IB Psychology SL Internal Assessment Example

Strong theoretical framing: the student introduces the multi-store model clearly and names the key memory stores. This gives the investigation a solid psychological basis and shows that the aim is grounded in established theory rather than being a stand-alone classroom activity.

The student connects Glanzer and Cunitz directly to the memory model, which helps explain why the replication matters. This is effective because it moves beyond description and shows how the earlier finding informs the present investigation.

Good relevance statement: the student explains why adolescent memory is worth investigating in an educational context. This makes the aim more meaningful because the reader can see a clear real-world application beyond replication alone.

The student should soften this explanation. Saying STM was 'no longer in use' is too absolute; a filler task is better described as reducing active maintenance or displacement of recency information, not proving STM stops functioning entirely.

This prediction is directionally clear, but it mixes primacy and recency and incorrectly refers to a 'control group' in a repeated-measures design. The student should state the IV as delay/no delay and the DV as recall from the recency position to keep the hypothesis method-accurate.

The null hypothesis is not operationalized clearly enough. It refers to 'the first and last few items' rather than the recency section targeted by the investigation, and it does not state the IV and DV precisely. The student should align the hypothesis with the actual measured recency recall.

The student could strengthen the relevance claim by briefly linking it to a specific educational mechanism, such as improved rehearsal strategies or reduced distraction during revision. A concrete example would make the practical value more convincing and less general.

The student should make this control more replicable by specifying the presentation rate, interval between words, and whether the same researcher delivered both lists. Concrete procedural detail would make it easier to judge reliability and reproduce the investigation.

This is a strong explanation of why the design is appropriate. The student does not just name the repeated-measures method; they explain how it improves comparability by reducing participant-related variation, which is exactly the kind of methodological reasoning needed here.

This is imprecise because the lists are not actually the same; the method uses two different 15-word lists. The student should explain that list length was controlled, while also addressing how equivalence in difficulty or word familiarity was ensured across the two lists.

Good control rationale: the student explains why word choice matters for memory performance. This strengthens the design because it shows awareness that recall differences could arise from vocabulary difficulty rather than the filler task.

This is a helpful materials description because it identifies the exact stimuli and their source. The choice of monosyllabic words is also methodologically sensible, since it reduces lexical complexity and helps isolate the effect of delay on recall.

This claim overstates what convenience sampling can do. The sample is accessible, but not automatically representative. The student should explain why the chosen participants are relevant to the research question, while avoiding unsupported claims about generalization.

This implementation creates an order confound because the later condition is always tested after the earlier one. In a repeated-measures study, the student should counterbalance the order of conditions or justify why order effects are negligible, otherwise improvements may reflect practice rather than the delay.

The ethical statement is good, but the student could strengthen it by briefly linking the consent/debrief procedures to the specific participants' age group. If the sample includes adolescents, showing how consent and withdrawal were handled appropriately would improve the explanation.

Clear procedural detail improves replicability here. The student specifies the recall window and how responses were recorded, which helps the reader understand exactly how data were gathered and why the procedure is consistent across participants.

The graph is a relevant visual summary because it directly compares the two conditions in the hypothesis. Using the same measure as the table helps the reader connect the descriptive and visual evidence quickly.

The analysis should be clearer about how the recency section was defined and why six words were selected. The student needs to justify this cutoff methodologically, because the interpretation of the means depends on which items are counted as 'recency' items.

This is a solid descriptive-statistics justification because the student explains why the chosen measures suit the dataset. Linking the mean and standard deviation to the data characteristics shows some awareness of when summary statistics are appropriate.

The student could strengthen the graph by labelling the two bars with the condition names on the x-axis and adding error bars. That would make the comparison easier to read and would show the variability alongside the means.

The student should be more precise and grammatical here, but more importantly they should interpret the difference in context. A mean gap of 0.68 words is small, so the analysis would be stronger if it commented on whether this is likely to be practically meaningful as well as statistically different.

The table title is focused and directly tied to the hypothesis. This is effective because it makes the reader immediately see that the descriptive data are about recency recall rather than the whole list.

This interpretation goes beyond what the standard deviation alone can show. Lower spread suggests more consistency, but it does not by itself prove that the task disturbed memory or focus. The student should reserve that causal claim for evidence that directly supports it.

The reasoning here is partly correct, but it should be more careful. The small effective $N$ means the critical $W$ value is used, yet the student still needs to show how the paired differences were ranked and why ties reduce the usable sample. Without that, the inferential steps are hard to verify.

What effect do the many zero-difference pairs have on the power of the test and on the strength of the conclusion? Reflecting on this would help the student explain not just the result, but also how the data structure limits what can be inferred.

The conclusion overstates what a non-significant result means. Failing to reject the null does not confirm that there is 'no measurable difference'; it only shows insufficient evidence of a difference in this sample. The student should avoid turning a null result into proof of equivalence.

This justification is not quite correct. Word recall scores are counts, not ordinal rankings, so the student should not describe them as ordinal rankings. The test may still be defensible because of the repeated-measures design and small effective sample, but the data type needs to be stated accurately.

The student selects an appropriate non-parametric test and gives a sensible design-based reason for it. This shows good awareness that a Wilcoxon signed-rank test is suited to repeated-measures comparisons when the data do not justify a parametric test.

The student should be more cautious here. A non-significant result does not strongly show that the MSM is limited; it may instead reflect low power, practice effects, or individual differences. The conclusion should be framed as tentative rather than as a direct challenge to the model.

The student evaluates a methodological difference between their study and Glanzer and Cunitz in a relevant way. This works well because it connects a design choice to a plausible reason the present findings may differ from the original study.

This is a strong attempt to explain the result using background theory. The student does not just restate the outcome; they propose a cognitively plausible reason why the filler task may not have eliminated recall differences.

The student could strengthen this modification by explaining why the proposed task better matches the cognitive demands of the investigation. A brief justification would make the improvement more persuasive and show clearer limitation-to-modification reasoning.

The final evaluation repeats the non-significant result, but it still overstates the conclusion by implying the theory is not supported because of the test outcome alone. The student should distinguish between a weak statistical result and a definitive theoretical refutation.

This modification is well linked to a real limitation in the repeated-measures design. The student shows good evaluative thinking by explaining how counterbalancing would directly reduce the specific order problem identified earlier.

The limitation is relevant and clearly explained. The student shows understanding that an experimental control can improve internal validity while still reducing how naturally the findings transfer to everyday memory situations.