经验取样法——一种收集“真实”数据的新方法

经验取样法是一种有效的密集纵向研究数据收集方法。该方法的优点正逐渐被广大研究者所认同,国外运用经验取样法取得的研究成果越来越多,而国内学者对经验取样法的研究尚处于探索阶段,运用此方法的实证研究更是凤毛麟角。通过对《应用心理学期刊》(Journal of Applied Psychology)最近5年(2010~2014年)发表的26篇采用经验取样法的实证研究论文的内容分析,发现这些文章全面体现了经验取样法的特点、研究内容、样本量、激励方式、数据收集和操作过程等该方法的操作要素。对其进行总结不仅有利于对经验取样法在组织行为学中的应用状况进行总结,而且也能促进这一方法在未来的组织行为学和人力资源管理中的发展和应用。     

经验取样法在组织行为学中的应用

在组织行为学研究中,研究者对个体的日常经验和短期过程越来越感兴趣,经验取样法为研究这些过程提供了必要的工具。尽管近年来经验取样法的研究成果在逐渐增加,但是研究者尤其是国内研究者对该方法在组织行为学研究中所适用的主题并未有整体的认识。通过分析发表在4种组织行为学国际顶级期刊《应用心理学期刊》(Journal of Applied Psychology)、《组织行为学杂志》(Journal of Organizational Behavior)、《人事心理学》(Personnel Psychology)和《组织行为和人的决策过程》(Organizational Behavior and Human Decision Processes)中最新发表的67篇实证文章,以及国内期刊中相关的7篇成果,发现该方法主要应用在捕捉个体短时或瞬时的状态和感受,本文将目前运用该方法的前沿研究主题分为四大类,分别为工作感受、工作体验、工作行为和工作影响。     

纵向研究的取样和测量

本文对心理发展中最重要的研究类型之一,纵向研究的取样和测量问题进行了讨论.指出了研究者在使用纵向研究时可能出现的问题,并提出了相应的解决方法.     

主观幸福感测量研究

主观幸福感回溯式自陈报告测量存在一定的不足,在此基础上研究者们发展了关注时间因素的即时测量方法,如经验取样法(ESM),生态瞬时评定法(EMA),日重现法(DRM),U指数(The U-Index)等.从传统测量走向多样化测量大大提高了主观幸福感测量的生态效度,同时也为国民幸福指数的构建与评估提供了一定参考.     

IRT_Δb法和修正LR法对矩阵取样DIF检验的有效性

矩阵取样测验包含多个题册,单个题册的总分不能直接作为匹配变量用于 DIF 检测。本研究首先基于模拟数据,同时采用 I RT_Δb法,以及用 I RT模型估计的考生能力作为匹配变量修订后的 L R法对矩阵取样测验进行DIF检测,分析二者进行DIF检测的有效性及其相关影响因素;并根据已有的LR法DIF判断标准划定出I RT_Δb法分类标准;最后使用实证数据加以验证。结果显示：矩阵取样测验中, I RT_Δb法和修正LR法均能较好地区分DIF量不同的题目;样本量、题册中DIF题目的比例和考生群体间真实能力的差异对两种方法的检验力、犯I类错误的概率和分类结果都有较大影响。     

儿童心理研究中的时间取样观察法

时间取样方法是在对儿童研究的过程中逐步发展起来的一种观察策略,被视为儿童发展研究的特有方法.目前,它越来越多地运用于儿童被试量较大的各种早期发展研究之中.本文将对此方法作一简要介绍.     

眼动轨迹匹配法：一种研究决策过程的新方法

眼动轨迹匹配法是近年来新兴的一种眼动数据分析方法, 该方法包括注视数据的预处理、兴趣区划分和编码、形成眼动轨迹字符串、计算相似性得分四个步骤。研究者采用眼动轨迹匹配法对决策过程理论及其影响因素进行的探索性研究, 证实了眼动轨迹匹配法在决策领域的可行性、精确性和高价值性。未来的研究应进一步利用眼动轨迹匹配法加强对各种决策理论及其影响因素的研究, 以揭示决策的认知过程, 构建更加完善的决策理论模型。     

单字法与字表法定向遗忘的比较研究

迄今为止,定向遗忘的研究一般用单字法和字表法展开,而何种方法占优势一直是研究者们争论的焦点。本文在前人．研究的基础上,通过操作指示符变量（记住,遗忘）,尝试着揭示两种方法研究定向遗忘的特点以及机制。     

