PES6251 Chap.6 Experiments, Hypotheses and Variables
Experiments, Hypotheses and Variables
Build a controlled contrast
Experiments manipulate an independent variable and measure a dependent variable, creating the foundation for causal inference when comparison, allocation and procedures are credible. Operational definitions state how constructs become interventions and outcomes. A hypothesis predicts a relationship or contrast, while the null states its absence in the population.
Randomisation, standardisation, blinding and fidelity checks target different rival explanations rather than serving as interchangeable quality labels.
Translate constructs into procedures
A concept such as coaching clarity becomes experimental only after the intervention specifies what coaches will say or do, how delivery will be monitored and what distinguishes conditions.
Confidence or performance becomes an outcome after the instrument, timing and scoring rule are named. Operational definitions allow another researcher to reproduce the contrast and reveal whether the measured response answers the theoretical question.
Attractive labels cannot compensate for vague delivery or an outcome recorded at the wrong time.
Choose the comparator by the rival explanation
A no-treatment group estimates ordinary change, while an active comparison can control attention, contact and expectation. Wait-list designs address access but may not match experience during the study.
Identify the alternative story most likely to explain improvement and choose a condition that tests it. Then standardise procedures that should remain equal. Differences in coach, practice exposure or assessment setting can otherwise become inseparable from the intended intervention.
Separate programme failure from delivery failure
A manipulation check documents whether conditions actually differed as planned.
Without it, a null result may mean that the intervention theory failed or simply that the programme was not delivered. Record adherence, contamination, missing sessions and unintended responses. Analyse outcomes according to the design, then use implementation evidence to explain what participants received.
This distinction makes the result useful for the next coaching decision and prevents statistical non-significance from being treated as proof that the idea has no value.
What this chapter covers
- 01
Independent and dependent variables
- 02
Operational definitions
- 03
Research and null hypotheses
- 04
Comparator choice
- 05
Random allocation and standardisation
- 06
Internal and external validity
Worked example · free
Build a feedback experiment
- 2Identify coach choice as a confounding route.
- 4Specify assignment, comparator and outcome timing.
- 3State the hypothesis and fidelity check.
Key terms
- Experiment
- A study that deliberately manipulates an independent variable and measures a dependent response.
- Null Hypothesis
- A statistical statement that the specified population contrast or association is absent.
- Comparator
- The condition used to estimate what would happen without the intervention of interest.
- Manipulation Check
- Evidence that intervention conditions actually differed in the intended way.
- Internal Validity
- The credibility of a causal explanation within the studied comparison.
Experiments, Hypotheses and Variables FAQ
Does manipulation alone prove causation?
No. Manipulation begins the causal argument, but the study still needs a credible comparator, defensible allocation, consistent procedures, adequate adherence and unbiased measurement. If another factor differs systematically between conditions, it can explain the outcome even though researchers changed the independent variable.
What makes a hypothesis testable?
It names measurable variables, a population and a relationship or contrast that observations could contradict. A directional hypothesis also states the expected direction when theory supports it. Broad claims such as coaching matters are not testable until coaching and the relevant outcome are operationally defined.
Why is an active comparator sometimes stronger than no treatment?
An active comparator can control attention, contact time and expectations that accompany an intervention. A no-treatment group estimates ordinary change but may differ in those experiences. Choose the comparator that rules out the most important rival explanation rather than the one that is easiest to organise.
Assessment move
For each experimental scenario, write the assigned condition, comparator, dependent variable and measurement time on separate lines. Add one manipulation check and one rival explanation. Practise changing causal verbs into association language when allocation or comparison is missing, then state the design change needed to restore the stronger claim.
Translate every abstract construct into an operational instruction, instrument, scoring rule and time point. Pair each protection with its target threat: standardisation for procedural differences, blinding for expectation or measurement effects, and fidelity checks for delivery.
Write the null and alternative hypotheses in words before symbols, identify the population parameter, and decide in advance what result would count as practically meaningful.
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