Latent Class Analysis: An example for reporting results
Latent Class Analysis: An example for reporting results
复制标题
DOI：
10.1016/j.sapharm.2016.11.011
复制
发表时间：
2017-11-01
影响因子：
3.9
通讯作者：
Schreiber, James B.
中科院分区：
文献类型：
--
作者：
Schreiber, James B.
目的：本文的目的是提供一个简短的非数学介绍潜在类分析（LCA）和演示的研究人员新的分析技术在药房和药房管理。LCA是一种数学技术，用于在可能存在未观察到的分类变量的集合时检查观察到的变量之间的关系。传统上，LCA集中在多分类观测变量，但最近的工作已经扩展了可以利用的数据类型。本导言中包含了提交审稿的稿件中应包含的信息的基本指南。为了进行分析，使用了LatentGold，但我还包含了基本的R代码，用于使用poLCA包运行LCA和LC回归。(C)2016 Elsevier Inc. All rights reserved.
Objective: The purpose of this paper is to provide a brief non-mathematical introduction to Latent Class Analysis (LCA) and a demonstration for researchers new to the analysis technique in pharmacy and pharmacy administration. LCA is a mathematical technique for examining relationships among observed variables when there may be collections of unobserved categorical variables. Traditionally, LCA focused on polytomous observed variables, but recent work has extended the types of data that can be utilized. Included in this introduction are basic guidelines for the information that should be part of a manuscript submitted for review. For the analysis, LatentGold is used, but I also include basic R code for running LCA and LC Regressions with the poLCA package. (C) 2016 Elsevier Inc. All rights reserved.