Publications by authors named "Amanda K Montoya"

8 Publications

  • Page 1 of 1

The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation.

Educ Psychol Meas 2021 Jun 12;81(3):413-440. Epub 2020 Aug 12.

Arizona State University, Tempe, AZ, USA.

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule ( = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.
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http://dx.doi.org/10.1177/0013164420942899DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072951PMC
June 2021

Key dimensions of post-traumatic stress disorder and endothelial dysfunction: a protocol for a mechanism-focused cohort study.

BMJ Open 2021 05 5;11(5):e043060. Epub 2021 May 5.

Psychology, University of California Los Angeles, Los Angeles, California, USA

Introduction: Both trauma exposure and post-traumatic stress disorder (PTSD) are associated with increased risk of cardiovascular disease (CVD), the leading cause of death in the USA. Endothelial dysfunction, a modifiable, early marker of CVD risk, may represent a physiological mechanism underlying this association. This mechanism-focused cohort study aims to investigate the relationship between PTSD (both in terms of diagnosis and underlying symptom dimensions) and endothelial dysfunction in a diverse, community-based sample of adult men and women.

Methods And Analysis: Using a cohort design, 160 trauma-exposed participants without a history of CVD are designated to the PTSD group (n=80) or trauma-exposed matched control group (n=80) after a baseline diagnostic interview assessment. Participants in the PTSD group have a current (past month) diagnosis of PTSD, whereas those in the control group have a history of trauma but no current or past psychiatric diagnoses. Endothelial dysfunction is assessed via flow-mediated vasodilation of the brachial artery and circulating levels of endothelial cell-derived microparticles. Two higher order symptom dimensions of PTSD-fear and dysphoria-are measured objectively with a fear conditioning paradigm and attention allocation task, respectively. Autonomic imbalance, inflammation, and oxidative stress are additionally assessed and will be examined as potential pathway variables linking PTSD and its dimensions with endothelial dysfunction. Participants are invited to return for a 2-year follow-up visit to reassess PTSD and its dimensions and endothelial dysfunction in order to investigate longitudinal associations.

Ethics And Dissemination: This study is conducted in compliance with the Helsinki Declaration and University of California, Los Angeles Institutional Review Board. The results of this study will be disseminated via articles in peer-reviewed journals and presentations at academic conferences and to community partners.

Trial Registration Number: NCT03778307; pre-results.
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http://dx.doi.org/10.1136/bmjopen-2020-043060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103395PMC
May 2021

Modeling motivation for alcohol in humans using traditional and machine learning approaches.

Addict Biol 2021 05 29;26(3):e12949. Epub 2020 Jul 29.

Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.

Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self-administration using a novel paradigm. Heavy drinkers (n = 67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60 mg%) and a progressive-ratio alcohol self-administration paradigm (maximum breath alcohol 120 mg%; ratio requirements range = 20-3 139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self-administration. K-means clustering was used to identify motivated (n = 41) and unmotivated (n = 26) self-administration trajectories. The data were analyzed using two approaches: a theory-driven test of a-priori predictors and a data-driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data-driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self-administration. Additional application of this model to AUD translational science and treatment development appear warranted.
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http://dx.doi.org/10.1111/adb.12949DOI Listing
May 2021

MIMIC Models for Uniform and Nonuniform DIF as Moderated Mediation Models.

Appl Psychol Meas 2020 Mar 12;44(2):118-136. Epub 2019 Apr 12.

University of California, Los Angeles, USA.

In this article, the authors describe how multiple indicators multiple cause (MIMIC) models for studying uniform and nonuniform differential item functioning (DIF) can be conceptualized as mediation and moderated mediation models. Conceptualizing DIF within the context of a moderated mediation model helps to understand DIF as the effect of some variable on measurements that is not accounted for by the latent variable of interest. In addition, useful concepts and ideas from the mediation and moderation literature can be applied to DIF analysis: (a) improving the understanding of uniform and nonuniform DIF as direct effects and interactions, (b) understanding the implication of indirect effects in DIF analysis, (c) clarifying the interpretation of the "uniform DIF parameter" in the presence of nonuniform DIF, and (d) probing interactions and using the concept of "conditional effects" to better understand the patterns of DIF across the range of the latent variable.
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http://dx.doi.org/10.1177/0146621619835496DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003182PMC
March 2020

Fitness, Sleep-Disordered Breathing, Symptoms of Depression, and Cognition in Inactive Overweight Children: Mediation Models.

Public Health Rep 2017 Nov/Dec;132(2_suppl):65S-73S

5 Georgia Prevention Institute, Augusta University, Augusta, GA, USA.

Objectives: We used mediation models to examine the mechanisms underlying the relationships among physical fitness, sleep-disordered breathing (SDB), symptoms of depression, and cognitive functioning.

