经验取样法的数据分析:方法及应用
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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.
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.
引文
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1.Mplus使用手册(第8版)可通过如下网址免费下载:http://www.statmodel.com
2.樊景立,梁建,陈志俊.(2012).实证研究的设计与评价.见陈晓萍,徐淑英,樊景立(编),组织与管理研究的实证方法(第二版)(pp.121-146).北京:北京大学出版社.
3.方杰,邱皓政,张敏强.(2011).基于多层结构方程模型的情境效应分析--兼与多层线性模型比较.心理科学进展,19(2),284-292.
4.方杰,邱皓政,张敏强,方路.(2013).我国近十年来心理学研究中HLM方法的应用述评.心理科学,36(5),1194-1200.
5.方杰,温忠麟,张敏强,任皓.(2014).基于结构方程模型的多层中介效应分析.心理科学进展,22(3),530-539.
6.方杰,张敏强,邱皓政.(2010).基于阶层线性理论的多层级中介效应.心理科学进展,18(8),1329-1338.
7.李文静,郑全全.(2008).日常经验研究:一种独具特色的研究方法.心理科学进展,16(1),169-174.
8.李育辉,黄飞.(2010).成对数据分析之行动者-对象互依性模型(APIM).心理科学进展,18(8),1321-1328.
9.林琳.(2017).拖延行为的干预:计划行为理论和实施意向的影响.心理学报,49(7),953-965.
10.刘红云,孟庆茂.(2003).纵向数据分析方法.心理科学进展,11(5),586-592.
11.刘红云,张雷.(2005).追踪数据分析方法及其应用.北京:科学教育出版社.
12.刘源,刘红云.(2015).潜变量量尺的拓展及研究展望.统计与决策,(6),8-12.
13.罗胜强,姜嬿.(2014).管理学问卷调查研究方法.重庆:重庆大学出版社.
14.孙旭,严鸣,储小平.(2014).坏心情与工作行为:中庸思维跨层次的调节作用.心理学报,46(11),1704-1718.
15.王济川,王小倩,姜宝法.(2011).结构方程模型:方法与应用.北京:高等教育出版社.
16.王孟成,叶浩生.(2014).计划缺失设计--通过有意缺失让研究更高效.心理科学进展,22(6),1025-1035.
17.张淑华,王可心.(2017).情绪、希望感与工作投入:来自经验取样法的证据.中国人力资源开发,34(11),65-75.
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