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Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data. 比较交叉分类数据的随机效应模型、普通最小二乘法或带有聚类稳健标准误差的固定效应。
IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-12-01 Epub Date: 2023-03-09 DOI: 10.1037/met0000538
Young Ri Lee, James E Pustejovsky

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

交叉分类随机效应建模(CCREM)是心理学、教育研究和其他领域分析交叉分类数据的常用方法。然而,当研究的重点是第 1 层的回归系数而不是随机效应时,普通最小二乘回归与聚类稳健方差估计(OLS-CRVE)或固定效应回归与 CRVE(FE-CRVE)可能是合适的方法。这些替代方法具有潜在优势,因为它们所依赖的假设条件比 CCREM 所要求的要弱。我们进行了蒙特卡罗模拟研究,比较了 CCREM、OLS-CRVE 和 FE-CRVE 在模型中的表现,包括同方差假设和外生性假设成立的条件和违反这些假设的条件,以及未建模随机斜率的条件。我们发现,当 CCREM 的假设条件全部满足时,其性能优于其他方法。然而,当违反同方差假定时,OLS-CRVE 和 FE-CRVE 的表现与 CCREM 相似或更好。当违反外生性假设时,只有 FE-CRVE 提供了足够的性能。此外,在存在未建模随机斜率的情况下,OLS-CRVE 和 FE-CRVE 比 CCREM 提供了更准确的推断。因此,我们推荐双向 FE-CRVE 作为 CCREM 的良好替代方案,尤其是在 CCREM 的同方差或外生性假设可能存在疑问的情况下。(PsycInfo Database Record (c) 2023 APA, 版权所有)。
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引用次数: 0
Improving hierarchical models of individual differences: An extension of Goldberg's bass-ackward method. 改进个体差异的层次模型:戈德伯格低音后退法的扩展。
IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-12-01 Epub Date: 2023-02-13 DOI: 10.1037/met0000546
Miriam K Forbes

Goldberg's (2006) bass-ackward approach to elucidating the hierarchical structure of individual differences data has been used widely to improve our understanding of the relationships among constructs of varying levels of granularity. The traditional approach has been to extract a single component or factor on the first level of the hierarchy, two on the second level, and so on, treating the correlations between adjoining levels akin to path coefficients in a hierarchical structure. This article proposes three modifications to the traditional approach with a particular focus on examining associations among all levels of the hierarchy: (a) identify and remove redundant elements that perpetuate through multiple levels of the hierarchy; (b) (optionally) identify and remove artefactual elements; and (c) plot the strongest correlations among the remaining elements to identify their hierarchical associations. Together these steps can offer a simpler and more complete picture of the underlying hierarchical structure among a set of observed variables. The rationale for each step is described, illustrated in a hypothetical example and three basic simulations, and then applied in real data. The results are compared with the traditional bass-ackward approach together with agglomerative hierarchical cluster analysis, and a basic tutorial with code is provided to apply the extended bass-ackward approach in other data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

戈德伯格(Goldberg,2006 年)提出的 "低位后向"(bass-ackward)方法被广泛应用于阐明个体差异数据的层次结构,以提高我们对不同粒度结构之间关系的理解。传统的方法是在层次结构的第一层提取一个成分或因子,在第二层提取两个成分或因子,以此类推,将相邻层次之间的相关性视为层次结构中的路径系数。本文对传统方法提出了三项修改建议,尤其侧重于研究层次结构各层次之间的关联:(a) 识别并移除在层次结构多层次中延续的冗余要素;(b) (可选)识别并移除伪要素;(c) 绘制剩余要素之间的最强关联图,以识别其层次关联。这些步骤结合在一起,可以更简单、更完整地描述一组观测变量之间的潜在层次结构。本文介绍了每个步骤的基本原理,通过一个假设例子和三个基本模拟进行了说明,然后将其应用于真实数据中。将结果与传统的后向基数法和聚类分层聚类分析进行了比较,并提供了在其他数据中应用扩展后向基数法的基本教程和代码。(PsycInfo Database Record (c) 2023 APA, 版权所有)。
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引用次数: 0
Ubiquitous bias and false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties. 统计交互作用分析中因模型错误规范而导致的无处不在的偏差和错误发现:结果分布和度量特性的作用。
IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-12-01 Epub Date: 2022-10-06 DOI: 10.1037/met0000532
Benjamin W Domingue, Klint Kanopka, Sam Trejo, Mijke Rhemtulla, Elliot M Tucker-Drob

Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable. We begin by illustrating that, for a variety of noncontinuously distributed outcomes (i.e., binary and count outcomes), attempts to use the linear model for recovery leads to catastrophic levels of bias and false discovery. Next, focusing on transformations of normally distributed variables (i.e., censoring and noninterval scaling), we show that linear models again produce spurious interaction effects. We provide explanations offering geometric and algebraic intuition as to why interactions are a challenge for these incorrectly specified models. In light of these findings, we make two specific recommendations. First, a careful consideration of the outcome's distributional properties should be a standard component of interaction studies. Second, researchers should approach research focusing on interactions with heightened levels of scrutiny. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

对交互作用效应的研究非常有意义,因为这些研究可以确定预测因素之间在解释结果方面的重要相互作用。以往的研究已经考虑了交互作用研究中统计偏差和实质性误解的几个潜在来源,但较少关注结果变量在此类研究中的作用。在此,我们将根据结果变量的分布和度量特性,考虑与交互作用参数估计相关的偏差和错误发现。我们首先说明,对于各种非连续分布的结果(即二元结果和计数结果),尝试使用线性模型进行复原会导致灾难性的偏差和错误发现。接下来,我们重点讨论了正态分布变量的转换(即删减和非区间缩放),结果表明线性模型再次产生了虚假的交互效应。我们提供了几何和代数直观的解释,说明为什么交互作用对这些指定不正确的模型是一个挑战。根据这些发现,我们提出了两项具体建议。首先,仔细考虑结果的分布特性应该成为交互作用研究的标准组成部分。其次,研究人员应加强对交互作用研究的审查。(PsycInfo Database Record (c) 2022 APA, 版权所有)。
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引用次数: 0
Why multiple hypothesis test corrections provide poor control of false positives in the real world. 为什么多重假设检验校正在现实世界中无法很好地控制假阳性?
IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-11-21 DOI: 10.1037/met0000678
Stanley E Lazic

Most scientific disciplines use significance testing to draw conclusions about experimental or observational data. This classical approach provides a theoretical guarantee for controlling the number of false positives across a set of hypothesis tests, making it an appealing framework for scientists seeking to limit the number of false effects or associations that they claim to observe. Unfortunately, this theoretical guarantee applies to few experiments, and the true false positive rate (FPR) is much higher. Scientists have plenty of freedom to choose the error rate to control, the tests to include in the adjustment, and the method of correction, making strong error control difficult to attain. In addition, hypotheses are often tested after finding unexpected relationships or patterns, the data are analyzed in several ways, and analyses may be run repeatedly as data accumulate. As a result, adjusted p values are too small, incorrect conclusions are often reached, and results are harder to reproduce. In the following, I argue why the FPR is rarely controlled meaningfully and why shrinking parameter estimates is preferable to p value adjustments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

大多数科学学科都使用显著性检验来得出实验或观察数据的结论。这种经典方法为控制一组假设检验中的假阳性数量提供了理论保证,因此对于寻求限制他们声称观察到的假效应或假关联数量的科学家来说,它是一个很有吸引力的框架。遗憾的是,这种理论保证只适用于极少数实验,真正的假阳性率(FPR)要高得多。科学家有很大的自由度来选择要控制的误差率、纳入调整的检验项目以及校正方法,因此很难实现强有力的误差控制。此外,假设往往是在发现意想不到的关系或模式后才进行检验的,数据分析有多种方式,而且随着数据的积累,分析可能会反复进行。因此,调整后的 p 值过小,往往会得出不正确的结论,结果也更难重现。在下文中,我将论证为什么很少对 FPR 进行有意义的控制,以及为什么缩小参数估计比调整 p 值更可取。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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引用次数: 0
Simulation studies for methodological research in psychology: A standardized template for planning, preregistration, and reporting. 心理学方法研究的模拟研究:计划、预先登记和报告的标准化模板。
IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-11-14 DOI: 10.1037/met0000695
Björn S Siepe, František Bartoš, Tim P Morris, Anne-Laure Boulesteix, Daniel W Heck, Samuel Pawel

