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相关概念视频

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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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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Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Dimensional Analysis01:23

Dimensional Analysis

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Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
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相关实验视频

Updated: Mar 15, 2026

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
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对于纵向和双向数据的动态测量不变度截止值.

Daniel McNeish1

  • 1Department of Psychology, Arizona State University, PO Box 871104, Tempe, AZ, 85287, USA. dmcneish@asu.edu.

Behavior research methods
|March 14, 2026
PubMed
概括

动态测量不变度 (DMI) 为行为研究提供了量身定制的切断值. 这项研究将DMI扩展到依赖组 (DG-DMI),为纵向和双向数据分析提供了新的指导.

科学领域:

  • 心理测量 心理测量 心理测量
  • 行为科学 行为科学
  • 统计建模 统计建模

背景情况:

  • 测量不变性确保了跨组或跨时间的得分可比性.
  • 传统的适合指数切断具有有限的概括性.
  • 动态测量不变性 (DMI) 提供定制的切割值,但仅限于独立组.

研究的目的:

  • 将动态测量不变度 (DMI) 切割值扩展到依赖组 (DG-DMI).
  • 为解释在模型中的适合指数差异提供指导,这些模型具有跨组相关的响应.
  • 为了解决对纵向和双向数据的当前方法的局限性.

主要方法:

  • 建议和描述 DG-DMI 方法.
  • 使用对依赖群体的统计建模,与群体之间的相关性.
  • 将GD-DMI应用于三个实证示例.

主要成果:

  • 展示DG-DMI用于评估依赖组中的测量不变性的应用.
  • 提供经验证据证明DG-DMI在纵向和二次数据中的实用性.
  • 为实际实施提供开源软件.

结论:

关键词:
适合指数差异的差异.纵向的不变性 纵向的不变性纵向测量的不变性测量等价性 测量等价性测量不变性 测量不变性

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  • 总局-DMI将DMI原则扩展到依赖组,增强测量不变性分析.
  • 拟议的方法为研究人员提供了重要的指导,研究人员使用纵向或双向数据.
  • 在复杂的数据结构中,DG-DMI促进了对复杂数据结构中得分可比性的更准确和更可概括的结论.