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Proč chceme děti: hodnota dítěte a preferovaný počet dětí v České republice

100%
Pakosta P.
Sociologický časopis. Czech Sociological Review
|
2009
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vol. 45
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issue 5
899-935
EN
Surveys often reveal that the number of children people would like to have is greater than the number they actually have. This article examines the question of why people actually want children and bases its answers on data from the 2006 Value of Children Survey, which reintroduces the value of children concept from the 1970s. The battery of survey questions used identified six dimensions of the value of children (The positives of parenthood; Natural drives and goals; Tradition and social status; Social pressure; Limitations and losses; and Decision inhibitors). The respondents, young people between the ages of 28 and 34, see the main reasons for deciding to have children in the positive feelings associated with raising children and with successful parenthood as a natural part of life. They associate parenthood less with responses about social norms and pressure or with rational considerations about all the pros and cons of having children, and they see parenthood as their own, individual decision. A data analysis based on a multinomial logistic regression shows that declared attitudes to a limited extent influence the preferred number of children and that the Czech population is still dominated by the idea of the two-child family with two biological parents, while declared voluntary childlessness is still a marginal phenomenon.
2
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Vybrané metody analýzy panelových dat

63%
Pakosta P., Fucik P.
Data a výzkum - SDA Info (Data and Research - SDA Info)
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2009
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vol. 3
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issue 1
77-96
EN
Advances in the statistical analysis of longitudinal data has been so rapid, that it has been difficult for empirically oriented social scientists to remain informed of all new developments in this important area of social methodology. This article offers some guidance on the use of various types of panel data analysis techniques, paying particular attention to the analysis of longitudinal panel data. The aim of this article is to describe in a succinct manner the logic underpinning a number of panel analysis techniques; outlining the types of inferences that can be drawn from employing specific techniques, and providing the reader with references to the literature associated with particular forms of panel data analysis. Five types of panel data analysis are discussed: Event history analysis, Sequential analysis, Hierarchical linear (or multi-level) modeling (with application to longitudinal data analysis), Structural equation modeling with longitudinal data, and use of Log- linear and Markov chain models for longitudinal data with categorical variables.
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