← HR Metrics·Performance & Development
Performance Improvement Rate
Rate of improvement in performance ratings period over period
How it’s computed
(current_avg - previous_avg) / previous_avg
What the evidence shows
Evidence (effect sizes, priors, validity) is syncing from Principia.
What this metric can show you
Performance Improvement Rate can tell roughly 23 pre-built stories — each a designed scene the data either confirms or it doesn’t. Bring your numbers and the Story Finder runs every one of these shapes against them.
specific to performance & development
It's two companies, split by manager
leadership-quality · T1
Most are fine — a tail is struggling
engagement · T1
The system isn't differentiating
measurement-health · T1
You've quietly stopped promoting from within
development-mobility · T1
Your ratings are compressing
performance · T1
universal shapes — any single metric can take these
A few large values are doing the talking
any focus · T1
A one-time event, not a trend
any focus · T1
It doesn't track — the premise is false
any focus · T1
It's concentrated — one group stands apart
any focus · T1
Scenes are pre-built; your data is the toggle. Browse the full deck or watch one play end-to-end in The Quiet Exodus.
Run it on your data
This metric is computed in the People Analytics Toolbox on your own numbers. See pricing — posted, no quotes.
sources: toolbox:metrics-catalog
What the literature says
The measurement literature behind this signal — sourced, so you can defend it.
“In compensation decision-making, the data on which action will be taken should be the referenced item and, as such, should be in the denominator of the calculation. That way, the percent difference will be expressed in terms of the reference. For example, market adjustment is…”
— Worldatwork Handbook Compensationmatch 53%
“It is common practice to assign a weight of importance to each key result based on impact, frequency, and relative significance to the overall list of responsibilities as well as to the department and the organization. Weights may be determined informally and discussed when…”
— Worldatwork Handbook Compensationmatch 51%
“The tilde (~) operator signifies “depends on,” so this is a perfect example of a dependent variable and an independent variable. summary() Get the results of the logistic regression Here’s the output: [image file=Image00160.jpg] [image file=Image00161.jpg] To predict whether an…”
— People Analytics Text Mining with Rmatch 50%
Resources: Worldatwork Handbook Compensation · People Analytics Text Mining with R