BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Kubik-Komar Agnieszka (University of Life Sciences in Lublin), Kuna-Broniowska Izabela (University of Life Sciences in Lublin)
Title
Decision procedure of the choice of statistical analysis method of repeated measurements data
Procedura decyzyjna wyboru statystycznej metody analizy danych z powtarzanymi pomiarami
Source
Logistyka, Logistyka - nauka, 2015, nr 5, CD 1, s. 263-270, rys., tab., bibliogr. 26 poz.
Keyword
Analiza danych, Podejmowanie decyzji, Błędy pomiarowe, Metody pomiarowe, Pomiary, Metody analityczne
Data analysis, Decision making, Measuring errors, Measuring methods, Measurement, Analytical methods
Note
streszcz., summ.
Abstract
W zintegrowanym zarządzaniu logistyczno-marketingowym kierownictwo przedsiębiorstwa coraz częściej wykorzystuje wyniki nowoczesnych eksperymentów przemysłowych. W pracy przedstawiono zasady wyboru metody analizy statystycznej wyników jednego z tego typu eksperymentów. Celem pracy było określenie uwarunkowań do stosowania kilku wybranych metod analizy danych z powtarzanych pomiarów oraz opracowanie procedury postępowania przy procesie decyzyjnym wyboru jednej z nich. Analiza tego typu danych jest często prowadzona w sposób nieprawidłowy, w szczególności korelacje pomiędzy pomiarami nie zawsze są brane pod uwagę. W pracy omówiono trzy podstawowe parametryczne metody analizy danych tego typu. Ścieżkę decyzyjną procedury wyboru metody analizy danych z powtarzanymi pomiarami zaprezentowano graficznie w postaci schematu. Opracowaną procedurę postępowania wzbogacono o praktyczne wskazówki dotyczące wyboru metody analizy danych z powtarzanymi pomiarami, które sformułowano w postaci wniosków.(abstrakt oryginalny)

In the integrated logistics and marketing management the company increasingly adopting the results of modern industrial experiments. The paper presents the prin ciple of selecting one of these types statistical analysis method. The aim of the study was to determine the conditions for application of a few selected methods for analyzing of repeated measurements data and to develop procedures for dealing with decisi on - making process of choosing one of them. Analysis of this type of data is often carried out in an irregular manner, in particular correlations between measurements are not always taken into account. Three basic parametric methods for the analysis of such data were considered and discussed. The decisional procedures for the methods of analysis with repeated measurements are presented graphically in the form of a diagram. The developed procedures for handling enriched with practical tips for choosing the me thods of data analysis with repeated measurements, which were formulated in the form of conclusions.(original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
Bibliography
Show
  1. Akritas M., G., Limitations of the rank transform procedure: A study of repeated measures design, Part I. Journal of the American Statistical Association 86, 1991, pp.457-460.
  2. Bathke, A.C., Harrar, S.W., Madden, H. L., How to compare small multivariate samples using non-parametric tests. Computaional Statistics and Data Analysis, 52, 2008, pp. 4951-4965.
  3. Bathke, A.C., Harrar, S.W., Nonparametric methods in multivariate factorial designs for large num-ber of factor levels. Journal of Statistical planning and Inference, 138, 1, 2008, pp.588-610.
  4. Bathke A. C., Schabenberger O., Tobias R. D., Madden L. V., Greenhouse-Geisser Adjustment and the ANOVA-Type Statistic: Cousins or Twins? The American Statistician. August 1, 63(3), 2009, pp. 239-246.
  5. Brunner E., Domhof E., Langer F., Nonparametric analysis for longitudinal data in factorial experi-ment, Wiley, 2002.
  6. Bruner E., Munzel U., Puri M., L., Rank score tests in factorial designs with repeated measures. Journal of Multivariate Analysis 70,2, 1999, pp. 286-317.
  7. Cornell. J.E., Young. D.M., Seaman. S.L., Kirk. R.E., Power comparisons of eight tests for sphericity in repeated measures designs. Journal of Educational Statistics, 17, 1992, pp. 233-249.
  8. Damon. R. A., Harvey W. R., Experimental Design . ANOVA. and Regression. Harper and Row, New York, 1987.
  9. Field A., A Bluffer's Guide to Sphericity. The British Psychological Society: Mathematical Statisti-cal &Computing Section Newsletter, 6, 1998, pp. 13-22.
  10. Gurka M.J., Selecting the best linear mixed model under REML. The American. Statistician, 60(1), 2006, pp 19-26.
  11. Harrar S.W., Bathke A.C., Nonparametric methods for unbalanced multivariate data and many factor levels. J. Multivariate Anal., 2008. http://dx.doi.org/10.1016/j.jmva.2008.01.005
  12. Harville D. A., Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems. Journal of the American Statistical Association, 72, 1977, pp. 320-340.
  13. Howell, D. C., Statistical Methods for Psychology (7th ed.). Belmont, CA: Wadsworth/Thomson Learning, 2010.
  14. Kariya, T., Kurata, H., Generalized Least Squares Estimators. John Wiley & Sons, Ltd, Chichester, UK, 2004
  15. Keselman, H.J., Algina, J., Kowalchuk, R.K., The analysis of repeated measures designs: a review. British Journal of Mathematical and Statistical Psychology, 54, 2001, pp. 1-20.
  16. Krzyśko M., Multivariate statistical analysis (in Polish). UAM, Poznań 2000.
  17. Linnel Nemec A.F.. Analysis of repeated measures and time series: An introduction with forestry examples. Biometrics information handbook no. 6. British Columbia Ministry of Forests, Victoria, BC. Canada, 1996.
  18. Littell R. C., Henry P. R., Ammerman C. B., Statistical Analysis of Repeated Measures Data Using SAS Procedures. J. Anim. Sci., 76, 1998, pp. 1216-1231.
  19. Morrison D. F., Multivariate statistical analysis. 4th edition, Pacific Grove, CA: Duxbury Press, 2004
  20. Munzel U., Tamhane A., C., Nonparametric Multiple Comparisons in Repeated Measures Designs for data with Ties. Biometrical journal 44, 6, 2002, pp. 762-779.
  21. Peck J., Wiggins J.: It Just Feel Good.: Customers' Affective Response to Touch and Its Influence on Persuasion, Journual of Marketing 70, 2006, 56-69.
  22. Sarhai H., Ojeda M. M., Analysis of Variance for Random Models, Unbalanced Data. Birkhäuser, Boston, Basel, Berlin, 2005.
  23. Skowronek E.: Marketing sensoryczny w zintegrowanym zarządzaniu logistyczno-marketingowym w ujęciu teoretycznym, Logistyka 2013, 2, 106-115
  24. Verbeke,G., Molenberghs, G., Linear Mixed Models for Longitudinal Data. Springer, 2000.
  25. Wallenstein S., Fleiss J.L., and Kingman A. Repeat Measurements Analysis of Dental Data. Journal of Dental Research, 59 (11), 1980, pp. 2021-2024
  26. Warner, R. M., Applied Statistics: From bivariate through multivariate techniques. Thousand Oaks: Sage Publications, 2008.
Cited by
Show
ISSN
1231-5478
Language
eng
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu