeF + prevRTinv + TrialNum + stream * Cond + (1 + T1.LetKenZ | itemsOnScreen) + (0 + stream | Subj) + (0 + stream | correctResponse) + (1 | Class)
## Data: LKrtNL.2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## REML criterion at convergence: -9702.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6715 -0.5866 0.0451 0.6458 4.3321
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## itemsOnScreen (Intercept) 2.174e-03 0.046631
## T1.LetKenZ 1.994e-04 0.014122 -0.82
## Subj streamT1 6.166e-04 0.024832
## streamT2 3.860e-04 0.019648 0.54
## correctResponse streamT1 3.821e-04 0.019547
## streamT2 6.436e-04 0.025369 0.91
## Class (Intercept) 4.608e-05 0.006788
## Residual 3.451e-03 0.058742
## Number of obs: 3646, groups: itemsOnScreen, 729; Subj, 75; correctResponse, 30; Class, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.201e-01 5.174e-02 6.920e+01 13.918 < 2e-16 ***
## T1PF_TotalZ 9.661e-03 3.488e-03 6.814e+01 2.770 0.00722 **
## T1AgeF 1.811e-02 8.150e-03 6.737e+01 2.222 0.02964 *
## prevRTinv 1.096e-02 4.622e-03 3.442e+03 2.372 0.01775 *
## TrialNum -3.368e-03 7.915e-04 3.041e+02 -4.255 2.79e-05 ***
## streamT2 3.519e-02 4.534e-03 6.233e+01 7.761 9.96e-11 ***
## CondRead -5.703e-03 9.699e-03 3.071e+00 -0.588 0.59701
## streamT2:CondRead 1.361e-02 6.895e-03 7.194e+01 1.975 0.05216 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) T1PF_T T1AgeF prvRTn TrilNm strmT2 CondRd
## T1PF_TotalZ -0.030
## T1AgeF -0.982 0.003
## prevRTinv -0.051 -0.031 -0.022
## TrialNum -0.080 -0.010 0.000 -0.026
## streamT2 -0.030 0.003 0.000 -0.059 -0.027
## CondRead -0.131 -0.065 0.056 0.000 -0.002 0.245
## strmT2:CndR 0.030 -0.003 0.001 -0.006 0.008 -0.515 -0.466#writeLines('Explained variance')
#rsquared.glmm(list(rtLKB.2))
writeLines('Sample size by condition')## Sample size by conditionsummary(aggregate(Cond~Subj,data=rtLKNL.2@frame,FUN=unique)$Cond)## Math Read
## 48 27writeLines('Trimmed observations:')## Trimmed observations:nrow(LKrtNL[abs(scale(resid(rtLKNL))) > 2 , ])## [1] 212writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(LKrtNL[abs(scale(resid(rtLKNL))) < 2 , ]) / nrow(LKrtNL)) * 100## [1] 4.945183performance::check_model(rtLKNL.2)
acf(resid(rtLKNL.2),main="autocorrelation of observations")
(rtLKNL.2bs <- confint(rtLKNL.2, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 0.0319227211 0.058976568
## .sig02 -1.0000000000 -0.184127502
## .sig03 0.0031711996 0.025825506
## .sig04 0.0189560296 0.030002886
## .sig05 0.2336553559 0.781315594
## .sig06 0.0147215038 0.024330197
## .sig07 0.0136198846 0.025795823
## .sig08 0.7432755801 1.000000000
## .sig09 0.0179518590 0.033240985
## .sig10 0.0000000000 0.015090205
## .sigma 0.0571364805 0.060283589
## (Intercept) 0.6256435664 0.819885645
## T1PF_TotalZ 0.0028301959 0.016626501
## T1AgeF 0.0022217139 0.033196759
## prevRTinv 0.0015597797 0.019531984
## TrialNum -0.0049193971 -0.001735082
## streamT2 0.0261570418 0.043518546
## CondRead -0.0258081839 0.014071841
## streamT2:CondRead 0.0007131408 0.0271738291.4.3.6.3 WLD Accuracy
accLDNL <-glmer(correctSelections ~ Cond + (1|target) + (1|Subj), family=binomial, data=LDaccNL, glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000) ))
LDaccNL.2 = LDaccNL[abs(scale(resid(accLDNL))) < 2 , ]
accLDNL.2 <-glmer(correctSelections ~ Cond + (1|target) + (1|Subj), family=binomial, data=LDaccNL.2, glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun=50000) ))
plot(effect("Cond",accLDNL.2), ylab="probability of correct response", main="Lexical Decision Accuracy", multiline=TRUE, ci.style="bars", xlab="", lines=list(col=c("black"), lty=c(1,2)))
summary(accLDNL.2)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( logit )
## Formula: correctSelections ~ Cond + (1 | target) + (1 | Subj)
## Data: LDaccNL.2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 50000))
##
## AIC BIC logLik deviance df.resid
## 1428.4 1450.4 -710.2 1420.4 1779
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1541 0.0646 0.1212 0.5248 2.2391
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subj (Intercept) 0.404 0.6356
## target (Intercept) 5.598 2.3660
## Number of obs: 1783, groups: Subj, 75; target, 30
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.58222 0.47706 5.413 6.21e-08 ***
