It still is unclear what you mean by "agreement scale" versus a satisfaction scale.
For example, in one questionnaire, did you have:
"I am satisfied with the cleanliness of the dining area" with response options ranging from strongly disagree-->strongly agree
which was changed to
"Please indicate the degree to which you were satisfied with the following aspects of the dining area:
1. cleanliness (very dissatisfied-->very satisfied)
.
.
You must provide as many details as possible in a succinct way when describing your design in your original post to SPSS-L or list members will either (a) ignore your post or (b) make assumptions which may or may not be true.
My guess is that Garry assumed that this was the same group of respondents measured twice, and therefore suggested an idea to see if there was a scaling factor across composite scores, but this simply does not apply to your scenario.
You have different questionnaires administered to different groups of people at different points in time.
My advice: Report the results separately. Do not conclude that the use of one questionnaire resulted in higher scores than use of another questionnaire. Accept the limitations of the study: You are asking different questions to different people at different points in time and therefore will likely obtain different responses. How are we to know/assume/evaluate that the questionnaires are measuring the same construct, given the study design? In the future, make sure that this flawed approach does not repeat itself.
Ryan
Art,Thanks for your response:1. Respondents are different in both waves2. We asked satisfaction on food, staff, and speed of service. We asked the same measures in wave 2 but on agreement scale.3. This is a non-profit work for community hospital restaurant. Unfortunately the decision maker had his own hypothesis on scales. We debated a lot but he still went ahead with scale change.4. Yes, scale magnitude were same.Re: regression, I am really not getting my head around on regression part, is t1 the wave 1 score of food and t3 score of food in wave 2? What's the dependent variable here? Note that I cannot run repeated measures as it is not the same respondent.Thanks,On 2013-04-28, at 8:46 AM, Art Kendall <[hidden email]> wrote:do you have the same respondents in both waves? Can you tie responses to individuals?
What did they agree with?
did you have a series of items with the same response scale to create a summative score, or do you have a single item?
You could do a regression as Gerry suggested.
On later waves you could as for both measures of performance. You would then have
t1 vs t3 for satisfaction and
t2 vs t3 for agreement
However, I do not think you can conclude at this time that performance dropped. You can conclude that the way that you measured performance changed.
Who changed the response format? Were the stems identical, similar?
Art Kendall Social Research ConsultantsOn 4/27/2013 8:24 PM, MR [via SPSSX Discussion] wrote:
Team,
I have one problem on my hand and am running out of options on which statistics to use in SPSS. First, I know that the what I want to do is not advisable but trust me, I have fought my battle on this. This is what I want to achieve:
Issue: We did wave 1 survey using 5-point satisfaction scale. The second wave was conducted using 5-point agreement scale. Expectedly, top-box scores from agreement scale when compared to top-box score of satisfaction scale was low by 10% points. For e.g., agreement scale top box in wave 2 came out as 50% while wave 1 it was 60%.
Goal: I have compared the historical data and conclude that score difference is purely due to scale change. However, i want to normalize the wave 2 score so that I can compare with wave 1. I know this is not advisable but I have to do this. I googled but could not find any statistics that helps to normalize the scores - indeed I don't know where to begin. I need a scientific method to normalize the scores so that they are comparable. I don't want to conclude that performance dropped by 10% just because scale changed.
Your wisdom and help is very much required.
Thanks,
Mike
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Art Kendall
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View this message in context: Re: Normalizing scores
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