complete.knit

Poor readers

Poor readers’ performance
ID	wr_time_z	wr_error_z	nwr_time_z	nwr_error_z	pa.bis	forward	backward	lexita.acc	it.ok.acc
LAE23	-2.88	-0.13	-3.36	-0.53	0.68	-0.10	-1.06	0.95	0.09
LAE38	-2.75	-0.26	-2.73	-1.70	0.52	0.52	-0.49	0.59	-1.17
LC19	-4.97	-0.65	-4.58	0.06	-1.48	-0.49	-1.10	-3.12	-2.29
MEN02	-2.96	-1.04	-2.10	-2.41	0.16	-0.49	-0.49	-0.76	-0.35
MEN10	-3.40	-0.69	-2.68	-0.53	0.12	-1.02	0.08	-0.20	-0.32
MEN29	-2.24	-2.36	-0.52	-4.55	-0.26	-0.10	-0.49	0.37	-0.72
MEN35	-0.12	-2.36	0.94	-2.54	-0.04	-0.10	0.08	-0.20	1.24

Compensated DYS

Compensated DYSs’ performance
ID	wr_time_z	wr_error_z	nwr_time_z	nwr_error_z	pa.bis	forward	backward	lexita.acc	it.ok.acc
LAUVEN	-0.58	-1.23	-1.05	-1.45	-0.71	0.71	0.71	-0.71	-0.71
LAE04	-4.58	1.04	-0.99	-0.43	0.79	-0.39	-0.07	-0.61	-0.79
LAE17	-1.15	-1.04	-1.33	-0.65	0.44	-1.50	0.74	0.26	-0.50
LC03	-2.75	-1.82	-0.44	-1.70	-1.29	-0.49	0.13	-0.08	-0.20
LC15	-3.00	-0.72	-1.47	-1.23	-0.99	0.45	-0.07	0.41	0.20
MEN22	-4.16	-0.69	-1.16	-1.34	-0.65	-1.02	-0.49	1.24	-0.45
MEN33	-3.33	0.43	-1.21	0.67	0.31	0.82	0.66	0.09	-0.32

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

Dyslexics’ profiles

Inferential analyses

Correlations

Lexical: All measures

df %>% 
  dplyr::select(ID, group, wr_time_z:nwr_error_z, age, pa.bis, backward, forward, lexita.acc, it.ok.acc) %>% 
  pivot_longer(names_to = "dimension", values_to = "reading", 3:6) %>% 
  mutate(
    measure = 
      case_when(
        dimension == "wr_time_z" ~ "fluency", 
        dimension == "wr_error_z" ~ "accuracy", 
        dimension == "nwr_time_z" ~ "fluency", 
        dimension == "nwr_error_z" ~ "accuracy", 
        ), 
    dimension = 
      case_when(
        dimension == "wr_time_z" ~ "lexical", 
        dimension == "wr_error_z" ~ "lexical", 
        dimension == "nwr_time_z" ~ "sublexical", 
        dimension == "nwr_error_z" ~ "sublexical", 
      )
  ) %>% 
  mutate(across(backward:it.ok.acc, ~scale(.x))) %>% 
  mutate_if(is.character, as.factor) -> df.model
  
df.model %>% filter(dimension == "lexical") %>% 
  
  lmer(formula = reading ~ 
         
         # pa.bis * group * measure + 
         # pa.bis * group + 
         pa.bis * measure + 
         
         # lexita.acc * group * measure + 
         lexita.acc * measure + 
         # lexita.acc * group + 
         
         it.ok.acc * group * measure + 
         
         backward * group * measure + 
         # backward * group + 
         # backward * measure + 
         # backward + 
         
         (1 |ID)
         
         ) -> m1

AIC	AICc	BIC	R2_conditional	R2_marginal	ICC	RMSE	Sigma
564.8965	569.7476	620.2496	0.8240664	0.6994607	0.4146072	0.7902413	0.9907325

	Chisq	Df	Pr(>Chisq)
(Intercept)	13.99	1	0.000
pa.bis	28.27	1	0.000
measure	4.45	1	0.035
lexita.acc	9.30	1	0.002
it.ok.acc	1.97	1	0.161
group	2.58	1	0.108
backward	2.42	1	0.119
pa.bis:measure	7.32	1	0.007
measure:lexita.acc	10.57	1	0.001
it.ok.acc:group	1.22	1	0.270
measure:it.ok.acc	14.91	1	0.000
measure:group	2.22	1	0.136
group:backward	1.36	1	0.244
measure:backward	2.61	1	0.106
measure:it.ok.acc:group	5.21	1	0.022
measure:group:backward	3.71	1	0.054

