二级潜增长模型的标准化估计:替代潜增长标准化方法的比较
Yifan Wang1,2, Zhonglin Wen1,2, Kit-Tai Hau3
1Center for Studies of Psychological Application, South China Normal University, China.
Multivariate behavioral research
|October 6, 2025
概括
对于二次潜增长模型 (LGMs) 的新一阶段方法提供了隐性变量 (eta-1) 的准确和简单的标准化. 这种方法通过确保可解释的生长参数来改善纵向数据分析.
科学领域:
- 心理测量 心理测量 心理测量
- 纵向数据分析 纵向数据分析
- 结构方程建模 结构方程建模
背景情况:
- 第二阶潜增长模型 (LGMs) 广泛用于纵向数据分析.
- 模型识别对于LGM中可解释的增长参数至关重要.
- 传统的隐性标准化方法有局限性,需要多个步骤,不能真正标准化隐性变量 (eta-1).
研究的目的:
- 为二级LGM提出一种新的1阶段隐性标准化方法.
- 确保隐性变量 (eta-1) 的真正标准化,平均值为0和方差为1.
- 与传统方法相比,证明拟议方法的准确性和简单性.
主要方法:
- 对二级LGM引入了一种新的1阶段建模程序.
- 该方法通过限制二次元件内的水平因子的平均值和方差来间接标准化eta-1.
- 通过使用不同潜在标准化技术的理论,模拟和实证数据进行了比较.
主要成果:
- 拟议的1阶段方法实现了eta-1的真正标准化 (平均值=0,方差=1).
- 新方法显示了与现有技术相比的目标准确性.
- 一个阶段的方法提供了比传统的多步骤方法更大的实施简单性.
结论:
- 一阶段隐性标准化方法在理论上提供了合理的实践进步,用于分析二阶段LGM的纵向数据.
- 这种方法提高了增长参数估计的可解释性和可靠性.
- 研究人员可以从这种简化而又准确的方法中获益,用于纵向结构分析.
相关概念视频
Testing a Claim about Standard Deviation
2.9K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.9K
Estimating Population Standard Deviation
3.3K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.3K
Estimating Population Mean with Unknown Standard Deviation
8.8K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
8.8K
Estimating Population Mean with Known Standard Deviation
9.6K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.6K
Empirical Method to Interpret Standard Deviation
9.3K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
This rule is used widely in statistics to calculate the proportion of data values...
9.3K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
1.1K