Methods: We conducted a cross-sectional secondary analysis of the cohorts involved in the 2003-2006 project PLAY (a trial of the effects of aerobic exercise on health and cognition) and the 2008-2011 SMART study (a trial of the effects of exercise on cognition). A total of 397 inactive overweight children aged 7-11 received a fitness test, standardized cognitive test (Cognitive Assessment System, yielding Planning, Attention, Simultaneous, Successive, and Full Scale scores), and depression questionnaire. Parents completed a Pediatric Sleep Questionnaire. We used bootstrapped mediation analyses to test whether SDB mediated the relationship between fitness and depression and whether SDB and depression mediated the relationship between fitness and cognition.

Results: Fitness was negatively associated with depression ( B = -0.041; 95% CI, -0.06 to -0.02) and SDB ( B = -0.005; 95% CI, -0.01 to -0.001). SDB was positively associated with depression ( B = 0.99; 95% CI, 0.32 to 1.67) after controlling for fitness. The relationship between fitness and depression was mediated by SDB (indirect effect = -0.005; 95% CI, -0.01 to -0.0004). The relationship between fitness and the attention component of cognition was independently mediated by SDB (indirect effect = 0.058; 95% CI, 0.004 to 0.13) and depression (indirect effect = -0.071; 95% CI, -0.01 to -0.17).

Conclusions: SDB mediates the relationship between fitness and depression, and SDB and depression separately mediate the relationship between fitness and the attention component of cognition.
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http://dx.doi.org/10.1177/0033354917731308DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5692181PMC
December 2017

The dark side of fluency: Fluent names increase drug dosing.

J Exp Psychol Appl 2017 09 22;23(3):231-239. Epub 2017 Jun 22.

Department of Psychology, The Ohio State University.

Prior research has demonstrated that high processing fluency influences a wide range of evaluations and behaviors in a positive way. But can high processing fluency also lead to potentially hazardous medical behavior? In 2 controlled experiments, we demonstrate that increasing the fluency of pharmaceutical drug names increases drug dosage. Experiment 1 shows that drugs with fluent names are perceived as safer than those with disfluent names and this effect increases drug dosage for both synthetically produced and herbal drugs. Experiment 2 demonstrates that people chose a higher dosage for themselves and for a child if the drug bears a fluent (vs. disfluent) name. Using linear regression based mediation analysis, we investigated the underlying mechanisms for the effect of fluency on risk perception in more detail. Contrary to prior research, we find that affect, but not familiarity, mediates the fluency-risk link. Our findings suggest that a drug name's fluency is a powerful driver of dosing behavior. (PsycINFO Database Record
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http://dx.doi.org/10.1037/xap0000131DOI Listing
September 2017

Why are some STEM fields more gender balanced than others?

Psychol Bull 2017 01 10;143(1):1-35. Epub 2016 Oct 10.

Department of Psychology, University of Washington.

Women obtain more than half of U.S. undergraduate degrees in biology, chemistry, and mathematics, yet they earn less than 20% of computer science, engineering, and physics undergraduate degrees (National Science Foundation, 2014a). Gender differences in interest in computer science, engineering, and physics appear even before college. Why are women represented in some science, technology, engineering, and mathematics (STEM) fields more than others? We conduct a critical review of the most commonly cited factors explaining gender disparities in STEM participation and investigate whether these factors explain differential gender participation across STEM fields. Math performance and discrimination influence who enters STEM, but there is little evidence to date that these factors explain why women's underrepresentation is relatively worse in some STEM fields. We introduce a model with three overarching factors to explain the larger gender gaps in participation in computer science, engineering, and physics than in biology, chemistry, and mathematics: (a) masculine cultures that signal a lower sense of belonging to women than men, (b) a lack of sufficient early experience with computer science, engineering, and physics, and (c) gender gaps in self-efficacy. Efforts to increase women's participation in computer science, engineering, and physics may benefit from changing masculine cultures and providing students with early experiences that signal equally to both girls and boys that they belong and can succeed in these fields. (PsycINFO Database Record
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http://dx.doi.org/10.1037/bul0000052DOI Listing
January 2017

Two-condition within-participant statistical mediation analysis: A path-analytic framework.

Psychol Methods 2017 03 30;22(1):6-27. Epub 2016 Jun 30.

Department of Psychology, The Ohio State University.

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of 2 different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this article we recast Judd et al.'s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path-analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al.'s method requires, because it relies only on an inference about the product of paths-the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path-analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses. (PsycINFO Database Record
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http://dx.doi.org/10.1037/met0000086DOI Listing
March 2017