Methodology
The focus of the proposed study is on patients suffering from head injuries and chronic traumatic encephalopathy, (CTE). Case control methodology is appropriate for this study.
To enhance efficiency, the case control methodology must assume a retrospective approach. This is because the proposed study involves examination of the existing cases of head injuries in the sampled population.
The study will sample the general population and the patients within the outpatient departments of public hospitals. The case groups and the control groups are important.
This is because they form the basis for assessments and comparisons (Sim & Wright, 2002). The principle aim for this methodology is to determine the exposure risk of CTE associated with head injuries.
Basically, the proposed study involves the comparison of the incidences of chronic traumatic encephalopathy. This must be done between the patients suffering from head injuries and the healthy individuals.
Two groups are prominent in the proposed methodology. This includes the case group and the control group.
Notably, the methodology requires a considerable time to attain desired results (Sim & Wright, 2002). Generally, the proposed methodology is appropriate for the study.
Participants
Both the case and control groups will be enrolled in the research. This explains why the proposed investigation is a case control study. The basic implication is that an empirically supported inclusion and exclusion criteria have to be adopted.
The cases include those participants who have previously suffered head injuries. On the other hand, the control group shall comprise other patients without any history of head injuries. This participant combination forms a perfect case control set.
The location of the proposed study has to be within selected public hospitals. Moreover, purposive sampling of all the participants within the selected hospitals is preferred.
The process must also observe the ethical and legal provisions for research. For instance, issues of informed consent have to be adequately addressed (Walker & Shostak, 2010). Purposive sampling helps to draw a representative sample.
The sample size shall be calculated using the Fischer’s formulae. This is because the representative sample is projected to be less than ten thousand. Generally, this study will involve a highly constricted sample size.
This is because the proposed study is retrospective and requires follow up for the enrolled participants. Thus, it is intensive and very costly within larger sample sizes.
Research Design
A combined approach for data collection is appropriate for the proposed study. In this approach, qualitative as well as quantitative strategies are applied during data collection process.
To enhance the process of triangulation, active clinical checkups and diagnosis are also necessary (Walker & Shostak, 2010). This is because the researcher is able to detect the new cases of CTE amongst the enrolled participants.
Qualitative and quantitative data management techniques shall be used in the study. Qualitative data analysis mainly entails discussions.
However, quantitative data analysis involves the use of sophisticated data management tools such as SPSS. Additionally, it involves the use of bar graphs, pie charts, and basic statistical inferences.
Instrumentation and Data Collection Plans
Qualitative data gathering to be used in the proposed study entail the use of key informant interviews, focus groups and personal observations. Quantitative approaches involve the active administration of assessment tools such as semi structured questionnaires (Keilegom & Wilson, 2011).
Because the proposed study is a case control, person-to-person data collection is appropriate. This will automatically rules out the possibility of a mailed survey.
Based on this projected sample, the participants shall report to the study sites. Consequently, the researcher shall conduct clinical monitoring, personal interviews, observations and focus groups during the reporting junctures.
Proposed Analysis of the Data
The results gotten from the proposed sample shall be varied. However, this depends on the inclination or objective of the data analysis (Fairclough, 2010). Results indicating the correlation between head injuries and occurrence of CTE are critical.
In addition, factors that influence the occurrence of CTE amongst those with head injuries are also expected. Additionally, issues about the efficiency of the clinical management strategies in head injuries are likely to emanate.
Determination and analysis of responses must be conducted through diverse mechanisms. For instance, similar causative factors linked to the occurrence of CTE must be grouped statistically. Analytically, such processes require sophisticated data management tools.
Correlation and regression analysis include some of the statistical tools applicable in the proposed study (Keilegom & Wilson, 2011). The proposed results will have significant implications on the rest of population.
For instance, certain revelations might dispel existing beliefs. There are different variables that the researcher may include in the proposed study.
The likelihood of occurrence of CTE and the incidences of head injuries are some of the variables. Observably, a correlation of the dependent and independent variable may be drawn from the two outlined examples.
“The occurrence or presence of head injury” remains as one of the crucial independent variable in the proposed study. Alternatively, “the occurrence of CTE” includes another vital dependent variable.
Therefore, it can be stated that the occurrence of CTE depends on the presence of head injury amongst the participants in the proposed study.
A more comprehensive and empirical decision making criteria must be used in the proposed study. In this context, various statistical inferences will have a critical role.
Comparison of the confidence intervals of various correlating factors will influence most decisions and assumptions in the proposed study. This is appropriate for all correlating factors (Sim & Wright, 2002).
This process entails the application of statistical distribution tables in determining the confidence intervals. Advanced computer software for data management must be used to enhance the high level of data processing.
Basically, this computer software must be applicable in the computation of various statistical inferences and associations. The SPSS program is most preferred for purposes of data analysis in the proposed study.
Validity and reliability measurements are critical for all empirical investigations. Application of content validity ensures that an appropriate and representative sample size is utilized.
In addition, criterion validity measurement when applied in the proposed study will help to test the precision of definite measures.
Construct validity may preferably be used in the proposed study to confirm whether the variables measure the proposed constructs (Fairclough, 2010). For purposes of objectivity and reliance, measurements on the reliability of factors within a study are vital.
In the proposed study, there are several measurement methodologies that might be applied for reliability. The determination and testing of reliability may automatically occur when certain results are gotten.
In such scenarios, these results must be tested for both internal and external consistency. There are other various applicable methodologies in measuring reliability.
The proposed study might involve the use of inter-observer or test-retests as strategies for measuring reliability. These processes are important because they improve the quality of data.
References
Fairclough, D. (2010). Design and Analysis of Quality of Life Studies in Clinical Trials. New York, NY: CRC Press.
Keilegom, I. & Wilson, W. (2011). Exploring research frontiers in contemporary statistics and econometrics: A festschrift for Léopold Simar. Berlin: Springer/Physica-Verlag.
Sim, J. & Wright, C. (2002). Research in health care: Concepts, designs and methods. Cheltenham: N. Thornes.
Walker, A. & Shostak, J. (2010). Common statistical methods for clinical research with SAS examples. Cary, NC: SAS Institute Inc.