Integrating statistical correlation with discrete multi-criteria decision making

Malik Haddad; David Sanders; Giles Tewkesbury; Nils Bausch

doi:10.1504/IJIDS.2021.113599

Integrating statistical correlation with discrete multi-criteria decision making

Malik Haddad, David Sanders, Giles Tewkesbury, Nils Bausch

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Abstract

This paper analyses two hypotheses that considers a correlation between the number of alternatives and the number of criteria considered in a Multiple Criteria Decision Making (MCDM) problem with the minimum percentage change required in the lowest criterion weight to change the outcome of a method. Two MCDM methods are considered, The Analytical Hierarchy Process (AHP) and The Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE II) were applied to the same sets of criteria weights and performance measures. More than two thousand randomly generated sets of criteria weights and performance measures are considered. The minimum percentage change in the lowest criterion weight required to change the outcome of a method is calculated. Pearson’s r parametric test is used to test the hypotheses. Results from parametric test were statistically significant and shows a weak negative correlation for hypothesis one and weak positive correlation for hypothesis two.

Original language	English
Pages (from-to)	1-15
Number of pages	15
Journal	International Journal of Information and Decision Sciences
Volume	13
Issue number	1
DOIs	https://doi.org/10.1504/IJIDS.2021.113599
Publication status	Published - 9 Mar 2021

Keywords

multiple criteria decision-making
MCDM
AHP
PROMETHEE II
correlation
criteria
Pearson's r parametric test
statistical analysis

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10.1504/IJIDS.2021.113599

author Final Version
Malik Haddad et al, Integrating statistical correlation with discrete multi-criteria decision-making, International Journal of Information and Decision Sciences, 2021 Vol.13 No.1, pp.1 - 15, DOI: 10.1504/IJIDS.2021.113599.
Accepted author manuscript (Post-print), 1.15 MB

Using Sensitivity Analysis to select discrete Multiple Criteria Decision Making methods for management and engineering
Haddad, M. (CoI), Sanders, D. (PI), Tewkesbury, G. (Team Member) & Bausch, N. (Team Member)
10/10/16 → 10/10/19
Project: Research

Top two per cent of researchers in the world
Sanders, D. (Recipient), 2 Nov 0001
Prize: National/international honour

Cite this

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title = "Integrating statistical correlation with discrete multi-criteria decision making",

abstract = "This paper analyses two hypotheses that considers a correlation between the number of alternatives and the number of criteria considered in a Multiple Criteria Decision Making (MCDM) problem with the minimum percentage change required in the lowest criterion weight to change the outcome of a method. Two MCDM methods are considered, The Analytical Hierarchy Process (AHP) and The Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE II) were applied to the same sets of criteria weights and performance measures. More than two thousand randomly generated sets of criteria weights and performance measures are considered. The minimum percentage change in the lowest criterion weight required to change the outcome of a method is calculated. Pearson{\textquoteright}s r parametric test is used to test the hypotheses. Results from parametric test were statistically significant and shows a weak negative correlation for hypothesis one and weak positive correlation for hypothesis two.",

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N1 - 12 month embargo to be set from date of online publication. Inderscience. Full citation and link to DOI required.

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AB - This paper analyses two hypotheses that considers a correlation between the number of alternatives and the number of criteria considered in a Multiple Criteria Decision Making (MCDM) problem with the minimum percentage change required in the lowest criterion weight to change the outcome of a method. Two MCDM methods are considered, The Analytical Hierarchy Process (AHP) and The Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE II) were applied to the same sets of criteria weights and performance measures. More than two thousand randomly generated sets of criteria weights and performance measures are considered. The minimum percentage change in the lowest criterion weight required to change the outcome of a method is calculated. Pearson’s r parametric test is used to test the hypotheses. Results from parametric test were statistically significant and shows a weak negative correlation for hypothesis one and weak positive correlation for hypothesis two.

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Integrating statistical correlation with discrete multi-criteria decision making

Abstract

Keywords

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Projects

Using Sensitivity Analysis to select discrete Multiple Criteria Decision Making methods for management and engineering

Prizes

Top two per cent of researchers in the world

Cite this