scholarly journals Neither measurement error nor speed-accuracy trade-offs explain the difficulty of establishing attentional control as a psychometric construct: Evidence from a latent-variable analysis using diffusion modeling

2021 ◽  
Author(s):  
Alodie Rey-Mermet ◽  
Henrik Singmann ◽  
Klaus Oberauer
Keyword(s):  
Measurement Error ◽  
Attentional Control ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Large Set ◽  
Diffusion Modeling ◽  
Latent Variable Analysis ◽  
Trade Offs ◽  
Speed Accuracy

Attentional control refers to the ability to maintain and implement a goal and goal-relevant information when facing distraction. So far, previous research has failed to substantiate strong evidence for a psychometric construct of attentional control. This has been argued to result from two methodological shortcomings: (a) the neglect of individual differences in speed-accuracy trade-offs when only speed or accuracy is used as dependent variable, and (b) the difficulty of isolating attentional control from measurement error. To overcome both issues, we combined hierarchical-Bayesian Wiener diffusion modeling with structural equation modeling. We re-analyzed the dataset from Rey-Mermet, Gade, and Oberauer (2018), which includes data from a large set of attentional-control tasks from young and older adults. Even when accounting for speed-accuracy trade-offs and removing measurement error, measures of attentional control failed to correlate with each other and to load on a latent variable. These results emphasize the necessity of rethinking attentional control.

scholarly journals Individual differences in inhibitory control: A latent variable analysis

2019 ◽  
Author(s):  
Anne Gärtner ◽  
Alexander Strobel
Keyword(s):  
Inhibitory Control ◽  
Latent Variable ◽  
Response Times ◽  
Previous Literature ◽  
Matching Task ◽  
Reaction Time Difference ◽  
Latent Variable Analysis ◽  
Trade Offs ◽  
Speed Accuracy ◽  
Variable Analysis

Inhibitory control represents a central component of executive functions and focuses on the ability to actively inhibit or delay a dominant response to achieve a goal. Although various tasks exist to measure inhibitory control, correlations between these tasks are rather small, partly because of the task impurity problem. To alleviate this problem, a latent variable approach has been previously applied and two closely related to each other yet separable functions have been identified: prepotent response inhibition and resistance to distractor interference. The goal of our study was a) to replicate the proposed structure of inhibitory control and b) to extend previous literature by additionally accounting for speed-accuracy trade-offs, thereby potentially increasing explained variance in the investigated latent factors. To this end, 190 participants completed six inhibitory control tasks (stroop task, antisaccade task, stop-signal task, Eriksen flanker task, shape-matching task, word-naming task). Analyses were conducted using standard scores (response times) as well as inverse efficiency scores (combining response times and error rates). In line with previous studies, we found generally low zero-order correlations between the six tasks. By applying confirmatory factor analysis using standard reaction time difference scores, we were not able to replicate a satisfactory model with good fit to the data. By using inverse efficiency scores, a single latent variable response–distractor inhibition emerged that resembled previous literature, but only four out of six tasks demonstrated significant factor loadings. Our results highlight the difficulty in finding robust inter-correlations between commonly used inhibitory control tasks, even when applying a latent variable analysis and accounting for speed-accuracy trade-offs.

scholarly journals Trade-offs among transport, support, and storage in xylem from shrubs in a semiarid chaparral environment tested with structural equation modeling

2021 ◽  
Vol 118 (33) ◽  
pp. e2104336118
Author(s):  
R. B. Pratt ◽  
A. L. Jacobsen ◽  
M. I. Percolla ◽  
M. E. De Guzman ◽  
C. A. Traugh ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variable ◽  
Structural Equation ◽  
Strong Support ◽  
Cell Types ◽  
Equation Modeling ◽  
Transport Efficiency ◽  
Xylem Structure ◽  
Trade Offs ◽  
Embolism Resistance

The xylem in plants is specialized to transport water, mechanically support the plant body, and store water and carbohydrates. Balancing these functions leads to trade-offs that are linked to xylem structure. We proposed a multivariate hypothesis regarding the main xylem functions and tested it using structural equation modeling. We sampled 29 native shrub species from field sites in semiarid Southern California. We quantified xylem water transport (embolism resistance and transport efficiency), mechanical strength, storage of water (capacitance) and starch, minimum hydrostatic pressures (Pmin), and proportions of fibers, vessels, and parenchyma, which were treated as a latent variable representing “cellular trade-offs.” We found that xylem functions (transport, mechanical support, water storage, and starch storage) were independent, a result driven by Pmin. Pmin was strongly and directly or indirectly associated with all xylem functions as a hub trait. More negative Pmin was associated with increased embolism resistance and tissue strength and reduced capacitance and starch storage. We found strong support for a trade-off between embolism resistance and transport efficiency. Tissue strength was not directly associated with embolism resistance or transport efficiency, and any associations were indirect involving Pmin. With Pmin removed from the model, cellular trade-offs were central and related to all other traits. We conclude that xylem traits are broadly governed by functional trade-offs and that the Pmin experienced by plants in the field exerts a strong influence over these relationships. Angiosperm xylem contains different cell types that contribute to different functions and that underpin trade-offs.

