Lucy,

This sounds pretty complex, because the automated scoring won't account
for the potential thrown-out trials, and it could end up being a challenge
to get right.

There are built-in functions LikertTrial and YesNoTrial that could be used
to do this, but they require keyboard input and a bit of set-up. There is
also a function in scales\bigfive.pbl called Likert() which I've copied
here:


##These helper functions require gTextBox, gHeader, and gFooter to work.
define Likert(question)
{


    ##spread should be a total of at least 640 pixels, or 80 per option
    skip <- 150

    ##Add the question:
    questionHead <- MakeLabel(gQuestionHead, gQuestFont)
    AddObject(questionHead,gWin)
    Move(questionHead,gVideoWidth/2,150)


    question <- MakeLabel(question,gQuestFont)
    AddObject(question,gWin)
    Move(question,gVideoWidth/2,300)

    left <- gVideoWidth/2-skip*3
    yval <- gVideoHeight/2+100

    backs <- []
    nums <- []
    texts <- []

    loop(i,[1,2,3,4,5])
     {

      x <- left + skip * i
      back <- Rectangle(x,yval+50,skip-10,180,MakeColor("white"),0)
      num <- MakeLabel(i+"",gQuestFont)
      Move(num, x,yval)
      AddObject(num,gWin)
      AddObject(back,gWin)


      optLabel  <- MakeTextBox(Nth(gLikertOptions,i),gSmallFont,skip-15,80)
      AddObject(optLabel,gWin)
      Move(optlabel,x-optLabel.width/2,yval+50)

      PushOnEnd(backs,back)
      PushOnEnd(nums,num)
      PushOnEnd(texts,optlabel)

     }


  Draw()
  t1 <- GetTime()
  response <-WaitForClickOnTarget(backs,[1,2,3,4,5])
  t2 <- GetTime()

  return [response,(t2-t1)]
}




 This should work fairly well for the confidence response, although you
may need to change where the text gets displayed or add a background
rectangle.  Just copy that function into the end of the cardsort.pbl
file, and then figure out where you want to collect the response (around
line 315 on my version; it might differ on 0.13 or older), where it reads
as below.

You can easily add the likert response right after the line where response
is collected


   if(gParams.UseMouse)
      {
         resp <-
WaitForClickOnTarget([comp1,comp2,comp3,comp4],["1","2","3","4"])

        likert <- Likert("How confident are you in your answer?")

      }else{
         resp <- WaitForListKeyPress(["1","2","3","4"])
         likert <- Likert("How confident are you in your answer?")

      }


You would need to be sure to record the likert data somehow, maybe into
the same .csv file as your main data, or maybe a new file (using the same
data file may throw off the new PAR scoring routines)

To allow you to throw things out, you would need to adapt this likert
scale function so it just asked 'yes' and 'no' (maybe call the function
YesNo); and then also insert this function like above.  But figuring out
how to incorporate that into  the scoring routines that follow would take
some work--you have to check whether they wanted to throw it out, and then
not incorporate it into the scorekeeping, but presumably still record the
trial so you have a record of it.  Let me know if you get stuck.

Shane

> Hi,
>
>
> I would like to be able to edit the Wisconsin card test to measure
> metacognition as Koren et al (2004) had done. I would like to add a
> measure of the participants' confidence in their answer and decide whether
> they want the response they gave to be recorded before they are given the
> feedback on each question.
>
>
> How do I add these 2 simple questions and is it possible for the
> participants to rate their confidence on a percentage scale?
>
>
> Many thanks in advance
>
> Lucy
>
>
> Koren, D., Seidman, L.J., Poyurovsky, M., Goldsmith, M., Viksman, P.,
> Zichel, S., Klein, E. (2004). the neuropsychological basis of insight in
> first-episode schizophrenia: a pilot metacognitive study. schizophrenia
> research, 70(2-3). 195-202. doi: 10.1016/j.schres.2004.02.004
>



------------------------------------------------------------------------------
HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
Find What Matters Most in Your Big Data with HPCC Systems
Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
Leverages Graph Analysis for Fast Processing & Easy Data Exploration
http://p.sf.net/sfu/hpccsystems
_______________________________________________
Pebl-list mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/pebl-list

Reply via email to