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Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary. 当结果是二元时,为什么不能使用系数差法估计中介效应?
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-29 DOI: 10.1080/00273171.2024.2418515
Judith J M Rijnhart, Matthew J Valente, David P MacKinnon

Despite previous warnings against the use of the difference-in-coefficients method for estimating the indirect effect when the outcome in the mediation model is binary, the difference-in-coefficients method remains readily used in a variety of fields. The continued use of this method is presumably because of the lack of awareness that this method conflates the indirect effect estimate and non-collapsibility. In this paper, we aim to demonstrate the problems associated with the difference-in-coefficients method for estimating indirect effects for mediation models with binary outcomes. We provide a formula that decomposes the difference-in-coefficients estimate into (1) an estimate of non-collapsibility, and (2) an indirect effect estimate. We use a simulation study and an empirical data example to illustrate the impact of non-collapsibility on the difference-in-coefficients estimate of the indirect effect. Further, we demonstrate the application of several alternative methods for estimating the indirect effect, including the product-of-coefficients method and regression-based causal mediation analysis. The results emphasize the importance of choosing a method for estimating the indirect effect that is not affected by non-collapsibility.

尽管以前有人警告过,当中介模型中的结果是二元的时候,不要使用系数差法来估计间接效应,但系数差法仍然被广泛应用于各个领域。之所以继续使用这种方法,大概是因为人们没有意识到这种方法混淆了间接效应估计和非可比性。在本文中,我们旨在说明用系数差法估计二元结果中介模型间接效应的相关问题。我们提供了一个公式,将系数差估计值分解为(1)非可比性估计值和(2)间接效应估计值。我们使用一个模拟研究和一个经验数据示例来说明非可比性对间接效应的系数差估计值的影响。此外,我们还演示了几种间接效应估计替代方法的应用,包括系数乘积法和基于回归的因果中介分析。结果强调了选择不受非可比性影响的间接效应估计方法的重要性。
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引用次数: 0
A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores. 从因果角度看缺失数据估算中的偏差:邪恶辅助变量对测验分数规范化的影响。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-20 DOI: 10.1080/00273171.2024.2412682
Erik Sengewald, Katinka Hardt, Marie-Ann Sengewald

Among the most important merits of modern missing data techniques such as multiple imputation (MI) and full-information maximum likelihood estimation is the possibility to include additional information about the missingness process via auxiliary variables. During the past decade, the choice of auxiliary variables has been investigated under a variety of different conditions and more recent research points to the potentially biasing effect of certain auxiliary variables, particularly colliders (Thoemmes & Rose, 2014). In this article, we further extend biasing mechanisms of certain auxiliary variables considered in previous research and thereby focus on their effects on individual diagnosis based on norming, in which the whole distribution of a variable is of interest rather than average coefficients (e.g., means). For this, we first provide the theoretical underpinnings of the mechanisms under study and then provide two focused simulations that (i) directly expand on the collider scenario in Thoemmes and Rose (2014, appendix A) by considering outcomes that are relevant to norming and (ii) extend the scenarios under consideration by instrumental variable mechanisms. We illustrate the bias mechanisms for two different norming approaches and exemplify the procedures by means of an empirical example. We end by discussing limitations and implications of our research.

多重估算(MI)和全信息最大似然估计等现代缺失数据技术的最重要优点之一,是可以通过辅助变量纳入有关缺失过程的额外信息。过去十年间,人们在各种不同条件下对辅助变量的选择进行了研究,最近的研究指出某些辅助变量,特别是对撞机可能会产生偏差效应(Thoemmes & Rose, 2014)。在本文中,我们将进一步扩展之前研究中考虑的某些辅助变量的偏差机制,从而关注它们对基于规范化的个体诊断的影响,在规范化中,我们关注的是变量的整体分布,而不是平均系数(如均值)。为此,我们首先提供了所研究机制的理论基础,然后提供了两个重点模拟:(i) 直接扩展 Thoemmes 和 Rose(2014 年,附录 A)中的对撞机情景,考虑与规范化相关的结果;(ii) 通过工具变量机制扩展所考虑的情景。我们说明了两种不同规范化方法的偏差机制,并通过一个实证例子举例说明了程序。最后,我们将讨论我们研究的局限性和影响。
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引用次数: 0
Make Some Noise: Generating Data from Imperfect Factor Models. 制造噪音从不完全性因子模型中生成数据。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-16 DOI: 10.1080/00273171.2024.2410760
Justin D Kracht, Niels G Waller

