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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).
Radius matching: all matches within a particular radius are used -- and reused between treatment units. Kernel matching: same as radius matching, except control observations are weighted as a function of the distance between the treatment observation's propesnity score and control match propensity score. One example is the Epanechnikov kernel.
Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. [1] For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed.
interrupted time series design; propensity score matching or weighting; instrumental variables; Panel analysis; Of all of these designs, the regression discontinuity design comes the closest to the experimental design, as the experimenter maintains control of the treatment assignment and it is known to "yield an unbiased estimate of the ...
When human participants perform under concurrent schedules of reinforcement, matching has been observed in some experiments, [8] but wide deviations from matching have been found in others. [9] Finally, if nothing else, the matching law is important because it has generated a great deal of research that has widened our understanding of operant ...
Probability matching is a decision strategy in which predictions of class membership are proportional to the class base rates.Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, then the observer using a probability-matching strategy will predict (for unlabeled examples) a class label of "positive" on 60% of instances ...
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An alternate way of summarizing the design trials would be to use a 4x3 matrix whose 4 rows are the levels of the treatment X 1 and whose columns are the 3 levels of the blocking variable X 2. The cells in the matrix have indices that match the X 1 , X 2 combinations above.