手法を比較

	ダブルブラインド複数ベースラインデザイン	シングル被験者実験計画
分野	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline
提唱年	1968 (multiple baseline); double-blind extension applied from 1980s onward in clinical behavioral research	1960s (Sidman 1960; formal applied codification by Kazdin and Baer in 1970s–1980s)
提唱者	Multiple baseline: Baer, Wolf & Risley (1968); double-blind procedural extension adapted from clinical trial methodology	Murray Sidman (foundational tactics); B. F. Skinner (applied behavior analysis lineage)
種類	Single-subject experimental design with blinded outcome assessment	Experimental research design
原典	Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.	Kazdin, A. E. (1982). Single-Case Research Designs: Methods for Clinical and Applied Settings. Oxford University Press. ISBN: 978-0195030440
別名	DB-MBD, blinded multiple baseline design, masked multiple baseline design, double-blind MBD	SSED, single-case experimental design, n-of-1 design, intrasubject replication design
関連	5	6
概要	The double-blind multiple baseline design is a single-subject experimental design in which an intervention is introduced sequentially across two or more independent baselines — behaviors, individuals, or settings — while outcome assessors (and ideally participants) remain unaware of which baseline is currently in the intervention phase. The double-blind procedural overlay reduces measurement bias and demand characteristics, strengthening causal inference beyond what a standard multiple baseline design offers.	Single-subject experimental design (SSED) establishes experimental control by repeatedly measuring one individual (or a small number of individuals) across baseline and intervention phases, using the participant as their own control. Instead of comparing groups, it compares the participant's own behavior across conditions over time. Widely used in applied behavior analysis, special education, rehabilitation, and clinical psychology, SSED allows causal inference from small or unique samples where group designs are impractical.