Wpływ układu czynnikowego na pomiar preferencji konsumentów metodą Maximum Difference Scaling

• Authors: Bartłomowicz Tomasz

Title variants

EN

Impact of factorial design on the measurement of consumers’ preferences using Maximum Difference Scaling

• Languages of publication: PL

Abstracts

EN

Measurement of consumer preferences is one of the most important elements of marketing research. In the measurement of consumers’ preferences Maximum Difference Scaling can be used. In this method, a fractional factorial design is used as the experiment, where a limited set of profiles (product or service) is taken into account. The resignation of full factorial design, in which the number of profiles exceeds the ability of respondents to assess, means choosing one of many possible factorial designs. The aim of this article is to present the results of the measurement of consumers’ preferences based on different fractional factorial designs using Maximum Difference Scaling. The article presents the results of measurement of consumers’ preferences using asymmetric, symmetric and balanced factorial designs and the MaxDiff R package.

Keywords

EN

measurement of consumers’ preferences; asymmetric; symmetric and balanced factorial designs; Maximum Difference Scaling (MaxDiff); R program

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Econometrics. Ekonometria. Advances in Applied Data Analytics
• Year: 2015
• Issue: 2 (48)
• Pages: 33-43

Contributors

author

Bartłomowicz Tomasz

References

• Agresti A., 2002, Categorical Data Analysis, second edition, Wiley, New York.
• Bartłomowicz T., Bąk A., 2013, Package MaxDiff. Maximum Difference Scaling R package, http://keii.ue.wroc.pl/MaxDiff/.
• Bartłomowicz T., 2014, Implementacja metody Maximum Difference Scaling w pakiecie MaxDiff programu R, Ekonometria, 4(46), Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław.
• Bąk A., Bartłomowicz T., 2013, Mikroekonometryczne modele wielomianowe i ich zastosowanie w analizie preferencji z wykorzystaniem programu R, [w:] Jajuga K., Walesiak M. (red.), Klasyfikacja i analiza danych – teoria i zastosowania, Taksonomia 20, PN UE we Wrocławiu nr 278.
• Bąk A., 2004, Dekompozycyjne metody pomiaru preferencji w badaniach marketingowych, Wydawnictwo Akademii Ekonomicznej we Wrocławiu, Wrocław.
• Bierlaire M., 1997, Discrete Choice Models, http://web.mit.edu/mbi/www/ michel.html. Cambridge, Massachusetts Institute of Technology.
• Cameron A.C., Trivedi P.K., 2009, Microeconometrics. Methods and Applications, Cambridge University Press, New York.
• Cohen S., 2003, Maximum Difference Scaling: Improved Measures of Importance and Preference for Segmentation, Sawtooth Software Conference Proceedings.
• Coombs C.H., Dawes R.M., Tversky A., 1977, Wprowadzenie do psychologii matematycznej, PWN, Warszawa.
• Croissant Y., 2008, Package mlogit. Multinomial logit model, http://www.R-project.org.
• Greene W.H., 2008, Econometric Analysis, 6th ed., Prentice Hall, Upper Saddle River.
• Gruszczyński M. (red.), 2010, Mikroekonometria. Modele i metody analizy danych indywidualnych, Wolters Kluwer, Warszawa.
• Hoffman S.D., Duncan G.J., 1988, Multinomial and Conditional Logit Discrete-Choice Models in Demography, Demography, vol. 25, no. 3, Population Association of America, http://www.jstor.org/stable/2061541.
• Jackman S., 2007, Models for Unordered Outcomes, Political Science 150C/350C, http://jackman.stanford.edu/classes/350C/07/unordered.pdf.
• Kuhfeld W.F., 1997, Efficient Experimental Designs Using Computerized Searches, SAS Institute,
• http://www.sawtoothsoftware.com/download/techpap/1997Proceedings.pdf.
• Kulikowski J.L., 1993, Komputery w badaniach doświadczalnych, PWN, Warszawa.
• Long J.S., 1997, Regression Models for Categorical and Limited Dependent Variables, SAGE Publications, Thousand Oaks – London – New Delhi.
• Louviere, J.J., 1991, Best-Worst Scaling: A Model for the Largest Difference Judgments, Working Paper, University of Alberta.
• Orme B., 2012, New MaxDiff Designer (V8.1) Offers Small Improvement for HB Estimation, Sawtooth Software, http://www.sawtoothsoftware.com/support/technical-papers/maxdiff-best-worst-scaling/.
• Powers D.A., Xie Y., 2008, Statistical Methods for Categorical Data Analysis, 2nd ed., Emerald, Bingley.
• R Development Core Team, 2011, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, http://cran.r-project.org/.
• Sawtooth Software, 2013, What is MaxDiff?, 2013, http://www.sawtoothsoftware.com/products/maxdiff-software/93-support/sales-support/238-maxdiff-method.
• So Y., Kuhfeld W.F., 1995, Multinomial Logit Models, http://support.sas.com/techsup/technote/ mr2010g.pdf.
• Wheeler R.E., 2010, Package AlgDesign. Algorithmic Experimental Design, http://www.R-project.org.
• Zwerina K.,1997, Discrete Choice Experiments in Marketing, Physica-Verlag, Heidelberg-New York.