Separate Paired Observer Agreement from Agreement Expected by Chance — Free
Two care observers can agree often simply because they use the same category frequently. Keep paired observations, category distance and the chance denominator visible before discussing a shared recording rubric.
The proof surface
A handoff brief transmits observations; this checks paired observers' ordinal agreement against chance, without interpreting a person's health.
InputObservation code | observer A category | observer B category; number of ordered categories (2–10)
Rare deviceA handoff brief transmits observations; this checks paired observers' ordinal agreement against chance, without interpreting a person's health.
Output artifactLinear-weighted agreement coefficient plus complete labeled working
Cost$0 local core · no account, card, paid key or subscription · filing proposal has no checkout
Sample, not your facts: Invented records: Morning cue | 0 | 0; Lunch cue | 1 | 2; Afternoon cue | 2 | 2; Evening cue | 3 | 4. Number of ordered categories (2–10) = 5; expected Linear-weighted agreement coefficient: 0.666667 linear-weighted κ. These are not quotations, verified observations or personal evidence.
Before using the observer-agreement tally plate
Practice with an agreed, non-clinical ordered rubric and fictional observation codes. Both observers must rate the same observation independently using categories numbered zero through one less than the setting. A number does not create a valid instrument: categories need meaningful shared definitions, and their order does not prove equal clinical spacing. Linear weights here make each additional category step the same modeled disagreement. This is a descriptive training aid, not an assessment of a patient, professional competence or treatment. Actual care records should stay in an approved system, and a qualified lead should decide whether this agreement model is suitable.
Why the flat version breaks
Percent matching is treated as a chance-corrected result
An exact-match percentage does not account for heavily used categories, and the weighted percentage gives partial credit under an explicit convention. Kappa answers a different descriptive question. Keep all three pieces visible rather than reporting whichever looks highest. A changed category mix can change kappa even with similar pairwise differences, so comparisons across sessions need context rather than a simplistic better/worse label.
The category count is inferred from observed use
If category four happens not to occur, the rubric still may have five categories. Shrinking the scale changes the distance weights and can manufacture a different coefficient. This tool requires the intended category count as a setting. It does not infer clinical severity from category numbers or decide that unused categories are unnecessary; that is a rubric-design question for the responsible human.
An undefined result is made reassuring
All ratings in the same category can make the expected-agreement correction denominator zero. Returning one would conceal the missing variation. The explicit error keeps the input and previous successful reading, and invites a suitable practice set rather than more confident language. A computed coefficient also cannot establish observer reliability without an appropriate sampling plan and professional review.
How to work the observer-agreement tally plate
Agree the categories and pairing
Write category definitions elsewhere before entering scores. The setting tells the parser how many ordered categories exist; it does not learn them from the maximum observed value. Enter each paired observation once with a neutral code. A missing score is not a zero rating. Different observation sets cannot be compared as if they were paired, and one observer’s later summary cannot replace their original rating. The app rejects categories outside the declared whole-number range.
Inspect raw agreement before correction
For each pair, show the absolute category distance and weight 1 − distance/(categories − 1). Exact agreement and near agreement remain visible as separate row facts. The raw exact-match count is also reported. A high weighted agreement can hide frequent one-step differences, so do not skip the rows. If the category definitions changed mid-rehearsal, split the sessions rather than presenting the combined tally as one fixed rubric.
Build the marginal chance model explicitly
Count how often observer A and observer B used each category. Expected weighted agreement averages the same weights over all combinations of those marginal counts, divided by the squared observation count. Compute κ = (observed − expected)/(1 − expected). This model assumes the margins are the relevant chance baseline; it is not a causal model of the observers. If expected agreement is one, the correction is undefined and no numeric kappa is invented.
Use disagreement to revise the recording process
Read differing pairs with the category definitions and source observation context. Ask the responsible care lead whether terms, observation timing or the rubric need clarification. Do not use a single descriptive coefficient to rank people or decide care. No confidence interval, sample-size adequacy claim or acceptance cutoff is supplied. The optional filing prototype keeps the tally and boundary together; its local history is only five small summaries and is not a clinical audit trail.
What the observer-agreement tally plate keeps distinct
Question
Before
Check this working
Percent matching is treated as a chance-corrected result
An exact-match percentage does not account for heavily used categories, and the weighted percentage gives partial credit under an explicit convention. Kappa answers a different descriptive question. Keep all three pieces visible rather than reporting whichever looks highest. A changed category mix can change kappa even with similar pairwise differences, so comparisons across sessions need context rather than a simplistic better/worse label.
Inspect raw agreement before correction
The category count is inferred from observed use
If category four happens not to occur, the rubric still may have five categories. Shrinking the scale changes the distance weights and can manufacture a different coefficient. This tool requires the intended category count as a setting. It does not infer clinical severity from category numbers or decide that unused categories are unnecessary; that is a rubric-design question for the responsible human.
Build the marginal chance model explicitly
An undefined result is made reassuring
All ratings in the same category can make the expected-agreement correction denominator zero. Returning one would conceal the missing variation. The explicit error keeps the input and previous successful reading, and invites a suitable practice set rather than more confident language. A computed coefficient also cannot establish observer reliability without an appropriate sampling plan and professional review.
