POPH90014 Chap.12 External Validity and Triangulation

External Validity and Triangulation

Define external validity

The course material gives this chapter a concrete anchor: Week 12 connects causal inference, sufficient-component causes, cross-sectional designs and population inference.

That external validity anchor controls how target population is explained and how triangulation is tested in changed practice.

External Validity and Triangulation is a quantitative decision problem built from external validity, target population and triangulation.

The aim is to assess transportability and triangulate across designs; a numerical result earns meaning only when the variables, units, assumptions and comparison are all explicit.

Begin with external validity: state what quantity it represents, the scale on which it is measured and the condition under which it changes.

Then map every symbol in the External Validity and Triangulation formula checkpoint to external validity before calculation begins.

Next connect target population to the calculation. Show the target population transformation line by line, preserve units and signs, and make any denominator or baseline visible.

A target population calculator output is not a method; the reader must be able to reconstruct why that operation answers the question.

Use triangulation to interpret or stress-test the result. Ask whether the triangulation magnitude is plausible, whether a boundary case behaves as expected and which conclusion would reverse if an assumption changed.

This is where computation becomes analysis rather than arithmetic.

When the task is to assess transportability and triangulate across designs, separate inputs supplied by the problem from quantities you derive.

Then report the triangulation result in the language of the course and attach the relevant uncertainty, limitation or decision consequence.

Trace target population

Build a representation check before solving.

Put external validity, target population and triangulation into a small symbol-and-units table, mark which values are observed and which are calculated, and predict the direction of the result before doing arithmetic. A sign, scale or unit mismatch in external validity then becomes visible at setup instead of being hidden inside a polished final number.

Run one sensitivity test after the baseline answer.

Change the input most closely connected to target population, hold the remaining assumptions fixed and recompute only the affected steps. Explain whether the movement in triangulation matches the mechanism.

This target population sensitivity shows which assumption controls the conclusion and prevents a single scenario from being presented as universal.

Use a three-column external validity error log for POPH90014: translation error, calculation error and interpretation error.

Record the exact line where the target population solution first diverged, rewrite that line, and check it with a limiting case or an independent calculation.

Correcting the first failed target population move is more useful than copying the complete solution again.

A complete response should make the task visible before the detail: identify what must be decided, define the relevant terms, connect the evidence to target population, and use triangulation to test the result.

The final sentence about triangulation should answer the question actually asked rather than merely repeat the topic.

The controlling limit is specific: representativeness alone does not establish causal transport and consistent bias can align studies falsely.

Keep that triangulation limit beside the worked example, because it separates a careful POPH90014 answer from one that sounds confident but claims more than the task or evidence supports.

For revision, retrieve external validity, target population and triangulation without notes, explain their relationship aloud, then complete a changed version of the application: assess transportability and triangulate across designs.

Record the first failed target population reasoning move and repair it before attempting another case.

What this chapter covers

• 01

  External validity

• 02

  Target population

• 03

  Triangulation

• 04

  Applying external validity

• 05

  Limits of target population and triangulation

Transport a stratum-specific effect

Key terms

External validity

Extent to which an inference applies to a specified target population or setting. This chapter uses the concept when students assess transportability and triangulate across designs. Use this definition when the task is to assess transportability and triangulate across designs.

Target population

Population about which the study seeks to support an inference or decision. It helps explain the reasoning required to assess transportability and triangulate across designs. Use this definition when the task is to assess transportability and triangulate across designs.

Triangulation

Use of evidence with different methods and biases to test the stability of an inference. Its limit matters because representativeness alone does not establish causal transport and consistent bias can align studies falsely. Use this definition when the task is to assess transportability and triangulate across designs.

External Validity and Triangulation FAQ

Exam move

Reconstruct the relationship among external validity, target population and triangulation; complete the chapter application without notes; then test the result against this limit: representativeness alone does not establish causal transport and consistent bias can align studies falsely.

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