Simulation studies are widely used for evaluating the performance of statistical methods in psychology. However, the quality of simulation studies can vary widely in terms of their design, execution, and reporting. In order to assess the quality of typical simulation studies in psychology, we reviewed 321 articles published in Psychological Methods, Behavior Research Methods, and Multivariate Behavioral Research in 2021 and 2022, among which 100/321 = 31.2% report a simulation study. We find that many articles do not provide complete and transparent information about key aspects of the study, such as justifications for the number of simulation repetitions, Monte Carlo uncertainty estimates, or code and data to reproduce the simulation studies. To address this problem, we provide a summary of the ADEMP (aims, data-generating mechanism, estimands and other targets, methods, performance measures) design and reporting framework from Morris et al. (2019) adapted to simulation studies in psychology. Based on this framework, we provide ADEMP-PreReg, a step-by-step template for researchers to use when designing, potentially preregistering, and reporting their simulation studies. We give formulae for estimating common performance measures, their Monte Carlo standard errors, and for calculating the number of simulation repetitions to achieve a desired Monte Carlo standard error. Finally, we give a detailed tutorial on how to apply the ADEMP framework in practice using an example simulation study on the evaluation of methods for the analysis of pre-post measurement experiments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

模拟研究被广泛用于评估心理学统计方法的性能。然而,模拟研究在设计、执行和报告方面的质量可能存在很大差异。为了评估心理学中典型模拟研究的质量,我们查阅了 2021 年和 2022 年发表在《心理学方法》、《行为研究方法》和《多元行为研究》上的 321 篇文章,其中 100/321 = 31.2% 的文章报告了模拟研究。我们发现,许多文章没有提供完整、透明的研究关键方面的信息,如模拟重复次数的理由、蒙特卡罗不确定性估计或重现模拟研究的代码和数据。为了解决这个问题,我们总结了 Morris 等人(2019 年)的 ADEMP(目的、数据生成机制、估计值和其他目标、方法、绩效衡量)设计和报告框架,并将其调整为心理学中的模拟研究。在此框架基础上,我们提供了 ADEMP-PreReg,一个供研究人员在设计、预注册和报告模拟研究时使用的分步模板。我们给出了估算常见性能指标、其蒙特卡洛标准误差以及计算达到所需的蒙特卡洛标准误差所需的模拟重复次数的公式。最后,我们通过一个关于事后测量实验分析方法评估的模拟研究实例,详细介绍了如何在实践中应用 ADEMP 框架。(PsycInfo Database Record (c) 2024 APA, all rights reserved)。
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引用次数: 0
Item response theory-based continuous test norming. 以项目反应理论为基础的连续测试规范化。
IF 7 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-10-14 DOI: 10.1037/met0000686
Hannah M Heister,Casper J Albers,Marie Wiberg,Marieke E Timmerman
In norm-referenced psychological testing, an individual's performance is expressed in relation to a reference population using a standardized score, like an intelligence quotient score. The reference population can depend on a continuous variable, like age. Current continuous norming methods transform the raw score into an age-dependent standardized score. Such methods have the shortcoming to solely rely on the raw test scores, ignoring valuable information from individual item responses. Instead of modeling the raw test scores, we propose modeling the item scores with a Bayesian two-parameter logistic (2PL) item response theory model with age-dependent mean and variance of the latent trait distribution, 2PL-norm for short. Norms are then derived using the estimated latent trait score and the age-dependent distribution parameters. Simulations show that 2PL-norms are overall more accurate than those from the most popular raw score-based norming methods cNORM and generalized additive models for location, scale, and shape (GAMLSS). Furthermore, the credible intervals of 2PL-norm exhibit clearly superior coverage over the confidence intervals of the raw score-based methods. The only issue of 2PL-norm is its slightly lower performance at the tails of the norms. Among the raw score-based norming methods, GAMLSS outperforms cNORM. For empirical practice this suggests the use of 2PL-norm, if the model assumptions hold. If not, or the interest is solely in the point estimates of the extreme trait positions, GAMLSS-based norming is a better alternative. The use of the 2PL-norm is illustrated and compared with GAMLSS and cNORM using empirical data, and code is provided, so that users can readily apply 2PL-norm to their normative data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
