Load the Data

load("data/TSIcor.rda")

Run some correlations

cor(TSIcor %>% 
      select(aa, ie, sleeph, cwt, ldfr, CVLT1t5, ps3,
             ps4, wm3, wm4),
    use = "pairwise.complete.obs") %>% 
  round(2)
##            aa    ie sleeph   cwt  ldfr CVLT1t5   ps3   ps4   wm3   wm4
## aa       1.00  0.72  -0.14 -0.13 -0.16   -0.09 -0.17 -0.22 -0.11 -0.16
## ie       0.72  1.00  -0.18 -0.19 -0.18   -0.10 -0.22 -0.23 -0.18 -0.18
## sleeph  -0.14 -0.18   1.00  0.02  0.10    0.07 -0.35  0.06 -0.02  0.05
## cwt     -0.13 -0.19   0.02  1.00  0.30    0.19  0.46  0.51  0.34  0.28
## ldfr    -0.16 -0.18   0.10  0.30  1.00    0.71  0.23  0.28  0.19  0.24
## CVLT1t5 -0.09 -0.10   0.07  0.19  0.71    1.00  0.15  0.26  0.15  0.33
## ps3     -0.17 -0.22  -0.35  0.46  0.23    0.15  1.00  0.94  0.36  0.95
## ps4     -0.22 -0.23   0.06  0.51  0.28    0.26  0.94  1.00  0.93  0.45
## wm3     -0.11 -0.18  -0.02  0.34  0.19    0.15  0.36  0.93  1.00  0.92
## wm4     -0.16 -0.18   0.05  0.28  0.24    0.33  0.95  0.45  0.92  1.00

No significant relationships found between variables, >.36 Highest correlations found between variables of interest: -hours of sleep and processing speed on the WAIS III (-0.35) -intrusive experience and processing speed on the WAIS IV (-0.23) -anxious arousal/intrusive experience and processing speed on the WAIS III and IV (-0.22)

ggplot(TSIcor, aes(x = ie, y = ps3)) +
  geom_smooth(se = 0) +
  geom_jitter(size = 0.5)

ggplot(TSIcor, aes(x = aa, y = ps4)) +
  geom_smooth(se = 0) +
  geom_jitter(size = 0.5)

TSIcor %>% 
  group_by(probfsl) %>% 
  summarize (
    avg_ps3 = mean(ps3, na.rm = TRUE)
  )
## # A tibble: 3 x 2
##   probfsl avg_ps3
## * <chr>     <dbl>
## 1 N          94  
## 2 Y          93.8
## 3 <NA>       96.0

Not sure yet how to quantify the Y/N question regarding “trouble sleeping”. Maybe tally reported sleep problems into a score from 0-3? (if individual reports nightmares, trouble falling asleep, and waking in sweat, they geta score of 3. Reports no problems and gets a score of 0)