Statistical Decisions When Modelling Effects of Teaching Quality
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| Title: | Statistical Decisions When Modelling Effects of Teaching Quality |
|---|---|
| Language: | English |
| Authors: | Carmen Köhler (ORCID |
| Source: | Educational Studies. 2026 52(3):364-391. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 28 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Teacher Effectiveness, Educational Research, Statistical Analysis, Models, Decision Making, Statistical Inference, Foreign Countries, Administrator Surveys, Teacher Surveys, Outcomes of Education, Interrater Reliability, Predictor Variables, Video Technology |
| Geographic Terms: | Germany |
| Assessment and Survey Identifiers: | Teaching and Learning International Survey |
| DOI: | 10.1080/03055698.2025.2492576 |
| ISSN: | 0305-5698 1465-3400 |
| Abstract: | The article provides a toolkit for pivotal necessary statistical steps in analysing data from studies conducted to investigate the influence of teaching quality on student outcomes. It further helps researchers make informed decisions about their choices for statistical checks and the analytical model. Issues we elaborate on are measures of reliability at different levels of the model and for different teaching quality assessments, and how decisions about the statistical model influence the estimated effects. We use data from a teaching quality video study (N = 958 students in 41 classes) to address these statistical issues and demonstrate the necessary steps when evaluating and analysing data from such studies. Results show that inferences from analyses can differ depending on the applied statistical model. These findings imply that practitioners should be cautious in interpreting the results from a single modelling approach and should consider running multiple models to compare the consistency of the results. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1504569 |
| Database: | ERIC |
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