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1

Wielowymiarowa analiza wariancji - MANOVA

100%
Aranowska E., Rytel J.
Psychologia Społeczna
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2010
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vol. 5
|
issue 2-3(14)
117-141
EN
The paper describes a model of the multivariate analysis of variance (MANOVA). First, we outline differences between this method and the univariate analysis of variance (ANOVA). We present basic repeated measures designs and point to the research designs that provide data which can be analyzed only with the MANOYA models. We describe formal structure of the MANOVA model and provide its basic definitions. We show how these definitions are related to terms of ANOVA. Development of the ANOVA logic into the MANOYA model is shown in relation to a discussion on independence between expected values of variables and their bivariate correlations (more precisely - means of variables and Pearson product-moment correlation coefficients). We explain how the assumptions, null hypotheses and test statistics of MANOVA have been developed from the ANOVA model. We point to the inconclusiveness of the formal MANOYA solution (lack of the one, established, test statistic) and show these test statistics which appeared most often in the statistical software in the last twenty years. We illustrate formalities of the model with one fictional example of a simple one-way MANOVA. Ali test statistics introduced in this paper were calculated by hand and compared with SPSS output. Moreover, an example of application of multivariate analysis of variance in psychological research was portrayed, using a study on evaluation of managers' performance. In this example, we emphasize reasons why it is necessary to complement MANOVA with another method: discriminant analysis.
2

ANALYSIS OF IMPACT OF USING THE TREND VARIABLES ON BANKRUPTCY PREDICTION MODELS PERFORMANCE

100%
Gavurová B., Janke F., Packová M.
Ekonomický časopis (Journal of Economics)
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2017
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vol. 65
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issue 4
370 – 383
EN
The main objective of this paper is to analyse the impact of trend variables on the predictive ability of the models constructed using two methods: discriminant analysis and decision tree technique. The second objective is to develop a new model with prediction accuracy higher by at least 10% in comparison with other models being currently used in the Slovak business environment (Altman model, Index IN05). After analysing and comparing these methods, we came to the conclusion that the most suitable method for developing the model was the decision tree technique. Using this technique we were able to extract classification rules for bankruptcy prediction and achieve predictive ability of about 85% which, in comparison with other models, showed higher predictive performance by about 10%. Moreover, we confirmed that by applying the dynamic approach predictive ability of the decision tree increased; however we did not derive the same result using the discriminant analysis method.
3
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Prognozowanie upadłości przedsiębiorstw za pomocą wybranych metod wielowymiarowej analizy statystycznej

100%
Dębkowska K.
Zarządzanie i Finanse
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2012
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vol. 1
|
issue 1
175-184
EN
This article aims to assess the efficiency of selected methods of multivariate analysis. The statistical fixed in predicting bankruptcy of enterprises. Compared the classification results of three methods: classification trees, regression of logit and discriminant analysis. The study created a base of Polish companies representing various sectors, among whom were both bankrupt and no-bankrupt, and the ratio between one and the others was 1:1. Each company is described by means of diagnostic variables as financial ratios. Data were collected for analysis on the basis of information contained in the Corporate Database Emerging Markets Information Service (EMIS).
4

Analiza dyskryminacyjna. Podstawowe założenia i zastosowania w badaniach społecznych

75%
Radkiewicz P.
Psychologia Społeczna
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2010
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vol. 5
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issue 2-3(14)
142-161
EN
This article is dedicated to the discriminant analysis - a statistical method that allows to test differences between groups of observations (two or more), based on a set of selected independent variables (predictors). It may be effectively applied to various fields of social sciences and practice (psychology, sociology, political science, economy, law). Linear combination of independent variables, obtained on a basis of the discriminant analysis model, serves as a criterion of assigning observations to different groups. Information carried by an independent variables is saved in a synthetic form as discriminant function scores. Discriminant analysis may have two goals: discrimination (separation) and classification (allocation). In the first case, a researcher tries to explain causes of differences between groups of observations by making use of their characteristics available as "disciriminating" variables. In the second case, a researcher seeks to find a mathematical equation, that combines observation's group characteristics in order to effectively predict the unknown group category to which an observation belongs. First part of the article contains a general description of the statistical model; the second one includes two empirical examples of its application - for two and for four groups of observations.
5

DISCRIMINANT ANALYSIS TO PREDICT THE IMPACT OF PERSONALITY TRAITS, SELF-ESTEEM, AND TIME SPENT ONLINE ON DIFFERENT LEVELS OF INTERNET ADDICTION RISK AMONG UNIVERSITY STUDENTS

75%
Servidio R.
Studia Psychologica
|
2019
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vol. 61
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issue 1
56 – 70
EN
The aim of the current study is to evaluate the predictive influence of Big Five personality traits, self-esteem, and time spent online in discriminating among a sample of university students classified as normal, mildly, and moderately addicted Internet users. Self-report measures were administered to 207 Italian university students aged 19 to 41 years. Results indicated no severe Internet addiction among the participants, but only a mild and moderate risk. Correlation analysis revealed a significant negative association between Internet addiction score and self-esteem. The discriminant analysis indicated two main functions that allow discrimination in terms of the influence of personality traits, self-esteem, and time spent online in three groups of participants. These results may have valid implications in assessing students engaged in intensive online activities, indicating that tailored approaches to their problems are particularly important in preventing the risk of Internet addiction disorder.
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