Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI can have various distributional forms with different degrees of missingness. However, when variables with missing data contain skip patterns (i.e. questions not applicable to some survey participants are thus skipped), implementation of MI may not be straightforward. In this research, we compare two approaches for MI when skip-pattern covariates with missing values exist. One approach imputes missing values in the skip-pattern variables only among applicable subjects (denoted as imputation among applicable cases (IAAC)). The second approach imputes skip-pattern covariates among all subjects while using different recoding methods on the skip-pattern variables (denoted as imputation with recoded non-applicable cases (IWRNC)). A simulation study is conducted to compare these methods. Both approaches are applied to the 2015 and 2016 Research and Development Survey data from the National Center for Health Statistics. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

The National Center for Health Statistics is assessing the usefulness of recruited web panels in multiple research areas. One research area examines the use of close-ended probe questions and split-panel experiments for evaluating question-response patterns. Another research area is the development of statistical methodology to leverage the strength of national survey data to evaluate, and possibly improve, health estimates from recruited panels. Recruited web panels, with their lower cost and faster production cycle, in combination with established population health surveys, may be useful for some purposes for statistical agencies. Our initial results indicate that web survey data from a recruited panel can be used for question evaluation studies without affecting other survey content. However, the success of these data to provide estimates that align with those from large national surveys will depend on many factors, including further understanding of design features of the recruited panel (e.g. coverage and mode effects), the statistical methods and covariates used to obtain the original and adjusted weights, and the health outcomes of interest.

While web surveys have become increasingly popular as a method of data collection, there is concern that estimates obtained from web surveys may not reflect the target population of interest. Web survey estimates can be calibrated to existing national surveys using a propensity score adjustment, although requirements for the size and collection timeline of the reference data set have not been investigated. We evaluate health outcomes estimates from the National Center for Health Statistics' Research and Development web survey. In our study, the 2016 National Health Interview Survey as well as its quarterly subsets are considered as reference datasets for the web data. It is demonstrated that the calibrated health estimates overall vary little when using the quarterly or yearly data, suggesting that there is flexibility in selecting the reference dataset. This finding has many practical implications for constructing reference data, including the reduced cost and burden of a smaller sample size and a more flexible timeline.

The most common approach for a scale construction is to create a scale as a sum of manifest variables (a "sum scale"). When we use the sum scale for analysis, we implicitly assume that there is a one-dimensional latent structure representing the manifest data on a multidimensional space. In this commentary, we review basics of identifying a latent structure using measured variables with a minimum linear algebra. We demonstrate the technique using Fisher's iris data as an illustration. We examine the relationships between resulting latent variables and the sum scale to evaluate goodness of the sum scale. As a practical solution, in general, we could create a sum scale using a set of positively and highly correlated measured variables. More care is needed when the data are not unidimensional.

This study examined gender differences within Black, Latino, and White subgroups in the utilization of comprehensive services and their relation to posttreatment substance use. Survey data were collected during the National Treatment Improvement Evaluation Study (NTIES), a prospective, longitudinal, multisite study of substance abuse treatment programs and their clients in the United States. The analytic sample consisted of 1,812 Blacks (734 women and 1,078 men), 486 Latinos (147 women and 339 men), and 844 Whites (147 women and 339 men) from 59 service delivery organizations. Results related to service utilization indicated that compared to men, women in all racial and ethnic groups needed and received more services targeted to their needs and reported more positive relations with service providers. Gender was a significant moderator of the relationship between service receipt and treatment outcomes for all racial and ethnic groups, but especially for the Latino subsample. Findings point to the need to consider race-specific gender differences in the development of culturally competent, comprehensive substance abuse treatment.