Researchers simulating covariance structure models sometimes add model error to their data to produce model misfit. Presently, the most popular methods for generating error-perturbed data are those by Tucker, Koopman, and Linn (TKL), Cudeck and Browne (CB), and Wu and Browne (WB). Although all of these methods include parameters that control the degree of model misfit, none can generate data that reproduce multiple fit indices. To address this issue, we describe a multiple-target TKL method that can generate error-perturbed data that will reproduce target RMSEA and CFI values either individually or together. To evaluate this method, we simulated error-perturbed correlation matrices for an array of factor analysis models using the multiple-target TKL method, the CB method, and the WB method. Our results indicated that the multiple-target TKL method produced solutions with RMSEA and CFI values that were closer to their target values than those of the alternative methods. Thus, the multiple-target TKL method should be a useful tool for researchers who wish to generate error-perturbed correlation matrices with a known degree of model error. All functions that are described in this work are available in the fungible R library. Additional materials (e.g., R code, supplemental results) are available at https://osf.io/vxr8d/.

模拟协方差结构模型的研究人员有时会在数据中加入模型误差,以产生模型失配。目前,最流行的误差扰动数据生成方法是 Tucker、Koopman 和 Linn(TKL)、Cudeck 和 Browne(CB)以及 Wu 和 Browne(WB)的方法。虽然所有这些方法都包含控制模型不拟合程度的参数,但没有一种方法能生成重现多重拟合指数的数据。为了解决这个问题,我们介绍了一种多目标 TKL 方法,它可以生成误差扰动数据,从而单独或共同再现目标 RMSEA 和 CFI 值。为了评估这种方法,我们使用多目标 TKL 方法、CB 方法和 WB 方法模拟了一系列因子分析模型的误差扰动相关矩阵。结果表明,与其他方法相比,多目标 TKL 方法产生的解的 RMSEA 值和 CFI 值更接近目标值。因此,多目标 TKL 方法对于希望生成具有已知模型误差的误差扰动相关矩阵的研究人员来说,应该是一个有用的工具。本研究中描述的所有函数均可在可互换的 R 库中找到。更多资料(如 R 代码、补充结果)可从 https://osf.io/vxr8d/ 获取。
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引用次数: 0
Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling. 探索心理网络建模中降低维度的估算程序。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-16 DOI: 10.1080/00273171.2024.2395941
Dingjing Shi, Alexander P Christensen, Eric Anthony Day, Hudson F Golino, Luis Eduardo Garrido

