data structures for persistence data

## 'data.frame':    620 obs. of  16 variables:
##  $ id_item    : Factor w/ 20 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ a_ab       : num  1.1 0.85 2.12 0.62 1.06 1.75 1.06 0.84 1.36 1.15 ...
##  $ b_ab       : num  1.81 2.43 1.74 1.61 1.59 1.41 1.63 1.16 2.16 2.08 ...
##  $ a_abd      : num  1.134 0.894 2.036 0.744 1.185 ...
##  $ b_abd      : num  1.62 2.11 1.72 1.03 1.29 ...
##  $ d_abd      : num  0.797 0.743 0.845 0.738 0.775 0.85 0.775 0.761 0.764 0.778 ...
##  $ rater_id   : Factor w/ 31 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ gender     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ age        : int  65 65 65 65 65 65 65 65 65 65 ...
##  $ education  : int  5 5 5 5 5 5 5 5 5 5 ...
##  $ response   : int  4 1 3 2 3 3 3 2 3 2 ...
##  $ specificity: int  -2 2 2 -3 -2 -2 -3 -3 -3 -3 ...
##  $ availabil  : int  -3 -2 -2 -2 -2 -2 -3 2 -2 -3 ...
##  $ comfort    : int  -1 -3 -1 -1 -1 -1 -1 -3 -2 0 ...
##  $ vague      : int  3 2 2 -2 3 2 2 -2 3 -3 ...
##  $ desirab    : int  -2 -1 -3 2 -2 -2 -2 -2 -3 -1 ...

data structures for presence data

data.p<-read.csv("presence.long1.csv")
cols<-c(1,7)
data.p[cols] <- lapply(data.p[cols], factor)
str(data.p)
## 'data.frame':    620 obs. of  16 variables:
##  $ id_item    : Factor w/ 20 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ a_ab       : num  1.1 0.85 2.12 0.62 1.06 1.75 1.06 0.84 1.36 1.15 ...
##  $ b_ab       : num  1.81 2.43 1.74 1.61 1.59 1.41 1.63 1.16 2.16 2.08 ...
##  $ a_abd      : num  1.134 0.894 2.036 0.744 1.185 ...
##  $ b_abd      : num  1.62 2.11 1.72 1.03 1.29 ...
##  $ d_abd      : num  0.797 0.743 0.845 0.738 0.775 0.85 0.775 0.761 0.764 0.778 ...
##  $ rater_id   : Factor w/ 31 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ gender     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ age        : int  65 65 65 65 65 65 65 65 65 65 ...
##  $ education  : int  5 5 5 5 5 5 5 5 5 5 ...
##  $ response   : int  1 0 1 1 1 1 1 1 1 1 ...
##  $ specificity: int  -2 2 2 -3 -2 -2 -3 -3 -3 -3 ...
##  $ availabil  : int  -3 -2 -2 -2 -2 -2 -3 2 -2 -3 ...
##  $ comfort    : int  -1 -3 -1 -1 -1 -1 -1 -3 -2 0 ...
##  $ vague      : int  3 2 2 -2 3 2 2 -2 3 -3 ...
##  $ desirab    : int  -2 -1 -3 2 -2 -2 -2 -2 -3 -1 ...

means and sd of items responses for persistence data (1-4 Likert type) and gender proportions

##           [,1]  [,2] [,3]  [,4] [,5] [,6]  [,7] [,8]  [,9] [,10] [,11]
## resp.mean 1.94 1.710 2.00 2.677 2.45 2.06 2.097 2.74 1.613 1.677  2.00
## resp.sd   1.09 0.938 1.03 0.979 1.15 1.09 0.978 0.93 0.882 0.832  1.02
##           [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## resp.mean 2.806 2.097  2.26 1.484  2.74  1.27  2.16   1.8 1.871
## resp.sd   0.946 0.944  1.12 0.851  1.12  0.64  1.04   1.1 0.991
## 
## female   male 
##     13     18

distributions of age and education

distributions of item responses

persistence data

item responses (1-4; rarely, sometimes, occasionally, all of the time)

presence data

recode item responses (depression responses: 1 coded to 0, all others coded to 1)

# item responses
q<-ggplot(data=data.p,aes(response))
q + geom_bar(width=0.5) + facet_wrap(~ id_item, , nrow = 4)

distributions of five sco-cognitive ratings

## specificity
sp<-ggplot(data=dat1,aes(specificity))
sp + geom_bar(width=0.5) + facet_wrap(~ id_item, , nrow = 4)

## availability
av<-ggplot(data=dat1,aes(availabil))
av + geom_bar(width=0.5) + facet_wrap(~ id_item, , nrow = 4)

## comfort
com<-ggplot(data=dat1,aes(comfort))
com + geom_bar(width=0.5) + facet_wrap(~ id_item, , nrow = 4)

## vague
vag<-ggplot(data=dat1,aes(vague))
vag + geom_bar(width=0.5) + facet_wrap(~ id_item, , nrow = 4)

#desirability
des<-ggplot(data=dat1,aes(desirab))
des + geom_bar(width=0.5) + facet_wrap(~ id_item, , nrow = 4)