What makes a driver 'High Impact' vs. 'Very High Impact'?
As a quick reminder, drivers are potential causal factors that affect outcome measures such as, but not limited to, overall employee engagement, a sense of belonging as well as perceptions of a great workplace culture. In determining how we differentiate between Low, Medium, High and Very High driver impact, we use the driver's Pearson r correlation tied to the intended outcome measure. Drivers are plotted on the X-Axis and those with less than a .20 Pearson r are considered “Low” impact drivers. Between .20 and less than .40 are “Medium” impact, between .40 and less than .60 are “High”, and .60 and greater are “Very High”. See the table below for additional detail. Minimum Correlation Maximum Correlation Low 0 < 0.2 Medium 0.2 < 0.4 High 0.4 < 0.6 Very High 0.6 1.0 As People Science consultants, we're always leveraging the Driver Impact report to help us understand how different survey measures impact key outcomes across different populations and tying that back to action taking strategies. We'd love to hear from our community...In what ways have you utilized Viva Glint's Driver Impact report to support key findings in your surveys?
Statistical models in Excel
Hi Guys, I am trying to see how I can build a simple statistical model in Excel. Let's say I have 10 salespeople. For each of them, they have sold X amount of the year. I want to see the relationship between data I've collected about their differing sales experience, industry experience, calls, demos and see whether there is a correlation in positively affecting their sales performance for that year. I haven't used statistical modeling in Excel very much. Does anyone have an indication how I can set it up in Excel? Any tips would be huge. Many thanks in advance.
