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A Level · Cambridge (CIE) · 9990

Interpret correlations and longitudinal studies

Read associations between measured variables without claiming causation, and evaluate research that follows participants over time.

Section RM-CORRELATIONS · Correlations

Cambridge International AS & A Level Psychology 9990 · 2028–2030 · Version 1 · RM-CORRELATIONS / RM-LONGITUDINAL

Original StudyForA teaching mapped to the Cambridge 2028–2030 syllabus. These lessons are independent study support, not Cambridge material, official mark schemes or awarded grades. No lesson reproduces a research article or Cambridge question.

Before you begin

  • This pathway supports the 2028–2030 syllabus only. AS candidates take Papers 1 and 2; A Level candidates also study two of the four specialist options and take Papers 3 and 4. The separate 2027 syllabus is not mapped here.

Work on paper. Try each question before revealing help, and explain why your method works.

Quick readiness check

Study time rises with test scores, and the scatter points follow a clear upward trend. Which description fits?

What you will learn

  • Interpret the direction and strength of a correlation
  • Operationalise co-variables
  • Explain why correlation does not establish causation
  • Evaluate longitudinal research and attrition

A correlation describes how two measured variables vary together

A positive correlation means higher values on one co-variable tend to accompany higher values on the other; a negative correlation means higher values on one tend to accompany lower values on the other. Strength concerns how consistently the paired values follow a pattern. A scatter graph can show direction, form and possible unusual values.

Correlation does not show that one co-variable caused the other. A third variable may influence both, the direction may be unclear, or the relationship may be coincidental. Operationalise both co-variables so the measurement is clear. Correlation is appropriate when variables are measured rather than deliberately manipulated.

A longitudinal design follows a participant or sample over time

Repeated measurements can show change and sequence within people or groups. A longitudinal study may include an experiment, but ‘longitudinal’ describes the time structure, not a single research method. Repeated participation can create practice effects, and participants may leave the study (attrition); those who remain may differ from those lost.

To evaluate a longitudinal design, ask whether the measures stay comparable across occasions, whether the interval fits the change being studied, and whether missing participants bias conclusions. Following the same people helps distinguish individual change from differences between separate samples, but it does not automatically establish causation.

Sources for this lesson

Pause and explain

What is a major threat when many participants leave a long-term study?

Put the idea to work

Worked example

A study finds that people who sleep longer report lower stress. What can the correlation establish, and what can it not establish?

Show the worked solution
  1. The variables are reported sleep duration and reported stress.
  2. A negative association means greater sleep duration tends to accompany lower stress scores.
  3. The measured association alone cannot show that extra sleep caused lower stress; other variables could influence both.

Answer It establishes a negative association between the measured variables, not a causal effect.

Try a topic test

Choose Easy, Normal or Difficult. Save your answers and open worked review after finishing.

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From guided practice to a new situation

Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.

1 · Guided

A scatter graph shows a strong negative correlation between daily screen time and reported sleep quality. Give one cautious conclusion and one alternative explanation.

Give me a hint

State the direction first, then explain why it is not causal evidence.

Compare my reasoning
  1. Higher screen-time measurements tend to accompany lower sleep-quality scores.
  2. This is a negative association.
  3. Work schedules, stress or another factor could influence both measurements.

The measured variables are strongly negatively associated, but the graph does not show that screen time caused lower sleep quality; a third variable could influence both.

Look for these in your work

  • Direction correct
  • Association distinguished from cause
  • Plausible alternative stated
2 · Independent

A study measures the same learners’ stress at the start and end of each school year for four years. Identify one strength and one limitation of its longitudinal design.

Give me a hint

Consider within-person change and repeated participation.

Compare my reasoning
  1. Repeated measures can reveal how the same learners’ scores change over time.
  2. Repeated questions may create response familiarity or practice effects.
  3. Learners who leave the study may differ from those retained, creating attrition bias.

Strength: tracks change in the same sample. Limitation: repeated measurement and attrition can affect results.

Look for these in your work

  • Strength relates to repeated follow-up
  • A design-specific limitation stated
  • Attrition considered
3 · Transfer

A researcher measures weekly exercise and mood in one survey, then recommends exercise as a treatment for low mood. Evaluate the inference.

Give me a hint

Check whether the design manipulated exercise and ruled out alternatives.

