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Combining Item Purification and Multiple Comparison Adjustment Methods in Detection of Differential Item Functioning. 结合项目净化和多重比较调整方法来检测差异项目功能。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-01-01 Epub Date: 2023-05-23 DOI: 10.1080/00273171.2023.2205393
Adéla Hladká, Patrícia Martinková, David Magis

Many of the differential item functioning (DIF) detection methods rely on a principle of testing for DIF item by item, while considering the rest of the items or at least some of them being DIF-free. Computational algorithms of these DIF detection methods involve the selection of DIF-free items in an iterative procedure called item purification. Another aspect is the need to correct for multiple comparisons, which can be done with a number of existing multiple comparison adjustment methods. In this article, we demonstrate that implementation of these two controlling procedures together may have an impact on which items are detected as DIF items. We propose an iterative algorithm combining item purification and adjustment for multiple comparisons. Pleasant properties of the newly proposed algorithm are shown with a simulation study. The method is demonstrated on a real data example.

许多差异项目功能(DIF)检测方法都依赖于逐项检测 DIF 的原则,同时考虑到其余项目或至少部分项目无 DIF。这些 DIF 检测方法的计算算法包括在一个称为 "项目净化 "的迭代过程中选择无 DIF 的项目。另一个方面是需要对多重比较进行校正,这可以通过一些现有的多重比较调整方法来实现。在本文中,我们证明了这两种控制程序的共同实施可能会对哪些条目被检测为 DIF 条目产生影响。我们提出了一种结合项目净化和多重比较调整的迭代算法。我们通过模拟研究展示了新提出算法的可喜特性。我们还在一个真实数据示例中演示了该方法。
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引用次数: 0
An Iterative Scale Purification Procedure on lz for the Detection of Aberrant Responses. lz上用于检测异常响应的迭代规模纯化程序。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-01-01 Epub Date: 2023-06-01 DOI: 10.1080/00273171.2023.2211564
Xuelan Qiu, Sheng-Yun Huang, Wen-Chung Wang, You-Gan Wang

Many person-fit statistics have been proposed to detect aberrant response behaviors (e.g., cheating, guessing). Among them, lz is one of the most widely used indices. The computation of lz assumes the item and person parameters are known. In reality, they often have to be estimated from data. The better the estimation, the better lz will perform. When aberrant behaviors occur, the person and item parameter estimations are inaccurate, which in turn degrade the performance of lz. In this study, an iterative procedure was developed to attain more accurate person parameter estimates for improved performance of lz. A series of simulations were conducted to evaluate the iterative procedure under two conditions of item parameters, known and unknown, and three aberrant response styles of difficulty-sharing cheating, random-sharing cheating, and random guessing. The results demonstrated the superiority of the iterative procedure over the non-iterative one in maintaining control of Type-I error rates and improving the power of detecting aberrant responses. The proposed procedure was applied to a high-stake intelligence test.

已经提出了许多适合个人的统计数据来检测异常反应行为(例如,作弊、猜测)。其中,lz是应用最广泛的指数之一。lz的计算假定项目和个人参数是已知的。事实上,它们往往必须根据数据进行估计。估计越好,lz的性能就越好。当异常行为发生时,个人和项目参数估计不准确,这反过来降低了lz的性能。在这项研究中,开发了一种迭代程序,以获得更准确的个人参数估计,从而提高lz的性能。在项目参数已知和未知的两个条件下,以及难度共享作弊、随机共享作弊和随机猜测三种异常反应方式下,进行了一系列模拟来评估迭代过程。结果表明,迭代程序在保持对I型错误率的控制和提高检测异常响应的能力方面优于非迭代程序。拟议的程序被应用于一项事关重大的情报测试。
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引用次数: 0
SMART Binary: New Sample Size Planning Resources for SMART Studies with Binary Outcome Measurements. SMART 二进制:用于二元结果测量的 SMART 研究的新样本量规划资源。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-01-01 Epub Date: 2023-07-17 DOI: 10.1080/00273171.2023.2229079
John J Dziak, Daniel Almirall, Walter Dempsey, Catherine Stanger, Inbal Nahum-Shani

