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Item response theory models in the measurement theory with the use of LTM package in R

100%
Brzezińska J.
Econometrics. Ekonometria. Advances in Applied Data Analytics
|
2018
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issue vol. 22 no. 1
11-25
EN
Item Response Theory (IRT) is an extension of the Classical Test Theory (CCT) and focuses on how specific test items function in assessing a construct. They are widely known in psychology, medicine, and marketing, as well as in social sciences. An item response model specifies a relationship between the observable examinee test performance and the unobservable traits or abilities assumed to underlie performance on the test. Within the broad framework of item response theory, many models can be operationalized because of the large number of choices available for the mathematical form of the item characteristic curves. In this paper we introduce several types of IRT models such as: the Rasch, and the Birnbaum model. We present the main assumptions for IRT analysis, estimation method, properties, and model selection methods. In this paper we present the application of IRT analysis for binary data with the use of the ltm package in R.
2
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A polytomous item response theory models using R

100%
Brzezińska J.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2016
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issue 2 (52)
43-52
EN
Item response theory (IRT) is widely used in educational and psychological research to model how participants respond to test items in isolation and in bundles. Item response theory has replaced classical measurement theory as a framework for test development, scale constructions, scree reporting and test evaluation. The most popular of the item response models for multiple choice tests are the one-parameter (i. e. the Rasch model) and threeparameter models. This is the general framework for specifying the functional relationship between a respondent’s underlying latent trait level, commonly known as ability in educational testing, or the factor score in the factor analysis tradition and an item level stimulus. In this paper, arguments are offered for continuing research and applying multidimensional IRT models. The position is also taken that multi-parameter IRT models have potentially important roles to play in the advancement of measurement theory about which models to use should depend on model fit to the test data. All calculations are conducted in R available from CRAN which is a widely-used and well-known environment for statistical computing and graphics.
3
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DISTANCE MEASURES IN AGGREGATING PREFERENCE DATA

100%
Zaborski A.
Acta Universitatis Lodziensis. Folia Oeconomica
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2014
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vol. 3
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issue 302
EN
The aim of this paper is to present aggregation methods of individual preferences scores by means of distance measures. Three groups of distance measures are discussed: measures  which use preference distributions for all pairs of objects (e.g. Kemeny’s measure, Bogart’s measure), distance measures based on ranking data (e.g. Spearman distance, Podani distance) and distance measures using permissible transformations to ordinal scale (GDM2 distance). Adequate distance formulas are presented and the aggregation of individual preference by using separate distance measures was carried out with the use of the R program.
4
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Analysis of latent class models in economic research

100%
Brzezińska J.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2016
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issue 4 (54)
36-47
EN
Latent variable models are used and applied in many areas of the social and behavioral sciences. The increasing availability of computer packages for fitting such models makes latent variable models popular, known and applied in many scientific areas. Latent variable models have a very wide range of applications, especially in the presence of repeated observations, longitudinal data, and multilevel data. The basic model postulates an underlying categorical latent variable; within any category of the latent variable the manifest or observed categorical variables are assumed independent of one another (the axiom of conditional independence). The observed relationships between the manifest variables are thus assumed to result from the underlying classification of the data produced by the categorical latent variable. In this paper we present the theoretical and methodological aspects of latent variable models, as well as their application in R software in the field of economic research.
5
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Visual techniques for categorical data in R

100%
Brzezińska J.
Econometrics. Ekonometria. Advances in Applied Data Analytics
|
2017
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issue 3 (57)
26-36
EN
Visualization is one of the most important parts of statistical analysis. In this paper we present a new method of multiple bar charts to display the frequencies of data tables split up into conditional relative frequencies of one target variable and the absolute frequencies of the corresponding combinations of the remaining explanatory variables. In this paper we present the R package extracat allowing for new graphical tools: rmp and cpcp plot [Pilhoefer, Unwin 2013]. The first plot uses the a crossover of mosaicplots and multiple barcharts to display the frequencies of a contingency table split up into conditional relative frequencies of one target variable and the absolute frequencies of the corresponding combination of the remaining explanatory variables. It provides a well-structured representation of the data with the possibility of easy interpretation. Another plot presented in the paper is the cpcp plot using parallel coordinates. Sequences of points are used to represent each of the variable categories, while ordering algorithms are applied to represent a hierarchical structure in the dataset.
6
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Statistical analysis of economic poverty in Poland using R

100%
Brzezińska J.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2018
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issue vol. 22 no. 2
45-53
EN
Economic poverty is one of the more common and complex problems in the modern world, as well as in Poland. This is a complex and multidimensional phenomenon, and therefore there is no single universally valid definition of poverty. This article presents a statistical analysis of economic poverty in Poland based on real data from the Central Statistical Office of Poland. An in-depth statistical analysis of the social situation of Poles will be presented, as well as an attempt to examine interdependencies in the occurrence of various forms of poverty and social exclusion in Poland. In the article, several multivariate statistical methods are presented together with the graphical presentation of results. We present a correspondence analysis with a perception map, as well as the advanced modern visualizing tool for categorical data. All the calculations were conducted using R software.
7