Compare my reasoning
  1. A one-time correlation can show an association between measured exercise and mood.
  2. It cannot establish which variable came first or exclude factors such as health or social support.
  3. A carefully controlled experiment or longitudinal design could test a more specific causal question, with ethical safeguards.

The survey does not justify the causal treatment claim. It shows only an association; temporal order, third variables and an appropriate ethical research design need consideration.

Look for these in your work

  • Identifies correlational evidence
  • Explains causal limits
  • Suggests a relevant next design with ethics

Common mistakes

  • Calling a negative correlation a weak correlation: direction and strength are different.
  • Inferring causation from an association or temporal order alone.
  • Treating every longitudinal study as an experiment.
  • Ignoring attrition when the people who remain may differ from those who leave.
Recall without your notes

What can you explain now?

Why can a correlation not by itself show that X causes Y?

Compare with the explanation

The variables are measured together, so a third variable or another direction of influence may explain the pattern.

Manipulating an IV and controlling alternatives is needed for a stronger causal test; even an experiment has limits.

After trying it yourself, choose your next review. This is your self-assessment.

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Make it stick

What does the direction and strength of a correlation tell you, and what additional evidence is needed for a causal claim?

If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.

Keep learning Psychology

Course scope and assessment

Seven original lessons map all 15 AS research-method and methodological-concept sections to explained Guided, Independent and Transfer practice. All 12 compulsory AS core studies have original study-specific teaching and staged practice; five additional A Level methodology lessons cover research design and validity. All 15 Clinical, all 15 Consumer (2.1.1–2.5.3) and all 15 Health (3.1.1–3.5.3) Psychology sections link to original teaching and staged practice. All 15 Organisational Psychology sections 4.1–4.5 now have original teaching and explained staged practice; the six new 4.4–4.5 lessons bring the planning map to 92 of 92 indexed sections. Consumer lessons cover physical and psychological environments; decision models, heuristics and mistakes; packaging, selling and buying; advertising, self-monitoring, product placement, brand recognition and slogans. Evaluation boundaries are explicit: studies use particular products, samples, tasks and settings; gaze, recall, self-report, EEG indices, ratings, choices and purchases are different outcomes. Health evidence is bounded: Laba et al. measured hypothetical preferences; Riekert and Drotar raise a non-response concern; Chung and Naya's cap tracked openings, not swallowing; Chaney et al. was a short caregiver-reported pilot; Yokley and Glenwick examined immunisation attendance in one public-health setting. MacLachlan et al. report one patient, not a controlled treatment effect; Brudvik et al. compared separate child, parent and physician ratings in one emergency department. Chandola et al. found a cohort association; Wang et al. measured responses during a monitored arithmetic task; Evans and Wener observed commuters; Bridge et al. found mixed self-rated outcomes in radiotherapy. Janis and Feshbach measured short-term self-reports in one school; Lewin et al. tested a rehabilitation package, not information alone. Tapper et al. and Fox et al. studied bounded school and worksite programmes; incentive effects do not establish transfer or rule out reporting effects. Weinstein measured comparative expectations, while Seligman’s framework is conceptual and Shoshani and Steinmetz used a school-level comparison. These findings do not establish universal health effects or diagnose individuals. Need-theory examples use a child measurement study, not a workplace test; SMART is a practical checklist, not a motivational theory. Landry et al. compared reward framing with student volunteers in a lab; this does not establish universal effects in workplaces. Cuadrado et al. (2008) measured undergraduate evaluations of presented leaders, not workplace performance or hiring; this does not establish a universal gender effect. The historical 1995 Munchausen criteria are not a current diagnostic checklist. Cambridge’s 2028–2030 Reference List treats Oruč et al. (1997) as an example of genetic explanation although its participants had bipolar I; gambling disorder is listed in Cambridge’s Clinical topic but classified under addictive behaviours in ICD-11. Candidates choose two options, but all four remain listed so schools can select a route. The 92-section count is a planning index, not a Cambridge score. Remaining work is deeper coverage review, separate-year syllabus reconciliation, timed-paper practice and human subject-expert review. The separate 2027 and 2024–2026 syllabuses are not reconciled into this route. Practice is not official grading, a timed-paper simulation or human subject-expert review.

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