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

要理解心理数据,研究变量的结构和维度至关重要。在本研究中,我们研究了网络心理测量模型中基于传统 GLASSO 的探索性图分析(EGA)的替代估计算法,以评估数据的维度结构。研究采用贝叶斯共轭或杰弗里斯先验来估计图结构,然后使用卢万群落检测算法来划分和识别节点群,从而检测出多维和单维因子结构。蒙特卡罗模拟表明,与基于 GLASSO 的 EGA 和传统的并行分析(PA)相比,这两种贝叶斯估计算法的性能相当或更好。在估计多维因子结构时,基于分析的方法(即 EGA.analytical)在准确性和平均偏差/绝对误差之间表现出最佳平衡,准确性与 EGA 并列最高,但误差最小。与 PA 相比,基于采样的方法(EGA.采样)精度更高,误差更小;与 EGA 相比,精度较低,但误差也较小。在不同的数据条件下,这两种算法的技术比 EGA 和 PA 具有更稳定的性能。在估计单维结构时,PA 技术表现最好,紧随其后的是 EGA,然后是 EGA.分析和 EGA.采样。此外,研究还探索了四种完整的贝叶斯技术,以评估网络心理测量学中的维度。结果表明,在样本量较小的情况下,使用贝叶斯假设检验或推导图结构的后验样本时,效果更佳。研究建议使用 EGA.分析技术作为评估维度的替代工具,并主张将 EGA.抽样方法作为一种有价值的替代技术。研究结果还表明,将基于正则化的网络建模 EGA 方法扩展到贝叶斯框架取得了令人鼓舞的成果,并讨论了这一工作领域的未来方向。该研究以 R 语言中的两个经验实例说明了这些技术的实际应用。
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引用次数: 0
A Review of Some of the History of Factorial Invariance and Differential Item Functioning. 因子不变性和差异项目功能的部分历史回顾。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-12 DOI: 10.1080/00273171.2024.2396148
David Thissen
The concept of factorial invariance has evolved since it originated in the 1930s as a criterion for the usefulness of the multiple factor model; it has become a form of analysis supporting the validity of inferences about group differences on underlying latent variables. The analysis of differential item functioning (DIF) arose in the literature of item response theory (IRT), where its original purpose was the detection and removal of test items that are differentially difficult for, or biased against, one subpopulation or another. The two traditions merge at the level of the underlying latent variable model, but their separate origins and different purposes have led them to differ in details of terminology and procedure. This review traces some aspects of the histories of the two traditions, ultimately drawing some conclusions about how analysts may draw on elements of both, and how the nature of the research question determines the procedures used. Whether statistical tests are grouped by parameter (as in studies of factorial invariance) or across parameters by variable (as in DIF analysis) depends on the context and is independent of the model, as are subtle aspects of the order of the tests. In any case in which DIF or partial invariance is a possibility, the invariant parameters, or anchor items in DIF analysis, are best selected in an interplay between the statistics and judgment about what is being measured.
因子不变量的概念起源于 20 世纪 30 年代,当时是衡量多因子模型是否有用的一个标准;如今,它已发展成为一种分析形式,支持对潜在变量的群体差异进行有效性推断。差异项目功能(DIF)分析产生于项目反应理论(IRT)的文献中,其最初目的是检测和去除对一个或另一个亚群有不同难度或偏见的测试项目。这两个传统在潜在变量模型的层面上是一致的,但它们各自的起源和不同的目的导致它们在术语和程序的细节上有所不同。本综述将追溯这两种传统的某些历史方面,最终得出一些结论,即分析人员如何借鉴这两种传统的要素,以及研究问题的性质如何决定所使用的程序。统计检验是按参数分组(如因子不变量研究)还是按变量跨参数分组(如 DIF 分析)取决于具体情况,与模型无关,检验顺序的微妙之处也是如此。在任何可能存在 DIF 或部分不变量的情况下,不变量参数或 DIF 分析中的锚项最好是在统计数据和对测量内容的判断之间进行选择。
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引用次数: 0
Determining Sample Size Requirements in EFA Solutions: A Simple Empirical Proposal. 确定 EFA 解决方案中的样本量要求:一个简单的经验建议
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-05-08 DOI: 10.1080/00273171.2024.2342324
Urbano Lorenzo-Seva, Pere J Ferrando

In unrestricted or exploratory factor analysis (EFA), there is a wide range of recommendations about the size samples should be to attain correct and stable solutions. In general, however, these recommendations are either rules of thumb or based on simulation results. As it is hard to establish the extent to which a particular data set suits the conditions used in a simulation study, the advice produced by simulation studies is not direct enough to be of practical use. Instead of trying to provide general and complex recommendations, in this article, we propose to estimate the sample size that is needed to analyze a data set at hand. The estimation takes into account the specified EFA model. The proposal is based on an intensive simulation process in which the sample correlation matrix is used as a basis for generating data sets from a pseudo-population in which the parent correlation holds exactly, and the criterion for determining the size required is a threshold that quantifies the closeness between the pseudo-population and the sample reproduced correlation matrices. The simulation results suggest that the proposal works well and that the determinants identified agree with those in the literature.

