reading_tests.knit

Z-scores

Word reading

# 1. Create group M and SD values by class (time, errors and number of syllables)
df %>% 
  mutate(
    group_time_mean = case_when(
      class == 1 ~ 92.88, 
      class == 2 ~ 90.29, 
      class == 3 ~ 65.39,
      class == 4 ~ 62, 
      class == 5 ~ 61.26,
      class == "uni" ~ 77.17, 
    ),
    group_time_sd = case_when(
      class == 1 ~ 18.7, 
      class == 2 ~ 19.13, 
      class == 3 ~ 14.4,
      class == 4 ~ 12.65, 
      class == 5 ~ 11.73, 
      class == "uni" ~ 16.99
    ),
    group_error_mean = case_when(
      class == 1 ~ 4.8, 
      class == 2 ~ 4.66, 
      class == 3 ~ 2.32, 
      class == 4 ~ 1.77, 
      class == 5 ~ 1.77,
      class == "uni" ~ 2.23
    ),
    group_error_sd = case_when(
      class == 1 ~ 3.04, 
      class == 2 ~ 3.11, 
      class == 3 ~ 2.57, 
      class == 4 ~ 1.7, 
      class == 5 ~ 1.79,
      class == "uni" ~ 2.25
    ),
    group_syl_mean = case_when(
      class == 1 ~ 3.95, 
      class == 2 ~ 4.08, 
      class == 3 ~ 4.91, 
      class == 4 ~ 5.16, 
      class == 5 ~ 5.21, 
      class == "uni" ~ 4.77
    ),
    group_syl_sd = case_when(
      class == 1~ 0.83, 
      class == 2 ~ 0.88, 
      class == 3 ~ 0.98, 
      class == 4 ~ 0.99, 
      class == 5 ~ 0.97,
      class == "uni" ~ 1.02
    ),
    syl_n_wr = case_when(
      # syllable count: 352 syllables 
      class == 1 ~ 353, 
      class == 2 ~ 353, 
      # syllable count: 56 syllables 2 lists + 98 syllables 2 lists (308)
      class == 3 ~ 308, 
      class == 4 ~ 308, 
      class == 5 ~ 308,
      class == "uni" ~ 352
    ), 
    wr_syl = syl_n_wr/wr_time
    )-> df 


# create z scores: (groupM - subjM)/groupSD

df %>% 
  mutate(
    wr_time_z = (group_time_mean - wr_time)/group_time_sd,
    wr_time_z = round(wr_time_z,2),
    
    wr_error_z = (group_error_mean - wr_errors)/group_error_sd,
    wr_error_z = round(wr_error_z,2),
    
    wr_syl_z = (group_syl_mean - wr_syl)/group_syl_sd,
    wr_syl_z = round(wr_syl_z,2)
  ) %>% 
  
  # remove group values
  
  dplyr::select(ID:AoO, wr_time:nwr_errors, wr_syl, wr_time_z:wr_syl_z) -> df

Nonword reading

# 1. Create group M and SD values by class (time, errors and number of syllables)
df %>% 
  mutate(
    group_time_mean = case_when(
      class == 1 ~ 77.91, 
      class == 2 ~ 75.51, 
      class == 3 ~ 57.07,
      class == 4 ~ 55.68, 
      class == 5 ~ 54.98,
      class == "uni" ~ 63.22 
    ),
    group_time_sd = case_when(
      class == 1 ~ 20.57, 
      class == 2 ~ 18.62, 
      class == 3 ~ 13.51,
      class == 4 ~ 12.43, 
      class == 5 ~ 11.62, 
      class == "uni" ~ 15.98
    ),
    group_error_mean = case_when(
      class == 1 ~ 4.66, 
      class == 2 ~ 4.84, 
      class == 3 ~ 3.16, 
      class == 4 ~ 2.9, 
      class == 5 ~ 2.67,
      class == "uni" ~ 3.14
    ),
    group_error_sd = case_when(
      class == 1 ~ 3.68, 
      class == 2 ~ 3.76, 
      class == 3 ~ 2.84, 
      class == 4 ~ 2.53, 
      class == 5 ~ 2.49,
      class == "uni" ~ 2.66
    ),
    group_syl_mean = case_when(
      class == 1 ~ 2.4, 
      class == 2 ~ 2.46, 
      class == 3 ~ 2.84, 
      class == 4 ~ 2.9, 
      class == 5 ~ 2.92, 
      class == "uni" ~ 2.96
    ),
    group_syl_sd = case_when(
      class == 1 ~ 0.57, 
      class == 2 ~ 0.56, 
      class == 3 ~ 0.62, 
      class == 4 ~ 0.61, 
      class == 5 ~ 0.6,
      class == "uni" ~ 0.76
    ),
    syl_n_nwr = case_when(
      # syllable count: 176 syllables 
      class == 1 ~ 176, 
      class == 2 ~ 176, 
      # syllable count: 56 list 1 + 98 list 2 = 154 
      class == 3 ~ 154, 
      class == 4 ~ 154, 
      class == 5 ~ 154,
      class == "uni" ~ 176
    ), 
    nwr_syl = syl_n_nwr/nwr_time
  ) -> df 

