Spoonerism

Participants

Table 1. Biographical information
N	Age (M)	SD	range
60	22.91	2.92	18 - 36

Table 2. Linguistic background
L1_IT	mother_L1	father_L1	N
Sì	Italiano e Veneto	Italiano e Veneto	2
Sì	Italiano	Italiano	52
Sì	Bulgaro	Bulgaro	1
Sì	Italiano e Dialetto Camuno	Italiano e Dialetto Camuno	1
Sì	Friulano della Bassa Friulana	Italiano	1
Sì	Italiano e Veneto	Italiano e Siciliano	1
Sì	Italiano	Italiano e Friulano	1
Sì	Tedesco	Siciliano	1

Table 3. Number of L2s
n_other_L2	N
0	1
1	18
2	15
3	14
4	12

Table 4. Diagnosis of dyslexia
DYS	N
No	54
Sì	6

### Exclude for reading analysis 
df %>% filter(ID != "SPOON01") -> df # pilot 

df %>% 
  mutate(exclude = case_when(
    ID == "SPOON04" ~ 1, 
    ID == "SPOON10" ~ 1,
    ID == "SPOON49" ~ 1, 
    ID == "SPOON52" ~ 1, 
    ID == "SPOON46" ~ 1, 
    ID == "SPOON44" ~ 1, 
    TRUE ~ 0
  )) -> df 
df %>% filter(exclude != 1) -> df

Table 5. Tested sample after exclusion
N	Age (M)	SD	range
54	22.89	3.04	18 - 36

Reading measures

Table 6. Reading performance
reading_measure	M	SD	range
Word reading time	-0.79	1.31	-4.23 - 1.46
Word reading errors	-0.25	1.42	-4.34 - 0.99
Nonword reading time	-0.81	1.42	-4.99 - 1.43
Nonword reading errors	-0.35	1.46	-6.71 - 1.18

Orthographic choice

Table 7. Orthographic Choice Task
Accuracy (M)	sd	range
0.98	0.03	0.84 - 1

Digit span memory

Table 8. Digit Span Task
task	Mean	SD	range
forward	6.98	1.21	4 - 9
backward	5.67	1.25	3 - 8

Correlations

Table 8. Correlations across control measures
	wr_time_z	wr_error_z	nwr_time_z	nwr_error_z	it.ok	forward	backward	age	n_other_L2
wr_time_z	1	0.43	0.83	0.47	0.39	0.32	0.15	0.04	0.19
wr_error_z	0.43	1	0.37	0.6	0.64	-0.09	-0.16	0.17	0.16
nwr_time_z	0.83	0.37	1	0.51	0.38	0.19	0.1	0.07	0.32
nwr_error_z	0.47	0.6	0.51	1	0.43	-0.05	0	0.15	0.21
it.ok	0.39	0.64	0.38	0.43	1	0	0.11	0.26	0.23
forward	0.32	-0.09	0.19	-0.05	0	1	0.27	-0.21	0.01
backward	0.15	-0.16	0.1	0	0.11	0.27	1	-0.19	-0.08
age	0.04	0.17	0.07	0.15	0.26	-0.21	-0.19	1	0.17
n_other_L2	0.19	0.16	0.32	0.21	0.23	0.01	-0.08	0.17	1

Item analysis

Item structure

Inferential analysis

Table 9. Counterbalancing
order_admin	n
L2-V2/L1-V2	7
L1-V3/L2-V3	8
L2-V1/L1-V1	8
L1-V2/L2-V2	6
L2-V3/L1-V3	7
L1-V4/L2-V4	7
L1-V1/L2-V1	6
L2-V4/L1-V4	5

Accuracy

condition	n
mixed	8
simple_nonvisual1	4
simple_visual2	4
simple_visual1	4
complex1	4
complex4	4
complex3	4
complex2	4

df.items %>% 
  
  glmer(formula = accuracy_item ~ 
          
          condition + 
          
          (1 + condition|ID) + (1|item_code), 
        
        family = "binomial", 
        control = glmerControl(optimizer = "bobyqa")
          ) -> m0

Anova(m0, type = "III") %>% as.data.frame() %>% 
  mutate_all(~round(.x,3)) %>% 
  kable(caption = "Simple model comparing fine-grained conditions") %>% kable_minimal()

Simple model comparing fine-grained conditions
	Chisq	Df	Pr(>Chisq)
(Intercept)	5.750	1	0.016
condition	46.435	7	0.000

emmeans(m0, pairwise ~ condition, adjust = "bonferroni")$emmeans %>% as.data.frame() %>% 
  mutate(emmean = plogis(emmean),
         emmean = round(emmean,2),
         SE = round(SE,2)) %>% 
  dplyr::select(1:3) %>% 
  kable(caption = "Estimated Marginal Means") %>% kable_minimal()

Estimated Marginal Means
condition	emmean	SE
complex1	0.68	0.31
complex2	0.86	0.38
complex3	0.83	0.40
complex4	0.64	0.37
mixed	0.93	0.34
simple_nonvisual1	0.93	0.48
simple_visual1	0.98	0.72
simple_visual2	0.99	1.11

emmeans(m0, pairwise ~ condition, adjust = "bonferroni")$contrast %>% as.data.frame() %>% 
  mutate(across(estimate:z.ratio, ~round(.x,2)),
         p.value = round(p.value,3)) -> emmeans2
color.me <- which(emmeans2$p.value <= .05)
emmeans2 %>% kable(caption = "Contrasts") %>% kable_minimal() %>% row_spec(color.me, bold = T)