儿童图形识别取样的时空策略

本研究探讨儿童图形识别取样的时间和空间策略。被试为24名六年级学生和24名三年级学生,刺激材料为20个不规则无意义几何图形。结果发现:六年级学生采取取样空间扩展为主,取样时间调整为辅的策略,三年级学生采用增加取样时间为主,扩展取样空间为辅的取样策略。任务难度加大时,被试采用恒定取样点时间和增加取样点数目的时-空策略。在空间取样策略发展上,学生年龄和任务难度的交互影响显著。     

判断偏差分析的认知-生态取样途径

对判断偏差的研究曾主要侧重于考察个体的动机和认知缺陷,而近期从认知-生态取样角度对判断偏差进行的研究则表明,判断偏差源于人与环境的交互作用。从认知-生态取样角度分析人的判断偏差着重考察以下四方面的问题：基于样本的判断涉及的多个变量,现实环境中刺激的分布,取样过程及其结果,取样过程中的心理预设与限制。认知-生态取样角度的研究对判断偏差产生的原因提供了一种全新的解释,同时,它也存在一些亟待解决的问题     

Estimation of the Coefficient of Variation with Minimum Risk: A Sequential Method for Minimizing Sampling Error and Study Cost

The coefficient of variation is an effect size measure with many potential uses in psychology and related disciplines. We propose a general theory for a sequential estimation of the population coefficient of variation that considers both the sampling error and the study cost, importantly without specific distributional assumptions. Fixed sample size planning methods, commonly used in psychology and related fields, cannot simultaneously minimize both the sampling error and the study cost. The sequential procedure we develop is the first sequential sampling procedure developed for estimating the coefficient of variation. We first present a method of planning a pilot sample size after the research goals are specified by the researcher. Then, after collecting a sample size as large as the estimated pilot sample size, a check is performed to assess whether the conditions necessary to stop the data collection have been satisfied. If not an additional observation is collected and the check is performed again. This process continues, sequentially, until a stopping rule involving a risk function is satisfied. Our method ensures that the sampling error and the study costs are considered simultaneously so that the cost is not higher than necessary for the tolerable sampling error. We also demonstrate a variety of properties of the distribution of the final sample size for five different distributions under a variety of conditions with a Monte Carlo simulation study. In addition, we provide freely available functions via the MBESS package in R to implement the methods discussed.     

项目特征曲线等值的抽样误差

现在,等值越来越受到各考试测验机构及测量学研究人员的重视,特别是项目反应理论等值的优越性更使他们有了信心。然而,很多人却没有注意到被试能力分布形态可能给等值结果带来的影响效果及程度。本研究以项目反应理论两级记分模型的项目参数等值在不同被试能力分布形态下的结果差异作为重点,探讨被试抽样偏差可能给项目特征曲线等值带来的误差问题。研究结果表明,被试能力分布形态会显著地影响项目参数等值的系数,特别地,能力分布的偏态系数与等值方程的截距存在显著的线性相关关系,但能力分布形态的变化对等值方程中斜率的影响并不明显     

Theoretically informed qualitative psychotherapy research: A primer

This paper presents an introduction to theoretically informed qualitative psychotherapy research (QPR). Although QPR researchers have traditionally remained silent on theory, we suggest this has resulted in an implicit and unacknowledged use of theory. We argue instead for a clear articulation of qualitative researchers' theory and outline how theory can be incorporated to inform the entire qualitative research process. This approach assumes the research problem is embedded in a clearly defined and articulated theoretical framework, which also informs data collection and data analysis. We outline how researchers can use explicit theoretical frameworks to inform research question formulation, data collection and data analysis and illustrate this with specific applications of the method in practice. We believe that starting from a declared theoretical framework sets up a dialogue between the research problem, the type of data required and their meaningful analysis and interpretation. This aims not only to achieve greater depth in the final product of research, but also to enhance its utility in terms of practice; it contributes to building, altering and differentiating theory; and it allows for greater transparency by openly articulating the theoretical framework that scaffolds the entirety of the research process.     

Sampling distributions of the entropy in visual scanning

Identifying true statistical dependencies in visual-scanning data involves showing that the observed scanning pattern is significantly more ordered than that which would be produced by a stratified random-sampling model. In the past, entropy has been used as the index to measure statistical order or dependency. Due to the unknown nature of the underlying sampling distributions of entropy, however, researchers have had to use relatively less powerful nonparametric statistical tests to determine significance. In this paper we present relevant portions of the family of sampling distributions of entropy and show that they are sufficiently normally distributed to allow the use of a more powerful parametric statistical test when attempting to distinguish among the different models of sampling.     

