Evidence map›Paper›PMID 35585278›Full record

ArticleBehavior research methods2023

Dynamic fit index cutoffs for one-factor models.

Daniel McNeish, Melissa G Wolf

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Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

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21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
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  7. Self and other mental health stigma among public safety professionals: a psychometric study.Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS · 2025
    Review
  8. Article
  9. Article
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  12. Development of a Self-Care Scale for Compound Caregivers.Healthcare (Basel, Switzerland) · 2024
    Article
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  18. Review
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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Daniel McNeishDepartment of Psychology, Arizona State University, PO Box 871104, Tempe, AZ, 85287, USA. dmcneish@asu.edu.
Melissa G WolfUniversity of California, Santa Barbara, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessing whether a multiple-item scale can be represented with a one-factor model is a frequent interest in behavioral research. Often, this is done in a factor analysis framework with approximate fit indices like RMSEA, CFI, or SRMR. These fit indices are continuous measures, so values indicating acceptable fit are up to interpretation. Cutoffs suggested by Hu and Bentler (1999) are a common guideline used in empirical research. However, these cutoffs were derived with intent to detect omitted cross-loadings or omitted factor covariances in multifactor models. These types of misspecifications cannot exist in one-factor models, so the appropriateness of using these guidelines in one-factor models is uncertain. This paper uses a simulation study to address whether traditional fit index cutoffs are sensitive to the types of misspecifications common in one-factor models. The results showed that traditional cutoffs have very poor sensitivity to misspecification in one-factor models and that the traditional cutoffs generalize poorly to one-factor contexts. As an alternative, we investigate the accuracy and stability of the recently introduced dynamic fit cutoff approach for creating fit index cutoffs for one-factor models. Simulation results indicated excellent performance of dynamic fit index cutoffs to classify correct or misspecified one-factor models and that dynamic fit index cutoffs are a promising approach for more accurate assessment of model fit in one-factor contexts.

Indexed as

Behavioral ResearchModels, StatisticalComputer SimulationEmpirical ResearchFactor Analysis, StatisticalHumans

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.