Intraclass Correlation Calculator
The formula
What the intraclass correlation tells you
The share one quantity represents of another — the core reading behind intraclass correlation — with a second value folded in for direct comparison.
Calculation: percentage = part ÷ whole × 100. Enter your inputs and every substitution is shown below the result.
Reading the result
Percentages without denominators mislead: always pair the share with the absolute figures above before acting.
Limitations and best practice
Bases must match (same period, same definition of “whole”); comparing a share computed on one base against another is the most common error in dashboards.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
When is a percentage misleading?
Can it exceed 100 %?
Source & methodology
Elementary percentage arithmetic.
Intraclass Correlation: what this tool answers
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. There are plenty of pages about Intraclass Correlation; almost none of them show the working. This one does: the formula, every input, a fully computed example, and a sensitivity analysis calculated live from this engine’s own defaults.
In Statistics & Data Science, units and significant figures carry meaning; keeping them attached to every output is the quiet professional standard this page maintains.
What makes Intraclass Correlation worth calculating is not precision but position: it tells you which side of a threshold you are on, and by how much. A rough figure that settles the direction beats an elaborate one that never commits.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here share = 14.24%, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Whole / total first, at ≈11.1 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
How Intraclass Correlation is computed, line by line
The engine implements a single stated model: percentage = part ÷ whole × 100. There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Part / amount (example value 235) is a quantity rather than a category — precision here is cheap and worth having. If your source quotes a figure before and after some deduction, decide which one the formula wants and be consistent for every field.
Whole / total (example value 1,650) is a quantity rather than a category — precision here is cheap and worth having. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
Compare with second value (example value 140) is a measured number in the unit named by the label; the formula does not convert for you. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
A fully worked Intraclass Correlation example
Abstract formulas are hard to trust, so here is the complete substitution using the default inputs, written the way an examiner would want it. Starting figures: Part / amount, Whole / total, Compare with second value. Run to the end, the share resolves to 14.24% — Minor but material share on the bands this tool uses.
- Divide 235.00 by 1,650.00 = 0.1424
- Multiply by 100 → 14.24 %
Second value as % of whole — 8.48%. Compare it against the main result of 14.24% before drawing conclusions.
Ratio part : whole — 0.142. Compare it against the main result of 14.24% before drawing conclusions.
Per 100 units — 14.24. This is the number experienced users quote when the headline alone would mislead.
Keep this worked example open while you enter your own figures: matching the shape of the substitution is the fastest way to spot a unit or convention mistake.
Scenario table: Intraclass Correlation at ±10 % and ±20 % of the key input
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Whole / total setting | Share |
|---|---|
| 80 % of the example value | 17.80% |
| 90 % of the example value | 15.82% |
| example inputs | 14.24% |
| 110 % of the example value | 12.95% |
| 120 % of the example value | 11.87% |
That spread — 11.87% at the low end, 17.80% at the high — is what a careful read of Intraclass Correlation looks like: a band you can state out loud, with the reason for each edge. Reproduce it in the form above by editing Whole / total alone; the substitution steps will follow your figures exactly.
What actually moves your Intraclass Correlation
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the share. It is the fastest honest answer to “what matters most in Intraclass Correlation?” — and it is specific to these defaults, because sensitivity moves with the starting point.
- Nudge Whole / total by ±10 % and Intraclass Correlation lands between 12.95% and 15.82% (sensitivity ≈11.1 %)
- Part / amount +10 % → Intraclass Correlation = 15.67% · −10 % → 12.82% (the share shifts ≈10.0 %)
Whole / total is the dominant lever here: ten percent either way moves the result by about 11.1 %, from 12.95% to 15.82%. That is where a careless figure does the most damage, so check it against a source, and treat Part / amount as the second priority (≈10.0 % per ten percent). The remaining fields can sit at safe defaults without materially changing the answer — knowing which is which is what separates a careful calculation from an anxious one.
What an Intraclass Correlation of 14.24% means in practice
The value above only becomes useful the moment you know what it is being measured against. The verdict attached to the default calculation — “Minor but material share” — is one band on a ladder your own figure may sit somewhere else on. Before treating the band as fact, note its width: ten percent either way on Whole / total already spans 12.95% to 15.82%. The practical reading rules: read the band as a starting hypothesis, then test it: change the two dominant inputs by ten percent and see whether the verdict survives. Verdicts that survive stress are the ones worth acting on.
Related measures answer related questions, and the distinctions matter: Intraclass Correlation is the quantity, Share is this tool’s reading of it, and the decision that follows belongs to neither of them separately.
Why Intraclass Correlation results go wrong — and how to prevent it
Bases must match (same period, same definition of “whole”); comparing a share computed on one base against another is the most common error in dashboards. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Reporting the headline and quietly discarding Second value as % of whole. The companions are where the shape of the trade-off survives the rounding.
This model has its own particular trap: Whole / total carries ≈11.1 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
The subtlest error is answering the wrong question precisely. Confirm that share is actually the quantity your decision needs — the arithmetic is flawless either way, which is what makes the mistake invisible.
A quick check for any Intraclass Correlation figure
- Verify each input against its original source document, not memory.
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Check Whole / total before anything else: it is the measured dominant input, ≈11.1 % of Intraclass Correlation per ten percent.
- Note the date and the inputs beside the number so the figure can be audited later.
- Check that every field the formula needs actually arrived from a document, not from recollection.
- Repeat the ±10 % test on the inputs that matter and see whether the decision — not just the number — still holds.