# create z scores: (groupM - subjM)/groupDS

df %>% 
  mutate(
    nwr_time_z = (group_time_mean - nwr_time)/group_time_sd,
    nwr_error_z = (group_error_mean - nwr_errors)/group_error_sd,
    nwr_syl_z = (group_syl_mean - nwr_syl)/group_syl_sd
  ) %>% 
  mutate(across(nwr_time_z:nwr_syl_z, ~ round(.x,2))) %>% 
  # remove group values 
  dplyr::select(ID:AoO, wr_time:wr_syl_z, nwr_syl:nwr_syl_z) -> df

Identify poor readers

# Exclusion criteria for TD 

df %>% 
  mutate(
    # reading time <= -2sd from mean -> exclude 
  cr_wr_time = case_when(
    group == "DYS" ~ "DYS",
    group == "TD" & wr_time_z <= -2 ~ "PR",
    group == "TD" & wr_time_z > -2 ~ "TD"
  ),
  cr_nwr_time = case_when(
    group == "YES" ~ "DYS",
    group == "TD" & nwr_time_z <= -2 ~ "PR",
    group == "TD" & nwr_time_z > -2 ~ "TD"
  ),
  cr_wr_error = case_when(
    group == "DYS" ~ "DYS", 
    group == "TD" & wr_error_z <= -2 ~ "PR", 
    group == "TD" & wr_error_z > -2 ~ "TD"
  ),
  cr_nwr_error = case_when(
    group == "DYS" ~ "DYS", 
    group == "TD" & nwr_error_z <= -2 ~ "PR", 
    group == "TD" & nwr_error_z > -2 ~ "TD"
  )) -> df 


df %>%
  group_by(ID) %>% 
  rowwise() %>% 
  mutate(group.exclusion = case_when(
    group == "DYS" ~ "DYS",
    group == "TD" & sum(cr_wr_time %in% c("PR"), 
                            cr_nwr_time %in% c("PR"), 
                            cr_wr_error %in% c("PR"), 
                            cr_nwr_error %in% c("PR")) >= 2 ~ "PR",
    group == "TD" & sum(cr_wr_time %in% c("PR"), 
                            cr_nwr_time %in% c("PR"), 
                            cr_wr_error %in% c("PR"), 
                            cr_nwr_error %in% c("PR")) < 2 ~ "TD",
    TRUE ~ NA_character_
  )) %>% 
  ungroup() -> df 

# df %>%
#   filter(group.exclusion == "PR") %>%
#   dplyr::select(ID, group, wr_time_z, wr_error_z, wr_syl_z, nwr_time_z, nwr_error_z, nwr_syl_z) %>%
#   kable(caption = "Poor readers without diagnosis (stricter criteria)") %>% kable_styling()
# 
# df %>% 
#   rowwise() %>% 
#   relocate(nwr_time_z, .after = wr_error_z) %>% relocate(nwr_error_z, .after = nwr_time_z) %>% 
#   mutate(reading.score = mean(c_across(wr_time_z:nwr_error_z)), 
#          reading.score = round(reading.score, 2)) %>% 
#   dplyr::select(ID, group, reading.score) %>% 
#   filter(group == "TD" & reading.score < -2) %>% 
#   kable(caption = "Poor readers without diagnosis (less strict criteria)") %>% kable_styling()

df %>% 
  filter(group.exclusion == "PR") %>% 
  dplyr::select(ID, group, fdp_wr_time, fdp_wr_error, fdp_nwr_time, fdp_nwr_error) %>% 
  kable(caption = "Poor readers (CRR = Criterio completamente raggiunto, PS = Prestazione sufficiente, RA = Richiesta attenzione, RII = Richiesta intervento immediato)") %>% kable_styling()

Poor readers (CRR = Criterio completamente raggiunto, PS = Prestazione sufficiente, RA = Richiesta attenzione, RII = Richiesta intervento immediato)
ID	group	fdp_wr_time	fdp_wr_error	fdp_nwr_time	fdp_nwr_error
LAE23	TD	RII	PS	RII	PS
LAE38	TD	RII	PS	RII	RA
LC19	TD	RII	PS	RII	PS
MEN02	TD	RII	RA	RII	RII
MEN10	TD	RII	PS	RII	PS
MEN29	TD	RII	RII	PS	RII
MEN35	TD	PS	RII	CRR	RII

