| { | |
| "original_study": { | |
| "claim": { | |
| "hypothesis": "The interaction of the use of memorization and school-average ability will be negative in its association with mathematical self-concept.", | |
| "hypothesis_location": "p. 401, Hypotheses and Research Questions Addressed by the Present Investigation section, point 6.", | |
| "statement": "Use of memorization moderated the effect of school-average ability on mathematical self-concept (-.089,p<0.001; ), with an effect size of -0.157.", | |
| "statement_location": "p. 409, Study Methods: Cognitive and Metacognitive Learning Strategies section; also Table 3 and Table 4 (for effect size).", | |
| "study_type": "Observational" | |
| }, | |
| "data": { | |
| "source": "Program for International Student Assessment (PISA) database - Organisation for Economic Cooperation and Development (OECD). (2005b).", | |
| "wave_or_subset": "year 2003", | |
| "sample_size": "265180", | |
| "unit_of_analysis": "students", | |
| "access_details": "not stated", | |
| "notes": "Students are nested within schools, and schools are nested within countries. N = 265,180 students who attended 10,221 schools in 41 countries. To enable multilevel modeling schools with 10 or fewer students were excluded from further analysis. Only students who completed math self-concept items were included. other confounding variables not considered here (e.g., school expenditure levels, teacher characteristics, and other individual student characteristics) are likely to have an impact upon academic performance and school climate, and these likely vary greatly across the many schools included in this sample. Also, no school policy or school practice variables were included in the present investigation. " | |
| }, | |
| "method": { | |
| "description": "The authors used three-level multilevel regression analyses to examine how school-average mathematics ability influenced students’ mathematics self-concept and whether this relationship was moderated by 16 socioeconomic and academic self-regulation constructs.", | |
| "steps": "1. The authors presumably obtained the data from the OECD technical report, cleaned it, and removed students who did not complete math self-concept items, and schools with 10 or fewer students. \n2. Then they standardised the values for mathematics ability, mathematics self-concept, and all the potential moderators (including memorisation) across the entire sample. \n3. A school-average mathematics ability variable was calculated by averaging each plausible value separately within each school. \n4. This school-average mathematics ability variable was not restandardized, thus keeping all variables in the same metric as the individual test scores.\n5. Cross-products with school-average ability were created for each potential moderator (again, including memorisation) but were not restandardized.\n6. Sample weights were used to prevent biased estimates of population parameters.\n7. The authors then ran three-level multilevel regression analyses (students within schools within countries) for each of the PISA plausible value of mathematics ability.\n8. Then the authors averaged the regression results to get final parameter estimates that represented the overall pattern across those regressions.\n9. Finally, the effect sizes comparable with Cohen’s d were calculated using Tymms’s (2004) formula.", | |
| "models": "multilevel regression", | |
| "outcome_variable": "Mathematics self-concept", | |
| "independent_variables": "Individual mathematics ability (linear and quadratic), school-average mathematics ability, use of memorization (moderator), and the interaction between school-average mathematics ability and use of memorization", | |
| "control_variables": "NA", | |
| "tools_software": "not stated" | |
| }, | |
| "results": { | |
| "summary": "Memorization use significantly moderated the relationship between school-average ability and mathematical self-concept, weakening this association (–0.089, p < 0.001; effect size = –0.157).", | |
| "numerical_results": [ | |
| { | |
| "outcome_name": "Mathematics self-concept", | |
| "value": "-0.089", | |
| "unit": "standard deviation (the DV was standardised)", | |
| "effect_size": "-0.157", | |
| "confidence_interval": { | |
| "lower": "not stated", | |
| "upper": "not stated", | |
| "level": "not stated" | |
| }, | |
| "p_value": "< .001", | |
| "statistical_significance": "true", | |
| "direction": "negative" | |
| } | |
| ] | |
| }, | |
| "metadata": { | |
| "original_paper_id": "10.3102/0002831209350493", | |
| "original_paper_title": "Big-Fish-Little-Pond Effect: Generalizability and Moderation—Two Sides of the Same Coin.", | |
| "original_paper_code": "not stated", | |
| "original_paper_data": "not stated" | |
| } | |
| } | |
| } |