Use disagreement to revise the recording process
The observer-agreement tally plate replaces this named manual reconciliation, not source verification or the responsible human's decision.
Run it on the samples, right here
FIRST-LOAD
HYPOTHESIS / PROTOTYPE — checkout unavailable. Calculation is local. A draft is saved automatically in this browser profile when storage is available; Reset to sample clears it. Optional Pro history stores only five summaries and has its own deletion control. State links encode your inputs and can remain in browser history, clipboard or recipients’ records; share only non-sensitive rows. Optional external AI formatting leaves this device. The required site analytics beacon reports page activity; shared URLs contain encoded inputs. Do not treat an encoded URL as private. The calculator has no input-collection endpoint.
Descriptive rubric rehearsal only: no diagnosis, care recommendation or competence certification. Use fictional/de-identified observations and review the definitions, sampling and disagreement with the responsible qualified care lead before any care-related use.
Data note: This observer-agreement tally plate calculates in the tab from Observation code | observer A category | observer B category. No input-collection endpoint, AI request or file upload is built into it. Drafts may be saved locally; explicit input-state links and optional external formatting can disclose the records. Use non-sensitive codes and clear the draft when finished.
Go deeper: the companion app files the same reading as a scored comparison matrix
The article demo above runs without limits. The companion app keeps a local history, exports the rows as CSV, prints the observer-agreement tally plate reading, and holds your drafts on this device — one complete free app run; the proposed $4 one-time filing layer is not for sale.
Keep the complete observer-agreement tally plate answer free; optional filing proposes its boundary-preserving print, row-and-summary CSV and five local reading summaries. The $4 one-time prototype is not for sale; another calculation remains free in the article demo.
Descriptive rubric rehearsal only: no diagnosis, care recommendation or competence certification. Use fictional/de-identified observations and review the definitions, sampling and disagreement with the responsible qualified care lead before any care-related use.
What this is built on
Declared local method: Count how often observer A and observer B used each category. Expected weighted agreement averages the same weights over all combinations of those marginal counts, divided by the squared observation count. Compute κ = (observed − expected)/(1 − expected). This model assumes the margins are the relevant chance baseline; it is not a causal model of the observers. If expected agreement is one, the correction is undefined and no numeric kappa is invented.
Every sample code, measurement, date, price, fingerprint and scenario is invented. Artifact checks do not verify reader data, policies, actual files, tickets, votes or health/accessibility outcomes.
Google’s official Gemini pricing page, fetched 2026-10-01, lists AI Studio access in its Free section, limited model access and free input/output tokens. Free-tier content may be used to improve products. Optional external formatting may require an account; limits/access can change. Manual local entry needs none. Do not send sensitive records.
Before: matching scores seemed sufficient proof of agreement. After: observed distances and the marginal chance baseline remain separate, inspectable quantities.
Setting: Number of ordered categories (2–10) = 5. Expected summary: 0.666667 linear-weighted κ.
The four paired ratings differ by a total of two category steps. With five categories, observed weighted agreement is 1 − 2/(4 × 4) = 0.875. The marginal category mixes give expected weighted agreement of 0.625. Kappa is (0.875 − 0.625)/(1 − 0.625), or two thirds. The same-observation pairing and the linear weighting convention are necessary to interpret this number; it is not a clinical reliability certificate.
Setting: Number of ordered categories (2–10) = 5. Expected summary: -0.714286 linear-weighted κ.
The observers systematically disagree in this invented reversed set. Observed weighted agreement is 0.25, while the marginal mixes give expected agreement of 0.5625. Kappa is −5/7, approximately −0.714286. Negative kappa is retained rather than clamped to zero. It describes agreement below this chance model, not a judgment that an observer is careless, dishonest or unqualified.
Sample C — boundary convention
East practice cue | 0 | 0
West practice cue | 4 | 4
Setting: Number of ordered categories (2–10) = 5. Expected summary: 1 linear-weighted κ.
Both pairs match and both categories occur, so the expected-agreement denominator is nonzero and kappa equals one. Two observations are still a tiny rehearsal. If every pair used only the same single category, observed and expected agreement would both be one and kappa would be undefined; the app asks for a varying set instead of reporting perfect reliability from a zero denominator.
Optional AI formatting, never the calculation
Manual entry completes this observer-agreement tally plate for free without signup. If available to you, the free AI Studio interface linked in the sources may format fictional or non-sensitive notes; external access may require an account. No API key or AI call is built into this tool. Free-tier content may be used to improve products. Review each cell and transcribe it to the labeled row schema; do not paste the JSON object into the row box.
Format only these fictional or non-sensitive notes for a observer-agreement tally plate. Return strict JSON shaped as {"rows": [{"label": "string", "cells": ["string", "string"]}], "setting": "string"}. The columns are Observation code | observer A category | observer B category; the setting is Number of ordered categories (2–10). Keep all supplied strings and quantities exactly; do not calculate, infer missing entries, invent dates or add advice. If any required value is missing, return an empty rows array and ask me for it separately. I will verify every cell against my source and manually transcribe rows using vertical bars before running the local calculator.
An AI response is not executed, fetched or trusted as a result. Missing values remain questions; the strict local parser checks the rows you actually enter.