EDUC915-REPLIES-Discussion Thread: Research Methodology
Use two responses from 2 different people uploaded to the files. The response needs its own response of at least 200 words and its own citations and references.
Response two-Needs a reply with at least 200 words with its own citations and references. You may be required to support their assertions with scholarly citations. Replies may be required to incorporate scholarly citations.
Leanne M.
Problem Statement
Across prelicensure nursing education, there is a problem of low retention and high attrition rates of nursing students, resulting in a decreased number of nursing professionals caring for the growing population (Schrum, 2020). There is a gap in the literature to determine if there is a correlation between prelicensure nursing students’ self-efficacy scores, student success, and the number of hours nursing students work. The population sample is prelicensure nursing students at a private not-for-profit, hospital-affiliated college in the State of Maine. Other research on diverse disciplines with higher self-efficacy scores (using the generalized self-efficacy scale) has a higher probability of completing a task or event successfully, compared to those with lower self-efficacy scores (Lazic, Jovanovic, & Gavrilov-Jerkovic, 2021; Prifti, 2022). Due to the demand for prelicensure nursing programs to have high completion rates and licensure pass rates from their accrediting bodies, examining how attrition and retention rates can improve is imperative for the sustainability of nursing programs (ACEN, 2023).
The accreditation commission for nursing in education (ACEN) requires prelicensure % nursing programs to have a licensure pass rate of 70% or higher to maintain accreditation (ACEN, 2023). As a result, nursing programs are implementing standardized “exit” testing to successfully predict student success in passing the licensure exam (Riley et al., 2023). The health education system (HESI) examination is a standardized test that can predict the probability of a student passing the licensure exam for prelicensure nursing students. Students who obtain 850 points or higher on the HESI have a 95% probability of passing the licensure exam (Riley, et al., 2023). Finally, nursing students who work in college (due to outside responsibilities) take time away from their studies, and this time away may decrease their chances of success in the nursing program. Therefore, the problem is if there is a correlation between the number of hours students prelicensure students work, their self-efficacy scores, and their ability to complete their nursing program successfully.
Purpose Statement
Since there is evidence from other disciplines surrounding self-efficacy scores with how likely an individual is to complete their goals, the benchmarks for prelicensure nursing students to achieve success on the NCLEX. Additionally, for prelicensure nursing students with increased responsibilities that remove them from their studies in nursing school, there is a need to determine if there is a correlation between these criteria. The purpose of this research study is to examine if there is a correlation between prelicensure nursing students’ self-efficacy scores, the number of hours students work, and their success with passing the HESI examination at the end of their nursing program.
Independent Variable – the number of hours a student works
Dependent Variables – student success in passing the prelicensure nursing program and self-efficacy scores
Research Study – Casual-comparative study
Research Questions
Does the number of hours a student works affect the score a student obtains on the exit HESI exam?
Does the number of hours a student works affect the student’s self-efficacy score?
Is there a relationship between the number of hours a student works, and the attrition rate or retention rate in the prelicensure nursing program?
Reference
ACEN. (2023). ACEN accreditation manual-section II. Retrieved from ACEN Accreditation Manual Policies: https://www.acenursing.org/accreditation-manual-policies/
Lazic, M., Jovanovic, V., & Gavrilov-Jerkovic, V. (2021). The general self-efficacy scale: New evidence of structural validity, measurement invariance, and predictive properties in relation to subjective well-being in Serbian samples. Current Psychology, 10, 699-710. Retrieved from https://link.springer.com/article/10.1007/s12144-018-9992-6
Prifti, R. (2022). Self–efficacy and student satisfaction in the context of blended learning courses. Open Learning: The Journal of Open, Distance and e-Learning, 37(2), 111-125. doi:https://doi.org/10.1080/02680513.2020.1755642
Schrum, R. A. (2020). Nursing student retention in an associate degree nursing program utilizing a retention specialist. Teaching and Learning in Nursing, 10(2), 80-87. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S1557308714001085
Shah, M., Fuller, B., Gouveia, C., Mee, C. L., Baker, R. S., & Ofelia, M. (2022). NCLEX-RN readiness: HESI Exit Exam validity and nursing program policies. Elsevier, 39, 131-138. doi:https://doi.org/10.1016/j.profnurs.2022.01.010
Patricia W.
