Research Proposal on Smoking Cessation

NURSING AND HEALTH

Nursing and Health: Research Proposal (Smoking Cessation)

Data Analysis

Data Analysis Plans For Demographic Variables

The demographic variables such as income, sex, race, income, and education would undergo a data analysis, most commonly known as univariate or bivariate analysis. Summary statistics would help analyze the demographics of the sample participants clearly and concisely. It is said that mean, median, mode, and measures of dispersion or variation support identifying the symmetry, skewness, or distribution of certain characteristics of the participants involved in the proposed research (Kaliyadan & Kulkarni, 2019). 

Descriptive Statistical Tests

The hypothesis for the given research question would be:

· Null hypothesis: The behavioral change techniques are more ineffective in smoking cessation than those not quitting for 6 months.

· Alternative Hypothesis: The behavioral change techniques are effective in smoking cessation compared to those not quitting for 6 months.

Different types of descriptive statistics are present that group the data in summarized form for gaining a better picture of the demographics of the selected sample (Ali & Bhaskar, 2016). The use of descriptive statistics would determine any errors present in the demographic data so that a surety is made beforehand that the data is free of mistakes before the analysis is made and results are presented (Guetterman, 2019). Gaining minimum or maximum interval values for the data would help check the frequencies and review whether values fall outside the range.

The ‘distribution’ could be calculated from factors like age and income brackets. ‘Central tendency’ could be calculated for education that could be the mean of the obtained educational degree of the participants. Moreover, ‘dispersion’ could be calculated using the standard deviation of ethnic group as an illustration. Giving meaning to the collected data is the main purpose of such statistical display so that summarizing the final results becomes more significant. Therefore, the normality of the data would be best depicted visually, such as the graphical representation of mean or standard deviations.