在常模参照心理测验中,一个人的表现是用一个标准化的分数(如智商分数)来表示与参照人群的关系。参照群体可以是连续变量,如年龄。目前的连续常模方法是将原始分数转换成与年龄相关的标准化分数。这种方法的缺点是只依赖原始测验分数,而忽略了单个项目反应的宝贵信息。我们建议用贝叶斯双参数对数(2PL)项目反应理论模型(简称 2PL-norm)代替原始测验分数建模,该模型的潜在特质分布的均值和方差与年龄相关。然后,利用估计的潜在特质得分和与年龄相关的分布参数得出常模。模拟结果表明,2PL-标准总体上比最流行的基于原始分数的标准方法 cNORM 和位置、尺度和形状的广义加法模型(GAMLSS)更准确。此外,2PL-norm 的可信区间明显优于基于原始分数方法的置信区间。2PL-norm 的唯一问题是其在规范尾部的性能略低。在基于原始分数的规范化方法中,GAMLSS 的表现优于 cNORM。在经验实践中,如果模型假设成立,则建议使用 2PL-norm 方法。如果模型假设不成立,或者只对极端性状位置的点估计感兴趣,那么基于 GAMLSS 的规范化方法是更好的选择。本文使用经验数据对 2PL-norm 的使用进行了说明,并与 GAMLSS 和 cNORM 进行了比较,同时还提供了代码,以便用户可以随时将 2PL-norm 应用于他们的常模数据。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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引用次数: 0
Comments on the measurement of effect sizes for indirect effects in Bayesian analysis of variance. 关于贝叶斯方差分析中间接效应效应大小测量的评论。
IF 7 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-10-10 DOI: 10.1037/met0000706
Sang-June Park,Youjae Yi
Bayesian analysis of variance (BANOVA), implemented through R packages, offers a Bayesian approach to analyzing experimental data. A tutorial in Psychological Methods extensively documents BANOVA. This note critically examines a method for evaluating mediation using partial eta-squared as an effect size measure within the BANOVA framework. We first identify an error in the formula for partial eta-squared and propose a corrected version. Subsequently, we discuss limitations in the interpretability of this effect size measure, drawing on previous research, and argue for its potential unsuitability in assessing indirect effects in mediation analysis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
贝叶斯方差分析(BANOVA)通过 R 软件包实现,为分析实验数据提供了一种贝叶斯方法。心理学方法》(Psychological Methods)一书中的教程广泛介绍了贝叶斯方差分析。本说明批判性地研究了在 BANOVA 框架内使用部分 eta-squared 作为效应大小度量来评估中介作用的方法。我们首先指出了部分等方公式中的一个错误,并提出了一个更正版本。随后,我们借鉴以往的研究,讨论了这种效应大小测量方法在可解释性方面的局限性,并论证了它可能不适合在中介分析中评估间接效应。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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引用次数: 0
The potential of preregistration in psychology: Assessing preregistration producibility and preregistration-study consistency. 心理学预注册的潜力:评估注册前的可生成性和注册前研究的一致性。
IF 7 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-10-10 DOI: 10.1037/met0000687
Olmo R van den Akker,Marjan Bakker,Marcel A L M van Assen,Charlotte R Pennington,Leone Verweij,Mahmoud M Elsherif,Aline Claesen,Stefan D M Gaillard,Siu Kit Yeung,Jan-Luca Frankenberger,Kai Krautter,Jamie P Cockcroft,Katharina S Kreuer,Thomas Rhys Evans,Frédérique M Heppel,Sarah F Schoch,Max Korbmacher,Yuki Yamada,Nihan Albayrak-Aydemir,Shilaan Alzahawi,Alexandra Sarafoglou,Maksim M Sitnikov,Filip Děchtěrenko,Sophia Wingen,Sandra Grinschgl,Helena Hartmann,Suzanne L K Stewart,Cátia M F de Oliveira,Sarah Ashcroft-Jones,Bradley J Baker,Jelte M Wicherts
Study preregistration has become increasingly popular in psychology, but its potential to restrict researcher degrees of freedom has not yet been empirically verified. We used an extensive protocol to assess the producibility (i.e., the degree to which a study can be properly conducted based on the available information) of preregistrations and the consistency between preregistrations and their corresponding papers for 300 psychology studies. We found that preregistrations often lack methodological details and that undisclosed deviations from preregistered plans are frequent. These results highlight that biases due to researcher degrees of freedom remain possible in many preregistered studies. More comprehensive registration templates typically yielded more producible preregistrations. We did not find that the producibility and consistency of preregistrations differed over time or between original and replication studies. Furthermore, we found that operationalizations of variables were generally preregistered more producible and consistently than other study parts. Inconsistencies between preregistrations and published studies were mainly encountered for data collection procedures, statistical models, and exclusion criteria. Our results indicate that, to unlock the full potential of preregistration, researchers in psychology should aim to write more producible preregistrations, adhere to these preregistrations more faithfully, and more transparently report any deviations from their preregistrations. This could be facilitated by training and education to improve preregistration skills, as well as the development of more comprehensive templates. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
