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How to Investigate Your Statistical Significance Testing

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This article covers a general framework for investigating unexpected statistical significance results and adjusting your document's Advanced Statistical Testing Assumptions accordingly. If you're setting up your statistical testing assumptions for the first time, see How to Apply Significance Testing in Displayr first. This article does not troubleshoot custom significance testing or testing run using Rules in Displayr.

1. Gather the details about the issue

2. Find what is causing the issue

3. Figure out a workaround or a solution

4. Contact Support

Requirements

1. Gather the details about the issue

You will use these details and information to help figure out what is causing the issue.

  1. Why do you consider the results unexpected? Are results unexpected across the board or with a particular result?
  2. Review the Advanced Statistical Testing Assumptions of the table.
  3. Use the alpha button to review the stat testing for a particular cell or cells (if comparing columns instead of using arrows and fonts). Note, it's easiest to duplicate the table and remove any Rules before trying this.
  4. Do any messages pop up or show in the results when you do this?
  5. Are any of the details about the statistical testing unexpected?
  6. Are there any rules applied to the table that change the statistics shown? Keep in mind that statistical testing is done before Rules are applied to the table.
  7. What type of data is shown in the columns? Can the same respondent be in more than one column? Are there hidden columns? Are there column spans?
  8. What statistic is shown in the cells of the table? Remember that Displayr only ever uses the Column % in proportions tests and the Average in numeric tests.
  9. What documentation and help information is available about the tool(s) or issue? You can search the Displayr Help Center or our sister software Q's wiki for more technical information on statistical tests and examples to explain testing.

2. Find what is causing the issue

Once you figure out what is causing the issue, then you can go to step 3.

  1. If a column isn't getting tested:
    1. If no columns are being tested on the table at all, then select the table, and under Object Inspector 26UI_ObjectInspector.png> Appearance > Significance > Advanced > Column Comparisons > Maximum columns to compare may be set too low.  Increase this to the number of columns you have in the table, or the widest span in your table.
    2. If it's a Total or NET column, confirm all steps are followed in How to Include the Main NET Column in Column Comparisons.
    3. Do some/all of its respondents overlap with other tested columns? By default, respondents in both columns are excluded from statistical testing; see the technical details for Overlaps on our wiki.
    4. Given the Overlap setting, does the column meet the minimum sample size?
    5. If using exception tests, does the column have a NOT or an opposing category to compare to? If the variable set used in the columns has only one category and missing data is excluded (so there is no 0 or not-selected category, or a hidden category to compare to), a statistical test cannot be conducted because there is no other category to compare to.
    6. Similarly, if each column is mutually exclusive only with missing data that is excluded, no one respondent has a non-missing value across the columns to compare to. As a result, a statistical test cannot be conducted because the columns being tested lack an opposing group. A clue that this might be an issue is if the percentages in the column are all 100%. This is typically seen more with complex BANNERs.
    7. If you are testing across mutually exclusive categories in a banner, be sure they are a part of the same variable set and are not set up as individual binary variables.
  2. If testing results change across tables or waves: 
    1. Confirm the Testing Assumptions are the same for both tables. Note that a table can use the default or modified settings for that table, so you need to look at each one individually.
    2. If Multiple Comparison Correction is turned on, please review How to Apply Multiple Comparison Correction to Statistical Significance Testing to ensure the setting is configured as expected.
    3. Look for any Object Inspector 26UI_ObjectInspector.png> Data > Rules applied to the table that affect significance testing. Note that accessing rules requires a Displayr license.
  3. Confirm your Advanced Statistical Testing Assumptions are what you want, given the documentation and your analytical goals. Some testing scenarios require multiple settings to be adjusted. Ensure inputs and settings are set up properly based on our documentation in the Help Center on how to do this.
  4. Test individual Advanced Statistical Testing Assumptions to see what triggers the issue.
    1. If there are obvious settings or aspects to test, try changing/confirming those one at a time to see if one fixes or triggers the issue. Check the details using the alpha button for a particular result as you go along.
    2. If nothing is found from the step above, re-create the issue from scratch by Restoring the default settings.
    3. Slowly change the settings one at a time to confirm they are affecting results as expected, using the alpha button for a particular result as you go along. Note which setting, in particular, triggers the unexpected result. 
    4. If re-creating from scratch begins with unexpected results by default, repeat steps 2.2 and 2.3 but use simpler data. For example, using a similar type of data from our Colas.sav data set).
    5. If using simpler data still yields an unexpected result, skip to 4. Contact Support to clarify how the statistical testing assumptions work.

3. Figure out a workaround or a solution

If the steps above don't lead to an obvious solution, use what you've learned to work toward a solution.

  1. Is the setting that caused the issue required, or can you work without it? You can replicate testing similar to other programs like How to Replicate Quantum Significance Tests.
  2. Should you adjust the statistical assumptions for this particular output? You can reduce the minimum sample size or adjust any other significance settings on an individual table level instead of applying them as the default testing settings.
  3. Can you get what you need using multiple tables or by structuring the data differently? 
    See How to Create a Table of Differences and How to Test Against the Previous Period without a Date/Time Variable for some ideas.
  4. For repeated measures testing or testing across brands and attributes, will stacking the data allow you to perform the testing you'd like? For example, stacking will allow you to compare respondents' brand answers as if each brand response was a unique respondent.
  5. Can you get something close but adequate to what you ultimately need with a different process? For example, if using a large, complex table as part of the significance testing, will breaking the table up into smaller parts allow you to run similar tests?
  6. Can your desired testing be done using a RULE? Note these rules are very rudimentary compared to our built-in testing and are more limited in application. You can use two popular rules: How to Apply Independent Samples Column Means and Proportions Tests and How to Show Row Comparisons in a Table or Visualization. Applying, modifying, and removing rules requires a Displayr license.

4. Contact Support

If you've worked through these steps and haven't found a solution or need help, please contact our support team at support@displayr.com or by clicking on Help Help.png > Contact support from within your specific document in Displayr. Please provide the details of what you learned in the steps above.

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