Post-hoc 1: PA vs. Measure

emtrends(m1, pairwise ~ measure, var = "pa.bis", infer = T, adjust = "bonferroni")$emtrends %>% 
  as.data.frame() %>% 
  mutate(across(pa.bis.trend:t.ratio, ~round(.x,2)), 
         p.value = round(p.value,3)) -> post.1
color.me <- which(post.1$p.value <= .05)
post.1 %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

measure	pa.bis.trend	SE	df	lower.CL	upper.CL	t.ratio	p.value
accuracy	0.61	0.12	122.88	0.39	0.84	5.32	0.000
fluency	0.28	0.12	122.88	0.05	0.50	2.39	0.018

emtrends(m1, pairwise ~ measure, var = "pa.bis", infer = T, adjust = "bonferroni")$contrasts %>% 
  as.data.frame() %>% 
  mutate(across(estimate:t.ratio, ~round(.x,2)),
         p.value = round(p.value,3)) -> post.1a
color.me <- which(post.1a$p.value <= .05)
post.1a %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

contrast	estimate	SE	df	lower.CL	upper.CL	t.ratio	p.value
accuracy - fluency	0.34	0.12	72	0.09	0.59	2.71	0.009

Post-hoc 2: Vocabulary vs. Measure

emtrends(m1, pairwise ~ measure, var = "lexita.acc", infer = T, adjust = "bonferroni")$emtrends %>% 
  as.data.frame() %>% 
  mutate(across(lexita.acc.trend:t.ratio, ~round(.x,2)), 
         p.value = round(p.value,3)) -> post.2
color.me <- which(post.2$p.value <= .05)
post.2 %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

measure	lexita.acc.trend	SE	df	lower.CL	upper.CL	t.ratio	p.value
accuracy	0.49	0.16	122.88	0.17	0.80	3.05	0.003
fluency	-0.07	0.16	122.88	-0.39	0.24	-0.47	0.640

emtrends(m1, pairwise ~ measure, var = "lexita.acc", infer = T, adjust = "bonferroni")$contrasts %>% 
  as.data.frame() %>% 
  mutate(across(estimate:t.ratio, ~round(.x,2)),
         p.value = round(p.value,3)) -> post.2a
color.me <- which(post.2a$p.value <= .05)
post.2a %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

contrast	estimate	SE	df	lower.CL	upper.CL	t.ratio	p.value
accuracy - fluency	0.56	0.17	72	0.22	0.9	3.25	0.002

Post-hoc 3: OK vs. Measure vs. Group

emtrends(m1, pairwise ~ group | measure, var = "it.ok.acc", infer = T, adjust = "bonferroni")$emtrends %>% 
  as.data.frame() %>% 
  mutate(across(it.ok.acc.trend:t.ratio, ~round(.x,2)), 
         p.value = round(p.value,3)) -> post.3
color.me <- which(post.3$p.value <= .05)
post.3 %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

group	measure	it.ok.acc.trend	SE	df	lower.CL	upper.CL	t.ratio	p.value
DYS	accuracy	0.45	0.32	122.88	-0.19	1.09	1.40	0.163
TD	accuracy	-0.01	0.25	122.88	-0.50	0.49	-0.02	0.982
DYS	fluency	1.79	0.32	122.88	1.16	2.43	5.58	0.000
TD	fluency	0.32	0.25	122.88	-0.18	0.81	1.26	0.209

### comparison of group performance 
emmeans(m1, pairwise ~ group | measure | it.ok.acc, at = list(it.ok.acc = c(-1,1)), adjust = "bonferroni")$contrasts %>% summary() %>% as.data.frame() %>% 
  mutate(across(estimate:t.ratio, ~round(.x,2)),
         p.value = round(p.value,3)) -> post.3b
color.me <- which(post.3b$p.value <= .05)
post.3b %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

contrast	measure	it.ok.acc	estimate	SE	df	t.ratio	p.value
DYS - TD	accuracy	-1	-1.20	0.57	122.88	-2.10	0.037
DYS - TD	fluency	-1	-2.96	0.57	122.88	-5.21	0.000
DYS - TD	accuracy	1	-0.28	0.67	122.88	-0.43	0.670
DYS - TD	fluency	1	-0.01	0.67	122.88	-0.01	0.991

