hierarchical linear models HLM v8.2 在社会研究等领域060678，研究数据往往具有层次结构。也就是说，研究的个体主体可以被分类或安排在群体中，这些群体本身具有影响研究的品质。显然，对这些数据的分析需要专门的软件。已经开发了分层线性和非线性模型（也称为多级模型），以允许在单个分析中研究任何级别的关系，同时不忽略与分层的每个级别相关的可变性。HLM将模型与结果变量相匹配，从而生成具有解释变量的线性模型，这些解释变量利用每个级别指定的变量来解释每个级别的变化。HLM不仅估计每个级别的模型系数，而且预测每个级别的每个采样单元的随机效应。
- can fit for two-, three- and four-level models with continuous, count, ordinal, and nominal outcome variables.
- is capable of fitting multivariate models where the variance at the lowest level of the hierarchy can assume a variety of forms/structures.
- offers a choice of three- and four-level nested models for cross-sectional and longitudinal models and four-way cross-classified and nested mixture models.
- allows the fitting of hierarchical models with dependent random effects (spatial design models).
- has the ability to estimate an HLM from incomplete data in the form of a completely automated approach that generates and analyses multiply imputed data sets from incomplete data. The model is fully multivariate and enables the analyst to strengthen imputation through auxiliary variables.
- includes the option to fit flexible combinations of Fixed Intercepts and Random Coefficients (FIRC) are now included in HLM2, HLM3, HLM4, HCM2, and HCM3.
- can analyze multiply-imputed/plausible value data.
- has an option to fit V-known models.
本页收录的具体版本如下：
HIERARCHICAL LINEAR MODELS (HLM v8.2) EN 单机激活版
您需要先赞赏 580元 才能下载此资源！立即赞赏