在非限制性或探索性因子分析(EFA)中,有很多关于样本大小的建议,以获得正确稳定的解。但一般来说,这些建议要么是经验法则,要么是基于模拟结果。由于很难确定特定数据集在多大程度上符合模拟研究中使用的条件,因此模拟研究提出的建议不够直接,没有实际用途。本文建议估算分析手头数据集所需的样本量,而不是试图提供笼统而复杂的建议。估算时要考虑到指定的 EFA 模型。该建议基于一个密集的模拟过程,在此过程中,样本相关矩阵被用作从父相关性完全成立的伪群体中生成数据集的基础,而确定所需规模的标准是一个阈值,该阈值量化了伪群体与样本再现相关矩阵之间的接近程度。模拟结果表明,该建议运行良好,所确定的决定因素与文献中的决定因素一致。
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引用次数: 0
Using Conditional Entropy Networks of Ordinal Measures to Examine Changes in Self-Worth Among Adolescent Students in High School. 使用条件熵网络正序计量法研究高中青少年学生自我价值的变化。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-07-12 DOI: 10.1080/00273171.2024.2372635
Emanuela Furfaro, Fushing Hsieh, Maureen R Weiss, Emilio Ferrer

We implement an analytic approach for ordinal measures and we use it to investigate the structure and the changes over time of self-worth in a sample of adolescents students in high school. We represent the variations in self-worth and its various sub-domains using entropy-based measures that capture the observed uncertainty. We then study the evolution of the entropy across four time points throughout a semester of high school. Our analytic approach yields information about the configuration of the various dimensions of the self together with time-related changes and associations among these dimensions. We represent the results using a network that depicts self-worth changes over time. This approach also identifies groups of adolescent students who show different patterns of associations, thus emphasizing the need to consider heterogeneity in the data.

我们采用了一种分析方法来进行序数测量,并用它来研究高中青少年学生样本中自我价值的结构及其随时间的变化。我们使用基于熵的测量方法来表示自我价值及其各个子域的变化,从而捕捉观察到的不确定性。然后,我们研究了高中一学期中四个时间点的熵的演变情况。我们的分析方法提供了关于自我各个维度的配置信息,以及这些维度之间与时间相关的变化和关联。我们用一个网络来描述自我价值随时间的变化。这种方法还能识别出表现出不同关联模式的青少年学生群体,从而强调了考虑数据异质性的必要性。
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引用次数: 0
The Effects of Questionnaire Length on the Relative Impact of Response Styles in Ambulatory Assessment. 在非卧床评估中,问卷长度对回答方式相对影响的影响。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-05-23 DOI: 10.1080/00273171.2024.2354233
Kilian Hasselhorn, Charlotte Ottenstein, Thorsten Meiser, Tanja Lischetzke

Ambulatory assessment (AA) is becoming an increasingly popular research method in the fields of psychology and life science. Nevertheless, knowledge about the effects that design choices, such as questionnaire length (i.e., number of items per questionnaire), have on AA data quality is still surprisingly restricted. Additionally, response styles (RS), which threaten data quality, have hardly been analyzed in the context of AA. The aim of the current research was to experimentally manipulate questionnaire length and investigate the association between questionnaire length and RS in an AA study. We expected that the group with the longer (82-item) questionnaire would show greater reliance on RS relative to the substantive traits than the group with the shorter (33-item) questionnaire. Students (n = 284) received questionnaires three times a day for 14 days. We used a multigroup two-dimensional item response tree model in a multilevel structural equation modeling framework to estimate midpoint and extreme RS in our AA study. We found that the long questionnaire group showed a greater reliance on RS relative to trait-based processes than the short questionnaire group. Although further validation of our findings is necessary, we hope that researchers consider our findings when planning an AA study in the future.

