Analyzing Responses to Likert Items


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This is a brief (exploratory) discussion of how to run statistical analysis of responses to Likert items. The data used is from a study I ran on users of the PARC Wikipedia dashboard "WikiDashboard" looking at how the tool changed perceptions of credibility.

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  • Incidentally, the box plots show that not all of the effects are actually going in the supposed direction.For instance, Accuracy/Trust are higher for “Low-Quality” items, and Coverage/Currency/Objectivity are higher for “High-Quality” items.Some Thoughts:While the subjects rated the B/C-Class Articles as less complete and more current than the Featured Articles, they rated the “lower-quality” articles as more accurate and more trustworthy overall than the Featured Articles. This makes more sense when you consider why these articles were flagged in the first place. The article for “hip hop music” was actually once a featured article, but is currently flagged for reasons relating to the scope of hip hop (“No references to any of the classic hip hop stars”, “Hip-hop and Rap are entirely different things”, etc.), so perhaps while the existing material in the article may have been seen as accurate, the article overall is unsatisfactory for its lack of coverage and currency. Regarding “hypnagogia”, one of the discussion points flagged even on the main article page is that the article should be merged with another article, “Threshold Consciousness”, indicating that the article may not cover the topic fully.
  • Again, we see an interesting pattern where they rated the “controversial” items as more credible on some measures and less credible on others. The fact that they rated the “controversial” articles higher on “coverage”, “currency”, and “objectivity” may have to do with the fact that these articles receive a high number of edits overall, meaning that the pages are frequently updated by a number of different editors? It’s interesting to see that in spite of seeing the “controversial” articles as more complete and objective, they still trust the non-controversial articles more.
  • Analyzing Responses to Likert Items

