Did you know that regression (or any GLM model) assumes that the Y value observations (outcome or dependent variable) are independent? If they’re not, your coefficients can be inflated and your predictions inaccurate.
For example, say you’re trying to find out what drives performance, engagement or retention in your teams; in other words, your dependent variable is performance, engagement or retention.
Now ask yourself a question: are the engagement scores of employees within a team independent of each other? Of course not: people in a team influence each other. For example, if Lao is unhappy about some aspect of work and disengaged, there’s a reasonable chance that other members of that team will feel the same and therefore their scores are not independent as required by GLM regression.
Or consider another example: if a team leader’s behaviour affects one team member’s performance, chances are this behaviour is influencing performance across the whole team. Therefore the team members’ ratings are not independent and you are violating a key regression assumption which will result in inaccurate predictions and poor recommendations to the company.
I discovered this vital fact when researching the drivers of couple romantic relationship satisfaction (a team with n=2) and quickly learned that if one partner is dissatisfied with the relationship, there’s a good chance the other will be dissatisfied as well. Thus the scores are not independent and regression will deliver inaccurate predictions. How did I fix it?
The solution is to use a technique called Multilevel Modelling (MLM) which doesn’t assume that outcome variables are independent; it’s certainly the best way to get accurate results and make good recommendations if you’re working with team data. In fact, I won’t do any #PeopleAnalytics team data project without it.
MLM has been in the news a lot in the past few years because political analysts used it to successfully predict Trump’s election wins and Brexit. Before this, they’d been using regression and getting their forecasts wrong.
If you want to avoid the errors made by political forecasters and make accurate predictions and valid recommendations about what drives team performance in your organization, move away from GLM regression to MLM.
More information available on request.