Five steps from raw inputs to a figure you can defend
Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for Intraclass Correlation takes a minute to learn and applies to every input above.
Step 1 — gather before you type. Intraclass Correlation depends on part / amount, whole / total, compare with second value. Collect them from a source document — a statement, a report, an invoice, a log — rather than from memory, because recalled inputs are the single largest cause of wrong results. A result worth acting on survives having its two shakiest inputs halved.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Share (14.24% for the example values), and note the verdict band. Estimates improve most when the worst case is written down in ink.
Step 3 — change one input at a time. Nudge a single field by ten percent, watch the headline and its companions move, then undo it. Two or three fields will dominate — the sensitivity table above already ranks them for this engine. Suspiciously tidy answers deserve the same scrutiny as untidy ones.
Step 4 — run the scenario trio. Expected, pessimistic, optimistic. The gap between pessimistic and expected is the number to plan against; the optimistic result is useful only as a ceiling. A figure nobody can reproduce is a figure nobody will act on twice.
Step 5 — write the one-line memo. “Share = 14.24% on this date, from these inputs.” That line is the entire deliverable; everything else on this page is how you earned the right to write it. Note the assumption you are least sure about; that is where the real risk lives.
Where Intraclass Correlation matters in practice
In Statistics & Data Science the figure computed here is usually the first one somebody asks to see. Having it — with the formula behind it — changes your position in the conversation from participant to reference. The fastest sanity test is to guess the answer first and see how far the calculation disagrees.
The companion outputs — Second value as % of whole and Ratio part : whole — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. Re-running a number costs seconds; retracting a decision costs considerably more.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. A figure nobody can reproduce is a figure nobody will act on twice.
People also ask about Intraclass Correlation
What affects Intraclass Correlation the most?
The short answer: Whole / total. The longer answer is the sensitivity table on this page, where every numeric input was probed by ten percent and the share recalculated — Whole / total moved it furthest, by roughly 11.1 % (12.95% to 15.82%).
Which matters more: part / amount or whole / total?
Whole / total, by a factor of about 1.1. Concretely: ten percent on Whole / total is worth ≈11.1 % of Intraclass Correlation, the same nudge on Part / amount only ≈10.0 %. That ranking is re-computed for each page from that page’s own defaults.
What is a typical Intraclass Correlation?
With the example inputs on this page the result is 14.24%, labelled “Minor but material share”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate Intraclass Correlation by hand?
Use the stated model — percentage = part ÷ whole × 100 — and substitute in the order the tool prints (it starts from Divide 235.00 by 1,650.00 = 0.1424). On the example inputs the hand route lands on 14.24%, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.
What happens to Intraclass Correlation if whole / total changes by 10 %?
The engine was re-run with that field moved by ten percent while everything else stayed fixed: the share goes across 12.95% to 15.82%, a swing of roughly 11.1 % around the 14.24% baseline. That ratio is the practical definition of how much this input matters.
Is the Intraclass Correlation result of 14.24% good or bad?
Good and bad are comparisons, so the honest test is three questions: where does your own Intraclass Correlation sit against the 14.24% reference above, how far would a plausible input error move it (≈11.1 % per ten percent on Whole / total), and what does the alternative option compute to?
Is Intraclass Correlation accurate?
The arithmetic is exact: percentage = part ÷ whole × 100 is evaluated with full floating-point precision and every substitution is printed in the worked example. What limits accuracy is input quality and the model’s assumptions, so treat the 14.24% result as the correct answer to the question you typed — and check the assumptions section before acting on it.
What affects Intraclass Correlation the most?
Sensitivity depends on where you start, but the inputs Part / amount and Whole / total typically move Intraclass Correlation further than the rest because they enter the formula multiplicatively. If you can only measure two things well, measure those and hold the others at conservative defaults.
When is a percentage misleading?
When the whole is small — 50 % of two is still two. Check absolute numbers alongside every share.
Can it exceed 100 %?
Yes — part can exceed whole for growth and coverage metrics; the ratio output makes that visible.
Does this Intraclass Correlation tool store my numbers?
No. The entire calculation runs in your browser session; nothing entered above is transmitted or saved, which is the correct behaviour for sensitive Science & Research figures and the reason you can re-run this page as often as you like.
What the labels on this page mean
- Second value as % of whole
- 8.48% in the default Intraclass Correlation calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Ratio part : whole
- 0.142 for the Intraclass Correlation worked case; it sits next to the main result because it carries context the headline leaves out.
- Per 100 units
- 14.24 in the default Intraclass Correlation calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Share
- The primary output of this tool: 14.24% for the default inputs, computed exactly from percentage = part ÷ whole × 100.
- Worked example
- The calculation written out in full so that nothing on this page has to be taken on trust.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “Minor but material share”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Intraclass Correlation moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Where the Intraclass Correlation model comes from
There is no black box on this page — the engine evaluates percentage = part ÷ whole × 100. Source of record: Elementary percentage arithmetic. Where a professional version of this calculation differs, it differs in inputs and jurisdiction-specific coefficients, not in the algebra — which is why re-checking your inputs beats hunting for a second calculator.
This page is an estimation aid, not professional advice. The design rule is simple: two people entering identical figures must get identical answers. The design constraint was set by usefulness rather than by comfort: an estimate you can interrogate beats an answer you have to trust.
After the number: what to do with it
Re-run the calculation with your own figures, stress the two most sensitive inputs by ±10 %, and only then act on the result.
The audience here is people who need one trustworthy number before their next decision. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.