08-27-2009 07:17 AM
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08-27-2009 08:52 AM
08-28-2009 08:22 AM
08-30-2009 02:22 AM
07-20-2015 10:11 AM
Is PROC GLM or TTEST appropriate for testing the differences in proportions/ratios of ordinal variables, such as Likert scale/survey variables?
Thank you very much,
07-21-2015 07:45 AM
Not really. Of the linear modeling techniques, look at GENMOD or GLIMMIX to handle proportions. The question I have is what meaning does a ratio of Likert variables have? Just for example, suppose in example 1, scale 1 returns a 1 and scale 2 returns a 2. The ratio is 0.5. In example 2, scale 1 returns a 2 and scale 2 returns a 4. The ratio is 0.5. Same ratio--but a very different meaning. Thus a bit more background on the data and your research question are needed before I can make an educated guess as to how I would analyze the data.
07-22-2015 09:46 AM
Thanks for the reply and question, Steve!
I work on a Customer Satisfaction team which reports/analyzes survey results for a major bank. All of our reports contain Top2 Box, and Bottom4 Box, scores for survey questions using the 10 pt Likert scale. A Top 2 Box score is a proportion and calculated as follows: (the count of responses to a particular survey question of a 9 or 10) divided by (the count of all responses to the same survey question).
For instance, let's say we have test and control based upon an IVR option which results in a group of customers selecting YES or NO to some question. And let's say we survey these customers, but we are only concerned with one of the survey questions, Q9, which is as follows: "Would you recommend our Company to a friend or colleague? Please use a scale of 1 to 10, where 1 is 'Definitely Not' and 10 is 'Definitely'" And then let's say we have the following data from the survey:
9 OR 10
What is the best way using SAS for me to determine if the difference between the Q9 Top 2 Box scores for the two groups in the table, above, are significant?
Again, thank you very much!
07-22-2015 10:53 AM
Quick easy method is PROC FREQ.
input group $ response weight;
N 0 56
N 1 61
Y 0 64
Y 1 81
proc freq data=one;
gives me a p value of 0.5474, so I would say that there is no evidence for a difference between the proportions marking the top 2 levels (.5214 for N, .5586 for Y).
This approach is different from calculating the average Likert score, or the ratio of two specific scores. It just dichotomizes the response.
Now if your data came from a sample from a known population of a given size, you might want to consider PROC SURVEYFREQ, to account for finite sampling adjustments.
07-22-2015 12:23 PM
Weights are the number of observations in each category. For instance, for group N, you have 117 responses, of which 61 were 'Top 2". That leaves 56 as not Top 2. I coded Top 2 as 1, and not Top 2 as 0 for the variable response.
08-28-2009 06:00 PM
04-17-2017 10:29 AM
I also have same problem. Please consider my example: I have a table for the number of incidents occurred in two-hour blocks (Ex. 0-2, 2-4, etc). The data for each block recorded over 4 years and it is heavily inflated with zeros. So I have a table like:
Day [0-2] [2-4] [4-6] ....... [10-0]
Match 1 0 1 1 ....... 13
March 2 1 2 0 ........ 2
March 30 0 10 0 2
How can I compare the proportion of the number of attacks occurred during different time blocks in March? Please give me a hand in coding as well. Thank you!