Researchers are commonly faced with the problem of missing data. This article presents theoretical and empirical information for the selection and application of approaches for handling missing data on a single variable. An actual data set of 492 cases with no missing values was used to create a simulated yet realistic data set with missing at random (MAR) data. The authors compare and contrast five approaches (listwise deletion, mean substitution, simple regression, regression with an error term, and the expectation maximization [EM] algorithm) for dealing with missing data, and compare the effects of each method on descriptive statistics and correlation coefficients for the imputed data (n = 96) and the entire sample (n = 492) when imputed data are inculded. All methods had limitations, although our findings suggest that mean substitution was the least effective and that regression with an error term and the EM algorithm produced estimates closest to those of the original variables.
Osteoporosis is an age related metabolic disease that primarily affects women and causes bone demineralization that results in fractures. Early identification of risk factors for osteoporosis and development of prevention programs is needed to halt the increasing incidence of the disease. Public health nurses (PHNs), with their emphasis on primary, secondary, and tertiary prevention with individuals and families, are in a unique position to protect the health of these vulnerable populations who are at risk for osteoporosis. This article describes the implementation and program evaluation of three osteoporosis prevention educational programs that use three levels of intensity of design. Each design is based upon the learning needs of the targeted audience. The goals of each program were to increase knowledge of osteoporosis, increase health beliefs, and increase the frequency of osteoporosis preventing behaviors. Theoretical aspects from adult learning and the Health Belief Model (HBM) were used to develop the programs. For the program evaluation, participants completed evaluation instruments before and 3 weeks after participating in an osteoporosis health education program. Participants in all programs had significantly higher levels of knowledge after completing the programs; however, overall, there was no change in health beliefs or behaviors. Implications of these findings are discussed.
Using a conceptual and nontechnical approach, the meaning of structural equation modeling (SEM) and the similarities to, and differences from, more commonly used procedures such as correlation, regression, path analysis, and factor analysis are explained. Application of the statistical technique is presented using data from a study of the relationships among stresses, strains, and physical health in a random sample of 492 community-dwelling elders aged 65 and older. Advantages of each statistical procedure are described. Theoretical issues related to the use of each procedure are presented with emphasis on the need for a sound theoretical model and match between the statistical procedure and the aims of the analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.