## CondRead -0.02325 0.21052 -0.110 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## CondRead -0.158writeLines('Explained variance')## Explained variancersquared.glmm(list(accLDNL.2))## Class Family Link Marginal Conditional AIC
## 1 glmerMod binomial logit 1.332458e-05 0.645935 1428.427writeLines('Sample size by condition')## Sample size by conditionsummary(aggregate(Cond~Subj,data=accLDNL.2@frame,FUN=unique)$Cond)## Math Read
## 48 27writeLines('Trimmed observations:')## Trimmed observations:nrow(LDaccNL[abs(scale(resid(accLDNL))) > 2 , ])## [1] 76writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(LDaccNL[abs(scale(resid(accLDNL))) < 2 , ]) / nrow(LDaccNL)) * 100## [1] 4.0882191.4.3.6.4 WLD Response Time
bc <- MASS::boxcox(lm(trialRTs ~ targettype + T1.LetKenZ + prevRT + T1PF_TotalZ + Cond, data=LDrtNL),plotit=FALSE)
lambda <- bc$x[which.max(bc$y)]
if (lambda == 0) lambda <- 0.1
LDrtNL$RTinv = LDrtNL$trialRTs^lambda
LDrtNL$prevRTinv = LDrtNL$prevRT^lambda
rtLDNL = lmer(RTinv ~ targettype + T1.LetKenZ + prevRTinv + T1PF_TotalZ + Cond + (1+prevRTinv|target) + (1|Subj),data=LDrtNL)
LDrtNL.2 = LDrtNL[abs(scale(resid(rtLDNL))) < 2 , ]
rtLDNL.2 = lmer(RTinv ~ targettype + T1.LetKenZ + prevRTinv + T1PF_TotalZ + Cond + (1+prevRTinv|target) + (1|Subj),data=LDrtNL.2)
LKrtef = effect("Cond",rtLDNL.2)
LKrtef$fit = LKrtef$fit^(1/lambda)
LKrtef$upper = LKrtef$upper^(1/lambda)
LKrtef$lower = LKrtef$lower^(1/lambda)
plot(LKrtef, ylab="Median response time (seconds)", main="Lexical Decision Response Times",
multiline=TRUE, ci.style="bars", xlab="", lines=list(col=c("black"), lty=c(1,2)))
summary(rtLDNL.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: RTinv ~ targettype + T1.LetKenZ + prevRTinv + T1PF_TotalZ + Cond + (1 + prevRTinv | target) + (1 | Subj)
## Data: LDrtNL.2
##
## REML criterion at convergence: -4477.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0982 -0.5682 -0.0715 0.5719 4.7782
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Subj (Intercept) 0.000795 0.02820
## target (Intercept) 0.003459 0.05881
## prevRTinv 0.003295 0.05740 -0.94
## Residual 0.001408 0.03752
## Number of obs: 1285, groups: Subj, 75; target, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.086226 0.018669 48.889145 58.185 < 2e-16 ***
## targettypewrong 0.016831 0.008089 27.647259 2.081 0.046840 *
## T1.LetKenZ -0.012869 0.006105 68.183531 -2.108 0.038729 *
## prevRTinv 0.039916 0.015454 31.974073 2.583 0.014578 *
## T1PF_TotalZ -0.017019 0.004942 67.997993 -3.444 0.000987 ***
## CondRead 0.004246 0.007533 67.970149 0.564 0.574875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trgtty T1.LKZ prvRTn T1PF_T
## trgttypwrng -0.192
## T1.LetKenZ -0.369 -0.003
## prevRTinv -0.847 -0.017 0.009
## T1PF_TotalZ -0.060 0.001 -0.185 0.022
## CondRead -0.212 -0.003 0.290 -0.013 -0.162writeLines('Explained variance')## Explained variancersquared.glmm(list(rtLDNL.2))## Class Family Link Marginal Conditional AIC
## 1 lmerModLmerTest gaussian identity 0.113886 0.5381346 -4503.849writeLines('Sample size by condition')## Sample size by conditionsummary(aggregate(Cond~Subj,data=rtLDNL.2@frame,FUN=unique)$Cond)## Math Read
## 48 27writeLines('Trimmed observations:')## Trimmed observations:nrow(LDrtNL[abs(scale(resid(rtLDNL))) > 2 , ])## [1] 67writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(LDrtNL[abs(scale(resid(rtLDNL))) < 2 , ]) / nrow(LDrtNL)) * 100## [1] 4.820144performance::check_model(rtLDNL)
acf(resid(rtLDNL.2),main="autocorrelation of observations")
(rtLDNL.2bs <- confint(rtLDNL.2, method = "boot", nsim = simulations, level = 0.95, parallel = bootparallel, ncpus = bootthreads))## 2.5 % 97.5 %
## .sig01 0.022947743 0.0337612316
## .sig02 0.013664174 0.0885066014
## .sig03 -0.985943981 -0.6127743629
## .sig04 0.019835472 0.0854363556
## .sigma 0.036041139 0.0390562154
## (Intercept) 1.043941424 1.1246766009
## targettypewrong 0.001115962 0.0332729634
## T1.LetKenZ -0.024563366 -0.0003043497
## prevRTinv 0.009077692 0.0748518071
## T1PF_TotalZ -0.026749376 -0.0070821460
## CondRead -0.009408172 0.01902880791.4.4 ANALYSIS 3 - Game Exposure
Here, we also include subjects which were excluded for other analysis (e.g. not playing enough, or repeating first grade). The variables hoursPlayed, levelsPlayed, sessionsPlayed, MaxLevel, itemsSeen, responsesGiven are combined by principal component analyses.
