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相关概念视频

Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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基于 lz* 的两步方法来识别努力的受访者.

Yilan Chen1, Yue Liu2, Hongyun Liu1

  • 1Faculty of Psychology, Beijing Normal University, Beijing 100875, China.

Journal of Intelligence
|February 26, 2026
PubMed
概括

这项研究通过使用数据挖掘来获得更好的项目参数估计,改善了教育测试中的个人适应性分析. 这有助于准确识别那些没有付出努力的受访者,即使他们的行为很严重.

科学领域:

  • 教育测量教育的测量
  • 心理测量 心理测量 心理测量
  • 数据挖掘 数据挖掘

背景情况:

  • 基于概率的个人适应统计 (lz*) 对于在教育评估中检测不努力的受访者至关重要.
  • 当没有努力的受访者存在时,偏差的项目参数估计会影响 lz* 的准确性.

研究的目的:

  • 开发一种更准确的方法来估计个人适应性分析的项目参数.
  • 为了提高lz*统计的准确性,识别非努力的受访者.

主要方法:

  • 建议采用两步方法,将数据挖掘与个人适应统计结合起来.
  • 使用K-means聚类来识别不同的受访者群体 (努力者与非努力者).
  • 对项目的参数进行了重新估计,仅使用来自识别的努力群的数据.

主要成果:

  • 从努力小组获得的项目参数估计结果被发现更准确.
  • 改进的lz*统计数据显示,在识别非努力的受访者方面,准确度提高了,特别是在高非努力严重程度下.
  • 拟议的方法有效地减轻了非努力的受访者引入的偏见.

结论:

  • 数据挖掘增强的两步方法为个人适应性分析提供了强大的方法.
关键词:
努力的受访者努力的受访者人体健康统计 l z * *两个步骤的方法.没有监督的学习算法.

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  • 这种技术提高了识别那些在教育评估中没有付出努力的受访者的可靠性.
  • 准确的项目参数估计对于人体适应统计数据的有效性至关重要.