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Dieste Tubio, Oscar ORCID: https://orcid.org/0000-0002-3060-7853, Fernández, Enrique, García Martínez, Ramón and Juristo Juzgado, Natalia
ORCID: https://orcid.org/0000-0002-2465-7141
(2011).
The Risk of Using the Q Heterogeneity Estimator for Software Engineering Experiments.
In: "International Symposium on Empirical Software Engineering and Measurement, ESEM 2011", 19/09/2011 - 23/09/2011, Banff, Albert, Canada. ISBN 978-1-4577-2203-5. pp. 68-76.
Title: | The Risk of Using the Q Heterogeneity Estimator for Software Engineering Experiments |
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Author/s: |
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Item Type: | Presentation at Congress or Conference (Article) |
Event Title: | International Symposium on Empirical Software Engineering and Measurement, ESEM 2011 |
Event Dates: | 19/09/2011 - 23/09/2011 |
Event Location: | Banff, Albert, Canada |
Title of Book: | Proceedings of International Symposium on Empirical Software Engineering and Measurement, ESEM 2011 |
Date: | 2011 |
ISBN: | 978-1-4577-2203-5 |
Subjects: | |
Freetext Keywords: | Meta-analysis , effect size , heterogeneity , reliability , statistical power , weighted mean difference (WMD) |
Faculty: | Facultad de Informática (UPM) |
Department: | Lenguajes y Sistemas Informáticos e Ingeniería del Software |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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All meta-analyses should include a heterogeneity analysis. Even so, it is not easy to decide whether a set of studies are homogeneous or heterogeneous because of the low statistical power of the statistics used (usually the Q test). Objective: Determine a set of rules enabling SE researchers to find out, based on the characteristics of the experiments to be aggregated, whether or not it is feasible to accurately detect heterogeneity. Method: Evaluate the statistical power of heterogeneity detection methods using a Monte Carlo simulation process. Results: The Q test is not powerful when the meta-analysis contains up to a total of about 200 experimental subjects and the effect size difference is less than 1. Conclusions: The Q test cannot be used as a decision-making criterion for meta-analysis in small sample settings like SE. Random effects models should be used instead of fixed effects models. Caution should be exercised when applying Q test-mediated decomposition into subgroups.
Item ID: | 11569 |
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DC Identifier: | https://oa.upm.es/11569/ |
OAI Identifier: | oai:oa.upm.es:11569 |
Official URL: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?... |
Deposited by: | Memoria Investigacion |
Deposited on: | 19 Jul 2012 10:53 |
Last Modified: | 20 Apr 2016 19:36 |