Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00484851" target="_blank" >RIV/67985807:_____/18:00484851 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14310/18:00105591
Result on the web
<a href="http://dx.doi.org/10.1111/eth.12713" target="_blank" >http://dx.doi.org/10.1111/eth.12713</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/eth.12713" target="_blank" >10.1111/eth.12713</a>
Alternative languages
Result language
angličtina
Original language name
Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences
Original language description
Within behavioural research, non-normally distributed data with a complicated structure are common. For instance, data can represent repeated observations of quantities on the same individual. The regression analysis of such data is complicated both by the interdependency of the observations (response variables) and by their non-normal distribution. Over the last decade, such data have been more and more frequently analysed using generalized mixed-effect models. Some researchers invoke the heavy machinery of mixed-effect modelling to obtain the desired population-level (marginal) inference, which can be achieved by using simpler tools - namely by marginal models. This paper highlights marginal modelling (using generalized estimating equations [GEE]) as an alternative method. In various situations, GEE can be based on fewer assumptions and directly generate estimates (population-level parameters) which are of immediate interest to the behavioural researcher (such as population means). Using four examples from behavioural research, we demonstrate the use, advantages, and limits of the GEE approach as implemented within the functions of the ‘geepack’ package in R.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Ethology
ISSN
0179-1613
e-ISSN
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Volume of the periodical
124
Issue of the periodical within the volume
2
Country of publishing house
DE - GERMANY
Number of pages
8
Pages from-to
86-93
UT code for WoS article
000419978200002
EID of the result in the Scopus database
2-s2.0-85040669856