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Computational problems in variable selection for multilevel models using stepwise regression

100%
Wdowicka H.
Przegląd Statystyczny
|
2023
|
vol. 70
|
issue 4
73-85
EN
Multilevel modelling is a methodology that allows the consideration of variability in the level of the studied variables and the nature of the relationships between them, depending on the affiliation of study units to higher-level units (groups). Additionally, by dividing the studied population into groups, it is possible to explain part of the variability of the estimated characteristic using higher-level characteristics. The usefulness of multilevel modelling in estimating socioeconomic characteristics was investigated in the author's previous works. However, with large populations characterised by a multilevel structure, a significant drawback of this approach is its high computational complexity, often resulting in unacceptably long computation times. The main objective of the article is to propose a simplification in the algorithm of forward stepwise multilevel regression, allowing a significant reduction in the time required for variable selection in the model. The considerations will be illustrated by constructing a multilevel model to examine the determinants of daily flows related to employment based on the matrix of employment-related population flows developed from the 2021 National Census of Population and Housing (NSP 2021).
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Využití víceúrovňových modelů při analýze kontextuálních efektů míry ekonomické aktivity na podporu přerozdělování v komparativních longitudinálních datech

86%
Petrúšek I.
Sociologický časopis. Czech Sociological Review
|
2024
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vol. 60
|
issue 1
67-93
EN
This article studies the links between a country’s labour force participation rate and attitudes towards income redistribution. The article also demonstrates how to specify a multilevel model when analysing contextual effects and it presents several types of random effects structures and options for centering explanatory variables in comparative longitudinal survey data. The contextual effect is decomposed into longitudinal and cross-sectional components for time-varying contextual variables, such as the labour force participation rate. The analysis of redistribution support based on ESS data from 27 countries and nine rounds shows how fundamentally the mentioned properties can influence substantive conclusions. The analyses presented in this article do not provide any evidence for a link between redistribution support and the labour force participation rate. However, the hypothetical configurations of multilevel models presented here cover all possible substantive effects of the labour force participation rate. Contextual effects analysis may thus lead to highly unreliable results when a multilevel model fails to control for the compositional effects of individual-level predictors, when it does not specify random effects at the level to which a substantial variation of the outcome variable may be attributed, and when it does not distinguish between the longitudinal and cross-sectional effects of time-varying variables.
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