    1. 1. Analyzing Responses to Likert Items<br />An Exploration of Data from a Credibility Study Involving WikiDashboard<br />(<br />by Sanjay Kairam<br />
    2. 2. WikiDashboard Study<br />The System<br />The Study<br />The Data<br />
    3. 3. WikiDashboard<br />“Social Dynamic Analysis Tool” for Wikipedia<br />Michael Scott (The Office): “Wikipedia is the best thing ever. Anyone in the world can write anything they want about any subject, so you know you are getting the best possible information”<br />What happens when we see who is doing the editing?<br />
    4. 4. WikiDashboard (Close-Up)<br />
    5. 5. WikiDashboard Study<br />Study conducted on Amazon Mechanical Turk<br />N = 288 subjects<br />Subjects paid $0.08 / HIT<br />“Please read and evaluate this Wikipedia Article.”<br />
    6. 6. Experiment Conditions<br />Participants each placed in 1 of 3 conditions (each N = 96):<br />Wiki Only (WO)<br />Wiki + History (WH)<br />WikiDashboard (WD)<br />
    7. 7. Articles Used<br />Each subject read 1 (of 8 possible) Wikipedia articles.<br />Article “Quality”:<br />“Low-Quality” articles were those flagged as “B-Class” or “C-Class” by the Wikipedia community.<br />“High-Quality” articles were those which had at one time been “Featured Articles”.<br />Article “Controversiality”:<br />“Controversial” articles were those on the extensive “List of Controversial Articles”.<br />
    8. 8. Survey<br />Self-Reported Expertise<br />“How familiar are you with the topic discussed on this Wikipedia page?”<br />Manipulation/Quality Checks<br />“In 5-20 words, please describe what this Wikipedia page is about.”<br />“Please describe one fact from the article that you found interesting.” (WO)<br />“Please name at least one user (by username or IP address) who has made multiple edits to this page. (WH, WD)<br />
    9. 9. Credibility Assessment<br />Assessing agreement with these statements:<br />“I believe that the information on this page is accurate.” (Accuracy)<br />“I believe that the information on this page is objective.” (Objectivity)<br />“I believe that the information on this page is current and up-to-date.” (Currency)<br />“I believe that this page fully covers the relevant information on the topic.” (Coverage)<br />“I trust the information on this page.” (Trust)<br />
    10. 10. Likert Item Responses<br />Participants answered using a 5-point scale:<br />-2: “Strongly Disagree”<br />-1: “Somewhat Disagree”<br />0: “Neither Agree nor Disagree”<br />+1: “Somewhat Agree”<br />+2: “Strongly Agree”<br />Now, what do we do with this data?<br />
    11. 11. Analyzing Likert Item Responses<br />Very often, we see papers reporting Likert responses using means:<br />What is the average of 1 “Somewhat Agree” and 3 “Somewhat Disagree”s?<br />Hint: It’s not “Somewhat Disagree and a Half”<br />In this case, what does a “mean” mean?<br />In most cases, an ANOVA would definitely not work as well, though people still try!<br />
    12. 12. Options for Analysis<br />Non-Parametric Tests for Ordinal Data<br />Conversion to an Interval Scale<br />Aggregating Items<br />
    13. 13. Mann-Whitney U Test<br />Also called “Mann-Whitney-Wolcoxon”, “Wilcoxon Rank-Sum”, or “Wilcoxon-Mann-Whitney” test.<br />Non-parametric test for assessing whether two independent samples of observations have equally large values.<br /><br />
    14. 14. Mann-Whitney U Test<br />Assumptions:<br />All observations from both groups are independent of each other.<br />The responses are ordinal or continuous measurements.<br />Null hypothesis includes symmetry between two populations considered<br />Under alternative hypothesis, probability of an observation from pop. X exceeding an observation from pop. Y is not equal to 0.5<br /><br />
    15. 15. Kruskal-Wallis ANOVA<br />What if we want to test more than 2 groups? (as we do, given our 3 experimental conditions) <br />Kruskal-Wallis ANOVA is an extension of Mann-Whitney U to 3 or more groups.<br />Also non-parametric, though it does assume that both distributions have a similar underlying shape.<br /><br />
    16. 16. Analysis Using Non-Parametric Tests<br />Do participants actually notice differences in article quality?<br />Mann-Whitney: Significant effects of article quality for ratings of Accuracy (p< 0.001), Coverage (p< 0.01), Currency (p< 0.001), and Trust (p< 0.001), with marginally significant effect on Objectivity (p< 0.096).<br />Kruskal-Wallis: Significant effect on ratings of Accuracy (p< 0.001), Coverage (p< 0.012), Currency (p< 0.001), and Trust (p< 0.001), with no significant effect on Objectivity.<br />
    17. 17. Sample Boxplots: Ratings by Article Quality<br />Accuracy<br />Coverage<br />
    18. 18. Analysis Using Non-Parametric Tests<br />Do participants notice differences in how “controversial” an article is?<br />Mann-Whitney: Significant effect on ratings of Coverage (p < 0.039), Currency (p < 0.039), Objectivity (p < 0.021), and Trust (p < 0.021), with no effect on ratings of Accuracy.<br />Kruskal-Wallis: Significant effect on ratings of Objectivity (p < 0.042), and marginally significant effect for Coverage (p < 0.077) and Currency (p < 0.083), but no significant effect on Accuracy or Trust.<br />
    19. 19. Analysis Using Non-Parametric Tests<br />What we really want to know, however, is whether using WikiDashboard or Wiki + History makes participants more sensitive to article quality or controversiality than participants using Wikipedia on its own.<br />Both tests only allow us to compare populations separated on the basis of a single variable, however, so we can’t explore these interaction effects.<br />
    20. 20. Conversion to Interval Scale<br />If there were a way to map our Likert item responses on to an interval scale, we could use more familiar/powerful statistical tests.<br />If we found that the mapped data was normal, for instance, we could use our usual parametric tests such as MANOVA, which would help us find these interaction effects.<br />
    21. 21. Conversion to Interval Scale<br />E.J. Snell (1964) describes a procedure for mapping ordered data, like Likert responses, to an assumed underlying continuous scale of measurement.<br />At the end, he emphasizes that “the usefulness of the proposed method depends upon the assumption that the underlying scale of measurement can be transformed to produce a normal distribution.”<br />Snell, E.J. A Scaling Procedure for Ordered Categorical Data, Biometrics 20(3), pp. 592-607 (1964).<br /><br />
    22. 22. Utilizing the Snell Conversion<br />The conversion procedure was used to transform the data – essentially mapped each response (ranging from -2 to +2) to a new point which ranged from roughly -1.00 to +4.05<br />Essentially, it looks as if only the distances between the values has changed.<br />
    23. 23. Histogram: Original Data<br />
    24. 24. Histogram: Snell-Converted Data<br />
    25. 25. Aggregating Likert Items<br />If we consider the various Likert items to be different measurements of a certain underlying trait (Credibility), then can we sum them and run parametric statistical tests?<br />Haven’t tried this yet – is this a valid approach?<br />
    26. 26. Analyzing Responses to Likert Items<br />by Sanjay Kairam<br />Email:<br />Twitter: @skairam<br />