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The Solomon four-group design is a research method developed by Richard Solomon in 1949. [1] It is sometimes used in social science, psychology and medicine. It can be used if there are concerns that the treatment might be sensitized by the pre-test. [2]
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 [1] with further developments by Yoon in 1987, [2] and Hwang, Lai and Liu in 1993. [3]
The PRISMA flow diagram, depicting the flow of information through the different phases of a systematic review. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is an evidence-based minimum set of items aimed at helping scientific authors to report a wide array of systematic reviews and meta-analyses, primarily used to assess the benefits and harms of a health care ...
The VIKOR method is a multi-criteria decision making (MCDM) method. It was originally developed by Serafim Opricovic in 1979 to solve decision problems with conflicting and noncommensurable (different units) criteria.
Meta-analysis is a method of synthesis of quantitative data from multiple independent studies addressing a common research question. An important part of this method involves computing a combined effect size across all of the studies.
The PICO process (or framework) is a mnemonic used in evidence-based practice (and specifically evidence-based medicine) to frame and answer a clinical or health care related question, [1] though it is also argued that PICO "can be used universally for every scientific endeavour in any discipline with all study designs". [2]
The Wald–Wolfowitz runs test (or simply runs test), named after statisticians Abraham Wald and Jacob Wolfowitz is a non-parametric statistical test that checks a randomness hypothesis for a two-valued data sequence.
In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretest–posttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned.