摘要
经验取样法是通过对调查对象多次重复测量进行数据收集的研究方法 ,近年来受到研究者广泛关注。本文主要基于《应用心理学期刊》(Journal of Applied Psychology)2010～2017年刊发的34篇文献,总结并述评经验取样数据分析中的四个关键成分及具体操作:数据的结构设置、数据清理、所用测量工具的信效度检验、假设检验方法。具体包括,数据由于重复抽样而形成不同的嵌套结构;数据清理涉及异常值、缺失值的识别与处理;信效度计算方法区别于一般研究;假设检验时,依据研究问题("变量之间的关系"与"变量随时间的变化")选择相应的模型构建和估计方式。此外,收集国内期刊刊发的12篇实证文献,将其与国际期刊中经验取样数据的分析步骤及操作方法进行比较。最后,对未来研究如何丰富及完善数据分析过程作了展望。
Experience sampling is an effective method of collecting longitudinal data, which draws much attention in recent years. We reviewed the data analysis process from five key steps, including data structure identifying, data cleaning, reliability and validity testing, and hypothesis testing. Each step was illustrated with examples from 34 empirical studies in Journal of Applied Psychology in recent 8 years. Specifically, data structures differ based on the sampling unit. Data cleaning involving dealing with abnormal data and missing data. Methods for testing reliability and validity differs from common studies. The selection of analytical approaches to hypothesis testing is contingent on the types of research questions. Besides, we further collected 12 empirical studies published in Chinese journals and compared the data analysis between studies of Journal of Applied Psychology and Chinese journals. Finally, the future studies of experience sampling data analysis were discussed as well.
作者
邢璐
骆南峰
孙健敏
李诗琪
尹奎
Xing Lu;Luo Nanfeng;Sun Jianmin;Li Shiqi;Yin Kui(School of Labor and Human Resources,Renmin University of China,Beijing 100872;Donlinks School of Economics and Management,University of Science and Technology Beijing,Beijing 100083)
出处
《中国人力资源开发》
CSSCI
北大核心
2019年第1期35-52,共18页
Human Resources Development of China
基金
中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目成果(18XNH049)
关键词
经验取样法
嵌套结构
数据清理
信效度
假设检验
Experience Sampling Method
Nested Data
Data Cleaning
Reliability and Validity,Hypothesis Testing