1.4.4.1 Descriptives
Here, we show a summary of the combined data with number of observations, means, range and standard deviation per variable and group.
tab <- tableby(Cond ~ EMT + FamRisk + Country + T1AgeF + HandScore + Gender + Lang + SON_1st + hoursPlayed + levelsPlayed + sessionsPlayed + MaxLevel + itemsSeen + responsesGiven + T1CF_TotalPc + T1PF_TotalPc + T1.LetKen + T1RANcT + T1RANoT, data = datExposure, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2)##
##
## | | Read (N=104) | Math (N=106) | Total (N=210) | p value|
## |:-----------------|:----------------------------:|:---------------------------:|:---------------------------:|-------:|
## |EMT | | | | 0.137|
## |- Mean (SD) | 0.01 (0.93) | 0.22 (1.15) | 0.12 (1.05) | |
## |- Median (Range) | -0.19 (-1.44, 3.00) | -0.12 (-1.49, 3.00) | -0.19 (-1.49, 3.00) | |
## |FamRisk | | | | 0.902|
## |- no | 87 (83.7%) | 88 (83.0%) | 175 (83.3%) | |
## |- yes | 17 (16.3%) | 18 (17.0%) | 35 (16.7%) | |
## |Country | | | | 0.559|
## |- B | 63 (60.6%) | 60 (56.6%) | 123 (58.6%) | |
## |- NL | 41 (39.4%) | 46 (43.4%) | 87 (41.4%) | |
## |T1AgeF | | | | 0.468|
## |- Mean (SD) | 6.25 (0.34) | 6.28 (0.30) | 6.26 (0.32) | |
## |- Median (Range) | 6.25 (5.46, 7.39) | 6.32 (5.55, 7.07) | 6.30 (5.46, 7.39) | |
## |HandScore | | | | 0.343|
## |- Mean (SD) | 0.03 (0.91) | 0.14 (0.84) | 0.08 (0.88) | |
## |- Median (Range) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | 0.35 (-3.20, 0.52) | |
## |Gender | | | | 0.169|
## |- female | 50 (48.1%) | 41 (38.7%) | 91 (43.3%) | |
## |- male | 54 (51.9%) | 65 (61.3%) | 119 (56.7%) | |
## |Lang | | | | 0.432|
## |- mono | 93 (89.4%) | 91 (85.8%) | 184 (87.6%) | |
## |- multi | 11 (10.6%) | 15 (14.2%) | 26 (12.4%) | |
## |SON_1st | | | | 0.825|
## |- Mean (SD) | -0.02 (1.03) | 0.02 (0.97) | -0.00 (1.00) | |
## |- Median (Range) | -0.02 (-2.47, 4.87) | -0.02 (-2.47, 3.24) | -0.02 (-2.47, 4.87) | |
## |hoursPlayed | | | | 0.678|
## |- Mean (SD) | 3.35 (1.01) | 3.30 (0.93) | 3.33 (0.97) | |
## |- Median (Range) | 3.41 (1.16, 5.23) | 3.07 (1.59, 5.24) | 3.25 (1.16, 5.24) | |
## |levelsPlayed | | | | < 0.001|
## |- Mean (SD) | 220.70 (69.38) | 393.51 (157.29) | 307.93 (149.34) | |
## |- Median (Range) | 219.00 (87.00, 377.00) | 349.50 (132.00, 1026.00) | 275.50 (87.00, 1026.00) | |
## |sessionsPlayed | | | | 0.937|
## |- Mean (SD) | 26.26 (6.46) | 26.20 (4.66) | 26.23 (5.61) | |
## |- Median (Range) | 26.00 (11.00, 37.00) | 26.00 (14.00, 35.00) | 26.00 (11.00, 37.00) | |
## |MaxLevel | | | | 0.012|
## |- Mean (SD) | 117.86 (59.56) | 134.43 (30.58) | 126.22 (47.82) | |
## |- Median (Range) | 96.50 (24.00, 265.00) | 136.00 (39.00, 178.00) | 132.00 (24.00, 265.00) | |
## |itemsSeen | | | | 0.008|
## |- Mean (SD) | 10219.71 (4292.51) | 8649.07 (4245.75) | 9426.91 (4330.88) | |
## |- Median (Range) | 10090.00 (2010.00, 20113.00) | 7485.00 (2827.00, 22782.00) | 8448.00 (2010.00, 22782.00) | |
## |responsesGiven | | | | < 0.001|
## |- Mean (SD) | 2480.16 (918.20) | 3718.32 (2074.57) | 3105.14 (1721.27) | |
## |- Median (Range) | 2486.50 (628.00, 4522.00) | 3171.00 (1019.00, 10877.00) | 2850.00 (628.00, 10877.00) | |
## |T1CF_TotalPc | | | | 0.382|
## |- Mean (SD) | 44.21 (25.62) | 47.26 (24.96) | 45.75 (25.27) | |
## |- Median (Range) | 37.00 (0.10, 99.00) | 50.00 (1.00, 91.00) | 37.00 (0.10, 99.00) | |
## |T1PF_TotalPc | | | | 0.802|
## |- Mean (SD) | 53.96 (28.82) | 52.95 (29.14) | 53.44 (28.91) | |
## |- Median (Range) | 55.00 (0.00, 100.00) | 55.00 (0.00, 100.00) | 55.00 (0.00, 100.00) | |
## |T1.LetKen | | | | 0.147|
## |- Mean (SD) | 14.12 (9.33) | 16.00 (9.42) | 15.07 (9.40) | |
## |- Median (Range) | 13.00 (0.00, 30.00) | 15.00 (0.00, 30.00) | 15.00 (0.00, 30.00) | |
## |T1RANcT | | | | 0.050|
## |- Mean (SD) | 68.71 (16.76) | 64.41 (14.86) | 66.54 (15.93) | |
## |- Median (Range) | 66.00 (39.00, 122.00) | 62.50 (40.00, 107.00) | 65.00 (39.00, 122.00) | |
## |T1RANoT | | | | 0.577|
## |- Mean (SD) | 76.27 (21.21) | 74.77 (17.36) | 75.51 (19.33) | |
## |- Median (Range) | 73.00 (43.00, 180.00) | 72.00 (40.00, 130.00) | 72.00 (40.00, 180.00) | |1.4.4.2 PCA
Principal component analysis factor loadings and histograms for relevant variables to show data coverage.
