The purpose of this quasi-experimental study is to identify the program’s potential to attract participants. The evaluation of clinical projects is often associated with the collection of quantitative data (through experimental or quasi-experimental designs) to identify the exact quantifiable outcome of the project (Cannon, 2017). Hence, the number of drug users applying to take part in the harm reduction program before and after the project implementation will be estimated. The number of the current participants and applicants of the existing program for drug users will be calculated at the beginning of the project. Records analysis will involve all the participants and applicants (irrespective of the participation status). Even those who withdraw from the project at any time during its implementation will be noted as participants.
At the end of the project, the number of the participants of the project and applicants for a new program (that will start right after the proposed incentive) will be calculated. The independent variable is the proposed harm reduction program that aims at attracting a larger audience and helping more drug users. The manipulated measures (in this case, the number of attracted people due to the developed project) are the dependent variable that is measured to capture the changes, if any (Eldridge, 2017). Thus, the dependent variables will be the number of applicants to a new project and the overall number of participants in the proposed project.
Such demographic data as age, gender, ethnicity, marital status, education, and employment will be noted. This information is necessary for the particular impact of the developed project on different cohorts (Eldridge, 2017). These findings will be utilized to further improve the program and make it more effective with different populations. The number of people who withdraw from the program will also be analyzed, and the reasons for their withdrawal, if available, will be analyzed.
The analysis of quantitative data is now facilitated by the use of technology. SPSS software will be utilized for the purpose of this study in order to ensure the reliability of the findings. As far as demographic data, descriptive statistics will be employed. This data analysis method is common as it enables the researcher to capture the peculiarities of the participants that can have an impact on the overall outcomes of the study (Ellis, 2016). The demographic characteristics of the applicants to the project before and after the implementation of the proposed harm reduction program will be compared. This analysis can help in identifying the exact cohorts interested in participating in the program.
The number of applicants to and participants of the harm reduction project before and after the proposed program will be calculated, and the change rate will be noted. The inferential analysis is instrumental in identifying the effectiveness of certain measures (Ellis, 2016). It is expected that the number of drug users seeking help through participation in the proposed harm reduction incentive will increase. In order to validate the results, the statistical significance will be measured with the help of the p-value analysis (Stockert, 2018). By checking the null hypothesis, the researcher may ensure that the developed program has certain outcomes and leads to statistically significant changes (Stockert, 2018). If the p-value is 0.05 or lower, the correlation between the measured variables will be seen as statistically significant. Therefore, the data are reliable and reflect the exact effects of the initiative.
References
Cannon, S. (2017). Quantitative research design. In C. Boswell & S. Cannon (Eds.), Introduction to nursing research (pp. 111-134). Jones & Bartlett Publishers.
Eldridge, J. (2017). Data analysis. In C. Boswell & S. Cannon (Eds.), Introduction to nursing research (pp. 375-402). Jones & Bartlett Publishers.
Ellis, P. (2016). Understanding research for nursing students (3rd ed.). Learning Matters.
Stockert, P. A. (2018). Evidence-based practice. In P. A. Potter et al. (Eds.), Essentials for nursing practice (9th ed.) (pp. 83-99). Elsevier Health Sciences.