Sequential Multiple-Assignment Randomized Trials (SMARTs) play an increasingly important role in psychological and behavioral health research. This experimental approach enables researchers to answer scientific questions about how to sequence and match interventions to the unique, changing needs of individuals. A variety of sample size planning resources for SMART studies have been developed, enabling researchers to plan SMARTs for addressing different types of scientific questions. However, relatively limited attention has been given to planning SMARTs with binary (dichotomous) outcomes, which often require higher sample sizes relative to continuous outcomes. Existing resources for estimating sample size requirements for SMARTs with binary outcomes do not consider the potential to improve power by including a baseline measurement and/or multiple repeated outcome measurements. The current paper addresses this issue by providing sample size planning simulation procedures and approximate formulas for two-wave repeated measures binary outcomes (i.e., two measurement times for the outcome variable, before and after intervention delivery). The simulation results agree well with the formulas. We also discuss how to use simulations to calculate power for studies with more than two outcome measurement occasions. Results show that having at least one repeated measurement of the outcome can substantially improve power under certain conditions.

连续多次随机试验(SMARTs)在心理和行为健康研究中发挥着越来越重要的作用。这种实验方法使研究人员能够回答有关如何根据个人独特、不断变化的需求来安排干预顺序和匹配干预措施的科学问题。目前已开发出多种用于 SMART 研究的样本大小规划资源,使研究人员能够规划 SMART 以解决不同类型的科学问题。然而,人们对二元(二分)结果的 SMART 研究规划的关注相对有限,因为相对于连续结果而言,二元结果往往需要更高的样本量。用于估算二元结果 SMARTs 所需样本量的现有资源并未考虑通过基线测量和/或多次重复结果测量来提高研究力量的潜力。本文针对这一问题,提供了样本量规划模拟程序和两波重复测量二元结果的近似公式(即对结果变量进行两次测量,分别在干预实施之前和之后)。模拟结果与公式非常吻合。我们还讨论了如何使用模拟来计算具有两个以上结果测量场合的研究的功率。结果表明,在某些条件下,对结果进行至少一次重复测量可以大大提高研究的功率。
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引用次数: 0
Skewness and Staging: Does the Floor Effect Induce Bias in Multilevel AR(1) Models? 偏度与分期:地板效应是否会导致多层次 AR(1) 模型出现偏差?
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-12-31 DOI: 10.1080/00273171.2023.2254769
M. M. Haqiqatkhah, O. Ryan, E. L. Hamaker
Multilevel autoregressive models are popular choices for the analysis of intensive longitudinal data in psychology. Empirical studies have found a positive correlation between autoregressive parame...
多层次自回归模型是分析心理学密集型纵向数据的热门选择。实证研究发现,多层次自回归模型与纵向数据之间存在正相关。
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引用次数: 0
Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence. 独特变量分析:一种检测局部依赖性的网络心理计量学方法。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 Epub Date: 2023-05-04 DOI: 10.1080/00273171.2023.2194606
Alexander P Christensen, Luis Eduardo Garrido, Hudson Golino

The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locally dependent pairs of variables using network modeling and a graph theory measure called weighted topological overlap (wTO). Using simulation, this approach is compared to contemporary local dependence detection methods such as exploratory structural equation modeling with standardized expected parameter change and a recently developed approach using partial correlations and a resampling procedure. Different approaches to determine local dependence using statistical significance and cutoff values are also compared. Continuous, polytomous (5-point Likert scale), and dichotomous (binary) data were generated with skew across a variety of conditions. Our results indicate that cutoff values work better than significance approaches. Overall, the network psychometrics approaches using wTO with graphical least absolute shrinkage and selector operator with extended Bayesian information criterion and wTO with Bayesian Gaussian graphical model were the best performing local dependence detection methods overall.

