In the present study, the data analyses section will comprise two components: demographic analysis and the analysis of the research variables.
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Descriptive Plan for Data Analysis for Demographic Variables
The analysis of demographic variables is targeted at comparing the demographic variables with the main variables studied in the research for identifying whether there are potential confounding relationships between the two (Alspelmeier, 2010). To determine whether there is an association between the demographic variables (participants’ age, sex, ethnicity, health care status) and the primary variables of interest in the present study (BMI and the success of the medical intervention), the following plan will be followed:
Conducting a series of univariate analyses, such as:
- Pearson’s chi-square test for measuring the relationships between categorical variables. In the case of the present study, the chi-square test for testing goodness of fit will be the most appropriate since it will allow the researcher to decide whether there are any differences between the experimental and the theoretical value (Alspelmeier, 2010).
- Independent sample t-test as well as the one-way ANOVA (analysis of variance) test for measuring the relationship between numerical and categorical variables (Alspelmeier, 2010) such as BMI and a certain demographic variable. For example, it is important to measure whether there is a relationship between the ethnicity of the study participants and their BMI (obesity status) due to the findings that ethnicity can relate to individuals’ obesity-related behaviors both among children and adults (Falconer et al., 2014)
Descriptive Plan for Data Analysis for Study Variables
The main analysis of variables will test the formulated hypotheses with the use of the univariate approach. Association between the participant’s BMI and the success of the medical intervention implementation will be tested through Pearson’s correlations, which measures the linear relationship between two variables. The second set of data analyses will compare the changes in BMI among participants that have been exposed to different types of interventions (alterations in the quality of care, diet interventions, exercise interventions) with the use of ANOVA procedures. It is recommended to conduct the text in three directions:
- The first ANOVA test will compare the BMI scores of the entire sample involved in the research on adult obesity (Alspelmeier, 2010).
- The second ANOVA test will compare the scores of only those participants who scored above the BMI obesity-related mean after the implementation of the medical intervention.
- The third ANOVA test will compare the scores of only those participants who scored below the BMI obesity-related mean after the implementation of the medical intervention.
The final set of analyses will use Pearson’s r for testing the remaining hypothesis. The first analysis will comprise of the correlation tests for determining the association of participants’ changes in weight and overall health scores after being exposed to the medical intervention. Overall, the presented data analysis plan will be focusing on measuring the effectiveness of healthcare and lifestyle interventions’ implementation for reducing the BMI of adults diagnosed with obesity. BMI is the key variable on which the present study will focus since it allows for a reliable and quick measurement of individual weight status. The issue of obesity has never been as important as today (Hruby, 2015); therefore, the present study will aim to measure whether obese adults could improve their health by integrating better quality of care or lifestyle interventions into their everyday lives.
Alspelmeier, J. (2010). How to write methods, results, and discussion.Web.
Falconer, C., Park, M., Croker, H., Kessel, A., Saxena, S., Viner, M., & Kinra, S. (2014). Can the relationship between ethnicity and obesity-related behaviors among school-aged children be explained by deprivation? A cross-sectional study. BMJ Open, 4, 1-10.
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Hruby, A. (2015). The epidemiology of obesity: A big picture. Pharmacoeconomics, 33(7), 673-689.