Contrasts
contrast	estimate	SE	df	z.ratio	p.value
complex1 - complex2	-1.08	0.45	Inf	-2.42	0.432
complex1 - complex3	-0.82	0.46	Inf	-1.79	1.000
complex1 - complex4	0.18	0.43	Inf	0.41	1.000
complex1 - mixed	-1.81	0.43	Inf	-4.21	0.001
complex1 - simple_nonvisual1	-1.87	0.54	Inf	-3.46	0.015
complex1 - simple_visual1	-3.01	0.76	Inf	-3.93	0.002
complex1 - simple_visual2	-3.94	1.15	Inf	-3.43	0.017
complex2 - complex3	0.26	0.49	Inf	0.53	1.000
complex2 - complex4	1.26	0.46	Inf	2.76	0.163
complex2 - mixed	-0.73	0.47	Inf	-1.56	1.000
complex2 - simple_nonvisual1	-0.79	0.58	Inf	-1.38	1.000
complex2 - simple_visual1	-1.93	0.79	Inf	-2.43	0.423
complex2 - simple_visual2	-2.86	1.16	Inf	-2.47	0.379
complex3 - complex4	1.00	0.45	Inf	2.23	0.714
complex3 - mixed	-0.99	0.47	Inf	-2.11	0.981
complex3 - simple_nonvisual1	-1.05	0.60	Inf	-1.75	1.000
complex3 - simple_visual1	-2.19	0.78	Inf	-2.82	0.135
complex3 - simple_visual2	-3.11	1.19	Inf	-2.61	0.252
complex4 - mixed	-1.98	0.45	Inf	-4.36	0.000
complex4 - simple_nonvisual1	-2.05	0.58	Inf	-3.53	0.012
complex4 - simple_visual1	-3.18	0.78	Inf	-4.09	0.001
complex4 - simple_visual2	-4.11	1.18	Inf	-3.49	0.014
mixed - simple_nonvisual1	-0.06	0.56	Inf	-0.12	1.000
mixed - simple_visual1	-1.20	0.76	Inf	-1.58	1.000
mixed - simple_visual2	-2.13	1.14	Inf	-1.87	1.000
simple_nonvisual1 - simple_visual1	-1.14	0.86	Inf	-1.33	1.000
simple_nonvisual1 - simple_visual2	-2.06	1.18	Inf	-1.75	1.000
simple_visual1 - simple_visual2	-0.93	1.32	Inf	-0.70	1.000

df.items %>%

  glmer(formula = accuracy_item ~ 
          
          it.ok * condition3 + 
          # it.ok + 
          # condition3 + 
        
          forward * condition3+ 
          # backward * condition3 + 
          # backward + 
          
          age + 
          n_other_L2 + 
          
          (1 + condition3|ID) + (1|item_code), 
        
        family = "binomial", 
        control = glmerControl(optimizer = "bobyqa")
          ) -> m1

### Model selection
drop1(m1, test = "Chisq")

## Single term deletions
## 
## Model:
## accuracy_item ~ it.ok * condition3 + forward * condition3 + age + 
##     n_other_L2 + (1 + condition3 | ID) + (1 | item_code)
##                    npar    AIC    LRT Pr(Chi)  
## <none>                  1549.9                 
## age                   1 1553.1 5.1860 0.02277 *
## n_other_L2            1 1551.9 3.9577 0.04666 *
## it.ok:condition3      1 1550.5 2.5544 0.10999  
## condition3:forward    1 1551.5 3.5140 0.06085 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Model assessing predictive ability of orthographic knowledge, reading skills and verbal STM
	Chisq	Df	Pr(>Chisq)
(Intercept)	2.55	1	0.110
it.ok	18.89	1	0.000
condition3	34.11	1	0.000
forward	12.77	1	0.000
age	5.45	1	0.020
n_other_L2	4.13	1	0.042
it.ok:condition3	2.74	1	0.098
condition3:forward	4.04	1	0.044

Estimated Marginal Means
condition3	emmean	SE
complex	0.74	0.20
simple	0.94	0.24

Interaction: Forward * Condition
condition3	forward.trend	SE	df	asymp.LCL	asymp.UCL	z.ratio	p.value
complex	0.50	0.14	Inf	0.22	0.77	3.57	0.000
simple	0.13	0.18	Inf	-0.23	0.49	0.71	0.477

Response time

df.rt %>% 
  mutate(response_time = log(response_time)) %>% 
  filter(!is.na(response_time)) %>% 

  
  lmer(formula = response_time ~ 
          
          # it.ok * condition3 + 
          it.ok + 
          condition3 + 
         
          # forward * condition3 + 
          # backward * condition3 + 
          backward + 
          forward + 
          
          # age + 
          # n_other_L2 + 
          
          (1 + condition3|ID) + (1|item_code)
          ) -> m2

drop1(m2, test = "Chisq")

## Single term deletions using Satterthwaite's method:
## 
## Model:
## response_time ~ it.ok + condition3 + backward + forward + (1 + condition3 | ID) + (1 | item_code)
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## it.ok      2.6367  2.6367     1 49.787 20.0189 4.465e-05 ***
## condition3 6.9411  6.9411     1 50.951 52.6996 2.130e-09 ***
## backward   0.2116  0.2116     1 48.649  1.6065   0.21101    
## forward    0.6718  0.6718     1 48.549  5.1008   0.02844 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Anova table
	Chisq	Df	Pr(>Chisq)
(Intercept)	19309.58	1	0.000
it.ok	20.02	1	0.000
condition3	52.70	1	0.000
backward	1.61	1	0.205
forward	5.10	1	0.024

