Adding to the burden of health disparities faced by rural, remote, geographically dispersed, and culturally distinct populations are methodological problems that compromise research aimed at addressing such disparities. These problems include low power from small sample sizes that discourage the use of modern statistical techniques, questionable validity with culturally distinct populations of measures developed for the majority culture, and the unacceptability of individual randomization in communalistic Native cultures. In response to these issues, the Center of Alaska Native Health (CANHR) proposes a conference that brings together methodologists and applied researchers. The content of the conference will focus on three main areas: (1) research designs and analysis methods that can maximize statistical power-examples include dynamic wait list research designs and Bayesian approaches;(2) strategies that respond to cultural distinctiveness to reduce error and bias, and (3) use of mixed methods that combine qualitative and quantitative methodologies. This proposed conference would also make use of several existing resources, most notably the contributions of the CANHR President's Professors, who are nationally prominent senior health disparities researchers. An outcome of this conference would include a series of methodological publications in a peer-reviewed journal. The two-day conference will aim to (1) develop applied, problem-oriented solutions to concrete design and analytic issues in small sample research using actual data from CANHR and other investigators, (2) encourage broader application of these methodological innovations to more general problems in multivariate and multilevel statistical techniques, and (3) evaluate the potential role of these innovative methods in describing phenomena, testing theory, and evaluating interventions. The conference sessions will be disseminated through HD web streaming, digital archiving of these sessions, and a proposed special journal section. The public health relevance of this conference includes its potential to advance health disparities research with rural and ethnic minority populations. Current CANHR studies likely to benefit from such a conference concern diabetes, nutrition, cardiovascular health, and the prevention and treatment of alcohol and drug addiction. There also will be broader application to general problems in multivariate and multilevel statistics, given their current requirement for large sample sizes. The conference aligns well with NIDA specific strategic goals to advance its prevention and treatment research, as well as its priority area in health disparities.
Culturally distinct American Indian and Alaska Native populations face health disparities evidenced by elevated disease rates, substandard sanitation and medical care, and media portrayals that dampen self-efficacy for improving health related behaviors. Small sample sizes and/or differential validity of research instruments are factors that compromise otherwise important research aimed at reducing health disparities in such populations. Based on over 15 years of experience in health disparities research, researchers at the Center for Alaska Native Health Research (CANHR) propose a conference bringing together methodologists and applied researchers to address the hazards of small sample research.
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| Henry, David; Dymnicki, Allison B; Mohatt, Nathaniel et al. (2015) Clustering Methods with Qualitative Data: a Mixed-Methods Approach for Prevention Research with Small Samples. Prev Sci 16:1007-16 |
| Fok, Carlotta Ching Ting; Henry, David; Allen, James (2015) Research Designs for Intervention Research with Small Samples II: Stepped Wedge and Interrupted Time-Series Designs. Prev Sci 16:967-77 |
| Fok, Carlotta Ching Ting; Henry, David (2015) Increasing the Sensitivity of Measures to Change. Prev Sci 16:978-86 |
| Henry, David; Fok, Carlotta Ching Ting; Allen, James (2015) Why Small is Too Small a Term: Prevention Science for Health Disparities, Culturally Distinct Groups, and Community-Level Intervention. Prev Sci 16:1026-32 |
