If you "standardize" scores by groups, you definitely remove any differences between groups
from the scoring. Is that desirable or acceptable? And, you might consider scoring by logits rather
than the z-transformation.
The size of the groups can bear on the question of whether you coarsen some measures.
You throw away less information if the 1-9 scores have a tiny sample size; if they have the
much-bigger N, that's more reason to preserve them, and suffer the "noise" introduced by
re-mapping arbitrarily somewhere in the 1-9 range -- it could be (2,8) or (3,7) or whatever,
not /necessarily/ the extremes -- if the whole range of scores is being used, you don't want
the (1,2=> 1, 9) to dominate the variance calculations.
When you do coarsen the data, consider your hypotheses and what you want to say.
Consider (No, ?, Yes) ... where "?" might be Indifferent/ Don't Know/ Missing. If you want
to write up, eventually, a statement about "NOs" (or one about YESes), you should chose to
collapse the /other/ two groups.
--
Rich Ulrich
From: SPSSX(r) Discussion <[hidden email]> on behalf of Zdaniuk, Bozena <[hidden email]>
Sent: Tuesday, November 14, 2017 3:03:20 PM
To: [hidden email]
Subject: combining scores from questions measuring the same construct but with different scales===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARDHello everyone, I would like to gather people’s ideas on how to combine data measuring the same construct but with different scales. I have done it in the past by turning scores into z-scores but are there other ways?
e.g, a question “do you like color blue?”
Data set 1:– Answer options “yes” and “no”
Data set 2:- Answer options “1-I don’t like it at all”, “2-I like it a little”, “3 – I like it a lot”
Data set 3: - Answer options “1-I don’t like it at all, 2, 3, 4, 5, 6, 7-I like it a lot”
Thanks so much for any ideas, pointers to literature, websites, etc.!
Cheers,
Bozena
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