Contrasts
contrast	estimate	SE	df	t.ratio	p.value
complex - simple	0.31	0.04	50.95	7.25	0

Error analysis

Percentage of each errors on all responses by condition
Condition	Substitutions (Mean)	N	Deletion (Mean)	N	Insertion (Mean)	N	Real word (Mean)	N	No Response (Mean)	N	Total observations
Complex	0.16%	141	0.03%	22	0.1%	84	0.03%	30	0.03%	28	880
Simple	0.05%	53	0.01%	9	0.01%	12	0.02%	24	0.02%	21	1100

Percentage of each errors on total errors by condition
Condition	Substitutions (Mean)	N	Deletion (Mean)	N	Insertion (Mean)	N	Real word (Mean)	N	No Response (Mean)	N	Total observations
Complex	0.53%	141	0.08%	22	0.31%	84	0.11%	30	0.1%	28	267
Simple	0.48%	53	0.08%	9	0.11%	12	0.22%	24	0.19%	21	111

Total target substitutions by condition (e.g., /k/ changed into /ʧ/ or /g/ changed /ʤ/ or vice versa)
Condition	Total target substitutions	Mean	Tot. Substitutions
complex	77	0.55%	141
simple	0	0%	53

IRT analysis

Point-biserial correlations

item_code	accuracy_item	correlation	condition	prompt_1	prompt_2	target_1	target_2
MIX_08	0.89	0.47	mixed	vetro	lasso	letro	vasso
MIX_01	0.93	0.47	mixed	ruota	latte	luota	ratte
MIX_03	0.87	0.27	mixed	viso	tazza	tiso	vazza
MIX_06	0.78	0.13	mixed	rosa	biro	bosa	riro
S1_04	0.91	0.11	simple_nonvisual1	fungo	salto	sungo	falto
S1_02	0.81	0.34	simple_nonvisual1	fango	suono	sango	fuono
SV2_03	0.89	0.09	simple_visual2	miele	nocca	niele	mocca
SV1_02	0.87	0.18	simple_visual1	benda	pera	penda	bera
SV1_04	0.96	0.18	simple_visual1	bocca	posa	pocca	bosa
SV2_01	0.96	-0.06	simple_visual2	malta	nave	nalta	mave
C1_04	0.37	0.28	complex1	verso	chiosco	cherso	viosco
C4_03	0.67	0.28	complex4	giorno	topo	torno	giopo
C3_04	0.76	0.57	complex3	tana	ciclo	ciana	ticlo
C2_03	0.78	0.43	complex2	ghiaia	sire	siaia	ghire
C1_02	0.76	0.09	complex1	nido	chiodo	chido	niodo
C4_01	0.61	0.56	complex4	gita	lago	lita	giago
C3_02	0.83	0.35	complex3	sole	cinta	ciole	sinta
C2_01	0.81	0.41	complex2	ghiro	riccio	riro	ghiccio
MIX_07	0.85	0.59	mixed	varco	ratto	rarco	vatto
MIX_02	0.93	0.37	mixed	dosso	vetro	vosso	detro
MIX_04	0.81	0.11	mixed	torta	rospo	rorta	tospo
MIX_05	0.91	0.24	mixed	rete	destra	dete	restra
S1_03	0.87	0.20	simple_nonvisual1	sosta	fava	fosta	sava
S1_01	0.93	0.26	simple_nonvisual1	salto	fuoco	falto	suoco
SV2_04	0.93	0.16	simple_visual2	nonna	miglio	monna	niglio
SV1_01	0.93	0.18	simple_visual1	palo	barba	balo	parba
SV1_03	0.96	0.12	simple_visual1	pila	baffo	bila	paffo
SV2_02	0.93	0.29	simple_visual2	ninfa	mela	minfa	nela
C1_03	0.61	0.34	complex1	chiave	regno	riave	chegno
C4_04	0.54	0.40	complex4	dama	gioco	giama	doco
C3_03	0.76	0.45	complex3	cima	nota	nima	ciota
C2_04	0.80	0.33	complex2	vista	ghianda	ghista	vianda
C1_01	0.83	0.52	complex1	chicca	litro	licca	chitro
C4_02	0.57	0.38	complex4	maga	giacca	giaga	macca
C3_01	0.61	0.48	complex3	cifra	lato	lifra	ciato
C2_02	0.80	0.31	complex2	mito	ghisa	ghito	misa

Rasch model (RM)

