LexITA

LexITA

Exclude participants

df %>% 
  filter(group == "DYS" | (group == "TD" & is.na(other_diagnoses))) %>% 
  mutate(ID = as.character(ID)) %>% 
  filter(!startsWith(ID, "VER")) -> df
# length(unique(df$ID)) 86
df %>%
  mutate(
    exclude = case_when(
      ID == "VER02" ~ 1, # ADHD - university - DYS
      ID == "VER04" ~ 1, # ADHD - university - DYS
      ID == "MEN12" ~ 1, # discalculia - 3rd - TD
      ID == "MEN18" ~ 1, # disgrafia, discalculia - 3rd - TD
      ID == "MEN26" ~ 1, # disgrafia - 5th - TD, 
      ID == "LAE33" ~ 1, # disgrafia, disortografia, 3rd - TD, 
      
      ### new exclusion criteria: 
      ID == "LAE38" ~ 1,
      ID == "LC19" ~ 1, 
      ID == "MEN29" ~ 1, 
      ID == "MEN02" ~ 1, 
      
      
      ID == "MEN10" ~ 1, 
      ID == "LAE23" ~ 1, 
      # ID == "MEN35" ~ 1, 
      TRUE ~ 0
    )
  ) %>%
  filter(exclude == 0) %>%
  
  # stricter crtiteria 
  # filter(group.exclusion != "PR") %>% 
  
  # less strict criteria 
  # filter(group == "DYS" | (group == "TD" & reading.score > -2)) %>%
  
  
  filter(ID != "VER01" & ID != "VER03") %>% 
  dplyr::select(-exclude) -> df

Statistic analysis

Accuracy

# Model accuracy 

df %>% mutate_if(is.character, as.factor) %>% 
  mutate(age = scale(age), age = as.double(age),
         accuracy = as.factor(accuracy), 
         frequency = log(frequency), frequency = as.double(frequency)) -> df 

df %>% glmer(formula = accuracy ~ 
        # group * condition + 
        group + 
        condition + 
        # name_school + 
        age * group + 
        (1 + condition|ID) + (1|item), 
      family = "binomial",
      control = glmerControl(optimizer = "bobyqa")) -> m1

## test model 
# drop1(m1, test = "Chisq")

Model accuracy

	Chisq	Df	Pr(>Chisq)
(Intercept)	82.57	1	0.000
group	21.67	1	0.000
condition	0.12	1	0.728
age	5.12	1	0.024
group:age	3.30	1	0.069

group	emmean	SE
DYS	0.98	0.29
TD	0.99	0.27

group	age.trend	SE	df	asymp.LCL	asymp.UCL	z.ratio	p.value
DYS	0.48	0.21	Inf	0.06	0.89	2.26	0.024
TD	0.01	0.15	Inf	-0.29	0.30	0.04	0.967

RT

df %>%
  # only correct answers
  filter(accuracy == 1) %>%
  mutate(rt = log(reaction_time)) -> df.model 

# model
df.model %>% 
  
  lmer(formula =
        rt ~
        group * condition + 
        # group +
        age +
        (1 + condition|ID) + (1|item)) -> m2

### test
#drop1(m2, test = "Chisq")
 # Anova(m2, type = "III")

Model RT

	Chisq	Df	Pr(>Chisq)
(Intercept)	19686.84	1	0.000
group	56.46	1	0.000
condition	22.15	1	0.000
age	0.24	1	0.624
group:condition	5.14	1	0.023

contrast	group	estimate	SE	df	z.ratio	p.value
nonword - real	DYS	0.24	0.05	Inf	4.71	0.000
nonword - real	TD	0.13	0.04	Inf	3.01	0.003

Plot

LexITA total score

• Variable tot.lexita comparable to LexTALE: tot.yes.words - 2* tot.yes.nonwords

Group	Accuracy (M)	sd	range
DYS	52.69	8.05	17
DYS	52.69	8.05	60
TD	57.06	2.01	48
TD	57.06	2.01	60

### Model accuracy 
df1 %>% 
  mutate_if(is.character, as.factor) %>% 
  ## add + 2 to avoid negative values and use glm()
  mutate(age = scale(age),
         age = as.double(age)) %>% 
 
  glm(formula = tot.lexita ~ group * age, family = poisson(link = "log")) -> m3

	tot lexita
Predictors	Incidence Rate Ratios	CI	p
(Intercept)	50.98	48.09 – 53.98	<0.001
group [TD]	1.12	1.05 – 1.20	0.001
age	1.08	1.01 – 1.15	0.018
group [TD] × age	0.93	0.86 – 1.00	0.041
Observations	80
R2 Nagelkerke	0.288

LDT: Correct responses

Group	Estimate	SE	CI
DYS	50.98	0.03	48.12 - 54.02
TD	57.06	0.02	54.97 - 59.23

LDT: Correct responses

	LR Chisq	Df	Pr(>Chisq)
group	10.49	1	0.001
age	5.65	1	0.017
group:age	4.18	1	0.041

AIC	AICc	BIC	R2_Nagelkerke	RMSE	Sigma	Score_log	Score_spherical
517.06	517.59	526.58	0.29	4.67	1	-3.18	0.11

emtrends(m3, pairwise ~ group, var = "age", adjust = "bonferroni", infer = T)$emtrends %>% summary() %>% as.data.frame() %>% 
  mutate(across(age.trend:p.value, ~round(.x,3))) %>% 
  kable() %>% kable_styling()

group	age.trend	SE	df	asymp.LCL	asymp.UCL	z.ratio	p.value
DYS	0.075	0.031	Inf	0.013	0.137	2.375	0.018
TD	0.000	0.018	Inf	-0.036	0.036	0.018	0.986