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Are You Measuring the Wrong Behaviour?

What you choose to count can change how you perform, so a convenient metric should not reward speed or volume at the expense of quality.

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Preview for Are You Measuring the Wrong Behaviour?

On this page

  • How goals direct attention during performance
  • What speed versus quality experiments reveal
  • Choosing measures that match the result you value

Introduction

The easiest behaviour to count is not always the behaviour you should optimise. A target such as “write 1,000 words”, “answer 40 questions” or “finish the workout in 30 minutes” can make action concrete, but it also tells you what deserves attention. If the number rewards output while ignoring accuracy, technique or usefulness, you can become better at hitting the metric while becoming worse at the performance the metric was meant to improve.

Goal Metrics illustration 1
Explanatory illustration 1

That is not an argument against measurable behaviour goals. Goal setting has a small but reliable positive effect on behaviour, and monitoring progress can improve goal attainment.[PubMed]pubmed.ncbi.nlm.nih.govUnique effects of setting goals on behavior change: Systematic review and meta-analysis - PubMed… The important qualification is that measurement is part of the intervention. A useful behaviour metric therefore needs two properties: it must be easy enough to guide action, and it must preserve the quality that makes the action worthwhile.

How goals direct attention during performance

Goals do more than record what happened afterwards. Goal-setting theory proposes that they direct attention and action towards goal-relevant activities, increase effort and persistence, and encourage the development of strategies for reaching the target.[sagepub.com]journals.sagepub.comSage Journals Work Motivation and Satisfaction: Light at the End of the TunnelSage JournalsWork Motivation and Satisfaction: Light at the End of the Tunnel - Edwin A. Locke, Gary P. Latham, 1990July 1, 1990…Published: July 1, 1990 This is one reason specific goals can outperform vague instructions such as “do your best”. It is also the reason specificity can have unintended effects.

Suppose two people are practising the same skill. One aims to “complete 30 repetitions”; the other aims to “complete 20 correct repetitions using the required technique”. The first target makes throughput conspicuous. The second makes acceptable performance part of what counts. Both are measurable behaviour goals, but they define success differently.

This quantity–quality problem has appeared in experimental research for decades. In six experiments involving 338 undergraduates, Janet Bavelas and Eric Lee examined tasks in which participants could vary both how much they produced and the quality of what they produced. On several tasks, higher quantitative goals generated more responses but responses further from the defined ideal. The researchers argued that the goal helped define the task for participants, producing a systematic trade-off between quantity and quality rather than simply increasing effort indiscriminately.[ResearchGate]researchgate.netResearchGate(PDF) Effects of goal level on performance: A trade-off of quantity and qualityDecember 1, 1978…Published: December 1, 1978

Research on more complex decisions points in the same general direction while making the picture less tidy. In an experiment involving 160 students making simulated investment decisions, Stephen Gilliland and Ronald Landis manipulated quantity and quality goals and found effects not only on performance but also on participants’ information-search strategies.[University of Arizona]experts.arizona.eduUniversity of ArizonaQuality and Quantity Goals in a Complex Decision Task: Strategies and Outcomes - University of ArizonaOctober 1, 1992…Published: October 1, 1992 The implication is important for self-improvement: a metric can change the route you take through a task, not merely the final number you record.

This is why a seemingly innocent target can misfire. “Read 50 pages” can encourage moving through pages rather than understanding an argument. “Clear 30 emails” can privilege easy messages over important difficult ones. “Do 100 practice questions” can reward guessing quickly unless checking mistakes is included. In each case, the problem is not measurement itself. It is that the chosen measure represents only one dimension of good performance.

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What speed-versus-quality experiments reveal

A particularly clear demonstration comes from a randomised experiment by David Cook and colleagues involving 80 secondary-school students learning a simulated surgical procedure. Participants repeatedly tied ligatures around a model blood vessel. After four repetitions, they were randomly assigned either a speed goal or a quality goal, and researchers measured both completion time and whether the resulting ligatures were leak-free.[PubMed]pubmed.ncbi.nlm.nih.govSpeed and quality goals in procedural skills learning: A randomized experiment - PubMedOctober 9, 2019…Published: October 9, 2019

The goal changed what improved. By the final repetition, the speed-goal group was 18% faster than the quality-goal group. But its odds of producing a high-quality, leak-free ligature were substantially lower: the reported odds ratio was 0.36. Even when performance time was taken into account, participants given the speed goal had higher odds of leaks after the intervention.[PubMed]pubmed.ncbi.nlm.nih.govSpeed and quality goals in procedural skills learning: A randomized experiment - PubMedOctober 9, 2019…Published: October 9, 2019

That experiment is unusually useful because the metric and the neglected quality dimension were both directly observable. A stopwatch could truthfully report that someone had become faster while the leak test simultaneously showed that the product had become worse. The speed number was not inaccurate; it was incomplete.

The lesson should not be exaggerated into “speed goals are bad”. Another randomised study of laparoscopic-skills training found benefits from achievable time goals among students.[PubMed]pubmed.ncbi.nlm.nih.govOpen source on nih.gov. And experimental warehouse research involving 212 participants found that quantity feedback could improve productivity without necessarily reducing quality; in a robustness experiment, quantity feedback was associated with improvements in both.[Tilburg University Research Portal]research.tilburguniversity.eduOpen source on tilburguniversity.edu.

So there is no universal law saying that increasing quantity or speed must reduce quality. The risk depends on the task, the person’s skill, the room available for shortcuts and whether quality is independently protected. The surgical ligature experiment demonstrates the danger particularly clearly, rather than proving that every productivity metric creates the same trade-off.

