Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different subgroups within populations who often share certain outward characteristics. The assumption underlying LCA is that membership in unobserved groups (or classes) can be explained by patterns of scores across survey questions, assessment indicators, or scales. The application of LCA is an active area of research and continues to evolve. As more researchers begin to apply the approach, detailed information on key considerations in conducting LCA is needed. In the present article, we describe LCA, review key elements to consider when conducting LCA, and provide an example of its application.

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This study examined the relationship between youth health insurance status, insurance type (public versus private), and youths' unmet health care needs. A secondary analysis was conducted using data from the 2016 National Survey of Children's Health, a nationally representative, cross-sectional survey of U.S. youth. The sample included data from caregivers of 40,723 Hispanic, non-Hispanic black, and non-Hispanic white youth (0-17 years old) and was 49% female. Mplus 8.2 was used and statistical models accounted for the complex survey design. Using unweighted and weighted descriptive statistics and weighted probit regression models, we found that youth without health insurance were significantly more likely to have unmet health care needs compared to those with either public or private health insurance. We further found no statistically significant difference in unmet needs between youth with public and private health insurance. Our findings suggest that increased access to health insurance coverage, regardless of insurance type, may be an important policy focus when addressing youths' unmet health care needs. Our findings can be used to guide future social work advocacy regarding health insurance policy.