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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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重生完整案例分析:一个辩护.

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概括
此摘要是机器生成的。

完整案例分析 (CCA) 可以是一个有效的方法来处理缺失的数据,即使缺失的不是随机的 (MNAR) 数据,当使用适当的共变量时. 这种基于原则的共变量调整方法为多重归算和其他基于MAR的方法提供了有价值的补充.

关键词:
有关因果推理的推理.归算是指指责一个人.缺失的数据 缺失的数据选择偏差是一种选择偏差.

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科学领域:

  • 统计 统计 统计 统计
  • 生物统计学 生物统计学
  • 流行病学 流行病学

背景情况:

  • 完整案例分析 (CCA) 在完全随机 (MCAR) 假设下经常被认为是无效的.
  • 现有的文献经常建议放弃CCA用于假设随机丢失 (MAR) 的方法.

研究的目的:

  • 倡导CCA的实用性与原则性的协变量调整.
  • 证明CCA可以补充基于MAR的方法,如多重归算.
  • 为了突出CCA的潜在有效性,即使缺少非随机 (MNAR) 数据.

主要方法:

  • 研究了共变量调整在完整案例分析 (CCA) 中的作用.
  • 分析了因果结构,确定了CCA中的偏差消除.
  • 在调整后的CCA中比较偏差减少与在MAR下多重归算.

主要成果:

  • 基于原则的共变量调整可以消除CCA中的偏差,即使使用MNAR数据.
  • 在某些场景中,调整后的CCA可能比多重归算更少偏差.
  • 共变量调整通常会减少CCA的偏差,即使它不能完全消除它.

结论:

  • 调整后的CCA是一种有价值的灵敏度分析,与多重归算一起.
  • 在仔细控制共变量的情况下,CCA应被视为缺少数据的原则方法.
  • 恢复CCA作为一个可行的统计方法是有道理的.