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From: | Alan Mead |
Subject: | Re: multiple response set |
Date: | Thu, 08 Jan 2015 10:21:34 -0600 |
User-agent: | Mozilla/5.0 (Windows NT 6.1; WOW64; rv:31.0) Gecko/20100101 Thunderbird/31.3.0 |
I've used SPSS to analyze multiple response data for years (decades,
actually) but never used MULT RESPONSE. I was curious what I was
missing, so I watched this video:
https://www.youtube.com/watch?v=-toBCDscCwQ and I'm still a bit
confused. You get the same data by running frequencies on the four
variables independently, right? If each response is optional, then one thing that is a bit of a PITA is detecting non-response, but that's not a big deal. For example, if the four possible responses to Q12 are encoded 1/0 in Q12A, Q12B, Q12C, and Q12D, then you can do this: count Q12MISS = Q12A A12B Q12C Q12D (1). execute. Everyone with Q12MISS=0 didn't respond to the question. For some questions, this is more important than individual responses (other times not). I'm not arguing against including it in PSPP, I'm just curious why it's an issue because it seems like it's really, really easy to get along without. What am I missing? BTW, there is another issue of multiple responses that DOESN'T work this way. When you have a test question labeled "Mark all that apply" and if your scoring is all or nothing then it's actually easier to handle this as a string. If they marked A, B and E on Q12, you encode their response as 'ABE'. Later you score it: "recode Q12 ('ABC'=1) (else=0) into Q12.Scored." If you're going to give partial credit for individual responses, it's usually easier to enter the individual responses as independent variables, but you could create them using string functions. So, again, SPSS without MULT RESPONSE seems perfectly adequate and MULT RESPONSE doesn't actually handle all multiple-responses situations. -Alan On 1/8/2015 8:22 AM, Matthias Faeth
wrote:
-- Alan D. Mead, Ph.D. President, Talent Algorithms Inc. science + technology = better workers +815.588.3846 (Office) +267.334.4143 (Mobile) http://www.alanmead.org Announcing the Journal of Computerized Adaptive Testing (JCAT), a peer-reviewed electronic journal designed to advance the science and practice of computerized adaptive testing: http://www.iacat.org/jcat |
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