Thesis advisor: Henry I. Braun / Secondary analyses of international large-scale assessments (ILSA) commonly characterize relationships between variables of interest using correlations. However, the accuracy of correlation estimates is impaired by artefacts such as measurement error and clustering. Despite advancements in methodology, conventional correlation estimates or statistical models not addressing this problem are still commonly used when analyzing ILSA data. This dissertation examines the impact of both the clustered nature of the data and heterogeneous measurement error on the correlations reported between background data and proficiency scales across countries participating in ILSA. In this regard, the operating characteristics of competing modeling techniques are explored by means of applications to data from PISA 2012. Specifically, the estimates of correlations between math self-efficacy and math achievement across countries are the principal focus of this study. Sequentially employing four different statistical techniques, a step-wise model refinement approach is used. After each step, the changes in the within-country correlation estimates are examined in relation to (i) the heterogeneity of distributions, (ii) the amount of measurement error, (iii) the degree of clustering, and (iv) country-level math performance. The results show that correlation estimates gathered from two-dimensional IRT models are more similar across countries in comparison to conventional and multilevel linear modeling estimates. The strength of the relationship between math proficiency and math self-efficacy is moderated by country mean math proficiency and this was found to be consistent across all four models even when measurement error and clustering were taken into account. Multilevel multidimensional mixture IRT modeling results support the hypothesis that low-performing groups within countries have a lower correlation between math self-efficacy and math proficiency. A weaker association between math self-efficacy and math proficiency in lower achieving groups is consistently seen across countries. A multilevel mixture IRT modeling approach sheds light on how this pattern emerges from greater randomness in the responses of lower performing groups. The findings from this study demonstrate that advanced modeling techniques not only are more appropriate given the characteristics of the data, but also provide greater insight about the patterns of relationships across countries. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.
| Identifer | oai:union.ndltd.org:BOSTON/oai:dlib.bc.edu:bc-ir_109191 |
| Date | January 2021 |
| Creators | Gurkan, Gulsah |
| Publisher | Boston College |
| Source Sets | Boston College |
| Language | English |
| Detected Language | English |
| Type | Text, thesis |
| Format | electronic, application/pdf |
| Rights | Copyright is held by the author. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-nc-sa/4.0). |
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