EDUF4044 Chap.6 Surveys, Experiments and Quantitative Evidence
Surveys, Experiments and Quantitative Evidence
This chapter reads quantitative evidence as a ladder from description to comparison, association and causation. It begins with construct definition and survey-item design, then examines sampling, nonresponse, baseline comparability, implementation fidelity and alternative explanations in classroom experiments.
Interpretation keeps denominators, absolute and relative change, magnitude, uncertainty, missing data and graph scales visible before a numerical result becomes an educational judgement.
What this chapter covers
- 01
Constructs and survey items
- 02
Response scales and nonresponse
- 03
Description and group comparison
- 04
Association and causal inference
- 05
Validity threats and fidelity
- 06
Magnitude, uncertainty and graph reading
Worked example · free
Auditing a classroom comparison
- +1Check aligned baseline measures. The marks shown are not a published assessment scheme; they divide the validity audit for practice.
- +1Identify non-random allocation.
- +1Document dosage and fidelity.
- +1Inspect common outcomes and missing data.
- +1Retain class and teacher alternatives.
Key terms
- Construct validity
- The extent to which evidence represents the concept the study claims to measure.
- Nonresponse
- Missing participation that may make respondents differ systematically from the invited population.
- Confounding
- An alternative factor associated with both the exposure and the outcome that can distort interpretation.
- Effect magnitude
- The size and direction of a difference or relationship, considered apart from statistical significance.
Surveys, Experiments and Quantitative Evidence FAQ
Does statistical significance show educational importance?
No. Significance concerns compatibility with a statistical model and is influenced by sample size. Examine magnitude, direction, uncertainty, measurement quality and the practical consequence for the educational decision.
Can a correlation prove that one variable causes another?
No. Correlation can describe association or support prediction, but causal interpretation needs a design that addresses temporal order and competing explanations. Name plausible confounders and avoid causal verbs when they remain unresolved.
Why report the survey response denominator?
A percentage without the invited and responding counts hides coverage. Voluntary respondents may differ from nonrespondents, so a precise percentage can describe the sample while still being a weak estimate of the broader cohort.
Assessment move
Read every quantitative result in a fixed order: construct, measure, denominator, sampling, comparison, magnitude, uncertainty and claim. Repair survey items by removing leading assumptions, double questions and vague periods. For intervention studies, draw a timeline of allocation, baseline, implementation and outcome, then list concurrent changes that could explain the result.
Translate the final number into a bounded educational decision. Practise survey design by defining one construct in a sentence and mapping its dimensions before writing items. Ask a peer to paraphrase each item and response option; disagreement reveals ambiguity that numerical coding would otherwise conceal. Pilot the complete survey, recording completion time, skipped items and device display.
For reported results, rewrite every percentage as a count over a denominator and decide whether that denominator is respondents, invited participants or the wider population. Compare absolute and relative changes and explain which representation is most useful for the educational decision.
When reading an intervention study, build a validity table with baseline inequality, selection, concurrent events, maturation, attrition, measurement change, contamination and fidelity. For each threat, note what the design did and what uncertainty remains. Separate internal validity from transfer: a controlled comparison may isolate a local effect while saying little about routine implementation elsewhere.
Inspect graphs by covering the title, reading axes and scales first, and predicting the cautious description before reading the author's interpretation. Where information permits, reproduce simple percentages or differences. End each exercise with a claim ladder: description, comparison, association or cause.
Select the highest rung actually supported and write one sentence about practical magnitude and one about remaining uncertainty. Build a small notebook of misleading numerical presentations from education news and rewrite each one. Restore the denominator, absolute values, time period and uncertainty, then replace causal wording where the design supports only association.
For a survey example, trace how one ambiguous item could produce several reasonable interpretations and therefore an unstable percentage. For a comparison, list the conditions needed for the groups to provide a fair counterfactual. Explain the result to a non-specialist without dropping the limitation. This translation practice strengthens both critical reading and the concise interpretation expected in a research plan.
Keep a vocabulary list that separates everyday and statistical meanings, including significant, random, representative, effect and control. For each term, write a misuse and a corrected interpretation. Apply the list when reading abstracts and news summaries, where strong everyday meanings often replace technical ones. This reduces overclaiming when you translate findings for educational practice.
Close by translating one reported number into its count, denominator, magnitude, uncertainty and appropriate rung on the claim ladder.
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