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Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).
A paired difference test is designed for situations where there is dependence between pairs of measurements (in which case a test designed for comparing two independent samples would not be appropriate). That applies in a within-subjects study design, i.e., in a study where the same set of subjects undergo both of the conditions being compared.
The method of pairwise comparison is used in the scientific study of preferences, attitudes, voting systems, social choice, public choice, requirements engineering and multiagent AI systems. In psychology literature, it is often referred to as paired comparison .
In mathematics, economics, and computer science, the stable marriage problem (also stable matching problem) is the problem of finding a stable matching between two equally sized sets of elements given an ordering of preferences for each element. A matching is a bijection from the elements
2. Match each participant to one or more nonparticipants on propensity score, using one of these methods: Nearest neighbor matching; Optimal full matching: match each participants to unique non-participant(s) so as to minimize the total distance in propensity scores between participants and their matched non-participants.
Matched pair testing is used to detect discrimination. The focus is to determine the presence of disparate treatment in the offering of goods and services during the sales process. The focus is to determine the presence of disparate treatment in the offering of goods and services during the sales process.
The PAPRIKA method's closest theoretical antecedent is Pairwise Trade-off Analysis, [34] a precursor to Adaptive Conjoint Analysis in marketing research. [35] Like the PAPRIKA method, Pairwise Trade-off Analysis is based on the idea that undominated pairs that are explicitly ranked by the decision-maker can be used to implicitly rank other ...
The method is complicated by additional constraints that make the problem it solves not exactly the stable matching problem. It has the advantage that the students do not need to commit to their preferences at the start of the process, but rather can determine their own preferences as the algorithm progresses, on the basis of head-to-head ...