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Criteria weighting by using the 5Ws & H technique

100%
Čančer V.
Business Systems Research Journal
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2012
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vol. 3
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issue 2
41-48
EN
Background: This paper introduces the use of the 5Ws & H technique, which is the creative problem solving technique based on who, what, when, where, why and how questions, for the establishing of the criteria weights in multi-criteria decision-making (MCDM). Objectives: The main goal of this paper is to adapt and complete the steps of the 5Ws & H technique, usually used in the problem definition phase, to establish the importance of criteria by the methods based on an interval scale. It also aims to verify the applicability of the proposed approach in the selection of the most appropriate blade. Methods/Approach: In terms of prescriptive approach, the creative 5Ws & H technique was used in the weighting step of the frame procedure for MCDM. During synthesis, the additive model was used, whereas interactions among criteria were considered by using the discrete Choquet integral. Results: The first result is a theoretical statement of the weighting scheme for a new decision mechanism. The second result is the application of this scheme in a real-world case-study. Considering interactions among criteria strengthened the decision-making basis in the selection of the most appropriate blade. Conclusion: The creative 5Ws & H technique proved useful in criteria weighting.
2
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Simultaneous interpretive structural modelling and weighting (SISMW)

100%
NASROLLAHI M., RAMEZANI J., SADRAEI M., FATHI M. R.
Operations Research and Decisions
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2022
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vol. 32
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issue 1
111-126
EN
Multi-criteria decision-making (MCDM) methods have been implemented in many fields. In the meantime, several methods have been proposed to obtain the weight of the criteria determined by various methods in different ways. In this paper, a new approach, called simultaneous interpretive structural modelling and weighting (SISMW), is proposed to solve a multi-criterion decision-making (MCDM) problem. Using SISMW, the weight of the criteria and the relationship between them could be determined simultaneously. In this approach, like the ISM method, pair comparison between criteria was made by the decision-maker to determine the relationships among the different criteria. With the help of this data, the weight of the criteria, as well as the causal (cause and effect) relationships between them, were determined in 12 steps. The main advantage of this method is that only one stage of data collection is required for obtaining weights and modelling, and so the research process may be faster. This may increase the reliability of the collected data because, in a one-step survey, the impact of time is minimized. This process can be useful for conceptualizing and developing theories to help decisionmakers understand the problem better.
3
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Non-response and Weighting Systems in Business Tendency Surveys: Are Expectations Influenced?

100%
Tomczyk E., Kowalczyk B.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2011
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vol. 86:Business Surveys, Business Cycles. Polish Contribution to the 30th CIRET Conference
101-119
EN
Rationality of economic agents belongs to the basic assumptions of neoclassical economic theory, and for decades it has inspired research on whether expectations are indeed formed rationally. Direct data on expectations are available mainly through business tendency surveys which are subject to various types of non-response problems. Inclination of industrial enterprises to respond may be correlated with values of measured variable, introducing response bias. Response bias may also occur as a result of introducing weighting systems to control variable size of respondents. The two key properties of rational expectations, on which the majority of empirical analyses of survey data are focused, are unbiasedness and orthogonality. We analyze several sample balance statistics and expectations series based on quantified survey data, taking into consideration issues of non-response and weighting schemes. Alternative definitions of expectations series aim to account for: 1) influence of arbitrary assumptions concerning weighting of individual data, 2) changing sample structure that results from non-response, 3) response rates varying with degree of optimism / pessimism of respondents. Results of our analysis indicate that expectations concerning relative changes in production are unbiased but not efficient with respect to freely available information, namely, observed relative changes in production (lagged three months) and expectations balance (lagged two months). This result holds for a range of weighting schemes and non-response issues analyzed, including changes introduced to sample structure by non-response, and increased inclination of “optimists” and “pessimists” to supply answers in the business tendency survey, as long as their shares remain constant in time.
4
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Sample Surveys of Households in Belarus: State and Perspectives

86%
Bokun N.
Statistics in Transition new series
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2013
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vol. 14
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issue 1
89-106
EN
The main principles, characteristics and problems of three sample surveys of households (HH), conducted by the State Statistics of Belarus are considered: 1) The Household Sample Surveys (on expenses and incomes), 2) Private Subsidiary Plots in rural areas (PSP) and 3) Labour Force Survey (LFS). For each of them the purpose, sampling plan, sample design, data collection mode, the methods of estimation and possible ways to improve the surveys are discussed.
5
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Objective weights for scoring: the automatic democratic method

86%
Tofallis C.
Multiple Criteria Decision Making
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2022
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vol. 17
69-84
EN
When comparing performance (of products, services, entities, etc.), multiple attributes are involved. This paper deals with a way of weighting these attributes when one is seeking an overall score. It presents an objective approach to generating the weights in a scoring formula which avoids personal judgement. The first step is to find the maximum possible score for each assessed entity. These upper bound scores are found using Data Envelopment Analysis. In the second step the weights in the scoring formula are found by regressing the unique DEA scores on the attribute data. Reasons for using least squares and avoiding other distance measures are given. The method is tested on data where the true scores and weights are known. The method enables the construction of an objective scoring formula which has been generated from the data arising from all assessed entities and is, in that sense, democratic.
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