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Apr 10, 2019 7:02 PM
(2126 views)

I have some survey data that has descrete variables measuring item response time in sec. to completion each questions. I would like to isolate respondents who are consistently taking a lot of time across all questions. Say a respondent takes more or less 30 secs or more to answer each question. Any ideas?

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An alternate approach is:

- Stack the item response columns, hopefully each column name identifies the Item/question
- The resulting table should have a column of respondent, Label (item col name) and Data (time)
- Here you might want to create a new column that is the z-score (standardized) time for each question, or at least the deviations from each questions median time [ to do this in 2 steps, create a column of the mean or median time By Label (item), then create a 2nd column that is :Data - {new column).
- Now you can get a display of Standardized or Deviations by respondent, this will display both mean and standard deviation. Note using Graph Builder you could sort the nominal X-axis by the Median.

There are multiple metrics, typically, order statistics (quantiles), to select/order/find the respondents with large decision time.

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Re: case-wise or row analysis question

Assuming the response time columns are in the same data table as the response columns, then you could easily create a new column which is the average of the all of the response time columns. Then using a Data Filter, you could eliminate all of the respondents that have a mean time over 30 seconds.

Jim

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Re: case-wise or row analysis question

I think I see what you are getting at. When you say create a new column

that is the average of all RT columns, would that yield an average RT for

the respondent cross that respondent's row? That might get a little

tricky. I have a number of feeling thermometers asking about liking for

Trump and Obama, etc. typically a respondent will rank one very high and

the other very low, with an average of 50 on a 100 point scale. What I am

trying to do is cull out respondents that may be trolling the survey one

way or another. There is a variable showing the total time it took to

complete which is useful but not quite what I want. I am thinking I want

to see a trend analysis, not knowing quite what that is as its outside my

range of expertise.

BTW I have discovered the local filter and like it a lot.

Thanks

John

that is the average of all RT columns, would that yield an average RT for

the respondent cross that respondent's row? That might get a little

tricky. I have a number of feeling thermometers asking about liking for

Trump and Obama, etc. typically a respondent will rank one very high and

the other very low, with an average of 50 on a 100 point scale. What I am

trying to do is cull out respondents that may be trolling the survey one

way or another. There is a variable showing the total time it took to

complete which is useful but not quite what I want. I am thinking I want

to see a trend analysis, not knowing quite what that is as its outside my

range of expertise.

BTW I have discovered the local filter and like it a lot.

Thanks

John

Highlighted

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An alternate approach is:

- Stack the item response columns, hopefully each column name identifies the Item/question
- The resulting table should have a column of respondent, Label (item col name) and Data (time)
- Here you might want to create a new column that is the z-score (standardized) time for each question, or at least the deviations from each questions median time [ to do this in 2 steps, create a column of the mean or median time By Label (item), then create a 2nd column that is :Data - {new column).
- Now you can get a display of Standardized or Deviations by respondent, this will display both mean and standard deviation. Note using Graph Builder you could sort the nominal X-axis by the Median.

There are multiple metrics, typically, order statistics (quantiles), to select/order/find the respondents with large decision time.

- Mark as New
- Bookmark
- Subscribe
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Re: case-wise or row analysis question

Thanks, this was a big help