Item difficulty

item_code	betas	accuracy_item	correlation	condition	prompt_1	prompt_2	target_1	target_2
C1_04	-2.43	0.37	0.28	complex1	verso	chiosco	cherso	viosco
C4_04	-1.56	0.54	0.40	complex4	dama	gioco	giama	doco
C4_02	-1.36	0.57	0.38	complex4	maga	giacca	giaga	macca
C4_01	-1.16	0.61	0.56	complex4	gita	lago	lita	giago
C3_01	-1.16	0.61	0.48	complex3	cifra	lato	lifra	ciato
C1_03	-1.16	0.61	0.34	complex1	chiave	regno	riave	chegno
C4_03	-0.84	0.67	0.28	complex4	giorno	topo	torno	giopo
C3_04	-0.26	0.76	0.57	complex3	tana	ciclo	ciana	ticlo
C3_03	-0.26	0.76	0.45	complex3	cima	nota	nima	ciota
C2_03	-0.12	0.78	0.43	complex2	ghiaia	sire	siaia	ghire
C2_04	0.01	0.80	0.33	complex2	vista	ghianda	ghista	vianda
C2_02	0.01	0.80	0.31	complex2	mito	ghisa	ghito	misa
S1_02	0.16	0.81	0.34	simple_nonvisual1	fango	suono	sango	fuono
C2_01	0.16	0.81	0.41	complex2	ghiro	riccio	riro	ghiccio
C1_01	0.32	0.83	0.52	complex1	chicca	litro	licca	chitro
C3_02	0.32	0.83	0.35	complex3	sole	cinta	ciole	sinta
MIX_07	0.49	0.85	0.59	mixed	varco	ratto	rarco	vatto
MIX_03	0.67	0.87	0.27	mixed	viso	tazza	tiso	vazza
S1_03	0.67	0.87	0.20	simple_nonvisual1	sosta	fava	fosta	sava
MIX_08	0.87	0.89	0.47	mixed	vetro	lasso	letro	vasso
MIX_05	1.11	0.91	0.24	mixed	rete	destra	dete	restra
MIX_02	1.38	0.93	0.37	mixed	dosso	vetro	vosso	detro
S1_01	1.38	0.93	0.26	simple_nonvisual1	salto	fuoco	falto	suoco
SV2_02	1.38	0.93	0.29	simple_visual2	ninfa	mela	minfa	nela
MIX_01	1.38	0.93	0.47	mixed	ruota	latte	luota	ratte

Latent dimension (Participants’ ability)

ID	DYS	ID_rasch	pers.par	mean_accuracy	sd
SPOON38	Sì	P35	-1.1629039	0.3888889	0.4944132
SPOON27	No	P24	-0.5248044	0.5833333	0.5000000
SPOON35	No	P32	-0.5248044	0.5555556	0.5039526
SPOON50	No	P44	-0.5248044	0.4444444	0.5039526
SPOON12	No	P9	-0.3258659	0.5277778	0.5063094
SPOON51	No	P45	-0.1291811	0.6388889	0.4871361
SPOON17	No	P14	0.2663387	0.6666667	0.4780914
SPOON21	Sì	P18	0.2663387	0.6111111	0.4944132
SPOON24	Sì	P21	0.2663387	0.6666667	0.4780914
SPOON26	No	P23	0.2663387	0.6666667	0.4780914
SPOON08	No	P6	0.4696014	0.6944444	0.4671766
SPOON14	No	P11	0.6798290	0.6944444	0.4671766
SPOON28	No	P25	0.6798290	0.6944444	0.4671766
SPOON41	No	P38	0.6798290	0.6944444	0.4671766
SPOON61	No	P54	0.6798290	0.6944444	0.4671766
SPOON11	No	P8	1.1342770	0.7500000	0.4391550
SPOON57	No	P50	1.1342770	0.8055556	0.4013865
SPOON58	Sì	P51	1.1342770	0.7222222	0.4542568
SPOON16	No	P13	1.3878322	0.8055556	0.4013865
SPOON23	No	P20	1.3878322	0.8055556	0.4013865
SPOON34	No	P31	1.3878322	0.8333333	0.3779645
SPOON39	No	P36	1.3878322	0.8055556	0.4013865
SPOON42	No	P39	1.3878322	0.8333333	0.3779645
SPOON53	No	P46	1.3878322	0.8055556	0.4013865
SPOON37	No	P34	1.6688875	0.8333333	0.3779645
SPOON47	No	P42	1.6688875	0.8611111	0.3507362
SPOON03	No	P2	1.9906999	0.8333333	0.3779645
SPOON15	No	P12	1.9906999	0.8333333	0.3779645
SPOON36	Sì	P33	1.9906999	0.8611111	0.3507362
SPOON48	No	P43	1.9906999	0.8611111	0.3507362
SPOON55	No	P48	1.9906999	0.8333333	0.3779645
SPOON13	No	P10	2.3777216	0.8888889	0.3187276
SPOON20	No	P17	2.3777216	0.8611111	0.3507362
SPOON25	No	P22	2.3777216	0.8888889	0.3187276
SPOON31	No	P28	2.3777216	0.8888889	0.3187276
SPOON33	No	P30	2.3777216	0.9166667	0.2803060
SPOON43	No	P40	2.3777216	0.8611111	0.3507362
SPOON56	No	P49	2.3777216	0.8888889	0.3187276
SPOON60	No	P53	2.3777216	0.9166667	0.2803060
SPOON02	No	P1	2.8840871	0.9166667	0.2803060
SPOON18	No	P15	2.8840871	0.9444444	0.2323107
SPOON19	No	P16	2.8840871	0.9166667	0.2803060
SPOON29	Sì	P26	2.8840871	0.9444444	0.2323107
SPOON30	No	P27	2.8840871	0.9166667	0.2803060
SPOON40	No	P37	2.8840871	0.8888889	0.3187276
SPOON54	No	P47	2.8840871	0.9166667	0.2803060
SPOON05	No	P3	3.6807844	0.9444444	0.2323107
SPOON06	No	P4	3.6807844	0.9444444	0.2323107
SPOON09	No	P7	3.6807844	0.9722222	0.1666667
SPOON22	No	P19	3.6807844	0.9444444	0.2323107
SPOON59	No	P52	3.6807844	0.9444444	0.2323107
SPOON07	No	P5	4.5477839	0.9722222	0.1666667
SPOON32	No	P29	4.5477839	0.9444444	0.2323107
SPOON45	No	P41	4.5477839	1.0000000	0.0000000

