About Qualtrics Stats iQ

Stats iQ from Qualtrics allows everyone, from beginners to expert analysts, to uncover meaning in data, identify hidden trends, and produce predictive models, with no technical SPSS or Excel training required. For more, see Overview of Stats iQ.

At Indiana University, you must have an existing Qualtrics license in order to use Stats iQ, and then purchase a Stats iQ license in addition to your Qualtrics license. Purchase a Stats iQ license via IUware.

View a short video demonstration.

Stats iQ:

  • Understands the structure of your data and chooses the right visualizations
  • Brings in data directly from Qualtrics surveys and seamlessly from other sources
  • Identifies problems with your data and results, and provides solutions
    • Quantifies experience drivers
    • Automatically decides, and applies, the right regression method
  • Automatically runs the right statistical tests and visualizations, and then translates results into simple language that anyone can put into action

Stats iQ can perform the following statistical analyses:

  • Bivariate:
    • T-test (two categories vs. numbers)
    • ANOVA (three or more categories vs. numbers)
      • This is sometimes called the "F-test". More specifically, ANOVA creates an "F statistic" that is then translated into a p-value. Stats iQ skips the step of showing the F statistic, but it is calculated as part of ANOVA.
    • Games-Howell post hoc tests (three or more categories vs. numbers)
    • Cohen's f
    • Correlation (numbers vs. numbers)
    • Pearson correlation
    • Spearman correlation
    • Point Biserial Correlation
    • Cohen's d
    • Paired t-test (numbers vs. numbers)
    • Fisher's Exact Test (two categories vs. two categories)
    • Chi-squared (three or more categories vs. categories)
    • Kramer's V
    • Z-test (categories vs. categories)
    • Time-series analysis
    • Difference in differences (DID, DD)
  • Regression:
    • Linear (when output variable is numbers)
      • OLS (traditional)
      • M-estimation (downweights outliers)
      • Ridge (useful if two input variables are highly correlated)
    • Logistic/Logit (when output variable is categories)

This is document aoqm in the Knowledge Base.
Last modified on 2017-09-18 13:44:54.

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