Clinical Question and Intervention
The clinical question for this research covers the following aspects. “In the primary care setting, for women of minorities aged 40-54, how effective is it for providers to initiate a conversation regarding recommended mammography screening versus not initiating conversation in increasing the rate of early detection of breast cancer over 6 months?” thus, the research is going to reveal the efficiency of initiating a conversation regarding recommended mammography screening for women of minorities aged 40-54.
The planned intervention within this research is a conversation with female patients regarding the procedure of mammography screening. This is likely to increase the percentage of women who initiate mammography screening and thus to improve the rates of detecting breast cancer in an early stage.
The outcomes that will be analyzed include the interview answers of both experimental and control groups. It is expected that the experimental group produces significantly better results than the control group in informative preparation to initiate mammography screening in the post-experimental interview. The results of pre- and post-experiment interviews will be calculated and analyzed with the use of descriptive statistics procedure.
Suggested Statistical Procedure
In conditions of experimental research involving two groups that are interviewed before and after the experiment, descriptive statistics is an appropriate procedure to analyze data. Descriptive statistics can be applied when a researcher needs to describe data or show their common features. It is also used to summarize the research data (Scott & Mazhindu, 2014). Descriptive statistics is commonly divided into two types, measures of central tendency and measures of variability about the typical value. Measures of central tendency usually include “the mean, mode and median” while measures of variability about the typical value comprise such aspects as “range, inter-quartile range, standard deviation and variance” (Scott & Mazhindu, 2014, p. 45). Descriptive statistics provide a diversity of calculations of different complexity to illustrate research in health care and interpret the obtained data. However, a researcher should be careful in applying descriptive statistics in general and percentage in particular because such interpretation can be misleading in the case of a small sample.
Relativity of Statistical Analysis to Method of Data Collection
Descriptive statistics is the most frequent type of statistic to interpret diverse data on health care research (Scott & Mazhindu, 2014). Since it is commonly applied to assess and monitor health services and their work, it is appropriate to be used in the current research because it investigates the impact of informative work provided by healthcare providers on the decisions of patients. This procedure is used, for example, in the assessment of a pregnancy leaflet efficiency for health promotion (Wright, Biya, & Chokwe, 2014). Since the study by Wright et al. (2014) has a similar purpose to the current research such as to improve the knowledge of patients and thus improve their health outcomes, the same procedure of descriptive statistics can be applied to interpret the obtained data. One of the aspects of descriptive statistics suitable for this research are percentages and proportions rations. They are commonly applied for summarizing information. The percentage is a good method to demonstrate a change in variables, particularly in the case when one issue is investigated. For the current research, percentage can be used to illustrate the awareness of the importance of mammography screening before and after the experiment in both experimental and control groups.
Scott, I., & Mazhindu, D. (2014). Statistics for healthcare professionals: An introduction (2nd ed.). London, UK: SAGE Publications Ltd.
Wright, S., Biya, T., & Chokwe, M. (2014). The effectiveness of a pregnancy leaflet to promote health in Tshwane, South Africa. Health SA Gesundheit, 19(1), 1-7, Web.