spoonerism.knit

Summary of analysis criteria

Criteria to calculate point-time values

Input end: The end of the researcher’s utterance.
Answer start: First syllable, excluding “ehm” or similar fillers. Even if the syllables stops, e.g., “du…” when target is “duna”.
Answer end: The end of the participant’s answer, including any corrections or changes they make.

Criteria to calculate rt for each trial

Formula: answer_end - input_end

Criteria for accuracy

2 points: correct answer
1 point: correct phoneme switch, wrong word
0 point: incorrect
NA VALUES: participants’ forgot the prompt or did not answer

Calculating RT

# reaction time 

df %>% 
  # mutate raw time: seconds to milliseconds 
  
  mutate(input_end = input_end*1000, 
         answer_start = answer_start*1000, 
         answer_end = answer_end*1000) %>% 
  
  # create rt variables NOTE: CHECK THIS! 
  
  mutate(rt_trial = answer_end - input_end) %>% 
  
  # remove columns 
  
  dplyr::select(-input_end, -answer_start, -answer_end) -> df

Calculating accuracy

# load accuracy data 
read_csv("ACC_spoonerism.csv") %>% 
  
# check this: exclude for different phonemes at the beginning 
  filter(item != "C" & item != "I") %>% 

# calculate accuracy
  
  mutate(accuracy = case_when(
    # if response is correct == 2
    response == target ~ 2,
    
    # if phoneme-switching only is correct == 1
    substr(response, 1, 1) == substr(target, 1, 1) ~ 1,
    
    #else == 0
    TRUE ~ 0
  )) %>% 
  
  # create accuracy as factor (1 only for correct answers)
  
  mutate(accuracy_fct = if_else(
    accuracy == 2, 1, 0
  )) %>% 
  
  # create a total accuracy score for each participant
  
  group_by(ID) %>% 
  mutate(tot_accuracy = sum(accuracy)) %>% 
  ungroup()-> acc

### check 
# length(unique(acc$ID)) #94

Speed-accuracy trade off

## Create speed-accuracy trade-off measure 

### BIS combines reaction times and error rates in a way that strongly attenuates speed-accuracy trade-offs (see the paper for details). 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'
}

### BIS with pa.rt (less strict accuracy score)

df1 %>% 
  select(ID, pa.acc, pa.rt) %>% 
  mutate(pa.acc = pa.acc/40) %>% 
  rename(
    group = ID,
    pc = pa.acc,
    mean_rt_c = pa.rt
  ) %>% BIS() %>% 
  select(group, bis) %>% 
  rename(ID = group) -> df.bis
left_join(df1, df.bis, by = "ID") %>% 
  rename(tot.bis.pa = bis)-> df1

### BIS with pa.rt2 (more strict accuracy score)

df1 %>% 
  select(ID, pa.acc, pa.rt2) %>% 
  mutate(pa.acc = pa.acc/40) %>% 
  rename(
    group = ID,
    pc = pa.acc,
    mean_rt_c = pa.rt2
  ) %>% BIS() %>% 
  select(group, bis) %>% 
  rename(ID = group) -> df.bis
left_join(df1, df.bis, by = "ID") %>% 
  rename(tot.bis.pa2 = bis)-> df1

Exclude participants

df1 %>% 
  filter(group == "DYS" | (group == "TD" & is.na(other_diagnoses))) %>% 
  filter(!startsWith(ID, "VER")) -> df1

df1 %>%
  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) -> df1

Descriptive analysis of RT, accuracy and BIS

Descriptive measures
Group	Accuracy (M)	SD	range	RT (M)	SD	range	Speed-Accuracy trade-off (M)	SD	range
DYS	30.41	7.28	10	9.29	0.50	8.27	-1.44	2.12	-6.47
DYS	30.41	7.28	40	9.29	0.50	10.10	-1.44	2.12	1.68
TD	36.98	3.09	26	8.54	0.45	7.45	0.99	0.91	-2.12
TD	36.98	3.09	40	8.54	0.45	9.36	0.99	0.91	2.13

Inferential Statistics

RT

# model rt ~ group 
df1 %>% 
  mutate_if(is.character, as.factor) %>% 
  mutate(pa.rt = log(pa.rt),
         age = scale(age), age = as.double(age)) %>% 
  
