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相关概念视频

Cross-Sectional Research01:50

Cross-Sectional Research

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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Cause and Effect01:53

Cause and Effect

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Correlation and Causation01:27

Correlation and Causation

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Crossover Experiments01:16

Crossover Experiments

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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
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推断与交叉滞后效应 - - 时间上的问题.

Charles C Driver1

  • 1Institute of Education, University of Zurich.

Psychological methods
|July 18, 2024
PubMed
概括
此摘要是机器生成的。

离散时间模型可能会误解连续过程,影响因果推理. 使用随机微分方程的连续时间建模为理解动态系统提供了更准确的方法.

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科学领域:

  • 量化心理学 量化心理学
  • 统计建模 统计建模
  • 因果推理因果推理

背景情况:

  • 矢量自回归 (VAR) 模型被广泛用于从交叉效应推断因果关系.
  • 解释这些交叉效应可能是有问题的,当连续的过程是离散的建模.

研究的目的:

  • 突出对连续过程的离散时间模型的解释问题.
  • 提出并展示连续时间建模作为准确因果推理的解决方案.

主要方法:

  • 使用模拟来证明离散时间模型存在的问题.
  • 随机微分方程 (SDEs) 被参数化为连续时间推理.
  • 分析了一个经验示例,使用了密集的纵向数据.

主要成果:

  • 离散时间模型可能不准确地表示连续过程,导致对因果关系效应的误解.
  • 连续时间模型揭示了比离散模型预期的更密集的效应矩阵.
  • 切换到连续时间建模需要仔细考虑规范化,时间延迟和模型顺序.

结论:

  • 连续时间建模为从动态系统推断因果关系提供了更准确的框架.
  • 当将连续时间方法应用于真实世界的数据时,适当的模型规范和参数解释至关重要.