Exploratory structural equation modeling (ESEM) allows items to cross-load on nontarget factors and provides flexibility in modeling multidimensional survey data. However, this flexibility comes at the cost of increased model complexity due to the estimation of a significantly greater number of parameters than conventional SEM. This study systematically evaluated the model fit indices in ESEM through a Monte Carlo simulation. Design factors included model complexity,atent factor correlations, magnitudes and percentages of major cross-loadings, and sample sizes. Results showed that both the number ofatent factors m and the item–factor ratio p/m had distinct impacts on rejection rates, particularly in underspecified models. Among the fit measures, ${\chi }^2$ tests and Mc demonstrated high power for detecting misspecification while, in general, elevating false positives. CFI and TLI provided a more balanced trade-off between false- and true-positive rates. When evaluating ESEM, it is important to consider multiple fit indices and justify the cutoff criteria across models with different factor structures.

To determine the impact of a home-delivered, medically tailored grocery intervention on glycemic control and diet quality among participants with type 2 diabetes (T2D) experiencing food insecurity. A single-arm prepost study was conducted. One hundred one English, Spanish, or Marshallese-speaking adults were recruited from food pantries in Northwest Arkansas (from August 2021 to February 2023). Twelve weekly T2D-appropriate food boxes with diabetes self-management education and support materials were home-delivered. Primary outcomes measured at preintervention and postintervention included hemoglobin A1c and diet quality (i.e., Healthy Eating Index-2015). Mixed-effects regressions controlling for age, sex, race/ethnicity, household size, education, and employment found hemoglobin A1c scores significantly decreased by 0.56% (units) at postintervention compared with preintervention (P = 0.01). No significant changes in Healthy Eating Index-2015 scores were found (P = 0.47). Future research may build on this study's findings and explore mechanisms whereby medically tailored groceries can benefit people across communities experiencing high rates of food insecurity and T2D.

Factorial invariance is critical for ensuring consistent relationships between measured variables and latent constructs across groups or time, enabling valid comparisons in social science research. Detecting factorial invariance becomes challenging when varying degrees of heterogeneity are present in the distribution of latent factors. This simulation study examined how changes in latent means and variances between groups influence the detection of noninvariance, comparing Bayesian and maximum likelihood fit measures. The design factors included sample size, noninvariance levels, and latent factor distributions. Results indicated that differences in factor variance have a stronger impact on measurement invariance than differences in factor means, with heterogeneity in latent variances more strongly affecting scalar invariance testing than metric invariance testing. Among model selection methods, goodness-of-fit indices generally exhibited lower power compared to likelihood ratio tests (LRTs), information criteria (ICs; except BIC), and leave-one-out cross-validation (LOO), which achieved a good balance between false and true positive rates.

Family members/support persons can shape the self-management decisions of persons with T2DM (PWDs), and their inclusion in DSMES may lead to improved outcomes. However, research on the effectiveness of family models of DSMES (Family-DSMES) is limited. We conducted a comparative effectiveness-implementation RCT of Family-DSMES and a standard model of DSMES (Standard-DSMES) delivered via telehealth. 550 diverse adult PWDs and their family members/support persons were recruited from eight primary care clinics in both rural and urban areas. Process data was collected to evaluate implementation characteristics. Biometric, behavioral, and psychosocial measures were collected from PWD. Mean attendance was slightly higher for PWDs in Family-DSMES than Standard-DSMES (62 % vs. 57 %, p = 0.096). Engagement (p = 0.319) and class time (p = 0.145) were similar across arms. PWDs in both arms experienced a decrease in HbA1c at all time points (p < 0.005). No statistically significant between-arm differences were found for biometric outcomes or behavioral outcomes including HbA1c. For diabetes-related helpful family involvement, the improvement in the Family-DSMES arm was significantly greater than in the Standard-DSMES arm (p = 0.024). Results demonstrate the ability to engage rural and diverse PWDs in a telehealth intervention with high fidelity to achieve clinically and statistically significant improvements in HbA1c.

The impact of nonnormality on parameter estimates and model-data fit has been extensively evaluated in confirmatory factor analysis models, but comparatively less attention has been given to path models, where nonnormality may occur in different combinations across exogenous, endogenous, and mediating variables. This simulation study examined the effects of varying degrees and configurations of nonnormality on path coefficient estimation, standard errors, statistical power, and model fit under five estimation methods: maximum likelihood (ML), robust maximum likelihood (MLR), mean-adjusted maximum likelihood (MLM), mean- and variance-adjusted maximum likelihood (MLMV), and bootstrap maximum likelihood (MLB). Sample size and severity of nonnormality were systematically varied across 21 distributional configurations. Results showed that relative bias of path coefficients was generally small and approached zero as sample size increased, although coefficients involving severely nonnormal variables showed larger bias and root mean squared error when samples were small. Robust estimators (MLR, MLM, MLMV, and MLB) produced more accurate standard error estimates than ML under nonnormal conditions, with MLB showing the smallest standard error bias in the smallest samples. Statistical power increased primarily as a function of the magnitude of the path coefficient and sample size, though severe nonnormality reduced power at small sample sizes. Fit index results indicated that CFI and TLI remained very high in most conditions, even when variables were severely nonnormal. SRMR was relatively unaffected by nonnormality and sample size, whereas RMSEA was more sensitive to both factors and improved markedly with larger samples. Overall, the findings suggest that robust estimators are preferable for path analysis with nonnormal data, particularly when sample sizes are small to moderate.