Cambridge IAS-Level · Exam Tips

Psychology (9990) Exam Tips

Master the critical distinctions between results and conclusions, learn to avoid the Paper 1 evaluation 'named issue' cap, and construct high-scoring, context-driven research designs for Paper 2 9990 Psychology exams.

5 min readUpdated: Jun 21, 2026

Exam at a Glance

Papers
2
Total Marks
120
Time Limit
3h
Question Types
3
PaperDurationMarksQuestionsWeightingQuestion Types
Paper 1 Approaches, Issues and Debates1h 30min601050%Structured Short Answer, Comparison Essay, Evaluation Essay
Paper 2 Research Methods1h 30min601050%Methodology & Short Answer, Scenario-Based Short Answer & Table Design, Case Study/Field Experiment Planning Essay, Methodological Evaluation
Grade Scale
ABCDEU
Calculator Policy

A silent scientific calculator is required where the syllabus permits one. It must NOT be graphical, programmable, or capable of symbolic algebra (CAS), and it must contain no stored programs or notes.

  • AO1: Knowledge and understanding (40%)
  • AO2: Application of knowledge and understanding (30%)
  • AO3: Analysis and evaluation (30%)

Built from real past papers and marking schemes (2023–2025).

Tips & Strategies

The 1-Mark Distinction: \( \text{Result} \neq \text{Conclusion} \)

One of the most frequent areas where AS Level Psychology candidates shed easy marks is the confusion between a result and a conclusion. Examiners repeatedly highlight this pitfall across core studies questions. A result is a factual, data-driven finding—often numerical or statistical. A conclusion, conversely, is a generic, reasoned psychological takeaway that explains what those findings actually mean conceptually.

Consider Andrade (doodling). A result would state: 'The doodling group recalled a mean of 7.5 names and places, whereas the control group recalled a mean of 5.8.' A conclusion must be conceptual: 'Doodling aids cognitive performance by helping to concentrate on a primary task and preventing daydreaming.' Similarly, in Dement and Kleitman (sleep and dreams), a result is: 'There was a high rate of dream recall (152 out of 191) when participants were awakened from REM sleep compared to NREM sleep.' A conclusion is: 'Dreaming is highly associated with the REM stage of sleep.'

To secure full marks, read the command words carefully. If a question asks for a 'conclusion', keep numerical statistics out of your answer and focus entirely on the broader psychological principle demonstrated by the study.

The Named Issue Trap: Locking Level 5 on Q10

In Paper 1 Section B, Question 10 asks you to evaluate a specific core study in terms of two strengths and two weaknesses, with a mandatory named issue (such as self-reports, generalisations, quantitative data, or validity). If you fail to address the named issue in your response, your score is immediately hard-capped—typically at a maximum of 6 out of 10 marks—no matter how brilliant the rest of your essay is.

Top scorers address the named issue first to ensure it receives exhaustive coverage. For example, if evaluating Hölzel et al. (mindfulness and brain scans) with 'self-reports' as the named issue, you must explicitly detail both a strength and a weakness of using the 39-item Five Facet Mindfulness Questionnaire (FFMQ). A strength is that the FFMQ generates highly objective, standardised quantitative data that allows for straightforward comparison of mindfulness scores before and after the 8-week MBSR program. A weakness is the vulnerability to social desirability bias, where participants might overestimate their 'acting with awareness' scores to please researchers, thereby lowering internal validity. Always anchor your evaluative points with contextual details from the study rather than relying on generic, copy-paste evaluation points.

Paper 2 Blueprinting: Triangulating Your Way to 10/10

In Paper 2 Section B (Question 10), you will be asked to design an original study (such as a case study, field experiment, or correlation) based on a novel scenario. This question uses a strict level-based marking grid where you must address four mandatory design components with deep operationalisation to access the 9–10 marks band.

When designing a case study (e.g., investigating Liam's attention abilities), you must detail:

  1. Participant background: Demographics, how the individual was identified, and the setting.
  2. Information collected: Exactly what data is collected (such as hours spent focusing, or performance on cognitive tasks).
  3. Triangulation (2+ techniques): You must use multiple data collection methods to verify findings (e.g., combining unstructured interviews with Liam, structured observations of his classroom behavior, and a standardised computerised attention test).
  4. Qualitative analysis: Detail how you will code or interpret his descriptive accounts of his attentional focus.

If designing a correlational study, never outline an independent and dependent variable! Correlations measure the relationship between two continuous, quantitative co-variables (for example, child's vocabulary size and the daily duration of play with toys). Ensure you explicitly state that the data would be plotted on a scatter graph with co-variables clearly labeled on the axes.

The Context Rule: Banishing 'Generic' Answers

For any scenario-based short-answer question in Paper 2 (such as Pedro's toy study, Dr Bakar's boredom experiment, or Dr Gul's train passengers), writing a generic methodological point scores a maximum of 1 mark out of 2. To get the second mark, you must link your explanation directly to the context of the scenario.

If a question asks: 'Outline how Pedro could ensure children understand their right to withdraw,' a generic answer like 'He could tell them they can leave the study' only earns partial credit. To secure full marks, you must contextualize it: 'Pedro could tell the children they do not have to stay in the room and play with the toys, and they can go home with their parents whenever they want.' Always look for characters, toys, locations, or specific tasks in the question stem and weave them directly into your sentences.

