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The use of a sequence of experiments, where the design of each may depend on the results of previous experiments, including the possible decision to stop experimenting, is within the scope of sequential analysis, a field that was pioneered [12] by Abraham Wald in the context of sequential tests of statistical hypotheses. [13]
Ex post facto recruitment methods are not considered true experiments, due to the limits of experimental control or randomized control that the experimenter has over the trait. This is because a control group may necessarily be selected from a discrete separate population. This research design is thus considered a quasi-experimental design.
A middle-school-level example illustrating this point is the family of balance puzzles, which includes the Twelve Coins puzzle. At the undergraduate level, one could compare Bevington's [4] GRIDLS versus GRADLS. The latter is far from optimal, but the former, which changes only one variable at a time, is worse.
Combined Single-subject research is used to gain added knowledge on the research question and are used to make group research run better. The combined design has arisen from a need to obtain answers to more complex research questions. Combining two or more single-case designs, such as A-B-A-B and multiple baseline, may produce such answers. [14]
Research design refers to the overall strategy utilized to answer research questions. A research design typically outlines the theories and models underlying a project; the research question(s) of a project; a strategy for gathering data and information; and a strategy for producing answers from the data. [ 1 ]
Alias: When the estimate of an effect also includes the influence of one or more other effects (usually high order interactions) the effects are said to be aliased (see confounding). For example, if the estimate of effect D in a four factor experiment actually estimates (D + ABC), then the main effect D is aliased with the 3-way interaction ABC ...
a design that is optimal for a given model using one of the . . . criteria is usually near-optimal for the same model with respect to the other criteria. — [ 16 ] Indeed, there are several classes of designs for which all the traditional optimality-criteria agree, according to the theory of "universal optimality" of Kiefer . [ 17 ]
An example of an unrandomized design would be to always run 2 replications for the first level, then 2 for the second level, and finally 2 for the third level. To randomize the runs, one way would be to put 6 slips of paper in a box with 2 having level 1, 2 having level 2, and 2 having level 3.