Orthographic Knowledge IT

Total score

### create total score 

df %>% 
  
  rowwise() %>% 
  ### regular score 
  mutate(total = successi - errori) %>% 
  ### penalizing errors 
  mutate(total2 = (successi - (2*errori))) %>% 
  
  ungroup() -> df 

Exclusion of participants

df %>% 
  filter(group == "DYS" | (group == "TD" & is.na(other_diagnoses))) %>% 
  filter(!startsWith(ID, "VER")) -> df 


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

Descriptive analysis

Group	Errors (M)	SD	range	Correct answers (M)	SD	range	TOTAL (M)	SD	range
DYS	3.66	3.31	0	33.21	9.73	14	29.55	10.18	12
DYS	3.66	3.31	11	33.21	9.73	51	29.55	10.18	50
TD	1.71	2.18	0	48.06	9.47	25	46.35	9.36	25
TD	1.71	2.18	12	48.06	9.47	60	46.35	9.36	59

Inferential Statistics

Total score

df %>% 
  mutate(age = scale(age), age = as.double(age)) %>% 
  mutate_if(is.character, as.factor) %>% 
  glm(formula = total ~ group + age, 
      family = "poisson") -> m1

	total
Predictors	Incidence Rate Ratios	CI	p
(Intercept)	28.57	26.64 – 30.59	<0.001
group [TD]	1.65	1.52 – 1.79	<0.001
age	1.08	1.04 – 1.12	<0.001
Observations	80
R2 Nagelkerke	0.866

LDT: Total score

Group	Estimate	SE	CI
DYS	28.57	0.04	26.66 - 30.61
TD	47.06	0.02	45.18 - 49.01

LDT: Total score

	LR Chisq	Df	Pr(>Chisq)
group	153.47	1	0
age	17.83	1	0

AIC	AICc	BIC	R2_Nagelkerke	RMSE	Sigma	Score_log	Score_spherical
631.17	631.49	638.32	0.87	9.02	1	-3.91	0.09

Errors

df %>% 
  mutate_if(is.character, as.factor) %>% 
  mutate(age = scale(age), age = as.double(age)) %>% 
  glm(formula = errori ~ group * age, 
      family = "poisson") -> m2

	errori
Predictors	Incidence Rate Ratios	CI	p
(Intercept)	4.15	3.41 – 4.99	<0.001
group [TD]	0.41	0.31 – 0.55	<0.001
age	0.59	0.46 – 0.74	<0.001
group [TD] × age	1.69	1.23 – 2.32	0.001
Observations	80
R2 Nagelkerke	0.480

LDT: Errors

Group	Estimate	SE	CI
DYS	4.15	0.10	3.43 - 5.02
TD	1.70	0.11	1.37 - 2.11

LDT: Errors

	LR Chisq	Df	Pr(>Chisq)
group	36.85	1	0.000
age	19.86	1	0.000
group:age	10.61	1	0.001

AIC	AICc	BIC	R2_Nagelkerke	RMSE	Sigma	Score_log	Score_spherical
351.98	352.51	361.51	0.48	2.38	1	-2.15	0.1

emtrends(m2, pairwise ~ group, var = "age", infer = T, adjust = "bonferroni")$emtrends %>% 
  as.data.frame() %>% 
  mutate(across(age.trend:z.ratio, ~round(.x, 2)),
         p.value = round(p.value,3)) %>% 
  kable() %>% kable_styling()

group	age.trend	SE	df	asymp.LCL	asymp.UCL	z.ratio	p.value
DYS	-0.53	0.12	Inf	-0.77	-0.3	-4.41	0.000
TD	-0.01	0.11	Inf	-0.22	0.2	-0.06	0.952

Correct answers

df %>% 
  mutate_if(is.character, as.factor) %>% 
  mutate(age = scale(age), age = as.double(age)) %>% 
  glm(formula = successi ~ group * age, 
      family = "poisson") -> m3

	successi
Predictors	Incidence Rate Ratios	CI	p
(Intercept)	33.48	31.17 – 35.89	<0.001
group [TD]	1.46	1.35 – 1.58	<0.001
age	0.98	0.91 – 1.06	0.618
group [TD] × age	1.11	1.01 – 1.21	0.022
Observations	80
R2 Nagelkerke	0.788

LDT: Correct responses

Group	Estimate	SE	CI
DYS	33.48	0.04	31.2 - 35.92
TD	48.82	0.02	46.91 - 50.82

LDT: Correct responses

	LR Chisq	Df	Pr(>Chisq)
group	89.26	1	0.000
age	0.25	1	0.618
group:age	5.21	1	0.022

AIC	AICc	BIC	R2_Nagelkerke	RMSE	Sigma	Score_log	Score_spherical
622.32	622.85	631.85	0.79	8.93	1	-3.84	0.1

emtrends(m3, pairwise ~ group, var = "age", infer = T, adjust = "bonferroni")$emtrends %>% as.data.frame() %>% 
  mutate(across(age.trend:z.ratio, ~round(.x, 2)),
         p.value = round(p.value,3)) %>% 
kable() %>% kable_styling()

group	age.trend	SE	df	asymp.LCL	asymp.UCL	z.ratio	p.value
DYS	-0.02	0.04	Inf	-0.10	0.06	-0.50	0.618
TD	0.08	0.02	Inf	0.04	0.12	4.12	0.000

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