Sample selection in the face of design constraints: Use of clustering to define sample strata for qualitative research.
Sample selection in the face of design constraints: Use of clustering to define sample strata for qualitative research.
复制标题
面对设计约束的样本选择：使用聚类来定义定性研究的样本层。
DOI：
10.1111/1475-6773.13100
复制
发表时间：
2019
影响因子：
3.4
通讯作者：
Damberg,CherylL
中科院分区：
文献类型：
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作者：
Burgette,LaneF;Escarce,JoséJ;Paddock,SusanM;Ridgely,MarjorieS;Wilder,WarrenG;Yanagihara,Dolores;Damberg,CherylL
目的对40个根据纵向护理成本指标分层的医生组织进行定性访谈，以描述正在部署的影响患者护理总成本的护理提供结构和流程的范围。数据来源三年的医生组织级护理总成本数据（n = 156，加州）研究设计我们使用混合和K均值聚类算法拟合护理数据的总成本，将医生组织的人口划分为基于以下的抽样层：3年成本轨迹（即成本曲线）。主要发现多元正态分布的混合可以将医生组织成本曲线分为由总成本水平、形状和群内变异定义的聚类。K均值聚类不适应不同水平的群内变异，导致更多的聚类被分配到不稳定的成本曲线。混合回归方法的重点过于异常的轨迹，是敏感的模型coding.ConclusionsStatistical聚类可以用来形成抽样层时，纵向措施是主要的兴趣。许多聚类算法是可用的;聚类算法的选择可以强烈地影响所产生的层，因为不同的算法侧重于观察到的数据的不同方面。
ObjectiveTo sample 40 physician organizations stratified on the basis of longitudinal cost of care measures for qualitative interviews in order to describe the range of care delivery structures and processes that are being deployed to influence the total costs of caring for patients.Data SourcesThree years of physician organization‐level total cost of care data (n = 156 in California) from the Integrated Healthcare Association's value‐based pay‐for‐performance program.Study DesignWe fit total cost of care data using mixture andK‐means clustering algorithms to segment the population of physician organizations into sampling strata based on 3‐year cost trajectories (ie, cost curves).Principal FindingsA mixture of multivariate normal distributions can classify physician organization cost curves into clusters defined by total cost level, shape, and within‐cluster variation.K‐means clustering does not accommodate differing levels of within‐cluster variation and resulted in more clusters being allocated to unstable cost curves. A mixture of regressions approach focuses overly on anomalous trajectories and is sensitive to model coding.ConclusionsStatistical clustering can be used to form sampling strata when longitudinal measures are of primary interest. Many clustering algorithms are available; the choice of the clustering algorithm can strongly impact the resulting strata because various algorithms focus on different aspects of the observed data.