Multilevel Modeling in Plain Language
Multilevel Modeling in Plain Language
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用简单语言进行多级建模
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
D. Pevalin
中科院分区：
文献类型：
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作者：
K. Robson;D. Pevalin
第1章什么是多级建模以及为什么要使用它？混合分析层次多层次建模的理论依据使用多层次模型的优势是什么？多层建模的统计原因OLS软件的假设本书是如何组织的第2章：随机截距模型：当截距变化时单层回归的回顾我们数据中的嵌套结构从随机截距模型开始到目前为止我们的发现意味着什么？将分组改为学校添加1级解释变量添加2级解释变量组均值居中交互模型拟合R平方呢？R平方？关于随机和固定效应的进一步假设和简短说明第3章：随机系数模型：当截距和系数变化时开始使用随机系数模型尝试不同的随机系数收缩扇入和扇出检查方差作为随机系数的二分变量多个随机系数关于简约性和拟合具有多个随机系数的模型的说明具有一个随机系数的模型随机和一个固定系数添加2级变量残差诊断建模的第一步对我们的基本模型进行进一步扩展的尝试下一步是什么？第四章：将结果传达给更广泛的受众创建日志格式的表格模型的固定部分空模型的重要性居中变量Stata命令使表格制作更容易你在谈论什么？具有随机系数的模型图呢？跨层级互动
Chapter 1: What Is Multilevel Modeling and Why Should I Use It? Mixing levels of analysis Theoretical reasons for multilevel modeling What are the advantages of using multilevel models? Statistical reasons for multilevel modeling Assumptions of OLS Software How this book is organized Chapter 2: Random Intercept Models: When intercepts vary A review of single-level regression Nesting structures in our data Getting starting with random intercept models What do our findings mean so far? Changing the grouping to schools Adding Level 1 explanatory variables Adding Level 2 explanatory variables Group mean centring Interactions Model fit What about R-squared? R-squared? A further assumption and a short note on random and fixed effects Chapter 3: Random Coefficient Models: When intercepts and coefficients vary Getting started with random coefficient models Trying a different random coefficient Shrinkage Fanning in and fanning out Examining the variances A dichotomous variable as a random coefficient More than one random coefficient A note on parsimony and fitting a model with multiple random coefficients A model with one random and one fixed coefficient Adding Level 2 variables Residual diagnostics First steps in model-building Some tasters of further extensions to our basic models Where to next? Chapter 4: Communicating Results to a Wider Audience Creating journal-formatted tables The fixed part of the model The importance of the null model Centring variables Stata commands to make table-making easier What do you talk about? Models with random coefficients What about graphs? Cross-level interactions Parting words