writeLines('PCA component loadings of reading condition')## PCA component loadings of reading conditionprcomp(readExposure[,12:17],scale=T)## Standard deviations (1, .., p=6):
## [1] 2.20393928 0.83852686 0.50713198 0.37704198 0.19142493 0.05947579
##
## Rotation (n x k) = (6 x 6):
## PC1 PC2 PC3 PC4 PC5 PC6
## hoursPlayed 0.3916816 0.46389432 0.3512667 -0.70978360 0.06344236 0.01347600
## levelsPlayed 0.4398018 -0.02069116 0.2489048 0.42083099 0.71758646 0.22839350
## sessionsPlayed 0.3658469 0.54475614 -0.7266712 0.19224675 -0.06471541 -0.01410298
## MaxLevel 0.3589094 -0.65066835 -0.4360174 -0.42682897 0.21663621 -0.16908380
## itemsSeen 0.4401304 -0.24352451 0.1028218 0.09566272 -0.57033493 0.63401531
## responsesGiven 0.4436048 -0.07029370 0.2931272 0.30135590 -0.32348643 -0.71895144writeLines('PCA component loadings of math condition')## PCA component loadings of math conditionprcomp(mathExposure[,12:17],scale=T)## Standard deviations (1, .., p=6):
## [1] 2.10208576 0.86411560 0.68352379 0.54207365 0.26901839 0.03346937
##
## Rotation (n x k) = (6 x 6):
## PC1 PC2 PC3 PC4 PC5 PC6
## hoursPlayed 0.4015318 0.15113785 -0.42408226 0.79641557 -0.03938854 -0.015956608
## levelsPlayed 0.4481709 0.16753175 0.27897981 -0.14992160 -0.81892883 -0.011195849
## sessionsPlayed 0.3873993 0.04261814 -0.71184497 -0.57817923 0.08399375 0.006004764
## MaxLevel 0.2647940 -0.96019347 0.03012305 0.06174105 -0.05226583 -0.021483786
## itemsSeen 0.4583667 0.09764005 0.33658744 -0.03704332 0.38241397 0.720737571
## responsesGiven 0.4556545 0.12557497 0.34842896 -0.06140329 0.41445989 -0.692574750hist(datExposure$ExposurePCA,main="Exposure distribution",xlab="Exposure principal component")
hist(datExposure$T1CF_TotalZ,main="CELF PA distribution at T1",xlab="CELF PA (z-score)")
hist(datExposure$EMT,main="Reading fluency distribution at T2",xlab="One minute reading (z-score)")
1.4.4.3 READ vs. MATH
RMexp <- gam(EMT ~ s(T1RANcT) + s(T1CF_TotalZ) + ti(T1CF_TotalZ, ExposurePCA, by=Cond) + Country, data=datExposure)
datExposure.2 = datExposure[abs(scale(resid(RMexp))) < 2, ]
RMexp.2 <- gam(EMT ~ s(T1RANcT) + s(T1CF_TotalZ) + ti(T1CF_TotalZ, ExposurePCA, by=Cond) + Country, data=datExposure.2)
par(mfcol=c(1,3))
par(mar=c(4,4,2,3))
fvisgam(RMexp.2,view=c("T1CF_TotalZ","ExposurePCA"), ylim=c(-2.1,2.1),zlim=c(-2.1,2.1), xlim=c(-2,2),
n.grid=250,cond=list(Cond="Math"),
xlab="PreTest PA (z-score)",
ylab="Exposure PCA (z-score)", show.diff=T, col.diff="white", alpha.diff=0.7,
main="A) Math", print.summary=F, add.color.legend = F, nCol=200, color=c("blue", "green", "red"), col=1)
fadeRug(subset(datExposure.2, Cond=="Math")$T1CF_TotalZ,subset(datExposure.2,Cond=="Math")$ExposurePCA, alpha=0.5,n.grid=1000,use.data.range=F,col="white")
box()
gradientLegend(valRange = c(-2,2), nCol=200 ,pos=0.87, side=4,color=c("blue", "green", "red"), col=1)
fvisgam(RMexp.2,view=c("T1CF_TotalZ","ExposurePCA"), ylim=c(-2.1,2.1),zlim=c(-2,2),xlim=c(-2,2),
n.grid=250, cond=list(Cond="Read"),
xlab="PreTest PA (z-score)", ylab="", show.diff=T,col.diff="white", alpha.diff=0.7,
main="B) Read", print.summary=F, add.color.legend = F, nCol=200, color=c("blue", "green", "red"), col=1)
fadeRug(subset(datExposure.2,Cond=="Read")$T1CF_TotalZ,subset(datExposure.2,Cond=="Read")$ExposurePCA, alpha=0.5,n.grid=1000,use.data.range=F)
box()
gradientLegend(valRange = c(-2,2), nCol=200 ,pos=0.87, side=4, color=c("blue", "green", "red"), col=1)
plot_diff2(RMexp.2,view=c("T1CF_TotalZ","ExposurePCA"), ylim=c(-2.1,2.1),zlim=c(-1,1),xlim=c(-2,2),
n.grid=250, comp=list(Cond=c("Read","Math")),
xlab="PreTest PA (z-score)",
ylab="",nCol=200,nlevels=4, show.diff=T,col.diff="white", alpha.diff=0.7,
main="C) B-A Difference", print.summary=F, add.color.legend = F, color=c("blue", "green", "red"), col=1)
fadeRug(datExposure.2$T1CF_TotalZ,datExposure.2$ExposurePCA,alpha=0.5,n.grid=1000, use.data.range=F)
box()
gradientLegend(valRange = c(-1,1), nCol=200 ,pos=0.87, side=4, color=c("blue", "green", "red"), col=1)
par(mfcol=c(1,1))
summary(RMexp.2)##
## Family: gaussian
## Link function: identity
##
## Formula:
## EMT ~ s(T1RANcT) + s(T1CF_TotalZ) + ti(T1CF_TotalZ, ExposurePCA,
## by = Cond) + Country
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.16331 0.05817 -2.808 0.00552 **