局部独立假设是指变量在以潜在变量为条件后是不相关的。违反这一假设所产生的常见问题包括模型不规范、模型参数有偏差以及对内部结构的估计不准确。这些问题不仅限于潜变量模型,也适用于网络心理测量学。本文提出了一种新颖的网络心理测量方法,利用网络建模和一种称为加权拓扑重叠(wTO)的图论测量方法来检测局部依赖变量对。通过模拟,本文将这种方法与当代的局部依赖性检测方法进行了比较,如采用标准化预期参数变化的探索性结构方程建模,以及最近开发的一种使用偏相关性和重采样程序的方法。此外,还比较了使用统计显著性和截断值确定局部依赖性的不同方法。我们生成了连续、多态(5 点李克特量表)和二态(二进制)数据,这些数据在各种条件下都有偏差。结果表明,截止值比显著性方法更有效。总体而言,使用具有图形最小绝对收缩的 wTO 和具有扩展贝叶斯信息准则的选择算子的网络心理计量学方法,以及具有贝叶斯高斯图形模型的 wTO 是总体上表现最好的局部依赖性检测方法。
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引用次数: 8
Reorienting Latent Variable Modeling for Supervised Learning. 面向监督学习的潜在变量建模。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 Epub Date: 2023-05-25 DOI: 10.1080/00273171.2023.2182753
Booil Jo, Trevor J Hastie, Zetan Li, Eric A Youngstrom, Robert L Findling, Sarah McCue Horwitz

Despite its potentials benefits, using prediction targets generated based on latent variable (LV) modeling is not a common practice in supervised learning, a dominating framework for developing prediction models. In supervised learning, it is typically assumed that the outcome to be predicted is clear and readily available, and therefore validating outcomes before predicting them is a foreign concept and an unnecessary step. The usual goal of LV modeling is inference, and therefore using it in supervised learning and in the prediction context requires a major conceptual shift. This study lays out methodological adjustments and conceptual shifts necessary for integrating LV modeling into supervised learning. It is shown that such integration is possible by combining the traditions of LV modeling, psychometrics, and supervised learning. In this interdisciplinary learning framework, generating practical outcomes using LV modeling and systematically validating them based on clinical validators are the two main strategies. In the example using the data from the Longitudinal Assessment of Manic Symptoms (LAMS) Study, a large pool of candidate outcomes is generated by flexible LV modeling. It is demonstrated that this exploratory situation can be used as an opportunity to tailor desirable prediction targets taking advantage of contemporary science and clinical insights.

尽管有潜在的好处,但使用基于潜变量(LV)建模生成的预测目标在监督学习中并不常见,而监督学习是开发预测模型的主要框架。在监督学习中,通常假设要预测的结果是清晰且容易获得的,因此在预测结果之前验证结果是一个陌生的概念,也是一个不必要的步骤。LV建模的通常目标是推理,因此在监督学习和预测上下文中使用它需要一个重大的概念转变。本研究提出了将LV模型整合到监督学习中所必需的方法调整和概念转变。研究表明,通过结合传统的LV建模、心理测量学和监督学习,这种整合是可能的。在这个跨学科的学习框架中,使用LV建模和基于临床验证器系统地验证产生实际结果是两个主要策略。在使用躁狂症状纵向评估(LAMS)研究数据的示例中,通过灵活的LV建模生成了大量候选结果。这表明,这种探索性的情况可以作为一个机会,利用当代科学和临床见解来定制理想的预测目标。
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引用次数: 0
betaDelta and betaSandwich: Confidence Intervals for Standardized Regression Coefficients in R. betaDelta 和 betaSandwich:R 中标准化回归系数的置信区间。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 Epub Date: 2023-04-25 DOI: 10.1080/00273171.2023.2201277
Ivan Jacob Agaloos Pesigan, Rong Wei Sun, Shu Fai Cheung

The multivariate delta method was used by Yuan and Chan to estimate standard errors and confidence intervals for standardized regression coefficients. Jones and Waller extended the earlier work to situations where data are nonnormal by utilizing Browne's asymptotic distribution-free (ADF) theory. Furthermore, Dudgeon developed standard errors and confidence intervals, employing heteroskedasticity-consistent (HC) estimators, that are robust to nonnormality with better performance in smaller sample sizes compared to Jones and Waller's ADF technique. Despite these advancements, empirical research has been slow to adopt these methodologies. This can be a result of the dearth of user-friendly software programs to put these techniques to use. We present the betaDelta and the betaSandwich packages in the R statistical software environment in this manuscript. Both the normal-theory approach and the ADF approach put forth by Yuan and Chan and Jones and Waller are implemented by the betaDelta package. The HC approach proposed by Dudgeon is implemented by the betaSandwich package. The use of the packages is demonstrated with an empirical example. We think the packages will enable applied researchers to accurately assess the sampling variability of standardized regression coefficients.

