A research study is strengthened when a theoretical or conceptual framework is incorporated into the study’s design. Identifying and incorporating a conceptual model into a study can be a challenge if the researcher is analyzing a population-based dataset since data are already collected. These data from a population-based dataset are usually collected from a large, comprehensive cross-sectional survey. Many cross-sectional surveys are conducted by government public health agencies and consist of a sample that is weighted, so the results are representative of the population. Therefore, the findings can be generalized to the population of interest.
This case study describes the use of the Aday and Andersen Access to Medical Care framework in the analysis of a population-based dataset, the National Survey of Children’s Health. The National Survey of Children’s Health is a cross-sectional survey conducted at regular intervals by the United States federal government public health agencies. The National Survey of Children’s Health consists of weighted samples and is representative at the national, regional, and state levels, therefore providing valuable information to plan, deliver, and evaluate key child and adolescent health services. This case study describes how the Aday and Andersen framework was used to select the research study’s independent variables to ascertain risk factors associated with the dependent (or outcome) variable: parents’ satisfaction with their children’s primary health care. An analysis was conducted with two of the National Survey of Children’s Health datasets, 2007 and 2011-2012, thereby allowing for a cross-sectional comparison over time. This case study will highlight how the use of the framework provided structure in the development of the research questions and analysis.