## CountryNL 0.17823 0.10190 1.749 0.08193 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(T1RANcT) 1.845 2.327 4.535 0.00932 **
## s(T1CF_TotalZ) 1.000 1.000 43.138 < 2e-16 ***
## ti(T1CF_TotalZ,ExposurePCA):CondRead 4.649 5.946 2.987 0.00883 **
## ti(T1CF_TotalZ,ExposurePCA):CondMath 1.000 1.000 0.203 0.65315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.473 Deviance explained = 49.8%
## GCV = 0.36377 Scale est. = 0.34429 n = 196writeLines('Sample size by condition')## Sample size by conditionaggregate(EMT~Cond,RMexp.2$model,FUN=NROW)## Cond EMT
## 1 Read 101
## 2 Math 95writeLines('Trimmed observations:')## Trimmed observations:nrow(datExposure[abs(scale(resid(RMexp))) > 2 , ])## [1] 14writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(datExposure[abs(scale(resid(RMexp))) < 2 , ]) / nrow(datExposure)) * 100## [1] 6.666667gam.check(RMexp.2)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 18 iterations.
## The RMS GCV score gradient at convergence was 5.126103e-08 .
## The Hessian was positive definite.
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(T1RANcT) 9.00 1.84 1.03 0.69
## s(T1CF_TotalZ) 9.00 1.00 0.94 0.23
## ti(T1CF_TotalZ,ExposurePCA):CondRead 16.00 4.65 1.06 0.78
## ti(T1CF_TotalZ,ExposurePCA):CondMath 16.00 1.00 1.04 0.74# also plot individual smooths
plot(RMexp.2,select=1, ylab="Reading fluency (z-score)")
abline(0,0)
plot(RMexp.2,select=2, ylab="Reading fluency (z-score)")
abline(0,0)
1.4.4.4 READ by Gender
RGexp <- gam(EMT ~ s(T1CF_TotalZ) + ti(T1CF_TotalZ, ExposurePCA, by=Gender), data=readExposure)
readExposure.2 = readExposure[abs(scale(resid(RGexp))) < 2, ]
RGexp.2 <- gam(EMT ~ s(T1CF_TotalZ) + ti(T1CF_TotalZ, ExposurePCA, by=Gender), data=readExposure.2)
par(mfcol=c(1,3))
fvisgam(RGexp.2,view=c("T1CF_TotalZ","ExposurePCA"), ylim=c(-2,2),zlim=c(-2,2),xlim=c(-2,2),
n.grid=250,cond=list(Gender="male"),
xlab="Phonological skills at pre-test(z-score)",
ylab="Exposure PCA (z-score)",show.diff=T,col.diff="white", alpha.diff=0.7,
main="D) Read: Male", print.summary=F,add.color.legend = F, nCol=200)
fadeRug(subset(readExposure.2, Gender=="male")$T1CF_TotalZ,subset(readExposure.2,Gender=="male")$ExposurePCA,
alpha=0.75,n.grid=1000,use.data.range=F)
box()
gradientLegend(valRange = c(-2,2), nCol=200 ,pos=0.87, side=4)
fvisgam(RGexp.2,view=c("T1CF_TotalZ","ExposurePCA"), ylim=c(-2,2),zlim=c(-2,2),xlim=c(-2,2),
n.grid=250,cond=list(Gender="female"),
xlab="Phonological skills at pre-test(z-score)",
ylab="",show.diff=T,col.diff="white", alpha.diff=0.7,
main="E) Read: Female", print.summary=F, add.color.legend = F, nCol=200)
fadeRug(subset(readExposure.2,Gender=="female")$T1CF_TotalZ,subset(readExposure.2,Gender=="female")$ExposurePCA,alpha=0.75,n.grid=1000,use.data.range=F)
box()
gradientLegend(valRange = c(-2,2), nCol=200 ,pos=0.87, side=4)
plot_diff2(RGexp.2,view=c("T1CF_TotalZ","ExposurePCA"), ylim=c(-2,2), zlim=c(-2,2), xlim=c(-2,2),
n.grid=250, comp=list(Gender=c("female","male")),
xlab="Phonological skills at pre-test(z-score)",
ylab="",nCol=200, nlevels=8,show.diff=T,col.diff="white", alpha.diff=0.7,
main="F) E-D Difference", print.summary=F, add.color.legend = F,se=F)
fadeRug(readExposure.2$T1CF_TotalZ,readExposure.2$ExposurePCA,alpha=0.75,n.grid=1000,use.data.range=F, col="white")
box()
gradientLegend(valRange = c(-2,2), nCol=200 ,pos=0.87, side=4)
par(mfcol=c(1,1))
summary(RGexp.2)##
## Family: gaussian
## Link function: identity
##
## Formula:
## EMT ~ s(T1CF_TotalZ) + ti(T1CF_TotalZ, ExposurePCA, by = Gender)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.19958 0.05703 -3.499 0.000789 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(T1CF_TotalZ) 8.396 8.830 10.765 < 2e-16 ***
## ti(T1CF_TotalZ,ExposurePCA):Genderfemale 10.077 11.630 8.281 < 2e-16 ***
## ti(T1CF_TotalZ,ExposurePCA):Gendermale 3.791 3.963 6.272 0.00027 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.742 Deviance explained = 80.1%
## GCV = 0.23032 Scale est. = 0.17564 n = 98writeLines('Sample size by condition')## Sample size by conditionaggregate(EMT~Gender,RGexp.2$model,FUN=NROW)## Gender EMT
## 1 female 47
## 2 male 51writeLines('Trimmed observations:')## Trimmed observations:nrow(readExposure[abs(scale(resid(RGexp))) > 2 , ])## [1] 6writeLines('Trimmed in percent:')## Trimmed in percent:(1-nrow(readExposure[abs(scale(resid(RGexp))) < 2 , ]) / nrow(readExposure)) * 100## [1] 5.769231gam.check(RGexp.2)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 16 iterations.