Yuan 和 Chan 使用多元三角法估算标准化回归系数的标准误差和置信区间。Jones 和 Waller 利用 Browne 的无渐近分布 (ADF) 理论,将先前的工作扩展到了数据非正态分布的情况。此外,Dudgeon 利用异方差一致(HC)估计器开发了标准误差和置信区间,与 Jones 和 Waller 的 ADF 技术相比,这些估计器对非正态性具有稳健性,在样本量较小的情况下性能更好。尽管取得了这些进步,但实证研究在采用这些方法方面进展缓慢。这可能是由于缺乏用户友好的软件程序来使用这些技术。我们在本手稿中介绍了 R 统计软件环境中的 betaDelta 和 betaSandwich 软件包。Yuan和Chan以及Jones和Waller提出的正态理论方法和ADF方法都由betaDelta软件包实现。Dudgeon 提出的 HC 方法由 betaSandwich 软件包实现。我们通过一个实证例子演示了软件包的使用。我们认为这些软件包可以帮助应用研究人员准确评估标准化回归系数的抽样变异性。
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引用次数: 0
On the Common but Problematic Specification of Conflated Random Slopes in Multilevel Models. 论多层次模型中常见但有问题的串联随机斜率规范。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 Epub Date: 2023-04-10 DOI: 10.1080/00273171.2023.2174490
Jason D Rights, Sonya K Sterba

For multilevel models (MLMs) with fixed slopes, it has been widely recognized that a level-1 variable can have distinct between-cluster and within-cluster fixed effects, and that failing to disaggregate these effects yields a conflated, uninterpretable fixed effect. For MLMs with random slopes, however, we clarify that two different types of slope conflation can occur: that of the fixed component (termed fixed conflation) and that of the random component (termed random conflation). The latter is rarely recognized and not well understood. Here we explain that a model commonly used to disaggregate the fixed component-the contextual effect model with random slopes-troublingly still yields a conflated random component. Negative consequences of such random conflation have not been demonstrated. Here we show that they include erroneous interpretation and inferences about the substantively important extent of between-cluster differences in slopes, including either underestimating or overestimating such slope heterogeneity. Furthermore, we show that this random conflation can yield inappropriate standard errors for fixed effects. To aid researchers in practice, we delineate which types of random slope specifications yield an unconflated random component. We demonstrate the advantages of these unconflated models in terms of estimating and testing random slope variance (i.e., improved power, Type I error, and bias) and in terms of standard error estimation for fixed effects (i.e., more accurate standard errors), and make recommendations for which specifications to use for particular research purposes.

对于具有固定斜率的多层次模型(MLMs)来说,人们普遍认为第一层次变量可能具有明显的群间固定效应和群内固定效应,如果不对这些效应进行分解,就会产生混淆的、难以解释的固定效应。然而,对于具有随机斜率的多变量模型,我们要澄清的是,可能会出现两种不同类型的斜率混淆:固定分量的斜率混淆(称为固定混淆)和随机分量的斜率混淆(称为随机混淆)。后者很少被认识到,也没有得到很好的理解。在这里,我们解释了一个常用于分解固定成分的模型--具有随机斜率的背景效应模型--令人不安的是,它仍然会产生混淆的随机成分。这种随机混淆的负面影响尚未得到证实。在这里,我们将证明这些负面影响包括对群组间斜率差异的重要程度的错误解释和推断,包括低估或高估这种斜率异质性。此外,我们还表明,这种随机混淆会产生不恰当的固定效应标准误差。为了在实践中帮助研究人员,我们划分了哪些类型的随机斜率规范会产生非膨胀随机成分。我们展示了这些非膨胀模型在估计和检验随机斜率方差(即改进的功率、I 类误差和偏差)以及固定效应的标准误差估计(即更准确的标准误差)方面的优势,并就特定研究目的使用哪种规范提出了建议。
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引用次数: 2
Pay Attention to the Ignorable Missing Data Mechanisms! An Exploration of Their Impact on the Efficiency of Regression Coefficients. 关注不可忽略的缺失数据机制!探讨其对回归系数效率的影响
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 Epub Date: 2023-04-11 DOI: 10.1080/00273171.2023.2193600
Lihan Chen, Victoria Savalei, Mijke Rhemtulla