	C1_01	C1_03	C1_04	C2_01	C2_02	C2_03	C2_04	C3_01	C3_02	C3_03	C3_04	C4_01	C4_02	C4_03	C4_04	MIX_01	MIX_02	MIX_03	MIX_05	MIX_07	MIX_08	S1_01	S1_02	S1_03	SV2_02
P1	0.96	0.85	0.61	0.95	0.95	0.94	0.95	0.85	0.96	0.93	0.93	0.85	0.82	0.88	0.79	0.99	0.99	0.97	0.98	0.97	0.98	0.99	0.95	0.97	0.99
P2	0.91	0.70	0.39	0.90	0.88	0.87	0.88	0.70	0.91	0.85	0.85	0.70	0.65	0.76	0.61	0.97	0.97	0.93	0.96	0.92	0.95	0.97	0.90	0.93	0.97
P3	0.98	0.93	0.78	0.98	0.98	0.97	0.98	0.93	0.98	0.97	0.97	0.93	0.91	0.94	0.89	0.99	0.99	0.99	0.99	0.98	0.99	0.99	0.98	0.99	0.99
P4	0.98	0.93	0.78	0.98	0.98	0.97	0.98	0.93	0.98	0.97	0.97	0.93	0.91	0.94	0.89	0.99	0.99	0.99	0.99	0.98	0.99	0.99	0.98	0.99	0.99
P6	0.69	0.33	0.12	0.65	0.62	0.59	0.62	0.33	0.69	0.55	0.55	0.33	0.29	0.41	0.25	0.86	0.86	0.76	0.83	0.72	0.79	0.86	0.65	0.76	0.86
P7	0.98	0.93	0.78	0.98	0.98	0.97	0.98	0.93	0.98	0.97	0.97	0.93	0.91	0.94	0.89	0.99	0.99	0.99	0.99	0.98	0.99	0.99	0.98	0.99	0.99
P8	0.81	0.49	0.21	0.79	0.76	0.73	0.76	0.49	0.81	0.71	0.71	0.49	0.44	0.57	0.40	0.93	0.93	0.86	0.90	0.83	0.88	0.93	0.79	0.86	0.93
P9	0.50	0.18	0.06	0.46	0.42	0.39	0.42	0.18	0.50	0.36	0.36	0.18	0.16	0.24	0.13	0.74	0.74	0.59	0.69	0.54	0.63	0.74	0.46	0.59	0.74
P10	0.94	0.77	0.49	0.93	0.92	0.90	0.92	0.77	0.94	0.89	0.89	0.77	0.73	0.82	0.69	0.98	0.98	0.95	0.97	0.95	0.96	0.98	0.93	0.95	0.98
P11	0.73	0.38	0.15	0.70	0.67	0.64	0.67	0.38	0.73	0.60	0.60	0.38	0.34	0.46	0.29	0.89	0.89	0.79	0.86	0.76	0.83	0.89	0.70	0.79	0.89
P12	0.91	0.70	0.39	0.90	0.88	0.87	0.88	0.70	0.91	0.85	0.85	0.70	0.65	0.76	0.61	0.97	0.97	0.93	0.96	0.92	0.95	0.97	0.90	0.93	0.97
P13	0.85	0.56	0.26	0.82	0.80	0.78	0.80	0.56	0.85	0.76	0.76	0.56	0.51	0.63	0.46	0.94	0.94	0.89	0.92	0.87	0.91	0.94	0.82	0.89	0.94
P14	0.64	0.29	0.10	0.61	0.57	0.54	0.57	0.29	0.64	0.50	0.50	0.29	0.25	0.36	0.22	0.84	0.84	0.72	0.80	0.68	0.76	0.84	0.61	0.72	0.84
P15	0.96	0.85	0.61	0.95	0.95	0.94	0.95	0.85	0.96	0.93	0.93	0.85	0.82	0.88	0.79	0.99	0.99	0.97	0.98	0.97	0.98	0.99	0.95	0.97	0.99
P16	0.96	0.85	0.61	0.95	0.95	0.94	0.95	0.85	0.96	0.93	0.93	0.85	0.82	0.88	0.79	0.99	0.99	0.97	0.98	0.97	0.98	0.99	0.95	0.97	0.99
P17	0.94	0.77	0.49	0.93	0.92	0.90	0.92	0.77	0.94	0.89	0.89	0.77	0.73	0.82	0.69	0.98	0.98	0.95	0.97	0.95	0.96	0.98	0.93	0.95	0.98
P18	0.64	0.29	0.10	0.61	0.57	0.54	0.57	0.29	0.64	0.50	0.50	0.29	0.25	0.36	0.22	0.84	0.84	0.72	0.80	0.68	0.76	0.84	0.61	0.72	0.84
P19	0.98	0.93	0.78	0.98	0.98	0.97	0.98	0.93	0.98	0.97	0.97	0.93	0.91	0.94	0.89	0.99	0.99	0.99	0.99	0.98	0.99	0.99	0.98	0.99	0.99
P20	0.85	0.56	0.26	0.82	0.80	0.78	0.80	0.56	0.85	0.76	0.76	0.56	0.51	0.63	0.46	0.94	0.94	0.89	0.92	0.87	0.91	0.94	0.82	0.89	0.94
P21	0.64	0.29	0.10	0.61	0.57	0.54	0.57	0.29	0.64	0.50	0.50	0.29	0.25	0.36	0.22	0.84	0.84	0.72	0.80	0.68	0.76	0.84	0.61	0.72	0.84
P22	0.94	0.77	0.49	0.93	0.92	0.90	0.92	0.77	0.94	0.89	0.89	0.77	0.73	0.82	0.69	0.98	0.98	0.95	0.97	0.95	0.96	0.98	0.93	0.95	0.98
P23	0.64	0.29	0.10	0.61	0.57	0.54	0.57	0.29	0.64	0.50	0.50	0.29	0.25	0.36	0.22	0.84	0.84	0.72	0.80	0.68	0.76	0.84	0.61	0.72	0.84
P24	0.45	0.16	0.05	0.41	0.38	0.34	0.38	0.16	0.45	0.31	0.31	0.16	0.13	0.20	0.11	0.70	0.70	0.54	0.64	0.49	0.59	0.70	0.41	0.54	0.70
P25	0.73	0.38	0.15	0.70	0.67	0.64	0.67	0.38	0.73	0.60	0.60	0.38	0.34	0.46	0.29	0.89	0.89	0.79	0.86	0.76	0.83	0.89	0.70	0.79	0.89
P26	0.96	0.85	0.61	0.95	0.95	0.94	0.95	0.85	0.96	0.93	0.93	0.85	0.82	0.88	0.79	0.99	0.99	0.97	0.98	0.97	0.98	0.99	0.95	0.97	0.99