## The RMS GCV score gradient at convergence was 5.461778e-08 .
## The Hessian was positive definite.
## Model rank = 42 / 42
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(T1CF_TotalZ) 9.00 8.40 0.99 0.41
## ti(T1CF_TotalZ,ExposurePCA):Genderfemale 16.00 10.08 1.19 0.98
## ti(T1CF_TotalZ,ExposurePCA):Gendermale 16.00 3.79 1.04 0.65plot(RGexp.2,select=1, ylab="Reading fluency (z-score)")
abline(0,0)
gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 3059382 163.4 4742435 253.3 NA 4742435 253.3
## Vcells 27392077 209.0 53101616 405.2 73728 82970076 633.11.5 Extra Analyses
1.5.1 LK Belgium
This analysis is not shown in the manuscript but presents a conventional approach to evaluate potential changes in letter knowledge with an ANOVA on the aggregated in-game assessment. Here, we only find a main effect of time which is confirmed by a post-hoc wilcoxon test, showing an overall increase form T1 to T2.
LKdatB = subset(datB, T2.LetKen >0 & T1.LetKen >0, select=c("Subj","Cond","T1.LetKen","T2.LetKen"))
LKdatB = melt(LKdatB,id.vars=c("Subj","Cond"))
LKdatB$variable <- sub(".LetKen","",LKdatB$variable)
names(LKdatB) <- c("Subj","Cond","Session","LettersCorrect")
LKB = aov(LettersCorrect~Cond*Session,LKdatB) # main effect of session
summary(LKB)## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 2 19 10 0.265 0.767
## Session 1 6398 6398 177.251 <2e-16 ***
## Cond:Session 2 161 80 2.227 0.111
## Residuals 200 7219 36
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1shapiro.test(LKdatB$LettersCorrect) # not normal, so wilcox.test##
## Shapiro-Wilk normality test
##
## data: LKdatB$LettersCorrect
## W = 0.95436, p-value = 3.742e-06hist(LKdatB$LettersCorrect)
boxplot(LKdatB$LettersCorrect~LKdatB$Cond+LKdatB$Session)
wilcox.test(LettersCorrect~Session, data=LKdatB, paired=T) # T2 > T1##
## Wilcoxon signed rank test with continuity correction
##
## data: LettersCorrect by Session
## V = 83.5, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 01.5.2 LK Netherlands
This analysis is not shown in the manuscript but presents a conventional approach to evaluate potential changes in letter knowledge with an ANOVA on the aggregated in-game assessment. Here, we only find a main effect of Time and a main effect of Condition
LKdatNL = subset(datNL, T2.LetKen >0 & T1.LetKen >0, select=c("Subj","Cond","T1.LetKen","T2.LetKen"))
LKdatNL = melt(LKdatNL,id.vars=c("Subj","Cond"))
LKdatNL$variable <- sub(".LetKen","",LKdatNL$variable)
names(LKdatNL) <- c("Subj","Cond","Session","LettersCorrect")
LKNL = aov(LettersCorrect~Cond*Session,LKdatNL) # main effect of session and main effect of condition
summary(LKNL)## Df Sum Sq Mean Sq F value Pr(>F)
## Cond 1 326 326.1 13.706 0.000302 ***
## Session 1 561 560.7 23.564 3.07e-06 ***
## Cond:Session 1 1 0.6 0.024 0.878262
## Residuals 146 3474 23.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1shapiro.test(LKdatNL$LettersCorrect) # not normal##
## Shapiro-Wilk normality test
##
## data: LKdatNL$LettersCorrect
## W = 0.89015, p-value = 3.816e-09hist(LKdatNL$LettersCorrect)
boxplot(LKdatNL$LettersCorrect~LKdatNL$Cond+LKdatNL$Session)
wilcox.test(LettersCorrect~Session, data=LKdatNL, paired=T) # T2 > T1##
## Wilcoxon signed rank test with continuity correction
##
## data: LettersCorrect by Session
## V = 297.5, p-value = 5.109e-08
## alternative hypothesis: true location shift is not equal to 0wilcox.test(LettersCorrect~Cond, data=LKdatNL) # Math > Read##
## Wilcoxon rank sum test with continuity correction
##
## data: LettersCorrect by Cond
## W = 3363.5, p-value = 0.002452
## alternative hypothesis: true location shift is not equal to 01.5.3 Raw PrePost
Here, we present the raw means for the outcome measures at both testing points