Improving online panel data usage in sales research

The exponential expansion of the Internet has fundamentally altered the nature of personal selling and sales management. The Internet has also created significant change for sales researchers and proffered a bevy of new means of conducting research. Online panel data collection is one such means that has recently proliferated. Online panel data collection imbues a host of advantages sales researchers may reap. However, there are a proportionate number of disadvantages and concerns with collecting and using this type of data. This article delineates said benefits and drawbacks and also provides a review of recent sales-related examples employing this method of data collection. Furthermore, key considerations in utilizing online panel data are advanced. Importantly, several ways to maximize the benefits and minimize the drawbacks inherent to online panel data are provided. This article aims to provide a resource for sales scholars in optimizing their usage of online panel data.     

Experience Sampling Methods: A Modern Idiographic Approach to Personality Research

Experience sampling methods are essential tools for building a modern idiographic approach to understanding personality. These methods yield multiple snapshots of people's experiences over time in daily life and allow researchers to identify patterns of behavior within a given individual, rather than strictly identify patterns of behavior across individuals, as with standard nomothetic approaches. In this article, we discuss the origin and evolution of idiographic methods in the field of personality and explain how experience sampling methods function as modern day idiographic methods in this field. We then review four primary ways in which experience sampling methods have been used to foster idiographic approaches in personality research. Specifically, we highlight approaches that examine individual differences in temporal and behavioral distributions, situation–behavior contingencies, daily processes, and the structure of daily experience. Following a brief methodology primer, we end by discussing future directions for idiographic experience sampling approaches in personality psychology and beyond.     

Job Performance as Multivariate Dynamic Criteria: Experience Sampling and Multiway Component Analysis

Questions about the dynamic processes that drive behavior at work have been the focus of increasing attention in recent years. Models describing behavior at work and research on momentary behavior indicate that substantial variation exists within individuals. This article examines the rationale behind this body of work and explores a method of analyzing momentary work behavior using experience sampling methods. The article also examines a previously unused set of methods for analyzing data produced by experience sampling. These methods are known collectively as multiway component analysis. Two archetypal techniques of multimode factor analysis, the Parallel factor analysis and the Tucker3 models, are used to analyze data from Miner, Glomb, and Hulin's (2010) experience sampling study of work behavior. The efficacy of these techniques for analyzing experience sampling data is discussed as are the substantive multimode component models obtained.     

Online Data Collection: Strategies for Research

Online data collection, through e‐mail and Web‐based surveys, is becoming an increasingly popular research methodology. In this article, the authors outline the benefits and limitations of this type of data collection to help researchers determine whether their data could be collected online in a way that retains the integrity of the data. A detailed procedure, including strategies to manage limitations, is given for researchers wishing to conduct their own online surveys.     

Sampling Development

Research in developmental psychology requires sampling at different time points. Accurate depictions of developmental change provide a foundation for further empirical studies and theories about developmental mechanisms. However, overreliance on widely spaced sampling intervals in cross-sectional and longitudinal designs threatens the validity of the enterprise. This article discusses how to sample development in order to accurately discern the shape of developmental change. The ideal solution is daunting: to summarize behavior over 24-hour intervals and collect daily samples over the critical periods of change. We discuss the magnitude of errors due to undersampling, and the risks associated with oversampling. When daily sampling is not feasible, we offer suggestions for sampling methods that can provide preliminary reference points and provisional sketches of the general shape of a developmental trajectory. Denser sampling then can be applied strategically during periods of enhanced variability, inflections in the rate of developmental change, or in relation to key events or processes that may affect the course of change. Despite the challenges of dense repeated sampling, researchers must take seriously the problem of sampling on a developmental time scale if we are to know the true shape of developmental change.     

A Computationally More Efficient and More Accurate Stepwise Approach for Correcting for Sampling Error and Measurement Error

Over the last decade or two, multilevel structural equation modeling (ML-SEM) has become a prominent modeling approach in the social sciences because it allows researchers to correct for sampling and measurement errors and thus to estimate the effects of Level 2 (L2) constructs without bias. Because the latent variable modeling software Mplus uses maximum likelihood (ML) by default, many researchers in the social sciences have applied ML to obtain estimates of L2 regression coefficients. However, one drawback of ML is that covariance matrices of the predictor variables at L2 tend to be degenerate, and thus, estimates of L2 regression coefficients tend to be rather inaccurate when sample sizes are small. In this article, I show how an approach for stabilizing covariance matrices at L2 can be used to obtain more accurate estimates of L2 regression coefficients. A simulation study is conducted to compare the proposed approach with ML, and I illustrate its application with an example from organizational research.