IB Psychology SL Internal Assessment Example

The student makes the theory-investigation link explicit by using Bransford and Johnson’s findings to justify the predicted effect. This is strong because the introduction does not stop at describing schema theory; it explains how the theory predicts performance in this task.

The student strengthens the introduction by explicitly connecting the replication to the target group and the research purpose. This is effective because it explains how the study is relevant to a present-day educational context, not just to psychology theory in the abstract.

The theory is defined accurately and in a way that is directly useful to the investigation. The student shows a sound understanding of schemas as organizing frameworks, which supports later claims about why pre-passage context should improve recall.

The student gives a clear and relevant rationale for replication by linking the age of the original study to possible changes in modern cognition. This works well because it shows why the investigation matters now, rather than simply saying the study is old.

The research hypothesis is directionally appropriate, but it is not fully operationalized because it does not specify the exact recall measure. The student should name the DV as the number of idea units recalled out of 14, which would make the hypothesis testable and more precise.

The variable definitions need clearer wording and more precise operationalization. Phrases like “weather presenting” and “quantized” are unclear, and the IV should state exactly that the illustration is shown before or after the passage. The DV should also be tied to a scored total, such as idea units out of 14.

The null hypothesis identifies the comparison groups, but it mixes the unmeasured outcome “understand” with recall. For a stronger hypothesis, the student should use only the measured DV and state it operationally, so the predicted comparison is fully aligned with the data collection method.

The use of two intact senior groups is practical, but it introduces a potential class-level confound because the groups may differ before the manipulation. The student should acknowledge this more explicitly and explain how it was controlled, or recommend a design that randomizes at the individual level if possible.

The student explains why an independent-samples design is suitable, rather than only naming the design. This is effective because it shows understanding of methodological trade-offs and links the design choice to a concrete threat to validity.

This statement conflicts with the later description of intact class allocation. If whole classes were assigned, the study was not individually randomly assigned, so the student should revise the description to reflect cluster allocation or explain how randomization actually occurred. This matters because it affects the risk of pre-existing group differences.

The student explains why standardized instructions matter, which strengthens the control of extraneous variables. This is a good example of moving beyond listing materials to showing how they support reliability and fairness across conditions.

The claim that this sample represented a “typical adolescent population” is too broad for a single private-school sample. The student should qualify this statement and explain the limits of representativeness, especially given the specific age range, school type, and English-as-a-second-language context.

The sample size is reported inconsistently with the earlier statement that there were 40 students. The student should correct the participant count and make sure the sample description is consistent throughout the report, since accurate sample information is essential for evaluating the study.

The participant description is detailed and useful because it includes sample size, age, gender, school context, and language background. This level of detail helps the reader judge the likely influences on recall and the scope of the conclusions.

Using the original context image is a strong material choice because it supports replication and comparison with the classic study. This improves methodological continuity and helps the reader see that the manipulation is anchored in the original investigation.

The student identifies the sampling method clearly and consistently. This helps the reader understand both the practical basis for recruitment and the limits of how far the findings can be generalized.

The markscheme is a crucial part of the method, but the student does not explain how synonym decisions were applied consistently. The student should clarify how scoring reliability was maintained, because subjective marking can affect the validity of the recall measure if criteria are not tightly defined.

The translated audio is a sensible adaptation for the participants, but the student does not justify how translation might affect equivalence with the original study. A stronger method section would explain why this version still measures the same construct and how wording was kept as close as possible to the source text.

This control is sensible, but it would be stronger if the student explained why the chosen time might still matter for adolescent attention or fatigue. That would show more precise thinking about how timing could affect memory performance.

This is a solid control, and the student could make it even stronger by briefly explaining why those conditions matter for recall. For example, linking classroom environment to attention and concentration would show a deeper understanding of how controls support internal validity.

This control is relevant and well chosen because it directly reduces the chance that one group was encouraged differently from the other. The student shows awareness that uniform instructions help protect the fairness of the comparison.

The recall task is described, but the scoring procedure for written responses is not tied back to the markscheme here. The student should explain how answers were scored against the 14 idea units, because that connection is essential for understanding how the raw data were produced.

This procedure step is unclear and appears to refer to the wrong numbers, so the sequence is difficult to follow. The student should rewrite the procedure so each step is logically ordered and unambiguous, especially because procedural clarity is important for replication.

The student interprets the descriptive statistics clearly and connects the numbers to the two conditions. This works well because the reader can immediately see the direction of the effect and the pattern of central tendency and spread.

The student goes beyond significance testing by commenting on the practical meaning of the difference. That is strong interpretation because it helps the reader understand why the result matters, not just whether it is statistically reliable.

The student makes an attempt to interpret the spread of the data rather than only reporting it. This is useful because it shows awareness that variability matters when judging whether the manipulation produced a consistent effect.

This conclusion goes beyond what standard deviations alone can show. Similar SDs do not prove that the manipulation caused the difference, so the student should use the t-test result and the design more carefully to justify causal interpretation.

The box-and-whisker plot is an appropriate choice because it compares the distributions of the two groups clearly. This helps the reader see not only the difference in central tendency but also the spread and overlap of scores.

The title is slightly misleading because the study is really about context timing/schema activation rather than context-dependent memory in the strict sense. The student should rename it so the graph matches the exact independent variable being tested.

What additional statistic would help readers judge the size of the effect, not just its significance? Considering an effect size would make the analysis more informative and help compare this replication with the original study.

The figure title signals the variable being compared and the direction of the investigation. This is effective because the graph is clearly tied to the hypothesis and not just presented as a decorative visual.

The statistical conclusion is mostly correct, but “accepted” is too strong for an IB-style discussion. The student should say the research hypothesis was supported or the null hypothesis was rejected, which is more precise and avoids implying absolute proof.

The student links the findings back to the theory and the hypothesis in a direct way. This is a strong interpretation because it shows the result is being used to answer the research question, not merely reported as a statistic.

The student begins the evaluation by comparing the replication with the original study, which is exactly the right starting point. This is effective because it shows the investigation is being judged against its theoretical and empirical context, not in isolation.

The evaluation should discuss the intact-class allocation more directly here, because it is a major limitation of the design. Without that, the discussion underplays a likely between-group confound that could have influenced recall independently of the context manipulation.

This is a thoughtful observation, but it would be stronger if the student explained whether this behavior was caused by the instructions or by the participants’ own interpretation of the task. The evaluation should connect the observation more explicitly to a limitation and its likely effect on validity.

The proposed modification is relevant and practical, and it is linked to the limitation just discussed. This works well because the student is not simply listing improvements; they are showing how a change would address a specific weakness.

This is a useful strength, and the evaluation could be improved further by adding a matching or random allocation modification to address the class-level assignment issue. That would show stronger linkage between the identified limitation and a realistic methodological fix.

The student identifies a relevant limitation and explains its impact on the reach of the findings. This is strong evaluation because it moves from description to implication, showing why the limitation matters for interpretation.

The modifications would be stronger if each one were explicitly tied to a particular limitation and justified in more detail. For example, the student should explain how clearer instructions would prevent drawings, or how multiple schools would reduce the effect of one school’s specific culture.

The limitation is clearly identified and directly linked to generalizability. This is strong because it shows the student understands that sample characteristics shape the extent to which conclusions can be applied to other groups.