1. Comparison of fluency tasks

a. Raw Scores

There are differences in mean and SD across fluency items. There are also differences in the correlations between those fluency items and other components of the TabCAT-EXAMINER score.
Letter n mean sd cor:flanker cor:Dot_count cor:run_dot cor:set_shift
word_f 294 16.099 4.626 0.111 0.155 0.202 0.124
word_l 435 15.309 4.199 0.057 0.286 0.142 0.15
word_s 425 18.416 4.868 0.134 0.354 0.253 0.156
word_t 289 17.08 4.902 0.098 0.34 0.236 0.085
word_m 2 19.5 7.778 n<5 n<5 n<5 n<5
word_b 2 15.5 4.95 n<5 n<5 n<5 n<5
word_n 1 11 NA n<5 n<5 n<5 n<5
word_c 1 16 NA n<5 n<5 n<5 n<5
word_fruit 285 16.639 4.288 0.3 0.197 0.209 0.265
word_veg 439 15.551 4.162 0.069 0.114 0.172 0.074

b. Binned Scores

The TabCAT-EXAMINER score converts continous varaiables to ordinal for factor creation. Here I checked to see if converting to ordinal removed some of the distribution and correaltion issues noted in the raw score section. Binning in this way did not make much of a difference.

## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
Letter n mean sd cor:flanker cor:Dot_count cor:run_dot cor:set_shift
word_f_fword_recode 294 8.238 2.21 0.112 0.159 0.203 0.135
word_s_fword_recode 425 9.304 2.213 0.155 0.374 0.256 0.17
word_t_fword_recode 289 8.702 2.308 0.113 0.357 0.227 0.088
word_l_lword_recode 435 8.372 2.053 0.053 0.291 0.14 0.142
word_s_lword_recode 425 9.706 2.075 0.152 0.349 0.24 0.167
word_t_lword_recode 289 9.128 2.191 0.105 0.357 0.247 0.086
veg_recode 439 7.695 1.831 0.071 0.112 0.168 0.073
word_fruit_animal_recode 285 5.832 1.441 0.286 0.18 0.21 0.244
word_fruit_veg_recode 285 8.172 1.956 0.301 0.196 0.228 0.262

c. Cross walk

Based on Monsell et al 2016. Uses equate library.

I next attempted to use cross walk data to convert across fluency items. The idea being, if we can convert say words (s) to words (f) then maybe we can combine those groups. Here I completed a cross walk from S to L. Then I completed a cross walk from T to S. Then from I used that T_to_S variable, and converted it to L. Now we have the original n=294 words (f) and n=1149 [n=435 original; n=425 who were s; n=289 who were T] words (l). Results are reported under “creation of factor”. I did a similar approach with the semantic fluency items. I converted fruits to animals and animals to veg. Then I used those models to move the “fruits to animals” variable to vegitables.

2. Creation of Factor Score

Full sample

All participants included. Shows data comparing a TabCAT-EXAMINER score created with the original sample and a score (TabCAT-EXAMINER crosswalk) created after converting fluency items (t etc.) to words (l).

## 
## 
## Table: Description of factors
## 
## |                |   n|   mean|    sd| median|    min|   max| range|   skew| kurtosis|
## |:---------------|---:|------:|-----:|------:|------:|-----:|-----:|------:|--------:|
## |Original Score  | 725|  0.775| 0.519|  0.805| -1.790| 2.162| 3.953| -0.354|    0.453|
## |Crosswalk Score | 725|  0.853| 0.531|  0.863| -1.423| 2.389| 3.812| -0.170|    0.278|
## |Crosswalk Diff. | 725| -0.078| 0.163| -0.067| -0.750| 0.421| 1.171| -0.640|    1.178|
## [1] "Values in heatmap are: Correlation (no covariates). Spearman used for spearman.list, pearson for other variables"

Restricted sample

Includes only participants who had missing fluency data. Shows data comparing a TabCAT-EXAMINER score created with the original sample and a score (TabCAT-EXAMINER crosswalk) created after converting fluency items (t etc.) to words (l). Within this restricted sample, the correlation between the TabCAT-EXAMINER and the Tabcat-EXAMINER cross walk remains high (.95). There are changes in the associations with the fluency factors, but I don’t think that invalidates anything.

## 
## 
## Table: Description of factors (restricted sample)
## 
## |                     |   n|   mean|    sd| median|    min|   max| range|   skew| kurtosis|
## |:--------------------|---:|------:|-----:|------:|------:|-----:|-----:|------:|--------:|
## |Original Score       | 269|  0.744| 0.538|  0.735| -1.790| 1.845| 3.635| -0.541|    0.864|
## |Crosswalk Score      | 269|  0.896| 0.575|  0.903| -1.423| 2.373| 3.796| -0.275|    0.267|
## |Crosswalk Difference | 269| -0.152| 0.198| -0.139| -0.750| 0.421| 1.171| -0.255|    0.118|
## [1] "Values in heatmap are: Correlation (no covariates). Spearman used for spearman.list, pearson for other variables"