Počet záznamů: 1  

Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach

  1. 1.
    SYSNO ASEP0537125
    Druh ASEPV - Výzkumná zpráva
    Zařazení RIVZáznam nebyl označen do RIV
    NázevSubjective well-being and the individual material situation in Central Europe: A Bayesian network approach
    Tvůrce(i) Švorc, Jan (UTIA-B)
    Vomlel, Jiří (UTIA-B) RID, ORCID
    Vyd. údajePraha: ÚTIA AV ČR, 2020
    EdiceResearch Report
    Č. sv. edice2387
    Poč.str.33 s.
    Forma vydáníTištěná - P
    Jazyk dok.eng - angličtina
    Země vyd.CZ - Česká republika
    Klíč. slovaSubjective Well-Being ; Income ; Economic Strain ; Material Deprivation ; Bayesian Networks ; Central Europe
    Vědní obor RIVBB - Aplikovaná statistika, operační výzkum
    Obor OECDStatistics and probability
    CEPGA17-08182S GA ČR - Grantová agentura ČR
    Institucionální podporaUTIA-B - RVO:67985556
    AnotaceThe objective of this paper is to explore the associations between the subjective well-being (SWB) and the subjective and objective measures of the individual material situation in the four post-communist countries of Central Europe (the Czech Republic, Hungary, Poland, and Slovakia). The material situation is measured by income, relative income compared to others, relative income compared to one’s own past, perceived economic strain, financial problems, material deprivation, and housing problems. Our analysis is based on empirical data from the third wave of European Quality of Life Study conducted in 2011. Bayesian networks as a graphical representation of the relations between SWB and the material situation have been constructed in five versions. The models have been assessed using the Bayesian Information Criterion (BIC) and SWB prediction accuracy, and compared
    with Ordinal Logistic Regression (OLR). Expert knowledge, as well as three different algorithms (greedy, Gobnilp, and Tree-augmented Naive Bayes) were used for learning the network structures. Network parameters were learned using the EM algorithm. Parameters based on OLR were learned for a version of the expert model. The Gobnilp model, the Markov equivalent to the greedy model, is BIC optimal. The OLR predicts SWB slightly better than the other models. We conclude that the objective material conditions' influence on SWB is rather indirect, through the subjective situational assessment of various aspects related to the individual material conditions.
    PracovištěÚstav teorie informace a automatizace
    KontaktMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Rok sběru2021
Počet záznamů: 1  

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