Sublexical: All measures

df.model %>% 
  
  filter(dimension == "sublexical") %>% 
  
  lmer(formula = reading ~ 
         
         pa.bis * group * measure + 
         # pa.bis * measure + 
         # pa.bis * group + 
         
         # lexita.acc * group * measure + 
         # lexita.acc * measure +
         # lexita.acc * group +
         # lexita.acc + 
         
         # it.ok.acc * group * measure + 
         # it.ok.acc * group +
         it.ok.acc * measure +
         
         backward * group * measure + 
         # backward * group + 
         # backward * measure + 
         # backward + 
         
         (1 |ID)
         
         ) -> m2

AIC	AICc	BIC	R2_conditional	R2_marginal	ICC	RMSE	Sigma
597.8943	601.6985	647.0971	0.7939116	0.5458077	0.546253	0.7922884	1.031352

	Chisq	Df	Pr(>Chisq)
(Intercept)	25.00	1	0.000
pa.bis	34.55	1	0.000
group	7.01	1	0.008
measure	0.62	1	0.431
it.ok.acc	0.37	1	0.543
backward	0.26	1	0.609
pa.bis:group	3.64	1	0.056
pa.bis:measure	30.96	1	0.000
group:measure	0.01	1	0.936
measure:it.ok.acc	14.36	1	0.000
group:backward	0.34	1	0.560
measure:backward	1.73	1	0.189
pa.bis:group:measure	8.15	1	0.004
group:measure:backward	3.24	1	0.072

Post-hoc 1: OK vs. Measure

emtrends(m2, pairwise ~ measure, var = "it.ok.acc", infer = T, adjust = "bonferroni")$emtrends %>% 
  as.data.frame() %>% 
  mutate(across(it.ok.acc.trend:t.ratio, ~round(.x,2)), 
         p.value = round(p.value,3)) -> post2.1
color.me <- which(post2.1$p.value <= .05)
post2.1 %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

measure	it.ok.acc.trend	SE	df	lower.CL	upper.CL	t.ratio	p.value
accuracy	-0.14	0.24	112.45	-0.61	0.32	-0.61	0.544
fluency	0.71	0.24	112.45	0.24	1.18	3.00	0.003

Post-hoc 2: PA vs. Measure vs. Group

emtrends(m2, pairwise ~ group | measure, var = "pa.bis", infer = T, adjust = "bonferroni")$emtrends %>% 
  as.data.frame() %>% 
  mutate(across(pa.bis.trend:t.ratio, ~round(.x,2)), 
         p.value = round(p.value,3)) -> post2.3
color.me <- which(post2.3$p.value <= .05)
post2.3 %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

group	measure	pa.bis.trend	SE	df	lower.CL	upper.CL	t.ratio	p.value
DYS	accuracy	0.91	0.15	112.45	0.60	1.22	5.88	0.000
TD	accuracy	0.35	0.24	112.45	-0.13	0.83	1.44	0.154
DYS	fluency	0.09	0.15	112.45	-0.22	0.40	0.58	0.565
TD	fluency	0.33	0.24	112.45	-0.16	0.81	1.34	0.181

### comparison of group performance 
emmeans(m2, pairwise ~ group | measure | pa.bis, at = list(pa.bis = c(-1,1)), adjust = "bonferroni")$contrasts %>% summary() %>% as.data.frame() %>% 
  mutate(across(estimate:t.ratio, ~round(.x,2)),
         p.value = round(p.value,3)) -> post2.3b
color.me <- which(post2.3b$p.value <= .05)
post2.3b %>% kable() %>% kable_styling() %>% row_spec(color.me, bold = T)

contrast	measure	pa.bis	estimate	SE	df	t.ratio	p.value
DYS - TD	accuracy	-1	-2.04	0.71	112.45	-2.88	0.005
DYS - TD	fluency	-1	-1.29	0.71	112.45	-1.81	0.073
DYS - TD	accuracy	1	-0.92	0.54	112.45	-1.70	0.092
DYS - TD	fluency	1	-1.76	0.54	112.45	-3.25	0.002

Lexical and sublexical reading predictors

Poor readers

Compensated DYS

Exclude participants

Dyslexics’ profiles

Inferential analyses

Correlations

Lexical: All measures

Post-hoc 1: PA vs. Measure

Post-hoc 2: Vocabulary vs. Measure

Post-hoc 3: OK vs. Measure vs. Group

Sublexical: All measures

Post-hoc 1: OK vs. Measure

Post-hoc 2: PA vs. Measure vs. Group