Data Analysis
Analyses were conducted in Mplus 8.8 (Muthén & Muthén, 2017) using
Mplus Automation (Hallquist & Wiley, 2018). Mplus’ MLR estimator was
used with full information maximum likelihood of handling missing data
for unconditional models. The data analyses were conducted in four
steps, following guidelines by Ryoo and colleagues (2018). First,
Confirmatory Factor Analyses were conducted to examine the properties of
maternal mental health assessments in one model (i.e., stress, anxiety,
and depression; Appendix A). CFA model fit was assessed using absolute
and relative indices. A Root Mean Square Error of Approximation (RMSEA;
Steiger & Lind, 1980) of less than.05 to .08, a Tucker-Lewis Index
(TLI; 1973) of above .95 and a Comparative Fit Index (CFI; Bender, 1990)
above .95 was considered evidence of adequate model fit (see Appendix A
for more information).
Then Latent Profile Analyses (LPA; 2-5 models) were estimated separately
at each timepoint to investigate configural measurement invariance LPA
enumeration was completed using statistical criteria such as Bayesian
Information Criteria (BIC), sample-size adjusted BIC (aBIC),
bootstrapped likelihood ratio test (BLRT), adjusted Lo-Mendell-Rubin
likelihood ratio test (aLMR), standardized entropy, and successful model
estimation; substantive considerations such as interpretability and
number of students in each latent profile were also considered (Wang &
Wang, 2020). Third, unconditional Latent Transition Analyses (LTA) were
conducted, testing measurement invariance across time points. Then,
conditional LTAs were conducted, incorporating the main effects.
Further, longitudinal LPA with formal tests of measurement invariance
using corrected likelihood ratio tests comparing the constrained and
unconstrained models; BIC was also used to compare these two sets of
models (Wang & Wang, 2020). Finally, to avoid local maximum, all LTA
start values were increased to 10,000, 2000, and 400 for the
longitudinal models (Gillet et al., 2017). Model specification of the
LPA and LTA models followed traditional LPA modelling with the
assumption of local independence (i.e., a diagonal residual covariance
matrix) and item variances constrained equal across profiles.
Finally, we tested differences in family and child characteristics
(e.g., children’s temperament and mental health measured) across our
different profiles within the wave. Due to limited sample sizes within
profiles, we did this using a series of ANOVAs. Post-hoc analyses were
conducted using Tukey’s B or Dunnett’s C (based on homogeneity of
variance) and chi-square tests. In cases where the homogeneity of
variance was met, to account for the unequal sample sizes across the
maternal mental health profiles, the harmonic mean was used to measure
the central tendency for the ANOVAs (Bortz, 2005; Rankin, 1974).
Parenting information was only collected prior to the pandemic and is
therefore only included in the comparison across profiles in our earlier
wave.