The impetus of the mass exodus of teachers in the education field has been caused by several school-level influences such as administrative support, teacher value, teacher flexibility, and the teacher’s working condition (Toropova, et al., 2020; Reeves, et, al; 2022, Skaalvik, et.al; 2016; Ingersoll, 2001). Crippling school-level factors lead to teacher burnout that impacts mental health. Symptoms of teacher burnout such as emotional exhaustion, low self-efficacy, and feelings of stress inspire the motivation for teachers to leave the education field (Skaalvik, 2016). Teachers who leave the profession see the grass greener in other occupational fields. Gall, et, al., (2007) support teacher attrition studies by positing that prediction research can provide knowledge about how factors can “predict various outcomes.” Pg. 4. For example, the school-level factor, working conditions can be a predictor of teacher burnout and attrition.
Further, (Marshall, 2022) touts that 76% of teachers’ surveys nationwide depict teachers considering leaving the teaching profession in the school year. Several educators believed that COVID is the main reason for teacher turnover; however, Bridgeland & Balfanz, (2020) advances that the current pandemic, COVID did not start teacher retention, but rather exposed the problems and gaps in education and that policymakers should do more than find solutions to the problem rather than react to them.
In a society, the expectation for quality school districts is to maintain high-quality instructors with the characteristics of preparedness, self-efficacy, and motivation to instruct students. The gaps or limits in the literature advance that more studies should focus on different groups of teachers rather than middle school teachers (Toropova, et, al., 2019). Consequently, my dissertation proposal is to correlate the job dissatisfaction of high school teachers with causal school-level factors such as working conditions, teacher value, teacher flexibility, and administrative support.
This research study will be a predictive correlational study with a minimum sample size of 66. I will use Regression Analysis statistics to analyze the relationships among variables. In addition, to support my study, in analyzing the working conditions of the school-level factors contributing to teacher job dissatisfaction, I will use the Maslach Burnout Inventory (Maslach, & Jackson, 1981). Likert-type scales will be used to collect data like the teacher’s feelings about school-level culture. I will also seek to find programs that help to mitigate in-service teachers’ attrition rates. (664 words)
Research questions are:
Which school-level factors show predictive relationships of teacher job dissatisfaction in high-school-level inner-city schools that lead to teacher burnout?
How are high-school level teachers affected by the way in which administrators support that instructional practice?
Which types of resources do high school teachers receive that help mitigates teacher turnover?
Resources
Bridgeland, J.M., & Balfanze, R. (2020). COVID-19 Is Exposing the Gaps in Our Education System. Let’s start fixing them. Retrieved from: https://www.edweek.org/leadership/opinion-covid-19-is-exposing-the-gaps-in-our-education-system-lets-start-fixing-them/2020/03Links to an external site.
Gall, M.D., Gall, J.P., & Borg, W.R. (2007). Educational research: An introduction. Pearson and AB.
Goldhaber, D., & Theobald, R. (2022). Teacher Attrition and Mobility in the Pandemic. Educational Evaluation and Policy Analysis, 0(0). https://doi.org/10.3102/01623737221139285Links to an external site.
Hoy, W.K., & Feldman, J.A. (1987). Organizational health: The concept and its measure. J. Res. Dev. Educ. 20:30-38.
Ingersoll, R. M. (2001). Teacher Turnover and Teacher Shortages: An Organizational Analysis. American Educational Research Journal, 38(3), 499–534. https://doi.org/10.3102/00028312038003499Links to an external site.
Marshall, D. T., Pressley, T., Neugebauer, N. M., & Shannon, D. M. (2022). Why teachers are leaving and what we can do about it. Phi Delta Kappan, 104(1), 6–11. https://doi.org/10.1177/00317217221123642
Maslach, C., & Jackson, S.E. (1981). The Measurement of Experience Burnout. Journal of Occupational Behavior, (2) 99-113. https://doi.org./10.1016/j.paid.2019.01.036Links to an external site.
Reeves, T. D., Hamilton, V., & Onder, Y. (2022). Which teacher induction practices work? linking forms of induction to teacher practices, self-efficacy, and job satisfaction. Teaching and Teacher Education, 109, 103546. https://doi.org/10.1016/j.tate.2021.103546
Skaalvik, E. M., & Skaalvik, S. (2016). Teacher stress and teacher self-efficacy as predictors of engagement, emotional exhaustion, and motivation to leave the teaching profession. Creative Education, 07(13), 1785–1799. https://doi.org/10.4236/ce.2016.713182
Toropova, A., Myrberg, E., & Johansson, S. (2020). Teacher job satisfaction: The importance of school working conditions and teacher characteristics. Educational Review, 73(1), 71–97. https://doi.org/10.1080/00131911.2019.1705247