The use of modern missing data techniques has become more prevalent with their increasing accessibility in statistical software. These techniques focus on handling data that are missing at random (MAR). Although all MAR mechanisms are routinely treated as the same, they are not equal. The impact of missing data on the efficiency of parameter estimates can differ for different MAR variations, even when the amount of missing data is held constant; yet, in current practice, only the rate of missing data is reported. The impact of MAR on the loss of efficiency can instead be more directly measured by the fraction of missing information (FMI). In this article, we explore this impact using FMIs in regression models with one and two predictors. With the help of a Shiny application, we demonstrate that efficiency loss due to missing data can be highly complex and is not always intuitive. We recommend substantive researchers who work with missing data report estimates of FMIs in addition to the rate of missingness. We also encourage methodologists to examine FMIs when designing simulation studies with missing data, and to explore the behavior of efficiency loss under MAR using FMIs in more complex models.

随着统计软件的日益普及,现代缺失数据技术的使用也越来越普遍。这些技术侧重于处理随机缺失数据(MAR)。虽然所有的随机缺失机制通常都被视为相同的,但它们并不相同。即使在缺失数据量保持不变的情况下,缺失数据对参数估计效率的影响也会因不同的 MAR 变化而不同;但在目前的实践中,只报告缺失数据率。MAR 对效率损失的影响可以通过缺失信息的比例 (FMI) 更直接地衡量。在本文中,我们将利用单预测因子和双预测因子回归模型中的 FMIs 来探讨这种影响。在 Shiny 应用程序的帮助下,我们证明了数据缺失导致的效率损失可能非常复杂,而且并不总是直观的。我们建议研究人员在处理缺失数据时,除了报告缺失率外,还要报告 FMIs 的估计值。我们还鼓励方法论专家在设计有缺失数据的模拟研究时检查 FMIs,并在更复杂的模型中使用 FMIs 探索 MAR 下的效率损失行为。
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引用次数: 0
Exploratory Bi-factor Analysis with Multiple General Factors. 具有多个一般因素的探索性双因素分析。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 Epub Date: 2023-04-10 DOI: 10.1080/00273171.2023.2189571
Marcos Jiménez, Francisco J Abad, Eduardo Garcia-Garzon, Luis Eduardo Garrido

Exploratory bi-factor analysis (EBFA) is a very popular approach to estimate models where specific factors are concomitant to a single, general dimension. However, the models typically encountered in fields like personality, intelligence, and psychopathology involve more than one general factor. To address this circumstance, we developed an algorithm (GSLiD) based on partially specified targets to perform exploratory bi-factor analysis with multiple general factors (EBFA-MGF). In EBFA-MGF, researchers do not need to conduct independent bi-factor analyses anymore because several bi-factor models are estimated simultaneously in an exploratory manner, guarding against biased estimates and model misspecification errors due to unexpected cross-loadings and factor correlations. The results from an exhaustive Monte Carlo simulation manipulating nine variables of interest suggested that GSLiD outperforms the Schmid-Leiman approximation and is robust to challenging conditions involving cross-loadings and pure items of the general factors. Thereby, we supply an R package (bifactor) to make EBFA-MGF readily available for substantive research. Finally, we use GSLiD to assess the hierarchical structure of a reduced version of the Personality Inventory for DSM-5 Short Form (PID-5-SF).

探索性双因素分析(EBFA)是一种非常流行的估算模型的方法,在这种模型中,特定的因素与单一的一般维度相关联。然而,在人格、智力和精神病理学等领域通常会遇到涉及不止一个一般因素的模型。针对这种情况,我们开发了一种基于部分指定目标的算法(GSLiD),用于执行具有多个一般因素的探索性双因素分析(EBFA-MGF)。在 EBFA-MGF 中,研究人员不需要再进行独立的双因素分析,因为多个双因素模型会以探索性的方式同时进行估计,从而避免了由于意外的交叉负荷和因素相关性而导致的估计偏差和模型指定错误。对九个相关变量进行的详尽蒙特卡罗模拟结果表明,GSLiD 的性能优于 Schmid-Leiman 近似方法,并且对涉及交叉负荷和一般因子纯项的挑战性条件具有鲁棒性。因此,我们提供了一个 R 软件包(bifactor),使 EBFA-MGF 可随时用于实质性研究。最后,我们使用 GSLiD 评估了简化版 DSM-5 人格问卷简表(PID-5-SF)的层次结构。
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引用次数: 0
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Multivariate Behavioral Research
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