http://hdl.handle.net/11089/1361 2026-04-03T21:42:03Z http://hdl.handle.net/11089/1898 Evaluation of College Students with Application of Latent Class Analysis Rybicka, Aneta; Pełka, Marcin In social sciences, especially in economy, to reveal relations between variables it’s easy to apply many known statistical tools when we deal with observable (measureable) variables. The problems appear when dealing with latent variables – that are not directly observed and they are of subjective matter. It’s also an important issue to measure relations between latent variables. The example of latent variables are preferences. The preferences play a very important role in economy. Very often real market decisions, choices (or answers in a questionnaire) are described by non-metric variables (nominal and ordinal). These variables are also called qualitative. The latent class analysis allows to reveal hidden relations between observable variables. The observable variables allow, with a specified probability, to find a non-observable phenomenon. The latent class analysis allows to analyze the qualitative data [see: McCutcheon 1987, p. 7; 11; Hagenaars 1993, p. 21–23]. LCA was introduced by Lazarsfeld in 1950 [1968]. The paper presents evaluation of college students with application of latent class analysis. To obtain such a goal data collected (winter recruitment of 2008/2009) by a college in Walbrzych was used. 2012-01-01T00:00:00Z http://hdl.handle.net/11089/1897 Statistical Inference from Complex Sample with SAS on the Example of Household Budget Surveys Bartosińska, Dorota Many sample surveys are not based on simple or unrestricted random samples, but usually on complex samples with stratification, clustering, unequal inclusion probabilities and multistage sampling. To estimate a parameter, all individual data from complex sample must be weighted by weights connected with sample selection scheme and statification and adjusted for nonresponse and noncoverage errors. Standard statistical computer software is correct for statistical inference from unrestricted random sample, but not from complex sample. The aim of this paper is to present utility of SAS software to statistical inference from complex sample. Data from Household Budget Survey 2008 were used in examples. 2012-01-01T00:00:00Z http://hdl.handle.net/11089/1896 Bayesian Exponential Survival Model in the Analysis of Unemployment Duration Determinants Grzenda, Wioletta The primary objective of the work is to identify demographic and socio-economic factors influencing the unemployment duration in the recent period in Poland. Different approaches to the problem have been applied. In this paper we have used a survival parametric model in Bayesian approach. The following determinants have been concerned in the model: sex, marital status, education level, information about continuing an education, region of Poland, and age of respondent. The empirical analysis is based on “Household budgets in 2008” survey of Central Statistical Office and indicates the main factors influencing unemployment duration. 2012-01-01T00:00:00Z http://hdl.handle.net/11089/1895 Applicability of the Multi- Group Confirmatory Factor Analysis to Construction of Business Sentiment Indicators Białowolski, Piotr The paper presents arguments that advocate for application of the multi-group confirmatory factor analysis as a tool for constructing sentiment indicators in business surveys. Reliable measurement and comparisons of the sentiment mean between periods require measurement invariance on its three basic levels-configural, metric and scalar invariance. It is hypothesized that only sets of questions that are internally coherent can serve as a group of proxies for business sentiment indicator. An attempt to construct two different sentiment indicators for manufacturing industry is performed. The results show that only for the set of coherent proxies it is possible to estimate model with measurement invariance. 2012-01-01T00:00:00Z