P27	0.96	0.85	0.61	0.95	0.95	0.94	0.95	0.85	0.96	0.93	0.93	0.85	0.82	0.88	0.79	0.99	0.99	0.97	0.98	0.97	0.98	0.99	0.95	0.97	0.99
P28	0.94	0.77	0.49	0.93	0.92	0.90	0.92	0.77	0.94	0.89	0.89	0.77	0.73	0.82	0.69	0.98	0.98	0.95	0.97	0.95	0.96	0.98	0.93	0.95	0.98
P30	0.94	0.77	0.49	0.93	0.92	0.90	0.92	0.77	0.94	0.89	0.89	0.77	0.73	0.82	0.69	0.98	0.98	0.95	0.97	0.95	0.96	0.98	0.93	0.95	0.98
P31	0.85	0.56	0.26	0.82	0.80	0.78	0.80	0.56	0.85	0.76	0.76	0.56	0.51	0.63	0.46	0.94	0.94	0.89	0.92	0.87	0.91	0.94	0.82	0.89	0.94
P32	0.45	0.16	0.05	0.41	0.38	0.34	0.38	0.16	0.45	0.31	0.31	0.16	0.13	0.20	0.11	0.70	0.70	0.54	0.64	0.49	0.59	0.70	0.41	0.54	0.70
P33	0.91	0.70	0.39	0.90	0.88	0.87	0.88	0.70	0.91	0.85	0.85	0.70	0.65	0.76	0.61	0.97	0.97	0.93	0.96	0.92	0.95	0.97	0.90	0.93	0.97
P34	0.88	0.62	0.32	0.86	0.84	0.82	0.84	0.62	0.88	0.80	0.80	0.62	0.58	0.70	0.53	0.95	0.95	0.91	0.94	0.90	0.93	0.95	0.86	0.91	0.95
P35	0.30	0.09	0.03	0.27	0.24	0.22	0.24	0.09	0.30	0.19	0.19	0.09	0.07	0.12	0.06	0.55	0.55	0.38	0.49	0.34	0.43	0.55	0.27	0.38	0.55
P36	0.85	0.56	0.26	0.82	0.80	0.78	0.80	0.56	0.85	0.76	0.76	0.56	0.51	0.63	0.46	0.94	0.94	0.89	0.92	0.87	0.91	0.94	0.82	0.89	0.94
P37	0.96	0.85	0.61	0.95	0.95	0.94	0.95	0.85	0.96	0.93	0.93	0.85	0.82	0.88	0.79	0.99	0.99	0.97	0.98	0.97	0.98	0.99	0.95	0.97	0.99
P38	0.73	0.38	0.15	0.70	0.67	0.64	0.67	0.38	0.73	0.60	0.60	0.38	0.34	0.46	0.29	0.89	0.89	0.79	0.86	0.76	0.83	0.89	0.70	0.79	0.89
P39	0.85	0.56	0.26	0.82	0.80	0.78	0.80	0.56	0.85	0.76	0.76	0.56	0.51	0.63	0.46	0.94	0.94	0.89	0.92	0.87	0.91	0.94	0.82	0.89	0.94
P40	0.94	0.77	0.49	0.93	0.92	0.90	0.92	0.77	0.94	0.89	0.89	0.77	0.73	0.82	0.69	0.98	0.98	0.95	0.97	0.95	0.96	0.98	0.93	0.95	0.98
P42	0.88	0.62	0.32	0.86	0.84	0.82	0.84	0.62	0.88	0.80	0.80	0.62	0.58	0.70	0.53	0.95	0.95	0.91	0.94	0.90	0.93	0.95	0.86	0.91	0.95
P43	0.91	0.70	0.39	0.90	0.88	0.87	0.88	0.70	0.91	0.85	0.85	0.70	0.65	0.76	0.61	0.97	0.97	0.93	0.96	0.92	0.95	0.97	0.90	0.93	0.97
P44	0.45	0.16	0.05	0.41	0.38	0.34	0.38	0.16	0.45	0.31	0.31	0.16	0.13	0.20	0.11	0.70	0.70	0.54	0.64	0.49	0.59	0.70	0.41	0.54	0.70
P45	0.55	0.22	0.07	0.51	0.47	0.44	0.47	0.22	0.55	0.41	0.41	0.22	0.18	0.27	0.16	0.78	0.78	0.63	0.73	0.59	0.68	0.78	0.51	0.63	0.78
P46	0.85	0.56	0.26	0.82	0.80	0.78	0.80	0.56	0.85	0.76	0.76	0.56	0.51	0.63	0.46	0.94	0.94	0.89	0.92	0.87	0.91	0.94	0.82	0.89	0.94
P47	0.96	0.85	0.61	0.95	0.95	0.94	0.95	0.85	0.96	0.93	0.93	0.85	0.82	0.88	0.79	0.99	0.99	0.97	0.98	0.97	0.98	0.99	0.95	0.97	0.99
P48	0.91	0.70	0.39	0.90	0.88	0.87	0.88	0.70	0.91	0.85	0.85	0.70	0.65	0.76	0.61	0.97	0.97	0.93	0.96	0.92	0.95	0.97	0.90	0.93	0.97
P49	0.94	0.77	0.49	0.93	0.92	0.90	0.92	0.77	0.94	0.89	0.89	0.77	0.73	0.82	0.69	0.98	0.98	0.95	0.97	0.95	0.96	0.98	0.93	0.95	0.98
P50	0.81	0.49	0.21	0.79	0.76	0.73	0.76	0.49	0.81	0.71	0.71	0.49	0.44	0.57	0.40	0.93	0.93	0.86	0.90	0.83	0.88	0.93	0.79	0.86	0.93
P51	0.81	0.49	0.21	0.79	0.76	0.73	0.76	0.49	0.81	0.71	0.71	0.49	0.44	0.57	0.40	0.93	0.93	0.86	0.90	0.83	0.88	0.93	0.79	0.86	0.93
P52	0.98	0.93	0.78	0.98	0.98	0.97	0.98	0.93	0.98	0.97	0.97	0.93	0.91	0.94	0.89	0.99	0.99	0.99	0.99	0.98	0.99	0.99	0.98	0.99	0.99
P53	0.94	0.77	0.49	0.93	0.92	0.90	0.92	0.77	0.94	0.89	0.89	0.77	0.73	0.82	0.69	0.98	0.98	0.95	0.97	0.95	0.96	0.98	0.93	0.95	0.98
P54	0.73	0.38	0.15	0.70	0.67	0.64	0.67	0.38	0.73	0.60	0.60	0.38	0.34	0.46	0.29	0.89	0.89	0.79	0.86	0.76	0.83	0.89	0.70	0.79	0.89