This distinction matters when choosing personal goals. If faster performance can be achieved only after the underlying skill is secure, speed may be a sensible later metric. If rushing creates errors, injuries, shallow learning or rework, speed is a poor primary target. A metric should reflect the current bottleneck in performance, not merely the dimension that produces the neatest graph.

Goal Metrics illustration 2
Explanatory illustration 2

When a useful measure becomes the wrong target

The wider problem is sometimes described through Goodhart’s law: once a measure is placed under strong pressure as a target, the relationship between that measure and the broader result it was supposed to represent can deteriorate. The idea originated in economic policy but has since been applied to areas such as education and healthcare assessment.[PubMed Central (PMC)]pmc.ncbi.nlm.nih.govOpen source on nih.gov.

At an organisational level, research on performance measurement documents familiar failure modes: concentrating on easy cases, under-reporting problems, “teaching to the test”, increasing quantity while reducing quality and otherwise improving the reported indicator without equivalent improvement in the underlying objective.[OUP Academic]academic.oup.comOpen source on oup.com. Analyses of target-driven public services have similarly warned against allowing a measurable part of performance to stand in for the whole.[DOI]doi.orgOpen source on doi.org.

Personal behaviour tracking is usually much lower stakes, but the underlying design problem is recognisable. A person can optimise a proxy:

  • counting books finished rather than ideas understood;
  • counting study hours rather than successful retrieval;
  • counting applications submitted rather than credible, tailored applications;
  • counting workouts while allowing technique or recovery to deteriorate;
  • counting writing output while accumulating material that requires extensive rewriting.

The practical question is therefore not simply “Can I measure this behaviour?” It is “What behaviour will this measure encourage when I am tired, rushed or tempted to make the number look good?”

That last test exposes weak metrics remarkably well. If there is an easy way to hit the number while defeating the purpose of the activity, the metric needs a quality safeguard.

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Choosing measures that match the result you value

A good behaviour metric does not need to capture every feature of performance. Trying to measure everything can make self-tracking so cumbersome that it stops helping. Instead, choose a simple action measure and protect the one or two quality dimensions that would make that measure misleading if they deteriorated.

One useful design is quantity subject to a quality threshold. Instead of “complete 40 practice questions”, use “complete and mark 30 questions, then review every error”. Instead of “write 1,000 words”, use “draft for 45 minutes and spend the final ten minutes checking the argument against the outline”. Instead of “finish three applications”, count only applications that meet a short definition of tailored: relevant evidence selected, employer requirements addressed and obvious errors checked.

Another is to make quality the primary behaviour when quality is the skill currently being learned. The surgical experiment suggests why this can matter for novices: different goals can promote different learning processes.[PubMed]pubmed.ncbi.nlm.nih.govSpeed and quality goals in procedural skills learning: A randomized experiment - PubMedOctober 9, 2019…Published: October 9, 2019 Goal-setting research also cautions that difficult performance goals can be less useful on novel or complex tasks when people still need to discover effective strategies.[ScienceDirect]sciencedirect.comScienceDirect Goal Setting TheoryScienceDirect Goal Setting Theory Early practice may therefore be better organised around correct execution, deliberate checking or learning a method; speed can become a target once competent performance is more stable.

A third option is to track paired measures when neither dimension can safely stand alone. “Pages read + retrieval questions answered correctly”, “running pace + technique/pain rule”, or “tasks completed + proportion needing rework” make the trade-off visible. Experimental research on incentives likewise shows that changing the weight placed on quality can shift attention from quantity towards quality.[Munich Personal RePEc Archive]mpra.ub.uni-muenchen.deOpen source on uni-muenchen.de. The point is not to create a personal dashboard with dozens of indicators, but to prevent one convenient number from silently redefining success.

Progress monitoring itself is worth retaining. A meta-analysis of 138 studies involving 19,951 participants found that interventions designed to increase monitoring increased both monitoring and subsequent goal attainment, with larger effects when progress was physically recorded or reported publicly.[PubMed]pubmed.ncbi.nlm.nih.govDoes monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence - PubMed… But that finding concerns the usefulness of monitoring; it does not establish that every possible thing you might monitor is equally informative. Tracking works best when the tracked behaviour deserves to be reinforced.

Goal Metrics illustration 3
Explanatory illustration 3

A simple test for a behaviour metric

Before committing to a number, ask what would happen if you became exceptionally good at maximising it. Would the result you ultimately care about probably improve too?

Three checks keep the metric aligned with that purpose:

  1. Name the behaviour the number will reward. “Five sessions” rewards showing up; “50 questions” rewards throughput; “under 25 minutes” rewards speed. Be explicit about what the number is asking your attention to favour.
  2. Identify the easiest bad way to hit it. Could you rush, choose easier work, skip checking, lower the standard or repeat low-value activity? If so, that failure mode deserves a constraint or a second measure.
  3. Define what still has to be true for a repetition to count. A completed problem may need to be marked; a strength-training repetition may need acceptable form; a study session may need retrieval rather than passive rereading. This converts quality from a vague aspiration into part of the behaviour definition.

The resulting goal may be slightly less elegant than a single streak or total, but it is more faithful to the purpose of behaviour goals. A useful metric should make the desired action easier to see and repeat without creating an incentive to hollow it out.

The aim is not to find a perfect measure. No personal metric captures an entire worthwhile outcome, and research shows that quantity–quality trade-offs are not inevitable in every task.[Erasmus University Rotterdam]pure.eur.nlOpen source on eur.nl. The safer principle is narrower: count what helps you act, but make sure the counting rule does not reward sacrificing the part of the performance you actually value.

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