ScholarGate
助手

方法对比

并排查看您选择的方法;存在差异的行会高亮显示。

交叉全因子实验×全因子实验×
领域实验设计实验设计
方法族Process / pipelineProcess / pipeline
起源年份Mid-to-late 20th century (crossover trials formalised ~1960s–1980s; full factorial DoE from Fisher ~1935)1926 (Fisher's foundational paper); codified by the 1950s–1960s
提出者Developed within the design-of-experiments tradition (R. A. Fisher and successors); crossover adaptation formalised by B. Jones and M. G. KenwardRonald A. Fisher
类型Within-subject full factorial experimental designExperimental design
开创性文献Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584883429Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130
别名within-subject full factorial design, repeated-measures full factorial experiment, crossover factorial trial, full factorial crossover designfull factorial design, complete factorial design, 2^k factorial design, FFD
相关66
摘要A crossover full factorial experiment combines the efficiency of a crossover (within-subject) design with the comprehensiveness of a full factorial design. Every participant receives all combinations of the factor levels across successive treatment periods, separated by washout intervals, allowing complete estimation of all main effects and interactions while using each participant as their own control.A full factorial experiment runs every possible combination of all chosen factor levels, making it the gold standard for simultaneously estimating main effects, two-way interactions, and higher-order interactions among multiple independent variables. Introduced through Ronald Fisher's foundational work on factorial designs in the 1920s and systematised by Box, Hunter, and Montgomery, it provides complete information about how factors act individually and in combination on an outcome.
ScholarGate数据集
  1. v1
  2. 2 来源
  3. PUBLISHED
  1. v1
  2. 2 来源
  3. PUBLISHED

前往搜索 下载幻灯片

ScholarGate方法对比: Crossover Full Factorial Experiment · Full Factorial Experiment. 于 2026-06-19 检索自 https://scholargate.app/zh/compare