1. First, create a predefined list that contains all the items in your Bandit MaxDiff exercise (say, 80 items if you have 80 total items in your study). To do this, open the Lists manager by clicking the Lists icon on the toolbar. Click Add Predefined List and specify a new list name for this list. Type or paste all the items for your Bandit MaxDiff exercise (or specify graphics for these items if you will be showing graphics).
2. While in the List manager, click Add Constructed List and specify a new list name. In the Parent list drop-down field, select the list of items you previously specified above in Step 1. In the Constructed List Instructions field, specify the following two lines:
BanditMaxDiff (MaxDiffExerciseName, Items)
SETLISTLENGTH (Items)
where instead of MaxDiffExerciseName, you specify the name of a MaxDiff Exercise that you will be creating in Step 3 (if you click the Check for Errors button, it will warn you that no MaxDiff exercise exists yet with that name, which is OK for now).
Instead of Items, you specify the number of items to show each respondent (assuming 30 or more items in your Bandit MaxDiff study, Items should typically be 30). More information about Items.
By default, a moderately aggressive level of adaptivity is employed when selecting which items to show each new respondent completing the questionnaire. You can change how aggressively to favor previously preferred items for Bandit MaxDiff by using the optional NumThompsonItems argument.
BanditMaxDiff (MaxDiffExerciseName, Items, NumThompsonItems)
More information about the NumThompsonItems argument.
3. Next, create your MaxDiff exercise. From the Write Questionnaire dialog, add a MaxDiff exercise to your Lighthouse Studio project with the same name as you specified for MaxDiffExercise in Step 2. On the Items tab, change the Existing List to select the constructed list you specified in Step 2.
Click the Design tab and specify the Number of Items (Attributes) with the same value you specified in Items in Step 2 (typically 30). Continuing with the Design tab, design the questionnaire just as you would for a typical MaxDiff study: decide how many items to show per set (typically 4 or 5), and how many sets (questions) to show each respondent (typically 8 to 24). Click Generate Design to generate the experimental design (typically with the default number of versions, 300).
(Notes: no prohibitions between items may be used in Bandit MaxDiff. Because one typically uses aggregate logit to analyze large item list bandit MaxDiff problems, designs with individual versions lacking connectivity are usable.)
4. Test your survey to make sure everything looks and functions as you expect. If you answer the MaxDiff questions by always favoring certain items, after 5 or 10 completed respondent records following this same preference strategy you should see that these items tend to appear more often than the other items for later respondents.
Remember that Bandit MaxDiff uses any completed practice records on the server to influence the items drawn for later respondents. So, make sure to RESET your survey on the data collection server prior to launching your study so that it deletes any practice data on the server, including the table that Bandit MaxDiff creates to store the mean preferences and variances for prior respondents. To delete practice data and the associated table of group preferences, you must RESET your survey on the data collection server by logging into the Admin Module. Just deleting respondent data without resetting the survey does not clean out the preferences and variances for these practice records in the Bandit MaxDiff table on the server.
5. Field your questionnaire, making sure to limit the rate of flow of respondents into the questionnaire. Respondent answers to the Bandit MaxDiff questions are only updated to the Bandit MaxDiff preferences table after the respondent finishes the entire survey (including any answers to the questions following the MaxDiff section). Therefore, if all respondents begin the survey at nearly the same time (before the first respondent has finished), no adaptive learning can occur and this would essentially negate all the potential benefits of Bandit MaxDiff. Therefore, we recommend that no more than about 10 to 20 respondents take the survey simultaneously for good results.
Because Bandit MaxDiff relies on past respondents' preferences to select the items to show next respondents, it should not be used with offline CAPI data collection (where only data from respondents completed on the same device would be referenced).