This case study is a quasi-experiment. This quantitative research will focus on the effects of patient satisfaction scores on healthcare organizations. Thus, the study will involve an estimation of the causal impact of patient satisfaction scores on different healthcare organizations. Thus, the setting will be ten different local hospitals and Nurses, doctors, and other staff members will form the sample selection as all their services determine the patient satisfaction scores. The research study will also use purposeful sampling through the selection of individuals and departments for the study because they can purposely provide information and understanding of the research problem and phenomenon in the study. In regards to confidentiality, privacy protection for the participating college and participants involved in the survey is paramount. Pseudonyms will replace the names of participants. All tools used to gather information, notebooks, and audio equipment would be stored in a locked box in the home of the researcher. Once automatic transcription of the data occurs, all research-gathering tools will undergo destruction. Time series design will be used to detect whether an intervention has an effect that is significantly greater than the trend (Elo, Kääriäinen, Kanste, Pölkki, Utriainen, & Kyngäs, 2014).
Since the subjects are randomly assigned to the control groups to ensure reliability and validity, the study will assume that the independent variable caused the observed outcome since the two groups should not differ from one another (Kyte, Ives, Draper, Keeley, & Calvert, 2013). Besides, to establish external validity, the effects of patient satisfaction scores on healthcare organizations were assessed in 10 different hospitals (Iwata, DeLeon, & Roscoe, 2013). This empirical study will use semi-structured interviews, which use predetermined questions and observations to investigate the relationship between the patient satisfaction scores and healthcare organizations. The interviews will help form the foundation for determining the relationship between the two variables. Pre-tests will not be needed in this study.
1. Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., & Kyngäs, H. (2014). Qualitative content analysis. Sage Open, 4(1), 2158244014522633.
2. Iwata, B. A., DeLeon, I. G., & Roscoe, E. M. (2013). Reliability and validity of the functional analysis are screening tool. Journal of Applied Behavior Analysis, 46(1), 271-284.
3. Kyte, D., Ives, J., Draper, H., Keeley, T., & Calvert, M. (2013). Inconsistencies in the quality of life data collection in clinical trials: a potential source of bias? Interviews with research nurses and trialists. PloS one,8(10), e76625.
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