A Comparative Study of the Power of Parametric and Permutation Tests for a Multidimensional Two-sample Location Problem

88%
Polko-Zając D.
Argumenta Oeconomica Cracoviensia
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2020
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issue 2(23)
69-79
EN
Objective: A comparison of multidimensional populations is a very interesting and common statistical problem. It most often involves verifying a hypothesis about the equality of mean vectors in two populations. The classical test for verification of this hypothesis is the Hotelling’s T2 test. Another solution is to use simulation and randomization methods to test the significance of differences between the studied populations. Permutation tests are to enable statistical inference in situations where it is not possible to use classical parametric tests. These tests are supposed to provide comparable power to parametric tests with a simultaneous reduction of assumptions, e.g. regarding the sample size taken or the distribution of the tested variable in the population. The purpose of this study is a comparative analysis of the parametric test, the (usual) permutation test, and the nonparametric permutation procedure using two-stage ASL determination. Research Design & Methods: The study considered the analysis of multivariate data. The paper presents theoretical considerations and refers to the Monte Carlo simulation. Findings: The article presents a permutational, complex procedure for assessing the overall ASL (achieved significance level) value. The applied nonparametric statistical inference procedure uses combining functions. A simulation study was carried out to determine the size and power of the test under normality. A Monte Carlo simulation made it possible to compare the empirical power of this test with that of Hotelling’s T2 test. The most powerful test was the permutation test based on a two-stage ASL determination method using the Fisher combining function. Implications/Recommendations: The advantage of the proposed method is that it can be used even when samples are taken from any type of continuous distributions in a population. Contribution: The proposed test can be used in the analysis of multidimensional economic phenomena.
8
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Visualization of categorical data using extracat package in R

88%
Brzezińska J.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2018
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issue vol. 22 no. 2
9-19
EN
Visualization in research process plays a crucial role. There are several advanced plots for visualizing categorical data, such as mosaic, association, double-decker, sieve or fourfold plot that are based on the graphical presentation of residuals in a contingency table. In this paper we present new methods for visualizing categorical data such as rmb, fluctile and scpcp plot available in extracat package in R. This package provides a well-structured representation of categorical data and allows for a detailed presentation of the relationship between categories in terms of proportions. We describe rmb, fluctile and cpcp. Those plots are based on the concept of multiple bar charts, a fluctuation diagram from a multidimensional table and parallel coordinates respectively. Such plots are mostly used for a visualization of a contingency table or a data frame; they can also be used for exploratory analysis and allows for a graphical presentation even for a high number of variables [Pilhöfer, Unwin 2013]. All the calculations and plots are obtained using R software.
9
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Pomiar i analiza preferencji wyrażonych z wykorzystaniem pakietu conjoint programu R

75%
Pełka M., Rybicka A.
Przegląd Statystyczny
|
2012
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vol. 59
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issue 3
302-315
PL
Conjoint analysis jest metodą statystyczną, w której preferencje empiryczne respondentów wobec różnych ofert (rzeczywistych lub hipotetycznych) są poddawane dekompozycji w celu określenia: funkcji użyteczności każdego atrybutu, względnego znaczenia każdego z nich, analizy udziałów w rynku oraz segmentacji konsumentów. Na rynku obecnie oferowane są różne oprogramowania komputerowe pozwalające na przeprowadzenie badań preferencji konsumentów z wykorzystanie metod conjoint analysis. W artykule przedstawiono pakiet conjoint programu R oraz opracowane pakiety i funkcje programu R, niezbędne w prowadzeniu empirycznych badań preferencji. Program R, ze względu na dostępność na zasadach licencji GPL, zdobywa coraz więcej zwo-lenników, zarówno wśród osób zajmujących się badaniami preferencji, jak i osób korzystają-cych z metod analizy wielowymiarowej. Natomiast pakiet conjoint jest odpowiedzią na fakt, że nie wszystkie kroki procedury conjoint analysis znalazły swe odzwierciedlenie w programie R. W szczególności dotyczy to oceny ważności atrybutów oraz symulacji udziałów w rynku na etapie analizy i interpretacji wyników. W artykule przedstawiono również wyniki badania preferencji konsumentów wina z wyko-rzystaniem pakietu conjoint, analizę udziałów w rynku wybranych profilów symulacyj-nych oraz segmentacje konsumentów wina.
EN
Conjoint analysis is a statistical method in which consumer preferences are decomposed in order to evaluate: utility function for each attribute, importance of each attribute, market shares simulations and segmentation of consumers. There are many different computer programs that can be applied for conjoint analysis re-search. The paper presents conjoint package of R software which are useful to evaluate empiri-cal preferences. The R program is more and more popular and many researchers are applying it. The conjoint package of R software is a response to a fact, that not all steps of conjoint analysis were programmed in R. In particular it concerns evaluation of attributes' importance, market share simulations and interpretation of results. The article presents also results of the evaluation of wine consumers' preferences with ap-plication of conjoint package, market share simulations and segmentation of consumers.
10

