Factor Score Path Analysis

Devlieger, Ines; Rosseel, Yves

doi:10.1027/1614-2241/a000130

Cited by 117 publications

(103 citation statements)

References 14 publications

(20 reference statements)

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“…We used a jigsaw piece modeling strategy in combination with factor score regression, single‐indicator latent variables, and bootstrapped standard errors to circumvent issues related to small sample size, multivariate non‐normality, and model complexity (Bollen, ; Devlieger & Rosseel, ; Enders, ; Hayduk & Littvay, ). Briefly, we first estimated the best‐fitting measurement model for each construct (i.e.…”

Section: Methodsmentioning

confidence: 99%

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Common mechanisms of executive attention underlie executive function and effortful control in children

Tiego

Bellgrove

Whittle

et al. 2019

Developmental Science

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Executive Function (EF) and Effortful Control (EC) have traditionally been viewed as distinct constructs related to cognition and temperament during development. More recently, EF and EC have been implicated in top‐down self‐regulation ‐ the goal‐directed control of cognition, emotion, and behavior. We propose that executive attention, a limited‐capacity attentional resource subserving goal‐directed cognition and behavior, is the common cognitive mechanism underlying the self‐regulatory capacities captured by EF and EC. We addressed three related questions: (a) Do behavioral ratings of EF and EC represent the same self‐regulation construct? (b) Is this self‐regulation construct explained by a common executive attention factor as measured by performance on cognitive tasks? and (c) Does the executive attention factor explain additional variance in attention deficit hyperactivity disorder (ADHD) problems to behavioral ratings of self‐regulation? Measures of performance on complex span, general intelligence, and response inhibition tasks were obtained from 136 preadolescent children (M = 11 years, 10 months, SD = 8 months), along with self‐ and parent‐reported EC, and parent‐reported EF, and ADHD problems. Results from structural equation modeling demonstrated that behavioral ratings of EF and EC measured the same self‐regulation construct. Cognitive tasks measured a common executive attention factor that significantly explained 30% of the variance in behavioral ratings of self‐regulation. Executive attention failed to significantly explain additional variance in ADHD problems beyond that explained by behavioral ratings of self‐regulation. These findings raise questions about the utility of task‐based cognitive measures in research and clinical assessment of self‐regulation and psychopathology in developmental samples.

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Section: Methodsmentioning

confidence: 99%

“…The factor score regression method results in unbiased parameter estimates in structural regression models when the factor score estimates are generated using the regression method and are used as an exogenous (i.e. independent) latent variable (Devlieger, Mayer, & Rosseel, ; Devlieger & Rosseel, ; Skrondal & Laake, ).…”

Section: Methodsmentioning

confidence: 99%

Common mechanisms of executive attention underlie executive function and effortful control in children

Tiego

Bellgrove

Whittle

et al. 2019

Developmental Science

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“…Alas, we had too few indicators to begin to address this issue. Thankfully, it is unlikely that our measures suffer from issues related to sample size or model complexity [27] because our sample size was large and a unidimensional model seemed appropriate.…”

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confidence: 99%

Intelligence and Religiosity among Dating Site Users

Kirkegaard¹,

Lasker

2019

Psych

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We sought to assess whether previous findings regarding the relationship between cognitive ability and religiosity could be replicated in a large dataset of online daters (maximum n = 67k). We found that self-declared religious people had lower IQs than nonreligious people (atheists and agnostics). Furthermore, within most religious groups, a negative relationship between the strength of religious conviction and IQ was observed. This relationship was absent or reversed in nonreligious groups. A factor of religiousness based on five questions correlated at −0.38 with IQ after adjusting for reliability (−0.30 before). The relationship between IQ and religiousness was not strongly confounded by plausible demographic covariates (β = −0.24 in final model versus −0.30 without covariates).

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“…Despite the frequency of questions about cohesion and heterogeneity in the organizational literature, Lang et al (2018Lang et al ( , 2019 for a heterogeneous variance model in a mixed effect framework and would likely require a multistage model whose estimates would require Croon's correction (Croon, 2002) in order to be unbiased (Devlieger & Rosseel, 2017;Kelcey, Cox, & Dong, 2019), mediation is another straightforward extension if modeling the random scale effects in the multivariate multilevel SEM framework (Hoffman, 2019). McNeish and Hamaker (2020) note such advantages of multilevel SEM over mixed effects models, noting that multilevel SEM allows any hypothesized structural relations to be modeled between random effects.…”

Section: Implications For Organizational Researchmentioning

confidence: 99%

Specifying Location–Scale Models for Heterogeneous Variances as Multilevel SEMs

McNeish¹

2020

Preprint

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Standard multilevel models focus on variables that predict the mean while the within-group variability is largely treated as a nuisance. Recent work has shown the advantage of including predictors for both the mean (the location submodel) and the variability (the scale submodel) within a single model. Constrained versions of the model can be fit in standard mixed effect model software, but the most general version with random effects in each of the location and scale submodels has been noted for being difficult to fit and estimate in software. However, the latest release of Mplus includes new capabilities that facilitate fitting the general version of the model as a multilevel SEM. This paper introduces the general form of the model that includes location and scale random effects (called the location-scale model) and notes how it can be envisioned as a multilevel SEM. We provide a tutorial with example analyses and Mplus code for the model with two-level cross-sectional data and three-level repeated measures data and discuss how such a model has potential to extend recent developments in organizational science.

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Factor Score Path Analysis

Cited by 117 publications

References 14 publications

Common mechanisms of executive attention underlie executive function and effortful control in children

Common mechanisms of executive attention underlie executive function and effortful control in children

Intelligence and Religiosity among Dating Site Users

Specifying Location–Scale Models for Heterogeneous Variances as Multilevel SEMs

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