研究预注册在心理学界越来越流行,但其限制研究者自由度的潜力尚未得到经验验证。我们使用了一个广泛的协议来评估 300 项心理学研究的预注册的可生成性(即根据现有信息可以正确开展研究的程度)以及预注册与相应论文之间的一致性。我们发现,预注册往往缺乏方法论细节,未披露的偏离预注册计划的情况也很常见。这些结果突出表明,在许多预先登记的研究中,研究人员的自由度仍然可能导致偏差。更全面的注册模板通常会产生更多可制作的预注册。我们没有发现预注册的可制作性和一致性随时间推移或在原始研究和复制研究之间存在差异。此外,我们还发现,与其他研究部分相比,变量的操作化预置通常更容易制作,一致性也更高。预登记与已发表研究之间的不一致主要体现在数据收集程序、统计模型和排除标准上。我们的研究结果表明,为了充分发挥预注册的潜力,心理学研究人员应致力于撰写更多可制作的预注册,更忠实地遵守这些预注册,并更透明地报告与预注册之间的任何偏差。这可以通过培训和教育来提高预注册技能,以及开发更全面的模板来实现。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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引用次数: 0
Lagged multidimensional recurrence quantification analysis for determining leader-follower relationships within multidimensional time series. 用于确定多维时间序列中领导者与追随者关系的滞后多维复现量化分析。
IF 7 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-10-10 DOI: 10.1037/met0000691
Alon Tomashin,Ilanit Gordon,Giuseppe Leonardi,Yair Berson,Nir Milstein,Matthias Ziegler,Ursula Hess,Sebastian Wallot
The current article introduces lagged multidimensional recurrence quantification analysis. The method is an extension of multidimensional recurrence quantification analysis and allows to quantify the joint dynamics of multivariate time series and to investigate leader-follower relationships in behavioral and physiological data. Moreover, the method enables the quantification of the joint dynamics of a group, when such leader-follower relationships are taken into account. We first provide a formal presentation of the method, and then apply it to synthetic data, as well as data sets from joint action research, investigating the shared dynamics of facial expression and beats-per-minute recordings within different groups. A wrapper function is included, for applying the method together with the "crqa" package in R. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
本文介绍了滞后多维复现量化分析。该方法是多维复现量化分析的扩展,可以量化多变量时间序列的联合动态,研究行为和生理数据中的领导者与追随者关系。此外,如果考虑到这种领导者与追随者的关系,该方法还能量化群体的联合动态。我们首先对该方法进行了正式介绍,然后将其应用于合成数据以及来自联合行动研究的数据集,调查不同群体中面部表情和每分钟节拍记录的共享动态。我们还提供了一个封装函数,用于将该方法与 R 中的 "crqa "软件包一起应用(PsycInfo Database Record (c) 2024 APA, all rights reserved)。
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引用次数: 0
Harvesting heterogeneity: Selective expertise versus machine learning. 收获异质性:选择性专业知识与机器学习。
IF 7 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Pub Date : 2024-10-07 DOI: 10.1037/met0000640
Rumen Iliev,Alex Filipowicz,Francine Chen,Nikos Arechiga,Scott Carter,Emily Sumner,Totte Harinen,Kate Sieck,Kent Lyons,Charlene Wu
The heterogeneity of outcomes in behavioral research has long been perceived as a challenge for the validity of various theoretical models. More recently, however, researchers have started perceiving heterogeneity as something that needs to be not only acknowledged but also actively addressed, particularly in applied research. A serious challenge, however, is that classical psychological methods are not well suited for making practical recommendations when heterogeneous outcomes are expected. In this article, we argue that heterogeneity requires a separation between basic and applied behavioral methods, and between different types of behavioral expertise. We propose a novel framework for evaluating behavioral expertise and suggest that selective expertise can easily be automated via various machine learning methods. We illustrate the value of our framework via an empirical study of the preferences towards battery electric vehicles. Our results suggest that a basic multiarm bandit algorithm vastly outperforms human expertise in selecting the best interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
长期以来,行为研究结果的异质性一直被认为是对各种理论模型有效性的挑战。但最近,研究人员开始认识到,异质性不仅需要承认,而且需要积极应对,尤其是在应用研究中。然而,一个严峻的挑战是,当预期会出现异质性结果时,经典的心理学方法并不适合提出切实可行的建议。在本文中,我们认为异质性要求将基础行为学方法与应用行为学方法以及不同类型的行为学专业知识区分开来。我们提出了一个新颖的行为专业知识评估框架,并认为选择性专业知识可以通过各种机器学习方法轻松实现自动化。我们通过对电池电动汽车偏好的实证研究来说明我们框架的价值。我们的研究结果表明,在选择最佳干预措施方面,基本的多臂强盗算法大大优于人类的专业知识。(PsycInfo Database Record (c) 2024 APA, all rights reserved)。
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引用次数: 0
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Psychological methods
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