Evidence
What the research says, and where it stops.
The research informs our design. It does not establish that Facets improves performance. No validation study of this questionnaire exists yet. Everything below is evidence about the premises Facets is built on: peer versus self ratings, what feedback does, and what makes a debrief work. Each finding is stated in plain language first, with the effect size and source beneath it.
Start with what we cannot claim
Facets has not been shown to improve team performance.
The items are original drafts, not a validated scale.
Individual change is below the noise floor at this team size.
Why peers rather than self-report
Self-ratings run higher
In the studies summarized, people rated their own job performance more highly than their supervisors did, on average. The self–supervisor correlation is a separate analysis in the same paper: about .22 across 115 samples and 37,752 people.
d = 0.32; Δ = .49 corrected for unreliability · Heidemeier & Moser (2009), 89 samples, 35,417 people, Western samples
One peer is weak evidence
Two peers rating the same person agreed only modestly. A single peer is therefore weak evidence, which is why the reports show a reliability band that widens when few people rated you.
interrater reliability .37 · Conway & Huffcutt (1997)
Who measures matters
The same teamwork training looked more effective when third-party observers measured it than when teams reported on themselves.
.80 vs .38 · McEwan et al. (2017), 72 interventions, 8,439 participants
Raters differ a lot
Much of the variation in multisource ratings comes from the individual rater rather than the person rated. This is why scores are also reported perceiver-centered.
~55–60% of variance · Scullen, Mount & Goff (2000)
The conclusion is not that self-knowledge is useless. Self and other ratings appear to capture partly different things, and the difference between them may carry information neither has alone. That is why Facets asks you to rate yourself, and separately to predict how your teammates rated you.
A self–peer correlation of .2 to .3 is typical in this literature, so a modest correlation does not by itself indicate a problem with a questionnaire.
What feedback actually does
Worse in over a third
Feedback made performance worse in more than a third of the measured effects. The average effect across all of them was positive (d = 0.41), which is the number usually quoted on its own.
38% of effects negative; average d = 0.41 · Kluger & DeNisi (1996), 131 papers, 607 effects, 12,652 participants
Little change alone
Peer-rated behavior change following multisource feedback with no structured follow-up was close to nothing.
d ≈ .05 · Smither, London & Reilly (2005), 24 longitudinal studies
Development-only works better
The effect was larger when feedback was used for development only than when it was mixed with administrative use. Tying feedback to a performance decision cut the effect to about a third.
.25 vs .08 · Smither, London & Reilly (2005)
Debriefs helped
In the studies reviewed, teams that debriefed typically did better than comparable teams that did not. The authors describe the effect as a 20–25% improvement, which is a percentile shift rather than a gain in output: the average debriefing team lands near the 75th percentile of teams that do not. Facilitated and structured debriefs outperformed unstructured ones, though the paper offers that as an explanation rather than as the basis of the headline figure. The average debrief ran 18 minutes.
d ≈ .67; .54 with outliers removed · Tannenbaum & Cerasoli (2013), 46 samples, N = 2,136
Read together, these suggest that feedback is not automatically helpful. It can make performance worse, particularly when it points at the person rather than the work, and the studies reviewed found larger effects when a structured conversation followed and when feedback was kept for development. Facets is designed around those findings. Whether it achieves the same results has not been tested.
The design follows from that. Every team survey includes its debrief agenda, and no account can read another person’s ratings or comments as written. A facilitator the team names sees more; who can see what sets it out.
What the team-level questions measure
The climate scales are written to constructs with meta-analytic support: psychological safety, direction and role clarity, coordination, task cohesion, relationship conflict as distinct from task conflict, information flow and transactive memory, team efficacy, reflexivity, and viability. Trust is asked about each teammate rather than about the team, and pooled. Team processes as a whole relate to team performance at around ρ = .31 across 40 samples covering some 3,100 teams.
Worth knowing about that literature: the individual process dimensions are hard to separate empirically — they load onto a higher-order factor — so the reports treat them as a profile to discuss rather than as independent scores to optimize one at a time.
Things this product deliberately does not use
Team IQ or the “c-factor”. Independent replication attempts have partially or wholly failed, and one of the original papers has a published correction.
Project Aristotle. Unpublished, with no methods released. The underlying psychological-safety research is real and cited directly instead.
Faultline effect sizes from the 2011 meta-analysis. Retracted in 2016.
Belbin team roles, and type-based frameworks generally. The factor structures do not reproduce.
What to check in any assessment tool
Two things are worth checking in the technical documentation of any assessment or feedback tool. First, whether peer or observer ratings appear anywhere, or whether every number traces back to self-report. Second, whether the outcome evidence is user satisfaction, meaning people saying the result described them well, rather than behavior or performance. High self-endorsement does not distinguish a valid instrument from a flattering one.
A third is the difference between the reliability of a score and the stability of a category assigned from it, such as a type or a top-five list. Categories derived from continuous scores are generally less stable than the scores beneath them.
Sources
Every figure on this page is drawn from a published meta-analysis or primary study, named inline. The full research brief — every figure traced to its source with its verification status, and a list of what has not yet been checked against a primary source — is sent by email.
This page is the argument. The questionnaire itself, with every question, its anchors, keying, scoring and thresholds, and a plain list of what has not yet been measured about it, is published separately so that the research claims and the product specification stay distinct.