IB Psychology HL Internal Assessment Example

The student gives a clear, accurate definition of schema theory. This works well because the concept is explained in a way that can support later analysis, rather than being named without meaning.

The link between theory and investigation is explicit here. The student shows how the replication is designed to test reconstructive memory, which strengthens the conceptual coherence of the introduction.

The aim is highly focused and specific: the student clearly identifies the population, the manipulated language feature, and the outcome being measured. This precision makes the investigation easy to follow and shows a clear line of inquiry.

The null hypothesis matches the research hypothesis and is clearly written in terms of the same operationalised variables. That consistency is important because it shows the student understands how the hypotheses relate to the planned test.

The hypothesis is well operationalised: both the IV and DV are named in measurable terms, and the directional prediction is clear. This makes the intended analysis straightforward and appropriate.

The relevance is explained rather than merely stated. By linking the topic to school and media contexts, the student shows why the question matters beyond the laboratory and why it has practical value.

The sampling method is clearly identified and justified in context. This is strong because the student explains why the technique was practical for the school setting rather than just naming it.

The exclusion criterion is relevant and well linked to the aim. The student shows awareness that prior knowledge could bias recall, which strengthens the sampling rationale and internal validity.

The student explains not only what was controlled, but why it mattered. This helps show how the materials were standardised to reduce extraneous variables in the speed estimates.

The student clearly describes the design and explains why it is suitable for this investigation. Connecting the design to order effects shows an understanding of how methodology affects validity.

The student could sharpen this explanation by separating random allocation from questionnaire placement. Clarifying that random assignment reduces pre-existing group differences would make the design rationale more methodologically precise.

How might limiting the target population to adolescents with limited driving experience affect the generalisability of the findings? Reflecting on this would help the student distinguish between a practical sample choice and the wider population the study could support.

The student could strengthen this by briefly noting the trade-off: reducing the questionnaire may improve focus, but it also limits the amount of data available for interpretation. A short justification of that balance would make the materials discussion more evaluative.

The materials choice is explained well here. By linking the simplified questionnaire to lower fatigue and a more direct test of the verb manipulation, the student shows thoughtful control of procedure and cognitive load.

The processed data table gives a clear summary of the descriptive statistics needed for comparison. This is effective because it presents the key values in a compact form before the inferential test is discussed.

The student should complete the explanation of standard deviation more precisely by linking it to spread around the mean. A fuller interpretation would help the reader understand whether the two groups were similarly variable or whether one was more dispersed.

The student clearly reports the main descriptive pattern and quantifies the size of the difference. This helps the reader see the direction of the effect before moving to significance testing.

What does this result allow the student to conclude, and what does it not allow them to conclude? Considering the difference between ‘no significant evidence’ and ‘proof of no effect’ would make the interpretation more precise.

The inferential result is clearly stated with the relevant values. This is good statistical communication because it allows the reader to follow the decision rule and verify the conclusion.

The student links the inferential result back to the actual data pattern, which is exactly what stronger analysis should do. This shows the difference between a numerical contrast and a statistically reliable effect.

The test choice is reported correctly, but the justification is inaccurate. Speed estimates are quantitative ratio data, so they are not simply ordinal because they were ranked for the test. The student should explain the test choice in terms of sample size, independence, and the non-parametric assumption, not misclassify the data type.

This justification is too broad. A small sample does not automatically rule out an independent-samples $t$-test; the student would need to discuss normality and measurement scale as well. Tightening this reasoning would make the statistical analysis more accurate.

The student begins to explain the non-significant result in relation to the procedure, which is a good evaluative move. This helps move the discussion beyond simply restating the outcome.

The student explains why this sample choice matters, rather than merely listing it as a feature. Linking prior knowledge to internal validity makes the evaluation more convincing.

The limitation is specific and clearly linked to the verb manipulation. This is strong evaluation because it does not state a generic weakness; it explains why this particular sample may have responded differently to the stimuli.

This is a relevant limitation because it is directly connected to the dependent variable: experience with driving could shape speed estimates. The student shows awareness that participant characteristics can influence recall judgments.

The modification is explicitly connected to the limitation and is clearly justified. This is the kind of practical improvement that shows the student understands how to translate evaluation into better design.

The student should make this explanation more analytical by linking each feature of the video to a specific mechanism, such as weaker schema activation or reduced realism. That would turn a plausible idea into a more convincing discussion of findings.

This is a relevant strength because it is directly tied to the independent-measures design and the hypothesis. The student explains how the design protects validity, which is exactly what evaluative commentary should do.

The student identifies a strong procedural control and explains its value for consistency. This is good evaluation because it shows how the method supports fairness across conditions.

The proposed modification is concrete and directly tied to the limitation of having too few questions. It would likely improve attention checking and response reliability if implemented carefully.

Could adding several extra questions also change the nature of the task and introduce new demand characteristics? Thinking about that trade-off would make the modification more sophisticated and better justified.