datB$EMTraw <- (datB$T2EMTaCor + datB$T2EMTbCor) / 2
tab <- tableby(Cond ~ EMTraw + T1CF_Total + T2CF_Total + T1CF_TotalPc + T2CF_TotalPc + T1PF_Total + T2PF_Total + T1PF_TotalPc + T2PF_TotalPc + T1.LetKen + T2.LetKen + T1RANcT + T2RANcT + T1RANoT +T2RANoT, data = datB, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | Math (N=56) | Passive (N=52) | Read (N=53) | Total (N=161) |
## |:-----------------|:---------------------:|:---------------------:|:---------------------:|:---------------------:|
## |EMTraw | | | | |
## |- Mean (SD) | 12.02 (7.79) | 10.05 (4.59) | 12.62 (7.43) | 11.58 (6.83) |
## |- Median (Range) | 10.00 (1.00, 41.50) | 10.50 (2.50, 22.00) | 12.50 (1.00, 32.50) | 10.50 (1.00, 41.50) |
## |T1CF_Total | | | | |
## |- Mean (SD) | 23.44 (9.64) | 23.38 (9.84) | 22.08 (8.27) | 22.96 (9.23) |
## |- Median (Range) | 26.00 (1.00, 40.00) | 24.50 (1.00, 42.00) | 23.00 (2.00, 39.00) | 24.00 (1.00, 42.00) |
## |T2CF_Total | | | | |
## |- Mean (SD) | 32.04 (7.67) | 29.58 (8.64) | 30.45 (6.39) | 30.72 (7.64) |
## |- Median (Range) | 33.00 (13.00, 44.00) | 31.00 (2.00, 44.00) | 31.00 (8.00, 43.00) | 31.00 (2.00, 44.00) |
## |T1CF_TotalPc | | | | |
## |- Mean (SD) | 38.22 (23.68) | 41.14 (23.91) | 35.74 (20.05) | 38.31 (22.56) |
## |- Median (Range) | 37.00 (1.00, 91.00) | 37.00 (1.00, 95.00) | 37.00 (5.00, 84.00) | 37.00 (1.00, 95.00) |
## |T2CF_TotalPc | | | | |
## |- Mean (SD) | 55.71 (25.32) | 50.50 (24.37) | 50.87 (20.49) | 52.44 (23.49) |
## |- Median (Range) | 56.50 (9.00, 98.00) | 50.00 (0.10, 99.00) | 50.00 (9.00, 95.00) | 50.00 (0.10, 99.00) |
## |T1PF_Total | | | | |
## |- Mean (SD) | 29.93 (5.66) | 30.21 (5.33) | 29.23 (5.79) | 29.79 (5.58) |
## |- Median (Range) | 30.00 (16.00, 40.00) | 31.00 (11.00, 38.00) | 30.00 (0.00, 37.00) | 30.00 (0.00, 40.00) |
## |T2PF_Total | | | | |
## |- Mean (SD) | 33.75 (4.50) | 32.50 (5.36) | 31.60 (4.65) | 32.64 (4.89) |
## |- Median (Range) | 34.00 (18.00, 40.00) | 33.00 (13.00, 40.00) | 32.00 (15.00, 40.00) | 33.00 (13.00, 40.00) |
## |T1PF_TotalPc | | | | |
## |- Mean (SD) | 45.67 (29.28) | 49.52 (25.94) | 44.29 (25.37) | 46.46 (26.89) |
## |- Median (Range) | 45.00 (0.00, 100.00) | 47.50 (0.00, 97.50) | 42.50 (0.00, 97.50) | 42.50 (0.00, 100.00) |
## |T2PF_TotalPc | | | | |
## |- Mean (SD) | 61.03 (26.92) | 55.91 (29.02) | 48.73 (24.33) | 55.33 (27.13) |
## |- Median (Range) | 55.00 (2.50, 100.00) | 55.00 (0.00, 100.00) | 47.50 (0.00, 100.00) | 55.00 (0.00, 100.00) |
## |T1.LetKen | | | | |
## |- Mean (SD) | 9.36 (6.42) | 11.27 (5.30) | 8.23 (6.70) | 9.59 (6.27) |
## |- Median (Range) | 8.00 (0.00, 26.00) | 10.00 (0.00, 25.00) | 6.00 (0.00, 25.00) | 8.00 (0.00, 26.00) |
## |T2.LetKen | | | | |
## |- Mean (SD) | 20.78 (5.97) | 21.32 (4.92) | 22.58 (6.84) | 21.59 (5.79) |
## |- Median (Range) | 21.00 (8.00, 30.00) | 21.00 (8.00, 32.00) | 25.00 (3.00, 30.00) | 22.00 (3.00, 32.00) |
## |T1RANcT | | | | |
## |- Mean (SD) | 67.50 (15.37) | 70.88 (15.92) | 74.96 (16.02) | 71.05 (15.97) |
## |- Median (Range) | 67.00 (40.00, 107.00) | 67.50 (48.00, 115.00) | 70.00 (49.00, 122.00) | 69.00 (40.00, 122.00) |
## |T2RANcT | | | | |
## |- Mean (SD) | 55.88 (11.42) | 57.37 (12.57) | 59.40 (14.01) | 57.51 (12.68) |
## |- Median (Range) | 55.00 (36.00, 78.00) | 56.00 (37.00, 103.00) | 58.00 (35.00, 93.00) | 56.00 (35.00, 103.00) |
## |T1RANoT | | | | |
## |- Mean (SD) | 76.25 (17.33) | 75.56 (17.49) | 