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Research in family psychology often focuses on understanding how multiple familial constructs develop over time. To examine these developmental processes, researchers frequently use a multivariate latent growth model (LGM) in which univariate LGMs are specified for each individual construct and then correlations are examined between the slopes and intercepts of different pairs of constructs. However, if the developmental associations among the constructs are hypothesized to derive from a higher-order common "cause" or factor, then a more appropriate model is the factor of curves (FOCUS) model. In this paper, we describe the FOCUS model for assessing the covariation among multiple developmental measures over time. We use empirical data to illustrate the benefits of the FOCUS model for testing whether a common factor, family academic orientation, is responsible for the interrelations among parental school perception, parental educational involvement, and children's academic competence from elementary school to high school. Results support that a higher-order family academic orientation construct can be used to characterize the developmental associations among parental school perception, parental educational involvement, and children's academic competence over time. We emphasize the importance of selecting a statistical model that matches one's theory of developmental change. (PsycINFO Database Record

Many US Latinos migrate or travel between the US and Mexico on a regular basis, defined as circular migration. Latinos with diabetes (n = 250) were surveyed about circular migration and their ability to use medications and perform recommended diabetes self-care activities. A review of medical charts was performed. Twenty-eight percent (n = 70) of patients traveled to Mexico during the last 12 months. Older Latinos were more likely to report traveling to Mexico and back into the US. Among those that traveled, 29 % reported use of less medication than they wanted to or were prescribed because of travel and 20 % ran out of medications. The rate of reported problem areas while traveling were 39 % (27/70) for following a diabetic diet, 31 % (21/70) for taking medication, and 37 % (26/70) for glucose self-monitoring. The results suggest that the structure of primary care and care coordination are important for this population to fully engage in diabetes self-care.

BackgroundLatinos from agricultural communities have a high prevalence of food insecurity and are at increased risk of obesity and diabetes, yet little is known about the associations between food insecurity and diabetes outcomes.ObjectiveTo examine the associations between food insecurity and diabetes outcomes among rural Latinos.MethodsCross-sectional survey with medical chart abstraction of 250 Latinos with diabetes. Primary outcomes are the control of 3 intermediate diabetes outcomes (hemoglobin A1C ≤8.0%, LDL-cholesterol ≤100 mg/dL, and blood pressure ≤140/90 mm Hg), a composite of control of the 3, and receipt of 6 processes of care. Secondary outcomes are cost-related medication underuse and participation in self-care activities.ResultsFifty-two percent of patients reported food insecurity and 1-in-4 reported cost-related medication underuse. Patients with food insecurity were more likely to report cost-related medication underuse [adjusted odds ratio (AOR)=2.49; 95% confidence intervals (CI), 1.30, 4.98; P=0.003], less likely to meet the composite measure for control of the 3 intermediate outcomes (AOR=0.24; 95% CI, 0.07, 0.84; P

A first-order latent growth model assesses change in an unobserved construct from a single score and is commonly used across different domains of educational research. However, examining change using a set of multiple response scores (e.g., scale items) affords researchers several methodological benefits not possible when using a single score. A curve of factors (CUFFS) model assesses change in a construct from multiple response scores but its use in the social sciences has been limited. In this article, we advocate the CUFFS for analyzing a construct's latent trajectory over time, with an emphasis on applying this model to educational research. First, we present a review of longitudinal factorial invariance, a condition necessary for ensuring that the measured construct is the same across time points. Next, we introduce the CUFFS model, followed by an illustration of testing factorial invariance and specifying a univariate and a bivariate CUFFS model to longitudinal data. To facilitate implementation, we include syntax for specifying these statistical methods using the free statistical software R.

Little is known about how neighborhood perceptions are related to diabetes outcomes among Latinos living in rural agricultural communities. Our objective was to examine the association between perceived neighborhood problems and diabetes outcomes. This is a cross-sectional survey study with medical record reviews of a random sample of 250 adult Latinos with type 2 diabetes. The predictor was a rating of patient ratings of neighborhood problems (crime, trash and litter, lighting at night, and access to exercise facilities, transportation, and supermarkets). The primary outcomes were the control of three intermediate outcomes [LDL-cholesterol (LDL-c)

A first-order latent growth model assesses change in an unobserved construct from a single score and is commonly used across different domains of educational research. However, examining change using a set of multiple response scores (e.g., scale items) affords researchers several methodological benefits not possible when using a single score. A curve of factors (CUFFS) model assesses change in a construct from multiple response scores but its use in the social sciences has been limited. In this article, we advocate the CUFFS for analyzing a construct’s latent trajectory over time, with an emphasis on applying this model to educational research. First, we present a review of longitudinal factorial invariance, a condition necessary for ensuring that the measured construct is the same across time points. Next, we introduce the CUFFS model, followed by an illustration of testing factorial invariance and specifying a univariate and a bivariate CUFFS model to longitudinal data. To facilitate implementation, we include syntax for specifying these statistical methods using the free statistical software R. [ABSTRACT FROM AUTHOR]