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Jaký je vliv obsahu a struktury televizního zpravodajství na vnímání důležitosti vybraného tématu českou veřejností?

100%
Kalvas F., Kreidl M.
Sociologický časopis. Czech Sociological Review
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2007
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vol. 43
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issue 2
333-360
EN
The authors empirically explore whether and to what extent the content and thematic structure of television news influence the perceived importance of an issue among the population. They present several methodological and conceptual innovations to traditional agenda-setting research. First, they utilise a data set that is somewhat unique in the Czech scientific community. It was constructed by combining data from repeated public opinion surveys and from content analysis of television news; both segments were collected over a period of more than four years. Unlike in most research, the data are not aggregated before the analysis. Second, the authors' choice of issue overcomes the problem of endogeneity that is ubiquitous in agenda-setting research. Third, the authors employ hierarchical linear models to represent adequately the multi-level and clustered data structure and to obtain unbiased estimates of model parameters. Finally, in addition to using standard measures of the intensity of media exposure, they utilise a rather unique set of explanatory variables that represent the homogeneity of media coverage and the relative salience of the issue. They conclude that the media salience of an issue does indeed increase its perceived importance among the population, but media homogeneity and relative salience induce no effect on perceived importance. Owing to a number of methodological improvements, the authors' results are more robust than those produced in previous research.
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Proč užívat hierarchické lineární modely?

100%
Soukup P.
Sociologický časopis. Czech Sociological Review
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2006
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vol. 42
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issue 5
987-1012
EN
The article briefly describes multilevel models and presents their simplest applications. After the methodological and statistical need for this procedure is explained, real data are used to demonstrate how a hierarchical linear model is constructed. The article presents models with a random intercept, models with random slopes, and models with explanatory variables measured at higher levels. In the conclusion, other possible applications of multilevel analysis are discussed, and the basic readings on multilevel analysis are presented.
3

Hierarchiczne modele liniowe. Co nam daja i kiedy warto je stosowac

75%
Zieliński M. W., Radkiewicz P.
Psychologia Społeczna
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2010
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vol. 5
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issue 2-3(14)
217-233
EN
Independence of observations is one of the key assumptions underlying regression analysis and other methods based on the general linear model. The assumption of independence of observations is met, when a score on an outcome variable obtained by an individual is not dependent on results of other persons. This article introduces the hierarchical linear modeling (HLM) - statistical method that is recommended, when there is a real chance, that the assumption of observations' independence is violated. The structure of our article is threefold. In the first part we present basic methodological reasons for applying HLM method, stressing its advantages in comparison to the traditional regression analysis based on the ordinary least squares estimation. The second part introduces the most important theoretical notions underlying hierarchical models - a division into fixed and random effects, a multilevel data structure (including cross-level interaction), and a specific approach to variance components. In the third part we show two empirical examples of HLM application, including a detailed interpretation of their results.
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