81.77 (23.82) | 77.86 (19.83) |
## |- Median (Range) | 72.00 (50.00, 120.00) | 76.50 (46.00, 127.00) | 76.00 (49.00, 180.00) | 76.00 (46.00, 180.00) |
## |T2RANoT | | | | |
## |- Mean (SD) | 69.93 (21.50) | 68.08 (19.37) | 73.55 (22.18) | 70.52 (21.06) |
## |- Median (Range) | 67.50 (43.00, 186.00) | 64.00 (40.00, 134.00) | 69.00 (41.00, 177.00) | 66.00 (40.00, 186.00) |datNL$EMTraw <- (datNL$T2EMTaCor + datNL$T2EMTbCor) / 2
tab <- tableby(Cond ~ EMTraw + T1CF_Total + T2CF_Total + T1CF_TotalPc + T2CF_TotalPc + T1PF_Total + T2PF_Total + T1PF_TotalPc + T2PF_TotalPc + T1.LetKen + T2.LetKen + T1RANcT + T2RANcT + T1RANoT +T2RANoT, data = datNL, numeric.stats=c("meansd","medianrange"))
summary(tab, text=TRUE, digits=2, test=F)##
##
## | | Math (N=48) | Read (N=38) | Total (N=86) |
## |:-----------------|:---------------------:|:---------------------:|:---------------------:|
## |EMTraw | | | |
## |- Mean (SD) | 23.78 (11.59) | 19.30 (10.19) | 21.76 (11.14) |
## |- Median (Range) | 19.50 (3.50, 45.00) | 16.50 (8.00, 45.00) | 18.00 (3.50, 45.00) |
## |T1CF_Total | | | |
## |- Mean (SD) | 31.54 (6.71) | 32.53 (6.64) | 31.98 (6.66) |
## |- Median (Range) | 32.00 (8.00, 41.00) | 32.50 (19.00, 44.00) | 32.00 (8.00, 44.00) |
## |T2CF_Total | | | |
## |- Mean (SD) | 36.73 (4.68) | 36.49 (4.95) | 36.62 (4.77) |
## |- Median (Range) | 38.00 (25.00, 44.00) | 36.00 (23.00, 44.00) | 37.00 (23.00, 44.00) |
## |T1CF_TotalPc | | | |
## |- Mean (SD) | 59.85 (19.35) | 63.68 (22.22) | 61.55 (20.63) |
## |- Median (Range) | 63.00 (9.00, 91.00) | 56.50 (25.00, 99.00) | 63.00 (9.00, 99.00) |
## |T2CF_TotalPc | | | |
## |- Mean (SD) | 72.50 (19.26) | 73.46 (17.64) | 72.92 (18.47) |
## |- Median (Range) | 75.00 (25.00, 95.00) | 75.00 (25.00, 98.00) | 75.00 (25.00, 98.00) |
## |T1PF_Total | | | |
## |- Mean (SD) | 33.17 (4.26) | 34.76 (3.51) | 33.87 (4.01) |
## |- Median (Range) | 33.50 (21.00, 40.00) | 35.00 (27.00, 40.00) | 35.00 (21.00, 40.00) |
## |T2PF_Total | | | |
## |- Mean (SD) | 35.88 (2.76) | 36.00 (3.43) | 35.93 (3.06) |
## |- Median (Range) | 36.00 (29.00, 40.00) | 37.00 (28.00, 40.00) | 36.00 (28.00, 40.00) |
## |T1PF_TotalPc | | | |
## |- Mean (SD) | 64.84 (26.55) | 72.50 (22.80) | 68.23 (25.11) |
## |- Median (Range) | 66.25 (7.50, 100.00) | 75.00 (22.50, 100.00) | 75.00 (7.50, 100.00) |
## |T2PF_TotalPc | | | |
## |- Mean (SD) | 73.80 (19.63) | 75.92 (22.75) | 74.74 (20.97) |
## |- Median (Range) | 75.00 (32.50, 100.00) | 82.50 (25.00, 100.00) | 75.00 (25.00, 100.00) |
## |T1.LetKen | | | |
## |- Mean (SD) | 24.50 (4.74) | 22.24 (6.36) | 23.50 (5.60) |
## |- Median (Range) | 26.00 (13.00, 30.00) | 24.00 (2.00, 30.00) | 25.00 (2.00, 30.00) |
## |T2.LetKen | | | |
## |- Mean (SD) | 28.46 (3.45) | 25.26 (6.12) | 27.31 (4.81) |
## |- Median (Range) | 29.00 (15.00, 32.00) | 27.00 (10.00, 32.00) | 29.00 (10.00, 32.00) |
## |T1RANcT | | | |
## |- Mean (SD) | 58.44 (11.80) | 59.39 (14.71) | 58.86 (13.09) |
## |- Median (Range) | 57.50 (41.00, 96.00) | 57.50 (39.00, 102.00) | 57.50 (39.00, 102.00) |
## |T2RANcT | | | |
## |- Mean (SD) | 52.46 (10.17) | 53.11 (11.87) | 52.74 (10.89) |
## |- Median (Range) | 49.00 (39.00, 81.00) | 50.00 (34.00, 85.00) | 49.00 (34.00, 85.00) |
## |T1RANoT | | | |
## |- Mean (SD) | 70.46 (15.11) | 65.68 (12.13) | 68.35 (14.00) |
## |- Median (Range) | 68.00 (40.00, 126.00) | 65.50 (43.00, 90.00) | 67.00 (40.00, 126.00) |
## |T2RANoT | | | |
## |- Mean (SD) | 66.54 (17.76) | 63.67 (15.70) | 65.31 (16.87) |
## |- Median (Range) | 64.00 (39.00, 120.00) | 63.00 (39.00, 111.00) | 63.00 (39.00, 120.00) |