What is the standard error for Viva Glint survey scores? Related, what constitutes a statistically meaningful change in scores? Research shows that the standard deviation of a 5-point scale raw score for survey items is typically between .7 and 1.0. Conservatively, for a group of 100 participants and an SD of 1, the standard error would be .10 (which is .02 of the 5 point scale). This suggests that 2 points of the 100 points in the Viva Glint score range would be the Standard Error, and so a difference of less than 4 points is likely NOT indicative of a change in scores. Or I would assume. Thoughts?

Hi Paul,

Not directly on point, but if you haven’t seen these already you might find them helpful based on your questions:

- Choosing Comparison Data | Glint Community (glintinc.com) (the tables at the very bottom have notable comparison guidance)
- People Science Explains: A Modern Approach to Measuring Engagement | Glint Community (glintinc.com) (the PDF linked at the bottom has some information on the research behind questions)

Thanks, Brian. It’s not really helping me with the Glint Score. The first table refers to percent favorable and the second refers to a mean (but on a 1-5 raw scale or the “mean” 0-100 conversion is not clear). I would have expected this to be easier to find… It may need to be an office hour question. Thanks again for getting me closer. The truth is out there. P

Okay, some fake data made me realize this was simple algebra. Every 1 unit change on a raw score (1, 2, 3, 4, 5) is associated with a 25 point change on the Viva Glint scale (0, 25, 50, 75, 100). Therefore, if the standard deviation for a survey item on the raw scale is 1, then the standard deviation on the Viva Glint scale is 25. Therefore, for a group of 100 participants, the Standard Error on the raw score is 1/100^.5 or 1/10 = .1 and the Standard Error on the Viva Glint score is 25/100^.5 or 25/10 = 2.5.

The implication is that a second survey (B) of those 100 participants would need to have a Viva Glint score that is > 1.96*2.5 or 4.9 points above or below the score of group A to be considered beyond the p=.05 level. So, a crude rule of thumb would be that a change in score of 5 points or greater (for a group of 100) would be statistically significant.

So, if you enter various group sizes, you get this table:

N of 20, 11 point difference or greater is significant

N of 50, 7 point difference or greater is significant

N of 100, 5 point difference or greater is significant

N of 150, 4 point difference or greater is significant

N of 250, 3 point difference or greater is significant

N of 500, ~ 2 point difference or greater is significant

N > 2,000, ~ 1 point difference or greater is significant

Comparing group scores of difference sizes (e.g., company of 1,000 vs benchmark of 10,000) is a bit beyond my stats knowledge at the moment, and I don’t feel like digging up an independent measures t-test formula. Suffice to say, these same guidelines are a conservative estimate for most score differences. Just be careful about very small groups.

Please correct my logic, math, statistics if I am wrong. Thanks. P

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