## [1] "Variance = 31.11%"

Selected items

item_code	betas	accuracy_item	correlation	condition	prompt_1	prompt_2	target_1	target_2
C1_04	-2.43	0.37	0.28	complex1	verso	chiosco	cherso	viosco
C4_04	-1.56	0.54	0.40	complex4	dama	gioco	giama	doco
C4_02	-1.36	0.57	0.38	complex4	maga	giacca	giaga	macca
C4_01	-1.16	0.61	0.56	complex4	gita	lago	lita	giago
C1_03	-1.16	0.61	0.34	complex1	chiave	regno	riave	chegno
C3_04	-0.26	0.76	0.57	complex3	tana	ciclo	ciana	ticlo
C3_03	-0.26	0.76	0.45	complex3	cima	nota	nima	ciota
C2_03	-0.12	0.78	0.43	complex2	ghiaia	sire	siaia	ghire
C2_04	0.01	0.80	0.33	complex2	vista	ghianda	ghista	vianda
C2_02	0.01	0.80	0.31	complex2	mito	ghisa	ghito	misa
S1_02	0.16	0.81	0.34	simple_nonvisual1	fango	suono	sango	fuono
C1_01	0.32	0.83	0.52	complex1	chicca	litro	licca	chitro
C3_02	0.32	0.83	0.35	complex3	sole	cinta	ciole	sinta
MIX_07	0.49	0.85	0.59	mixed	varco	ratto	rarco	vatto
MIX_03	0.67	0.87	0.27	mixed	viso	tazza	tiso	vazza
MIX_08	0.87	0.89	0.47	mixed	vetro	lasso	letro	vasso
MIX_05	1.11	0.91	0.24	mixed	rete	destra	dete	restra
SV2_02	1.38	0.93	0.29	simple_visual2	ninfa	mela	minfa	nela

Validation

Original number: 36 items
Removed because of pbc < 0.20: 11 items
IRT: 25 items
Goal: 18

### Excluded items after IRT 
df.item.analysis %>% dplyr::select(item_code, betas) -> items
left_join(df, items, by = "item_code") %>% filter(!is.na(betas)) -> df 
# df %>% summarize(n = n_distinct(item_code))
df %>% filter(item_code != "C3_01" & 
              item_code != "C4_03" &  
              item_code != "C2_01" &  
              item_code != "S1_03" & 
              item_code != "MIX_02" & 
              item_code != "S1_01" & 
              item_code != "MIX_01"
              ) -> df

