Dataset for: Measuring local competitiveness: comparing and integrating two methods PCA and AHP.

Published: 27-04-2020| Version 2 | DOI: 10.17632/z72tgw2phx.2
Katarzyna A. Kurek


The dataset presents the socioeconomic data that was used to built the 24 local indicators for a selection of 63 municipalities in Poland. Furthermore, these local indicators were processed by the Principal Component Analysis (PCA) and the Analytical Hierarchy Procedure (AHP) in order to obtain the singular index that measures the competitiveness of each examined municipality. Following the research question of the study related to this dataset we asked if the competitiveness indexes obtained by the PCA and AHP are comparable in the situation of secondary data as presented. In order to answer the research question the rankings of the municipalities competitiveness indexes were created. Moreover, a mixed methods approach was taken resulting in additional two sets of local competitiveness indexes developed by the mixed method no 1 and no 2. The integration of the methods lied in applying the structure of the PCA and AHP models to the weights generated by each technique to the 24 indicators. Hence, we yielded two mix models between the one method model structure with the other method yielded weights values. By the non parametric testing we were able to check the magnitude of convergence between the PCA and AHP generated local competitivness indexs. Statistical observation of the indexes rankings allows for interpretation of the degree of comparable results from the two, different in principles the PCA and AHP multicriteria decision-making methods.


Steps to reproduce

1. Aggregation of the socioeconomic data 2. Principal Component Analysis by the SPSS software 3. Analytical Hierarchy Process by the Superdecision software 4. Mixed method development 5. Competitiveness indexes ranking non-parametric testing