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Autor
Brzezińska Justyna (University of Economics in Katowice, Poland)
Tytuł
Independence Analysis of Nominal Data with the Use of Log-Linear Models in R
Źródło
Statistics in Transition, 2012, vol. 13, nr 2, s. 311-320, rys., tab., bibliogr. 19 poz.
Słowa kluczowe
Modele logarytmiczno-liniowe, Wielowymiarowa analiza statystyczna, Analiza wielokryterialna, Zmienne jakościowe
Log-linear models, Multi-dimensional statistical analysis, Multicriteria analysis, Qualitative variables
Uwagi
Materiały z Kongresu Statystyki Polskiej: The 100th Anniversary of the Polish Statistical Association, 2012.
summ.
Abstrakt
Log-linear models are used to analyze the relationship between two or more categorical (e.g. nominal or ordinal) variables. The term log-linear derives from the fact that one can, through logarithmic transformations, restate the problem of analyzing multi-way frequency tables in terms that are very similar to ANOVA. Specifically, one may think of the multi-way frequency table to reflect various main effects and interaction effects that add together in a linear fashion to bring about the observed table of frequencies. There are several types of models between dependence and independence: homogenous association, partial association, conditional association and null model. Expected cell frequencies are obtained with the use of iterative proportional fitting algorithm (IPF) [Deming, Stephen 1940]. The next step is to derive model coefficients for single variables as well as for interaction parameter and the most useful tool for interpreting model parameter is odds and odds ratio. Log-linear models are available in R software with the use of loglm function in MASS library and glm function in stats library. In this paper log-linear analysis will be presented with the use of available packages on empirical datasets in economic area. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
Pełny tekst
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Bibliografia
Pokaż
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  12. ISHII-KUNTS, M., 1994. Ordinal log-linear models, Sage University Papers.
  13. KNOKE, D., BURKE P. J., 1980. Log-linear Models, Quantitative Applications in the Social Science" 20, Sage University Papers, Sage Publications, Newbury Park, London, New Delhi.
  14. LINDSEY, J. K., 1973. Inferences from Sociological Survey Data: A Unified Approach, New York, Elsevier.
  15. MADSEN, M., 1976. Statistical analysis of multiple contingency tables. Two examples. Scand. J. Statist. 3, 97-106.
  16. PEARSON, K., 1900. On a criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling, Philos. Mag. Ser. 5, 50,157-175.
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Cytowane przez
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ISSN
1234-7655
Język
eng
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