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相关概念视频

Random Sampling Method01:09

Random Sampling Method

10.9K
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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Systematic Sampling Method01:17

Systematic Sampling Method

9.9K
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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Stratified Sampling Method01:16

Stratified 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. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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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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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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相关实验视频

Updated: May 25, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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比较创建推特用户全国随机样本的方法.

Meysam Alizadeh1, Darya Zare2, Zeynab Samei3

  • 1Department of Political Science, University of Zurich, Zurich, Switzerland.

Social network analysis and mining
|February 28, 2025
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概括

1%流方法最好样本美国推特用户人口准确性,但是缓慢的,只有实时. 当历史数据或用户参与至关重要时,界限框方法是合适的替代方案.

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科学领域:

  • 社会科学 社会科学 社会科学
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 计算社会科学 计算社会科学

背景情况:

  • 推特数据被广泛用于社会和计算机科学研究.
  • 一个常见的研究目标是创建来自特定国家用户的代表性样本.
  • 现有采样Twitter用户的方法缺乏全面的比较分析.

研究的目的:

  • 实施和比较四种常见的方法来生成随机的Twitter用户样本在美国:1%流,边界框,位置查询和语言查询.
  • 根据推特和用户级度量以及它们在估计美国人口中的准确性来评估这些方法.

主要方法:

  • 实施四种不同的抽样策略:1%流,边界框,位置查询和语言查询.
  • 使用推特级别的指标 (例如,推特频率) 和用户级别的指标 (例如,关注者,帐户年龄) 对收集的数据进行比较分析.
  • 对每个采样方法的人口估计准确性的评估.

主要成果:

  • 1%流方法给出了用户的推特量,参与度指标和年轻账户,同时也显示了最低的人口估计错误.
  • 与其他方法相比,通过1%流采样的用户不成比例地是男性.
  • 1%流方法耗时,仅限于实时数据,不适合专注于用户参与的研究.

结论:

  • 1%流方法对于获得统计学上准确的美国推特用户样本来进行人口估计是优越的.
  • 当1%流的局限性 (时间,历史数据,参与重点) 成为关键研究约束时,界限盒方法成为可行的替代方案.