Exclude participants

df %>%
  dplyr::select(ID, group, wr_time_z, wr_error_z, nwr_time_z, nwr_error_z, group.exclusion) %>%
  rowwise() %>%
  mutate(reading.score = mean(c_across(wr_time_z:nwr_error_z)),
         reading.score = round(reading.score,2)) %>%
  ungroup() %>%

  mutate(
    exclude = case_when(

      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

      TRUE ~ 0
    )
  ) %>%

  ### exclude
  filter(exclude == 0) %>%
  filter(!startsWith(ID, "VER")) %>%

  ### other exclusion criteria! 
  # filter(group.exclusion == "DYS" | (group.exclusion == "TD")) %>%
  # filter(group == "DYS" | (group == "TD" & reading.score > -2)) %>%
  
  dplyr::select(-exclude) -> df


### check sample 
df %>%
  summarize(
    n = n(), .by = "group"
  ) %>% 
  kable(caption = "Final sample") %>% kable_styling()

Final sample
group	n
DYS	29
TD	57

Descriptive Statistics

Real word time (z) by group
group	mean	sd	range
DYS	-4.15	2.49	-14.12
DYS	-4.15	2.49	-0.58
TD	-0.71	1.21	-4.97
TD	-0.71	1.21	1.65

Real word errors (z) by group
group	mean	sd	range
DYS	-2.75	2.58	-9.07
DYS	-2.75	2.58	1.04
TD	-0.01	0.83	-2.36
TD	-0.01	0.83	1.18

Nonwords time (z) by group
group	mean	sd	range
DYS	-3.03	1.93	-8.35
DYS	-3.03	1.93	-0.44
TD	-0.34	1.18	-4.58
TD	-0.34	1.18	1.80

Nonwords time (z) by group
group	mean	sd	range
DYS	-3.04	3.05	-11.78
DYS	-3.04	3.05	0.75
TD	-0.29	1.32	-4.55
TD	-0.29	1.32	1.29

Inferential Statistics

Differences by groups in word reading performance

lm(wr_time_z ~ group, data = df)-> wr.time
lm(wr_error_z ~ group, data = df) -> wr.error

tab_model(wr.time, wr.error)

	wr time z			wr error z
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	-4.15	-4.80 – -3.51	<0.001	-2.75	-3.35 – -2.15	<0.001
group [TD]	3.44	2.65 – 4.24	<0.001	2.74	2.00 – 3.49	<0.001
Observations	86			86
R² / R² adjusted	0.471 / 0.465			0.392 / 0.385

Word reading time: Predicted values
Group	Estimate	SE	CI
DYS	-4.15	0.32	-4.8 - -3.51
TD	-0.71	0.23	-1.17 - -0.25

Word reading time
	Sum Sq	Df	F value	Pr(>F)
group	227.98	1	74.77	0
Residuals	256.11	84	NA	NA

AIC	AICc	BIC	R2	R2_adjusted	RMSE	Sigma
343.9051	344.1978	351.2682	0.4709468	0.4646486	1.725688	1.746112

Word reading errors: Predicted values
Group	Estimate	SE	CI
DYS	-2.75	0.30	-3.35 - -2.15
TD	-0.01	0.22	-0.44 - 0.42

Word reading errors
	Sum Sq	Df	F value	Pr(>F)
group	144.67	1	54.13	0
Residuals	224.50	84	NA	NA

AIC	AICc	BIC	R2	R2_adjusted	RMSE	Sigma
332.5781	332.8708	339.9412	0.3918814	0.3846419	1.615705	1.634826

Differences by groups in nonword reading performance

lm(nwr_time_z ~ group, data = df) -> nwr.time
lm(nwr_error_z ~ group, data = df) -> nwr.error

tab_model(nwr.time, nwr.error)

	nwr time z			nwr error z
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	-3.03	-3.58 – -2.49	<0.001	-3.04	-3.80 – -2.28	<0.001
group [TD]	2.69	2.02 – 3.36	<0.001	2.75	1.81 – 3.69	<0.001
Observations	86			86
R² / R² adjusted	0.432 / 0.425			0.289 / 0.280

Nonword reading time: Predicted values
Group	Estimate	SE	CI
DYS	-3.03	0.27	-3.58 - -2.49
TD	-0.34	0.20	-0.73 - 0.05

Nonword reading time
	Sum Sq	Df	F value	Pr(>F)
group	138.90	1	63.78	0
Residuals	182.94	84	NA	NA

AIC	AICc	BIC	R2	R2_adjusted	RMSE	Sigma
314.9707	315.2634	322.3338	0.4315857	0.4248188	1.458492	1.475753

Nonword reading errors: Predicted values
Group	Estimate	SE	CI
DYS	-3.04	0.38	-3.8 - -2.28
TD	-0.29	0.27	-0.83 - 0.26

Nonword reading errors
	Sum Sq	Df	F value	Pr(>F)
group	145.62	1	34.07	0
Residuals	359.07	84	NA	NA

AIC	AICc	BIC	R2	R2_adjusted	RMSE	Sigma
372.9666	373.2593	380.3297	0.2885322	0.2800623	2.043345	2.067528

Reading Tests

Z-scores

Word reading

Nonword reading

Identify poor readers

Exclude participants

Descriptive Statistics

Inferential Statistics

Differences by groups in word reading performance

Differences by groups in nonword reading performance

Plots