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# Choosing and defining variables

- Concept

Saying exactly what is measured, with which instrument and cut-off, so that anyone would get the same result.

Updated Nov 13, 2026

## Key points

- Dependent = outcome; independent = exposure or intervention; control = confounders or covariates measured so they can be adjusted for.
- Operational definition: which instrument, which technique, how many measurements, which cut-off and on what scale.
- Scales: nominal, ordinal, interval and ratio; turning a continuous variable into categories loses information.
- Measure only the variables the question needs: every extra variable costs time and quality.
- Confounders are identified in the protocol, before data collection, so that they can be measured.

On this page

- The basics Key points
- What it is
- Key facts
- Worked example
- The role of variables in a study
- Markers
- In practice
- Common pitfalls
- Memory aid

- Study Test yourself

- Connections Connections

## What it is

The variables in a study play different roles: the dependent variable (outcome), the independent variable (exposure or intervention) and control variables (possible confounders and other covariates). An operational definition turns a concept (‘hypertension’, ‘physical inactivity’) into a measurable procedure: instrument, technique, timing, cut-off and scale. Without it, two studies using the same word measure different things.

## Key facts

- Conceptual definition: what the variable means. Operational definition: how it is measured in this study.
- Example: physical inactivity = less than 150 minutes a week of moderate activity, by a validated questionnaire on the last 7 days.

## Worked example

- Data: a cross-sectional study of 250 adults; variable ‘hypertension’. An operational definition is needed before measuring.
- Definition A: mean of 2 readings ≥ 140/90 mmHg or use of antihypertensives. 62 meet it: 62 / 250 = 24.8 %.
- Definition B: a 130/80 mmHg threshold or treatment. 104 meet it: 104 / 250 = 41.6 %.
- With the same data, prevalence changes by 41.6 − 24.8 = 16.8 points because of the definition alone.
- Scales: systolic pressure is continuous quantitative; ‘hypertension yes/no’ is nominal binary; hypertension grades are ordinal.
- Reading: the protocol fixes the definition, a validated device, the technique and the number of readings; otherwise two studies' prevalences cannot be compared.

## The role of variables in a study

| Role | What it is | Example |
| --- | --- | --- |
| Dependent (outcome) | What is to be explained or predicted | Blood pressure at 6 months |
| Independent (exposure) | The possible cause or the intervention | Salt intake; an education programme |
| Control (confounder) | Linked to exposure and outcome, without being intermediate | Age, body mass index |
| Intermediate | Lies on the causal path between exposure and outcome | Weight gained because of the diet |

## Markers

- DV
- IV
- Confounder

## In practice

- When reading a paper, look for the operational definition of the outcome: different definitions explain many figures that do not match.
- Surveillance case definitions are operational definitions shared across the whole country.

## Common pitfalls

- Using a concept without defining how it is measured.
- Adjusting for an intermediate variable: it removes part of the real effect.
- Categorising a continuous variable with cut-offs chosen after seeing the data.

## Memory aid

‘If it cannot be repeated, it is not defined’: the operational definition is the exact recipe for the measurement.

## Test yourself

Think of the answer, then tap the question to see it.

## What does an operational definition include?

The instrument, technique, timing, number of measurements, cut-off and scale of the variable.

## In a study of salt and blood pressure, what role does age play?

Control variable: a possible confounder, linked both to salt intake and to blood pressure.

## Of 400 people, 80 meet one definition and 140 a broader one. How much does prevalence change?

From 20 % (80/400) to 35 % (140/400): 15 points.

## Why is an intermediate variable not adjusted for?

Because it lies on the causal path: adjusting for it removes part of the effect being measured.

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## Connections

## Related 5

- [Types of variables](https://durgical.com/en/epidemiology/types-of-variables/)
- [Validity and reliability](https://durgical.com/en/epidemiology/validity-and-reliability/)
- [Questionnaire design](https://durgical.com/en/epidemiology/questionnaire-design/)
- [Confounding](https://durgical.com/en/epidemiology/confounding/)
- [Outcomes, surrogate and composite end points](https://durgical.com/en/epidemiology/outcomes-surrogate-and-composite-end-points/)

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Educational content on epidemiological and statistical methods; the examples use made-up numbers to teach the calculation. Not a clinical or legal guide: current Colombian rules are in the official sources.

Sources: [Principles of Epidemiology | Lesson 2 - Section 2](https://archive.cdc.gov/www_cdc_gov/csels/dsepd/ss1978/lesson2/section2.html) (Centers for Disease Control and Prevention, 2012); [Collecting Data](https://www.cdc.gov/field-epi-manual/php/chapters/collecting-data.html) (Centers for Disease Control and Prevention (The CDC Field Epidemiology Manual), 2024).

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Representative model, for educational purposes only.

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License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Cite as: DurgiPedia (durgical.com), https://durgical.com/en/epidemiology/choosing-and-defining-variables/