顺序回归模型变得简单:关于参数解释,数据模拟和功率分析的教程
Filippo Gambarota1, Gianmarco Altoè1
1Department of Developmental Psychology and Socialization, University of Padova, Padova, Italy.
概括
顺序回归模型为有序数据提供了比指标模型更好的统计推断和预测. 本教程简化了理解和应用这些模型,使用基于模拟的方法和R函数.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 数据分析 数据分析
背景情况:
- 顺序数据 (例如,利克尔特尺度,评分) 在心理学中很常见,但通常使用度量模型进行分析,导致统计推断和预测问题.
- 理解顺序回归参数和进行功率分析的挑战可能会阻碍它们的采用.
研究的目的:
- 使用可访问的基于模拟的方法来呈现顺序回归模型.
- 为了澄清参数解释,并演示数据模拟技术的顺序预测器.
- 为了说明顺序回归模型的功率分析.
主要方法:
- 介绍了一般的顺序回归模型,其组件和假设.
- 解释了对logit和probit顺序模型的参数解释.
- 用2x2相互作用和数值类型预测器相互作用的例子演示数据模拟.
主要成果:
- 为理解顺序回归提供了一个基于模拟的框架.
- 展示了适用于复杂设计的实际数据模拟方法.
- 介绍了一个权力分析的例子,用于顺序回归,可扩展到多个预测器.
结论:
- 基于模拟的方法和定制的R函数可以消除顺序回归模型的神秘性.
- 本教程有助于更好地统计推断和预测心理研究中的顺序数据.
- 可访问的代码和函数可用于复制模拟和应用顺序回归.
相关概念视频
Mechanistic Models: Compartment Models in Individual and Population Analysis
32
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...
32
Regression Analysis
5.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.6K
Parametric Survival Analysis: Weibull and Exponential Methods
374
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
374
Introduction To Survival Analysis
188
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
188
Statistical Methods to Analyze Parametric Data: ANOVA
295
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
295
Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K


