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A multiple baseline design is a style of research involving the careful measurement of multiple persons, traits or settings both before and after a treatment. This design is used in medical, psychological and biological research to name a few areas. It has several advantages over AB designs which only measures a single case. It is important to note that the start of treatment conditions is staggered (started at different times) across individuals. Because treatment is started at different times we can conclude that changes are due to the treatment rather than to a chance factor. By gathering data from many subjects (instances), inferences can be made about the likeliness that the measured trait generalizes to a greater population. In multiple baseline designs, the experimenter starts by measuring a trait of interest, then applying a treatment before measuring that trait again. Treatment should not begin until a stable baseline has been recorded, and should not finish until measures regain stability.〔Christ, T. (2007). Experimental control and threats to internal validity of concurrent and nonconcurrent multiple baseline designs. Psychology in the Schools, 44(5), 451-459. .〕 If a significant change occurs across all participants the experimenter may infer that the treatment is effective. Multiple base-line experiments are most commonly used in cases where the dependent variable is not expected to return to normal after the treatment has been applied, or when medical reasons forbid the withdrawal of a treatment. They often employ particular methods or recruiting participants. Multiple base-line designs are associated with potential confounds introduced by an experimenter bias which must be addressed in order to preserve objectivity. Particularly, researchers are advised to develop all test schedules and data collection limits beforehand. == Recruiting participants == Although multiple baseline designs may employ any method of recruitment, it is often associated with "ex post facto" recruitment. This is because multiple baselines can provide data regarding the consensus of a treatment response. Such data can often not be gathered from ABA (reversal) designs for ethical or learning reasons. Experimenters are advised not to remove cases that do not exactly fit their criteria, as this may introduce sampling bias and threaten validity.〔 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Multiple baseline design」の詳細全文を読む スポンサード リンク
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