How temporal leadership boosts employee innovative job performance

2020 ◽  
Vol 24 (1) ◽  
pp. 23-42 ◽  
Author(s):  
Juncheng Zhang ◽  
Wendelien van Eerde ◽  
Josette M.P. Gevers ◽  
Weichun Zhu
Keyword(s):  
Job Performance ◽  
Latent Variable ◽  
Structural Equation ◽  
Design Methodology ◽  
Time Pressure ◽  
Equation Modeling ◽  
Content Type ◽  
Latent Variable Analysis ◽  
Rule Breaking ◽  
Variable Analysis

PurposeThe purpose of this paper is to examine how temporal leadership relates to employee innovative job performance through pro-social rule breaking for efficiency (PSRB_E) and vigor. As such, it draws from both motivational and affective perspectives to investigate the way in which leaders manage employees' time to boost employee innovation at work.Design/methodology/approachThis study adopts a two-source survey with coworker dyads from multiple organizations in China. Two hundred and three focal employees rated temporal leadership, time pressure and vigor. Each focal employee was rated by a coworker on PSRB_E and innovative job performance. Structural equation modeling (SEM) and bootstrapping techniques are used to examine the hypothesized relationships with the R package for latent variable analysis (i.e. lavaan).FindingsThe results suggest that temporal leadership positively relates to employees' innovative job performance through the mediations of their PRSB_E and vigor, respectively.Originality/valueThis study is one of the first that sought to understand the effect of temporal leadership on employee innovative job performance. It also casts light upon the motivational and affective mechanisms underlying such a linkage.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

scholarly journals Postpartum depression symptoms in survey-based research: a structural equation analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Che Wan Jasimah Bt Wan Mohamed Radzi ◽  
Hashem Salarzadeh Jenatabadi ◽  
Nadia Samsudin
Keyword(s):  
Postpartum Depression ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Public Health Problem ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Postpartum Women ◽  
Unhealthy Food ◽  
High Level

Abstract Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

scholarly journals Insights Into the Correspondence Between Land Use Pattern and Spatial Distribution of Rail Transit Services

2021 ◽  
Author(s):  
Zhongqi Wang ◽  
Qi Han ◽  
Bauke de Vries ◽  
Li Dai
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Operations Management ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Rail Transit ◽  
Land Use Patterns ◽  
Transit Services ◽  
The Relationship

AbstractThe identification of the relationship between land use and transport lays the foundation for integrated land use and transport planning and management. This work aims to investigate how rail transit is linked to land use. The research on the relationship between land use and rail-based transport is dominated by the impacts of rail projects on land use, without an in-depth understanding of the reverse. However, it is important to note that issues of operation management rather than new constructions deserve greater attention for regions with established rail networks. Given that there is a correspondence between land use patterns and spatial distribution of heavy railway transit (HRT) services at such regions, the study area (i.e., the Netherlands) is partitioned by the Voronoi diagram of HRT stations and the causal relationship between land use and HRT services is examined by structural equation modeling (SEM). The case study of Helmond (a Dutch city) shows the potential of the SEM model for discussing the rail station selection problem in a multiple transit station region (MTSR). Furthermore, in this study, the node place model is adapted with the derivatives of the SEM model (i.e., the latent variable scores for rail service levels and land use characteristics), which are assigned as node and place indexes respectively, to analyze and differentiate the integration of land use and HRT services at the regional level. The answer to whether and how land use affects rail transit services from this study strengthens the scientific basis for rail transit operations management. The SEM model and the modified node place model are complementary to be used as analytical and decision-making tools for rail transit-oriented regional development.

Attributional Style as a Predictor of Hopelessness Depression

2006 ◽  
Vol 20 (4) ◽  
pp. 447-458 ◽  
Author(s):  
Edward D. Sturman ◽  
Myriam Mongrain ◽  
Paul M. Kohn
Keyword(s):  
Attributional Style ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Epidemiological Studies ◽  
Psychomotor Retardation ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Negative Events ◽  
Hopelessness Depression

Stable and global attributions for negative events were tested as predictors of hopelessness depression symptoms, obtained from a diagnostic interview for a past depressive episode in a sample of 102 graduate students. All participants were administered the Structured Clinical Interview for DSM–IV, Center for Epidemiological Studies Depression Scale, Personal Style Inventory, and a modified version of the Extended Attributional Style Questionnaire. A stable and global attributional style for negative events was significantly associated with a composite of hopelessness depression symptoms. A regression analysis revealed that attributional style significantly postdicted hopelessness depression symptoms when controlling for both sociotropy and autonomy. Structural equation modeling supported a model in which stable and global attributions predicted a latent variable, which we refer to as a motivational deficit, involving psychomotor retardation and fatigue as indicators. Therefore, this study obtained some support for the hopelessness model and highlights the vulnerability posed by attributional style ( Abramson, Metalsky, & Alloy, 1989 ).

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

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