pathmodelfit-package {pathmodelfit}

pathmodelfit: Path Component Fit Indices for Latent Structural Equation Models

Description

Functions for computing fit indices for evaluating the path component of latent variable structural equation models. Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) <doi:10.1177/1094428117736137>. Also included are fit indices described by Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.

Author(s)

Maintainer: Steven Andrew Culpepper [e-mail] (ORCID)

Authors:

• Larry Williams [e-mail]

References

Hancock, G. R., & Mueller, R. O. (2011). The reliability paradox in assessing structural relations within covariance structure models. Educational and Psychological Measurement, 71(2), 306-324.

McNeish, D., & Hancock, G. R. (2018). The effect of measurement quality on targeted structural model fit indices: A comment on Lance, Beck, Fan, and Carter (2016). Psychological Methods, 23(1), 184–190. https://doi.org/10.1037/met0000157

O'Boyle, E. H., Jr., & Williams, L. J. (2011). Decomposing model fit: Measurement vs. theory in organizational research using latent variables. Journal of Applied Psychology, 96(1), 1–12. https://doi.org/10.1037/a0020539

Williams, L. J., & O’Boyle, E. H. (2011). The myth of global fit indices and alternatives for assessing latent variable relations. Organizational Research Methods, 14, 350-369.

Williams, L. J., O’Boyle, E. H., & Yu, J. (2020). Condition 9 and 10 tests of model confirmation: A review of James, Mulaik, and Brett (1982) and contemporary alternatives. Organizational Research Methods, 23, 1, 6-29.

Examples

library(lavaan)

model4 <- '
Ldrrew =~ LdrrewI1 + LdrrewI2 + LdrrewI3
Jobcom =~ JobcomI1 + JobcomI2 + JobcomI3
Jobsat =~ JobsatI1 + JobsatI2 + JobsatI3
Orgcom =~ OrgcomI1 + OrgcomI2 + OrgcomI3
Jobsat ~ Ldrrew + Jobcom
Orgcom ~ Jobsat'

data(mediationVC)

fit <- sem(model4, sample.cov = mediationVC, sample.nobs = 232)
pathmodelfit(fit)