Digit Span Memory

Exclude participants

df %>% 
  filter(group == "DYS" | (group == "TD" & is.na(other_diagnoses))) %>% 
  mutate(ID = as.character(ID)) %>% 
  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

Descriptive stats

Group	Forward (M)	SD	range	Backward (M)	SD	range
DYS	5.97	0.91	4	4.24	1.9	0
DYS	5.97	0.91	8	4.24	1.9	8
TD	6.63	1.11	4	5.39	1.4	3
TD	6.63	1.11	9	5.39	1.4	8

Inferential statistics

Forward

# model forward
df %>% mutate_if(is.character, as.factor) %>% mutate(age = scale(age), age = as.double(age)) -> df
glm(forward ~ 
     # group * age, 
      group + 
      age, 
     family = poisson(link = "log"),
     data = df) -> m1

	forward
Predictors	Incidence Rate Ratios	CI	p
(Intercept)	5.97	5.10 – 6.93	<0.001
group [TD]	1.11	0.92 – 1.35	0.287
age	1.00	0.91 – 1.09	0.987
Observations	80
R2 Nagelkerke	0.094

Digit span (F): Verbal STM

Group	Estimate	SE	CI
DYS	5.97	0.08	5.12 - 6.96
TD	6.63	0.06	5.94 - 7.39

Digit span F: Verbal STM

	LR Chisq	Df	Pr(>Chisq)
group	1.14	1	0.285
age	0.00	1	0.987

AIC	AICc	BIC	R2_Nagelkerke	RMSE	Sigma	Score_log	Score_spherical
315.85	316.16	322.99	0.09	1.03	1	-1.94	0.11

Backward

# model backward
glm(backward ~ 
     group + age,   
    family = poisson(link = "log"),
   data = df) -> m2

	backward
Predictors	Incidence Rate Ratios	CI	p
(Intercept)	4.12	3.42 – 4.93	<0.001
group [TD]	1.33	1.06 – 1.66	0.013
age	1.07	0.96 – 1.18	0.214
Observations	80
R2 Nagelkerke	0.151

Digit span (F): Verbal STM

Group	Estimate	SE	CI
DYS	4.12	0.09	3.43 - 4.95
TD	5.46	0.06	4.85 - 6.16

Digit span F: Verbal STM

	LR Chisq	Df	Pr(>Chisq)
group	6.33	1	0.012
age	1.54	1	0.215

AIC	AICc	BIC	R2_Nagelkerke	RMSE	Sigma	Score_log	Score_spherical
326.25	326.56	333.39	0.15	1.55	1	-2	0.11

Composite score

df %>% 
  rowwise() %>% 
  mutate(memory = mean(c_across(forward:backward))) %>% 
  ungroup() -> df 


df %>% 
  lm(formula = memory ~ group + age) -> m3

### Model output 
tab_model(m3)

	memory
Predictors	Estimates	CI	p
(Intercept)	5.04	4.64 – 5.43	<0.001
group [TD]	1.01	0.51 – 1.52	<0.001
age	0.16	-0.08 – 0.41	0.188
Observations	80
R2 / R2 adjusted	0.171 / 0.149

Anova(m3, type= "II")

## Anova Table (Type II tests)
## 
## Response: memory
##           Sum Sq Df F value    Pr(>F)    
## group     17.087  1 15.8465 0.0001544 ***
## age        1.907  1  1.7683 0.1875149    
## Residuals 83.028 77                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plot