  # model formula
  lm(formula = 
       pa.rt ~ 
       # group * age
       group + 
       age 
     ) -> m1 

tab_model(m1)

	pa rt
Predictors	Estimates	CI	p
(Intercept)	9.32	9.14 – 9.49	<0.001
group [TD]	-0.79	-1.01 – -0.56	<0.001
age	-0.05	-0.16 – 0.06	0.377
Observations	80
R² / R² adjusted	0.389 / 0.373

Spoonerism: RT
Group	Estimate	SE	CI
DYS	9.32	0.09	9.14 - 9.49
TD	8.53	0.07	8.4 - 8.66

Spoonerism: RT
	Sum Sq	Df	F value	Pr(>F)
group	10.29	1	47.18	0.000
age	0.17	1	0.79	0.377
Residuals	16.79	77	NA	NA

AIC	AICc	BIC	R2	R2_adjusted	RMSE	Sigma
110.13	110.67	119.66	0.39	0.37	0.46	0.47

Accuracy

# Accuracy 
df1 %>% 
  # prepare data 
  mutate_if(is.character, as.factor) %>% 
  mutate(age = scale(age), age = as.double(age)) %>% 
  
  # model   
  glm(formula = 
       
       pa.acc ~ 
       group * age, 
       # group, 
       # age, 
      
      family = poisson(link = "log")
     
     ) -> m2

tab_model(m2)

	pa acc
Predictors	Incidence Rate Ratios	CI	p
(Intercept)	28.79	26.61 – 31.07	<0.001
group [TD]	1.28	1.17 – 1.41	<0.001
age	1.13	1.04 – 1.22	0.004
group [TD] × age	0.88	0.80 – 0.97	0.008
Observations	80
R² Nagelkerke	0.483

Spoonerism: Accuracy
Group	Estimate	SE	CI
DYS	28.79	0.04	26.64 - 31.1
TD	36.94	0.02	35.27 - 38.7

Spoonerism: Accuracy
	LR Chisq	Df	Pr(>Chisq)
group	30.67	1	0.000
age	8.48	1	0.004
group:age	6.99	1	0.008

AIC	AICc	BIC	R2_Nagelkerke	RMSE	Sigma	Score_log	Score_spherical
497.32	497.85	506.84	0.48	4.61	1	-3.06	0.11

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

Age * Group
group	age.trend	SE	df	asymp.LCL	asymp.UCL	z.ratio	p.value
DYS	0.12	0.04	Inf	0.04	0.20	2.91	0.004
TD	0.00	0.02	Inf	-0.05	0.04	-0.19	0.849

BIS

# BIS 
df1 %>% 
  # prepare data 
  mutate_if(is.character, as.factor) %>% 
  mutate(age = scale(age), age = as.double(age)) %>% 
  
  # model   
  lm(formula = 
       
       tot.bis.pa ~ 
       group * age 
       # group, 
       # age 
     
     ) -> m3

tab_model(m3)

	tot bis pa
Predictors	Estimates	CI	p
(Intercept)	-1.86	-2.44 – -1.28	<0.001
group [TD]	2.84	2.14 – 3.55	<0.001
age	1.02	0.38 – 1.65	0.002
group [TD] × age	-1.04	-1.78 – -0.30	0.007
Observations	80
R² / R² adjusted	0.467 / 0.446

Spoonerism: RT
Group	Estimate	SE	CI
DYS	-1.86	0.29	-2.44 - -1.28
TD	0.98	0.20	0.58 - 1.38

Spoonerism: RT
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	79.76	1	41.13	0.000
group	126.18	1	65.06	0.000
age	19.83	1	10.23	0.002
group:age	15.18	1	7.83	0.007
Residuals	147.39	76	NA	NA

AIC	AICc	BIC	R2	R2_adjusted	RMSE	Sigma
285.91	286.72	297.82	0.47	0.45	1.36	1.39

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

Age * Group
group	age.trend	SE	df	lower.CL	upper.CL	t.ratio	p.value
DYS	1.02	0.32	76	0.38	1.65	3.20	0.002
TD	-0.02	0.19	76	-0.41	0.36	-0.13	0.899

Spoonerism task

Summary of analysis criteria

Criteria to calculate point-time values

Criteria for accuracy

Calculating RT

Calculating accuracy

Speed-accuracy trade off

Exclude participants

Descriptive analysis of RT, accuracy and BIS

Inferential Statistics

RT

Accuracy

BIS

Plot