2019
DOI: 10.31234/osf.io/9vcnz
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Dyadic Measurement Invariance and Its Importance for Replicability in Romantic Relationship Research

Abstract: Comparisons of group means, variances, correlations, and/or regression slopes involving psychological variables rely on an assumption of measurement invariance-that the latent variables under investigation have equivalent meaning and measurement across group. When measures are noninvariant, replicability suffers, as comparisons are either conceptually meaningless, or hindered by inflated Type I error rates. We propose that the failure to account for interdependence amongst dyad members when testing measurement… Show more

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Cited by 6 publications
(5 citation statements)
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“…Fourth, we used recently recommended techniques to test dependent-group invariance in a dyadic structure. This involves modeling observed and latent factors in both groups in a single structure while accounting for the interrelationship between dyad members by adding covariances between cross-group latent factors and pairs of item errors [22,24]. We followed the recommended invariance-testing sequential order, comparing increasingly constrained models: configural (no equality constrains), metric (equal factor loadings) and scalar invariance (equal intercepts) [30,31].…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Fourth, we used recently recommended techniques to test dependent-group invariance in a dyadic structure. This involves modeling observed and latent factors in both groups in a single structure while accounting for the interrelationship between dyad members by adding covariances between cross-group latent factors and pairs of item errors [22,24]. We followed the recommended invariance-testing sequential order, comparing increasingly constrained models: configural (no equality constrains), metric (equal factor loadings) and scalar invariance (equal intercepts) [30,31].…”
Section: Methodsmentioning
confidence: 99%
“…Next, we examined the CDDUX measurement invariance across self-and parent-reports to establish its convergent validity. We utilized recently developed techniques to model dyadic data and their equivalence across time and groups (dyadic measurement invariance; e.g., [21][22][23][24][25]). These techniques allow us to statistically account for dependencies within dyads while retaining the power of dependent-group data analysis [21].…”
Section: Structural Validation and Dyadic Child-parent Measurement In...mentioning
confidence: 99%
“…As alluded to above, we believe that the statistical methods used for most of our research questions severely limit the speed of understanding mechanisms in this area of study. On the one hand, relationship researchers are certainly leaders in adopting newer methods to answer questions of process (e.g., contributing to new models on the longitudinal modeling of couple-related dynamics; Bolger & Laurenceau, 2013; Sakaluk et al, 2021). On the other hand, we do not yet see many of these models capturing reciprocal and recursive mechanisms in the study of relationships and health.…”
Section: Intensive Longitudinal Approachesmentioning
confidence: 99%
“…In other words, the scaling of items is equivalent across partners. Scalar invariance indicates whether the amount of variation in each group is equivalent, and that each groups' mean differences are interpretable [51,52]. Power analyses for invariance testing suggests the use of changes in alternative fit indices (ΔAFI).…”
Section: Dyadic Confirmatory Factor Analysis and Measurement Invariancementioning
confidence: 99%