Score calculation & descriptive statistics

df %>% 
  group_by(ID) %>% 
  mutate(spoon.acc = sum(accuracy_item),
         spoon.rt = mean(response_time[accuracy_item == 1], na.rm = T)) %>% 
  ungroup() %>% 
  dplyr::select(ID:exclude, spoon.acc, spoon.rt) %>% 
  distinct(ID, .keep_all = T) -> df
  
### create speed-accuracy score tradeoff using BIS measure
### BIS combines reaction times and error rates in a way that strongly attenuates speed-accuracy trade-offs (see the paper for details). We here provide code to calculate BIS in Matlab, R, and Excel format. If you use one of these functions, please cite: Liesefeld, H. R. & Janczyk, M. (2019). Combining speed and accuracy to control for speed-accuracy trade-offs(?). Behavior Research Methods, 51, 40-60. doi:10.3758/s13428-018-1076-x

BIS <- function(data) {
  n <- length(data$group)    # sample size to correct var()-function result (which uses n-1)
  srt <- sqrt( ((n-1)/n) * var(data$mean_rt_c) )     # sample standard deviation across all rts
  spc <- sqrt( ((n-1)/n) * var(data$pc) )            # sample standard deviation across all rts
  mrt <- mean(data$mean_rt_c)                        # mean across all rts
  mpc <- mean(data$pc)                               # mean across all pcs
  zrt <- (data$mean_rt_c-mrt)/srt                    # standardized rts
  zpc <- (data$pc-mpc)/spc                           # z-standardized pcs
  data$bis <- zpc - zrt                              # Balanced Integration Score
  
  return(data)                                       # return data.frame with added variable 'bis'
}

df %>% 
  dplyr::select(ID, spoon.acc, spoon.rt) %>% 
  rename(
    group = ID,
    pc = spoon.acc, 
    mean_rt_c = spoon.rt
  ) %>% BIS() %>% 
  dplyr::select(group, bis) %>% 
  rename(ID = group) -> df.bis
left_join(df, df.bis, by = "ID") %>% rename(spoon.bis = bis) -> df

Descriptive results
Accuracy (M)	SD	range	RT (M)	SD	range	Speed-accuracy trade off (M)	SD	range
13.59	3.67	4-19	7795.47	4751.88	2817.37-29106.77	0	1.61	-5.24-2.22

Correlations

Accuracy

Response time

Speed-accuracy trade-off

Linear regressions

Accuracy

## Single term deletions
## 
## Model:
## spoon.acc ~ it.ok + forward + backward
##          Df Deviance    AIC    LRT Pr(>Chi)   
## <none>        40.977 282.91                   
## it.ok     1   48.264 288.20 7.2872 0.006945 **
## forward   1   48.996 288.93 8.0188 0.004629 **
## backward  1   41.283 281.22 0.3054 0.580493   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Accuracy ~ Italian Orthographic Knowledge & Verbal STM
term	estimate	std.error	statistic	p.value
(Intercept)	2.600	0.038	69.063	0.000
it.ok	0.107	0.041	2.607	0.009
forward	0.110	0.039	2.839	0.005
backward	0.022	0.040	0.552	0.581

Response time

## Single term deletions
## 
## Model:
## spoon.rt ~ it.ok + forward + backward
##          Df Sum of Sq        RSS    AIC  Pr(>Chi)    
## <none>                 832570058 886.20              
## it.ok     1 324763469 1157333527 901.65 2.941e-05 ***
## forward   1  42616558  875186616 886.84    0.1038    
## backward  1  13513987  846084045 885.05    0.3556    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

RT ~ Italian Orthographic Knowledge & Verbal STM
term	estimate	std.error	statistic	p.value
(Intercept)	7833.670	566.591	13.826	0.000
it.ok	-2491.213	569.823	-4.372	0.000
forward	-929.961	587.203	-1.584	0.120
backward	541.312	606.972	0.892	0.377

Speed-accuracy trade-off

## Single term deletions
## 
## Model:
## spoon.bis ~ it.ok + forward + backward
##          Df Sum of Sq     RSS    AIC  Pr(>Chi)    
## <none>                 75.351 26.649              
## it.ok     1    42.169 117.521 48.205 1.213e-06 ***
## forward   1    18.128  93.479 36.075 0.0007244 ***
## backward  1     0.037  75.388 24.675 0.8716389    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Trade off ~ Italian Orthographic Knowledge & Verbal STM
term	estimate	std.error	statistic	p.value
(Intercept)	0.005	0.170	0.026	0.979
it.ok	0.898	0.171	5.237	0.000
forward	0.607	0.177	3.433	0.001
backward	-0.028	0.183	-0.155	0.877

Spoonerism

Ilaria Venagli, Chiara Melloni, Chiara Carraro

2024-07-20

Spoonerism

Participants

Reading measures

Orthographic choice

Digit span memory

Correlations

Item analysis

Inferential analysis

Accuracy

Response time

Error analysis

IRT analysis

Point-biserial correlations

Rasch model (RM)

Selected items

Validation

Score calculation & descriptive statistics

Correlations

Accuracy

Response time

Speed-accuracy trade-off

Linear regressions

Accuracy

Response time

Speed-accuracy trade-off