Animal Guidelines & Ethical Truths

When evaluated on animal research guidelines (such as in the Hassett et al. monkey toy preferences study or Fagen et al. elephant training study), candidates often make sweeping, incorrect claims. A common misconception is that standard procedures like anesthesia, analgesia, or euthanasia are strictly banned. In reality, these procedures are accepted and necessary tools when used responsibly to actively reduce pain and suffering during or after invasive trials. Furthermore, the guideline of 'species' does not mean avoiding animals altogether; it dictates that researchers must carefully select the least sentient species that can still successfully fulfill the study's scientific objectives.

The 5-Minute Habit That Saves a Grade

With 90 minutes allocated for 60 marks on both Paper 1 and Paper 2, you have exactly 1.5 minutes per mark. Use the first 5 minutes of Paper 2 to skim Section B and read the design scenario. This allows your subconscious brain to process the requirements of the high-value 10-mark design task while you systematically work through the short-answer questions in Section A. In Paper 1, use a similar planning strategy for the similarities/differences question (Q9b) and the evaluation essay (Q10) to map out your comparative criteria and core evidence before committing pen to paper.

Calculator Programs

Table mode for roots & turning points

Scientific calculator (e.g. Casio fx-991 series)

Purpose: Tabulate \(y\) across a range of \(x\) to locate sign changes (roots) and approximate maxima/minima.

When to use it: Solving or sketching a function when you want to find where its graph crosses or turns.

Steps
Enter the function in TABLE mode, set the start, end and step, then read where the sign of \(y\) changes or where it peaks.

Exam note: Allowed, but the calculator must be silent, non-graphical, non-programmable and free of stored content; always show the working the mark scheme requires.

Statistics mode (mean, SD & regression)

Scientific calculator (e.g. Casio fx-991 series)

Purpose: Read the mean \(\bar{x}\) and standard deviation directly, and the gradient/intercept (and \(r\)) of a linear regression for bivariate data.

When to use it: Any data-handling, statistics, or required-practical analysis question.

Steps
Enter the data in STAT mode (1-VAR or A+BX), then recall \(\bar{x}\), \(\sigma\) or the regression coefficients.

Exam note: Allowed, but the calculator must be silent, non-graphical, non-programmable and free of stored content; always show the working the mark scheme requires.

Carry exact values with Ans & memory

Scientific calculator (e.g. Casio fx-991 series)

Purpose: Keep full-precision intermediate values to avoid rounding errors.

When to use it: Multi-step calculations where premature rounding loses the final accuracy mark.

Steps
Use Ans, STO/RCL or the M+ memory to reuse the unrounded result of each step; round only the final answer.

Exam note: Allowed, but the calculator must be silent, non-graphical, non-programmable and free of stored content; always show the working the mark scheme requires.

Equation solver — to CHECK your working

Scientific calculator (e.g. Casio fx-991 series)

Purpose: Use the built-in EQN/SOLVE mode to verify roots of quadratics or simultaneous equations you have already solved by algebra.

When to use it: As a check only, after solving by hand.

Steps
Enter the coefficients in EQN mode (or use SOLVE) and confirm they match your worked solution.

Exam note: Allowed, but the calculator must be silent, non-graphical, non-programmable and free of stored content; always show the working the mark scheme requires.

Common Mistakes

  1. 1highMarks at stake: 2Core Studies (General Principles)

    Confusing a statistical result of a core study with its conceptual conclusion.

    How to avoid it: Provide raw numerical data or factual findings when asked for a 'result' (e.g. 26 of 40 participants pressed 450V in Milgram), and provide a broad psychological explanation when asked for a 'conclusion' (e.g. individuals will obey an authority figure even to the point of harming another).
  2. 2highMarks at stake: 4Evaluation Essays (Paper 1 Q10)

    Omitting or minimally addressing the named issue in the Paper 1 10-mark evaluation essay.

    How to avoid it: Write about the named issue (e.g., self-reports, ecological validity, quantitative data) first and expand both a strength and a weakness of this issue in depth before evaluating other aspects.
  3. 3highMarks at stake: 2Research Methods (General Principles)

    Providing circular or tautological definitions of psychological and methodological terms.

    How to avoid it: Avoid using the word itself in its definition. For example, do not define 'opportunity sampling' as 'sampling when you have an opportunity'; instead, define it as 'selecting participants who are easily available and present at the specific time and location of the study.'
  4. 4mediumMarks at stake: 10Research Methods Design (Paper 2)

    Designing an experimental design with IV/DV manipulation when a correlational study is requested in Paper 2.

    How to avoid it: Ensure that for correlational designs you measure two continuous, quantitative variables (co-variables) without any active grouping or intervention, and state that the results will be plotted on a scatter graph.
  5. 5highMarks at stake: 3Saavedra and Silverman (button phobia)

    Writing completely generic methodological evaluations of core studies without anchoring them to the actual study context.

    How to avoid it: Always link your evaluation to the specific features of the study. Do not just say 'it was a case study of one boy so it lacks generalisability'; specify that the boy's phobia in Saavedra and Silverman was caused by a highly unique traumatic event involving a bowl of buttons falling on him in a classroom, making his experience unrepresentative.
  6. 6mediumMarks at stake: 4Research Methods (Data Presentation)

    Drawing bar charts with separate, non-touching bars when graphing consecutive or continuous age groups/categories.

    How to avoid it: When plotting consecutive continuous data categories (such as age ranges 20-29, 30-39, etc.), ensure the bars are touching on the x-axis, and fully label both axes with descriptive titles and scale units.

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