Title |
Analyzing Complex Longitudinal Data in Educational Research: A Demonstration With Project English Language and Literacy Acquisition (ELLA) Data Using xxM
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Published in |
Frontiers in Psychology, June 2018
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DOI | 10.3389/fpsyg.2018.00790 |
Pubmed ID | |
Authors |
Oi-Man Kwok, Mark Hok-Chio Lai, Fuhui Tong, Rafael Lara-Alecio, Beverly Irby, Myeongsun Yoon, Yu-Chen Yeh |
Abstract |
When analyzing complex longitudinal data, especially data from different educational settings, researchers generally focus only on the mean part (i.e., the regression coefficients), ignoring the equally important random part (i.e., the random effect variances) of the model. By using Project English Language and Literacy Acquisition (ELLA) data, we demonstrated the importance of taking the complex data structure into account by carefully specifying the random part of the model, showing that not only can it affect the variance estimates, the standard errors, and the tests of significance of the regression coefficients, it also can offer different perspectives of the data, such as information related to the developmental process. We used xxM (Mehta, 2013), which can flexibly estimate different grade-level variances separately and the potential carryover effect from each grade factor to the later time measures. Implications of the findings and limitations of the study are discussed. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 20% |
United Kingdom | 1 | 20% |
Switzerland | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 60% |
Scientists | 2 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 20 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 4 | 20% |
Lecturer | 3 | 15% |
Student > Doctoral Student | 3 | 15% |
Student > Master | 2 | 10% |
Lecturer > Senior Lecturer | 1 | 5% |
Other | 2 | 10% |
Unknown | 5 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 4 | 20% |
Social Sciences | 4 | 20% |
Engineering | 3 | 15% |
Linguistics | 1 | 5% |
Mathematics | 1 | 5% |
Other | 1 | 5% |
Unknown | 6 | 30% |