A Multivariate Statistical Analysis of the Information Society in Poland

63%
Brzezińska J.
Argumenta Oeconomica Cracoviensia
|
2018
|
issue 19
83-101
EN
Widespread access to high-speed Internet, user-friendly public e-services and the increasing digital competence of society are the main goals for the coming years according to the latest reports published by the Central Statistical Office in Poland. These goals are included in the Operational Programme Digital Poland. This technological development is also connected with the development of economic areas and public services. The rapidly increasing significance of information and electronic services, and thus the application of information and communication technologies (ICT) in the economy, public administration (central and local), and in the everyday life of citizens has triggered a new transformation trend – a transformation towards the information society. This term describes a society for which the processing of information with the use of ICT solutions creates significant economic, social and cultural value. In this paper we present the current state, main aspects, vision and mission of the information society in Poland and carry out a statistical analysis of the information society in Poland using multivariate statistical methods. All the calculations are based on data from the Central Statistical Office and they are conducted using R software.
PL
Powszechny dostęp do szybkiego internetu, przyjazne dla użytkownika e-usługi publiczne i rosnące kompetencje cyfrowe społeczeństwa są głównymi celami na najbliższe lata według najnowszych raportów opublikowanych przez Główny Urząd Statystyczny w Polsce. Cele te zawarte są w Programie Operacyjnym Polska Cyfrowa. Rozwój technologiczny wiąże się również z rozwojem obszarów gospodarczych i usług publicznych. Szybko rosnące znaczenie informacji i elektroniki usług, a tym samym zastosowania technologii informacyjnych i komunikacyjnych (Information and Communication Technologies – ICT) w gospodarce, administracji publicznej (centralnej i lokalnej), a także w codziennym życiu obywateli spowodowało nowy trend transformacji – transformację w kierunku społeczeństwa informacyjnego. Termin ten opisuje społeczeństwo, dla którego przetwarzane informacje z wykorzystaniem rozwiązań ICT stwarzają istotną ekonomiczną, społeczną oraz kulturową wartość. W artykule przedstawiono obecny stan, główne aspekty oraz wizję i misję społeczeństwa informacyjnego w Polsce. Przeprowadzono statystyczną analizę społeczeństwa informacyjnego w Polsce za pomocą wielowymiarowych metod statystycznych. Wszystkie obliczenia oparto na danych pochodzących z Głównego Urzędu Statystycznego i wykonano z wykorzystaniem programu R.
11

Comparative Analysis of the Ordering of Polish Provinces in Terms of Social Cohesion

63%
Dehnel G., Walesiak M., Obrębalski M.
Argumenta Oeconomica Cracoviensia
|
2019
|
issue 1(20)
71-85
EN
The article describes an assessment of the social cohesion of Polish provinces. The assessment was based on classical metric and interval-valued data using a hybrid approach combining multidimensional scaling with linear ordering. In the first step, after applying multidimensional scaling, the objects of interest were represented in a two-dimensional space. In the second step, the objects were linearly ordered based on the Euclidean distance from the pattern object. Interval-valued variables characterize the objects of interests more accurately than do metric data. Classic data are of an atomic nature, i.e. an observation of each variable is expressed as a single real number. By contrast, an observation of each interval-valued variable is expressed as an interval. Interval-valued data were derived by aggregating classic metric data on social cohesion at the level of districts to the province level. The article describes a comparative analysis of the results of an assessment of the social cohesion of Polish provinces based on classical metric data and interval-valued data.
PL
Ocenę spójności społecznej województw Polski przeprowadzono na podstawie klasycznych danych metrycznych oraz symbolicznych interwałowych z wykorzystaniem podejścia hybrydowego łączącego zastosowanie skalowania wielowymiarowego z porządkowaniem liniowym. W pierwszym kroku w wyniku zastosowania skalowania wielowymiarowego otrzymano wizualizację badanych obiektów w przestrzeni dwuwymiarowej. Następnie przeprowadzono porządkowanie liniowe zbioru obiektów na podstawie odległości Euklidesa od wzorca rozwoju. Zmienne symboliczne interwałowe opisują badane obiekty precyzyjniej niż metryczne dane klasyczne. Dane klasyczne mają charakter atomowy. Obserwacja na każdej zmiennej wyrażona jest w postaci jednej liczby rzeczywistej, z kolei dla zmiennych symbolicznych interwałowych obserwacja na każdej zmiennej ujęta jest w postaci przedziału liczbowego. W celu otrzymania danych symbolicznych interwałowych zastosowano dwustopniowe gromadzenie danych. Najpierw zgromadzono dane klasyczne dotyczące spójności społecznej według powiatów Polski, a następnie poddano je agregacji do poziomu województw, otrzymując dane symboliczne interwałowe. W artykule przeprowadzono analizę porównawczą wyników badania spójności społecznej województw Polski uzyskanych na podstawie klasycznych danych metrycznych oraz danych symbolicznych interwałowych.
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