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相关概念视频

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

24.8K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
24.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Nominal Level of Measurement00:56

Nominal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
29.7K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.5K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Case Studies01:22

Case Studies

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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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相关实验视频

Updated: Jul 18, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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在单个案例研究中使用数量和比例数据进行多层次建模:演示和评估.

Haoran Li1, Wen Luo1, Eunkyeng Baek1

  • 1Department of Educational Psychology, Texas A&M University.

Psychological methods
|August 21, 2023
PubMed
概括

本研究介绍了一般化的线性混合模型 (GLMMs),用于分析单个案例实验设计 (SCEDs) 中的计数和比例数据. 这项研究提供了实际指导和基于模拟的建议,以有效地应用GLMM.

科学领域:

  • 统计 统计 统计 统计
  • 行为研究方法 行为研究方法

背景情况:

  • 单个案例实验设计 (SCED) 经常产生计数或比例结果数据.
  • 现有的统计方法可能无法完全捕捉这些数据的复杂性.

研究的目的:

  • 介绍和说明一类新的通用线性混合模型 (GLMMs),用于分析SCED中的计数和比例数据.
  • 为研究人员提供关于应用GLMM的实际指导,包括过度分散,估计和解释等方面.

主要方法:

  • 对于计数和比例数据的GLMM的口头插图.
  • 详细讨论GLMM框架组件:过度分散,估计,推断,模型选择和系数解释.
  • 实证示例和模拟研究来评估GLMM的性能.

主要成果:

  • 模拟研究评估了GLMM在偏差和治疗效应 (即时和趋势) 覆盖率方面的表现.
  • 研究了统计测试的实证I型错误率.
  • 结果为在SCED研究中使用GLMM时的统计决策提供了基础.

结论:

  • 在SCED中,GLMM为分析计数和比例数据提供了一个强大的框架.
  • 该研究为SCED研究人员提供了必要的信息,并概述了方法学家的未来方向.

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  • 在实施GLMMs时,提供了关于健全统计决策的建议.