Introduction
In order to gain a better and an in-depth understanding of a research question, the researcher must use a data analysis plan that satisfies all the needs of the research design and the respective variables. As such, data analysis for this research would be done using the SPSS software. The beauty of using this statistical analysis software is that it offers a wide variety of statistical features that can be applied for different types of variables, both demographic and study variables.
Plan for demographic data analysis
Understanding the demographics of the participant’s is pretty essential in making accurate and relevant interpretations of the data. Understanding the demographic variables also goes a long way in finding any existing correlations between demographic characteristics and study variables. For instance, age or even economic status of the participants may have an impact on their ability to undertake self-care tasks for diabetes management. As such, for the purpose of analyzing demographic data, descriptive statistics would be used. The SPSS program would be used to find various measures of central tendency, precisely, the mean, mode and the median. Additionally, distribution tests would also be run for various demographic variables such as age, race, employment status and economic status.
Plan for analysis of study variables
The study variables for this research include physical exercise patterns, dietary patterns, diabetes self-management and occurrence of diabetes-related complications/co-morbidities. Correlation statistics (Pearson’s correlation co-efficient) at a confidence level of 5% would be used to find correlations between the intervention and the participants’ performance on the above-mentioned study variables. Additionally, to foster a better understanding of the research question, correlations would also be determined between the study variables and the demographic variables such as age.
