Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with categorical, continuous, or count distributions. A Monte Carlo simulation study is conducted to compare the performance of the new technique to two commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudo-class draws. Simulation results show that the model-based approach produces substantially less biased estimates of the effect compared to either classify-analyze technique, particularly when the association between the latent class variable and the distal outcome is strong. In addition, we show that only the model-based approach is consistent. The approach is demonstrated empirically: latent classes of adolescent depression are used to predict smoking, grades, and delinquency. SAS syntax for implementing this approach using PROC LCA and a corresponding macro are provided.

Despite recent methodological advances in latent class analysis (LCA) and a rapid increase in its application in behavioral research, complex research questions that include latent class variables often must be addressed by classifying individuals into latent classes and treating class membership as known in a subsequent analysis. Traditional approaches to classifying individuals based on posterior probabilities are known to produce attenuated estimates in the analytic model. We propose the use of a more inclusive LCA to generate posterior probabilities; this LCA includes additional variables present in the analytic model. A motivating empirical demonstration is presented, followed by a simulation study to assess the performance of the proposed strategy. Results show that with sufficient measurement quality or sample size, the proposed strategy reduces or eliminates bias.

High rates of comorbidity are observed between internalizing and externalizing problems, yet the developmental dynamics of comorbid symptom presentations are not yet well understood. This study explored the developmental course of latent profiles of internalizing and externalizing symptoms across kindergarten, 1st, and 2nd grade. The sample consisted of 336 children from an urban, low-income community, selected based on relatively high (61%) or low (39%) aggressive/oppositional behavior problems at school entry (64% male; 70% African American, 20% Hispanic). Teachers reported on children’s symptoms in each year. An exploratory latent profile analysis of children’s scores on aggression/oppositionality, hyperactivity/inattention, anxiety, and social withdrawal symptom factors revealed 4 latent symptom profiles: comorbid (48% of the sample in each year), internalizing (19–23%), externalizing (21–22%), and well-adjusted (7–11%). The developmental course of these symptom profiles was examined using a latent transition analysis, which revealed remarkably high continuity in the comorbid symptom profile (89% from one year to the next) and moderately high continuity in both the internalizing and externalizing profiles (80% and 71%, respectively). Internalizing children had a 20% probability of remitting to the well-adjusted profile by the following year, whereas externalizing children had a 25% probability of transitioning to the comorbid profile. These results are consistent with the hypothesis that a common vulnerability factor contributes to developmentally stable internalizing-externalizing comorbidity, while also suggesting that some children with externalizing symptoms are at risk for subsequently accumulating internalizing symptoms.

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Introduction: The purpose of this study was to examine transitions in smoking from adolescence into emerging adulthood and to identify factors that might infl uence these transitions, specifi cally, movement into and out of light and intermittent smoking.Methods: This study used Markov models to examine movement across three stages of smoking (nonsmoking, light and intermittent smoking, and heavy smoking) from adolescence into emerging adulthood. Biannual data were collected from 990 young men and women from the 12th grade until 2 years after high school.Results: At each timepoint, most youth were nonsmokers. Those who were heavy smokers in 12th grade had a 79% chance of also being heavy smokers 2 years after high school. Between 17% and 21% of participants were light and intermittent smokers at each timepoint, and the likelihood of remaining so at the next timepoint ranged from 56% to 72%. Less than one-half of the 12th-grade light and intermittent smokers were light and intermittent smokers 2 years later, and 3% of the sample were light and intermittent smokers across all assessments. Prevalence and transition rates did not differ by gender. College attendees reported less smoking than nonattendees before and after their transition to college, and attendees compared with nonattendees who smoked were less likely to transition from light and intermittent to heavy smoking and remain heavy smokers. Binge drinking was significantly related to 12th-grade smoking stage and to transitions from nonsmoking to smoking. Overall, few emerging adults maintained light and intermittent smoking consistently over time.

As more young people are identified with autism spectrum diagnoses without co-occurring intellectual disability (i.e. high-functioning autism spectrum disorder; HFASD), it is imperative that we begin to study the needs of this population. We sought to gain a preliminary estimate of the scope of the problem and to examine psychiatric risks associated HFASD symptoms in university students. In a large sample (n = 667), we examined prevalence of ASD in students at a single university both diagnostically and dimensionally, and surveyed students on other behavioral and psychiatric problems. Dependent upon the ascertainment method, between .7 per cent and 1.9 per cent of college students could meet criteria for HFASD. Of special interest, none of the students who were found to meet diagnostic criteria (n = 5) formally for HFASD in this study had been previously diagnosed. From a dimensional perspective, those students scoring above the clinical threshold for symptoms of autism (n = 13) self-reported more problems with social anxiety than a matched comparison group of students with lower autism severity scores. In addition, symptoms of HFASD were significantly correlated with symptoms of social anxiety, as well as depression and aggression. Findings demonstrate the importance of screening for autism-related impairment among university students.

Abstract. Substantial uncertainties surround the sensitivities and specificities of diagnostic techniques for urinary schistosomiasis. We used latent class (LC) modeling to address this problem. In this study, 220 adults in three villages northwest of Accra, Ghana were examined using five Schistosoma haematobium diagnostic measures: microscopic examination of urine for detection of S. haematobium eggs, dipsticks for detection of hematuria, tests for circulating antigens, antibody tests, and ultrasound scans of the urinary system. Testing of the LC model indicated non-invariance of the performance of the diagnostic tests across different age groups, and measurement invariance held for males and females and for the three villages. We therefore recommend the use of LC models for comparison between and the identification of the most accurate schistosomiasis diagnostic tests. Furthermore, microscopy and hematuria dipsticks were indicated through these models as the most appropriate techniques for detection of S. haematobium infection.