在心理学和生命科学领域,非卧床评估(AA)正日益成为一种流行的研究方法。然而,有关问卷长度(即每份问卷的项目数)等设计选择对非卧床评估数据质量的影响的知识仍然非常有限。此外,威胁数据质量的应答方式(RS)也几乎没有在 AA 的背景下进行过分析。当前研究的目的是在一项 AA 研究中,通过实验操纵问卷长度,并调查问卷长度与 RS 之间的关联。我们预计,与问卷较短(33 个条目)的小组相比,问卷较长(82 个条目)的小组将表现出更多的对 RS 的依赖。学生(n = 284)在 14 天内每天接受三次问卷调查。在 AA 研究中,我们在多层次结构方程建模框架下使用了多组二维项目反应树模型来估计中点和极端 RS。我们发现,相对于基于特质的过程,长问卷组比短问卷组更依赖于 RS。尽管我们的研究结果还需要进一步验证,但我们希望研究人员今后在计划 AA 研究时能考虑到我们的研究结果。
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引用次数: 0
Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators. 具有短时间序列和时变变量的多层次潜差结构方程模型:Frequentist and Bayesian Estimators: A Comparison of Frequentist and Bayesian Estimators.
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-05-31 DOI: 10.1080/00273171.2024.2347959
Young Won Cho, Sy-Miin Chow, Christina M Marini, Lynn M Martire

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

使用微分方程进行连续时间建模是一种很有前途的技术,可用于对纵向数据的变化过程进行建模。在拟合这种模型的方法中,潜在微分结构方程建模(LDSEM)方法在结构方程建模(SEM)框架内定义了潜在的衍生变量,从而使研究人员能够利用 SEM 框架的优势来建立模型、进行估计、推理和比较。但仍有一些问题尚未解决,包括 LDSEM 的多层次变化在较短时间长度(如 14 个时间点)下的表现,尤其是在涉及耦合多变量过程和时变协变量时。此外,使用贝叶斯估计法来促进具有复杂和高维随机效应结构的多层次 LDSEM(M-LDSEM)模型估计的可能性尚未得到研究。我们进行了一系列蒙特卡罗模拟,评估了拟合 M-LDSEM 的三种可能方法,包括:频数主义单水平和双水平稳健估计法以及贝叶斯双水平估计法。我们的研究结果表明,贝叶斯方法优于其他频数法。时变协变量的影响得到了很好的恢复,耦合参数的偏差最小,特别是使用贝叶斯估计器的高阶导数信息。最后,我们提供了一个实证例子来说明该方法的适用性。
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引用次数: 0
Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods. 测试心理测量网络中的条件独立性:对三种贝叶斯方法的分析。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-05-11 DOI: 10.1080/00273171.2024.2345915
Nikola Sekulovski, Sara Keetelaar, Karoline Huth, Eric-Jan Wagenmakers, Riet van Bork, Don van den Bergh, Maarten Marsman

Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal structure underlying psychological processes. Thus, it is crucial to have an appropriate method for evaluating conditional independence and dependence hypotheses. Bayesian approaches to testing such hypotheses allow researchers to differentiate between absence of evidence and evidence of absence of connections (edges) between pairs of variables in a network. Three Bayesian approaches to assessing conditional independence have been proposed in the network psychometrics literature. We believe that their theoretical foundations are not widely known, and therefore we provide a conceptual review of the proposed methods and highlight their strengths and limitations through a simulation study. We also illustrate the methods using an empirical example with data on Dark Triad Personality. Finally, we provide recommendations on how to choose the optimal method and discuss the current gaps in the literature on this important topic.

网络心理测量学使用图形模型来评估心理变量的网络结构。其分析的一项重要任务是确定哪些变量在网络中是不相关的,即与其他网络变量无关。这种有条件的独立结构是了解心理过程因果结构的入口。因此,采用适当的方法评估条件独立性和依赖性假设至关重要。检验此类假设的贝叶斯方法可以让研究人员区分网络中变量对之间缺乏联系(边)的证据和缺乏联系(边)的证据。网络心理计量学文献中提出了三种贝叶斯方法来评估条件独立性。我们认为这些方法的理论基础并不广为人知,因此我们对所提出的方法进行了概念性回顾,并通过模拟研究强调了这些方法的优势和局限性。我们还通过一个有关黑暗三合会人格数据的实证例子来说明这些方法。最后,我们就如何选择最佳方法提出了建议,并讨论了目前在这一重要课题上的文献空白。
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引用次数: 0
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Multivariate Behavioral Research
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