![]() ![]() ![]() The RI below was generated from ahp.ri with 500000 simulations (which takes some time), as follows: See the documentation for ahp.ri to generate your own RI based on a specific number of dimensions and random seed. The \(RI\) when five attributes are present is 1.11. The methodology of data generation is explained at the end of this vignette.īased on the Saaty scale, a pairwise comparison matrix of \(N\) attributes for the \(k^\) is the maximum eigenvalue of the pairwise comparison vector and \(n\) is the number of attributes. Respondents are asked to make pairwise comparisons for a range of attributes, and indicate their priorities for each of them.Īfterwards, we load the data needed, city200, which consists of randomly generated data of 200 individuals based on the weights provided in Saaty (2004). The preferred characteristics are absolutely more importantĪ Saaty scale is composed of 9 items on each end (17 options per pairwise comparison) where decision-makers are asked to indicate how much attribute/ characteristic A is more preferred to B (or vice versa), and how much it is preferred in a 9-point scale. The preferred characteristics are strongly more important The preferred characteristics are moderately more important The preferred characteristics are slightly more important Two characteristics are equally important ![]() The ahpsurvey package provides a workflow for researchers to quantify and visualise inconsistent pairwise comparisons that aids researchers in improving the AHP design and adopting appropriate analytical methods for the AHP.Ī gentle introduction of the AHP survey methodology: Rating Censoring observations with inconsistency is likely to result in a greatly decreased statistical power of the sample, or may lead to unrepresentative samples and nonresponse bias. Even if an electronic version that allows immediate feedback of consistency ratio is used, respondents asked to repeatedly change their answers are likely to be mentally fatigued. Inconsistent choices are also prevalent in AHP conducted in the survey format, where it is impractical for enumerators to identify and correct for inconsistent responses on the spot when the surveys are delivered in paper format. Hitherto, there are no good ways of computing and visualising the heterogeneity amongst AHP decision-makers, which is common in survey data. However, researchers looking to adopt the AHP in the analysis of survey data often have to manually reformat their data, sometimes even involving dragging and copying across Excel spreadsheets, which is painstaking and prone to human error. The tools currently available in R for the analysis of AHP data, such as the packages ahp by Glur (2018) and Prize by Dargahi (2016), are excellent tools for performing the AHP at a small scale and offers are excellent in terms of interactivity, user-friendliness, and for comparing alternatives. While most applications of the AHP are focused on implementation at the individual or small-scale, the AHP was increasingly adopted in survey designs, which involve a large number of decision-makers and a great deal of heterogeneity in responses. The Analytic Hierarchy Process (AHP), introduced by Saaty (1987), is a versatile multi-criteria decision-making tool that allows individuals to rationally weigh attributes and evaluate alternatives presented to them. ![]()
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