Plans for Data Collection
The sample for the study will be nurses from my working place. I will approach the qualified nurses and ask them about their willingness to participate in the study and the amount of personal and professional information they are willing to share. Since not all of the participants will be eager to give their personal information, I will create a survey on SurveyMonkey, which all participants can access and answer. I will print the survey and administer to the participants to respond in a few minutes of their time while still in the workplace. A paper-based survey will be easier to complete and will not consume much time. I will also print more copies of the survey for the respondents. The data from the SurveyMonkey will be analyzed through SPSS to get results that will be used for decision-making.
Plans for Data Entry
Data analysis forms a critical part of a study as it provides the results to be reported. Experts have developed various data analysis programs, which are used depending on the nature of the study. Data analysis methods applied for qualitative data differ from those used for quantitative data. Since the study is a quantitative research, I will use a statistical method to analyze the data. In this case, the Statistical Package for the Social Sciences (SPSS) will be the most efficient approach. According to Norris, Qureshi, Howitt, and Cramer (2014), SPSS is used in research to analyze data and make predictions. Although the statistical package was originally developed for use in social sciences, it has become a critical tool for analyzing data in other fields, including nursing.
Various steps are involved when collecting and analyzing data using SPSS. The first stage will be loading the excel file with all the collected data, including demographic records and data to test different variables in the study, including a burnout and nursing settings. I will load the data using the most appropriate tabular form for easy analysis. The second step is importing the raw data into SPSS through the created excel file. The data will be analyzed using the program once imported. The third phase will be giving the SPSS specific commands depending on the desired results of the analysis. I will use a tool with particular guidelines on how to use it and analyze the data to obtain correct results. In the process of learning how to use SPSS, I have realized that with the commands, using the statistical method is easy and straightforward.
To present the data in an understandable form, I will retrieve it from the statistical package. The output from software is presented efficiently and accurately, providing the researcher with a better idea of suitable future experiments and a direction for further data collection and analysis. Notably, the most effective method of presenting the data is using graphs and charts. Once an input is made to the statistical package, the analyst can automatically create the graphs and charts based on the variables (demographics, nursing setting, and burnout). Finally, I will make deductions depending on the outcome of the analysis. The final objective of using SPSS for data analysis is to come at a conclusion to test the hypothesis to answer the research question (Ott & Longnecker, 2015). The outcome of the report enables the researcher to conclude and predict the future within a minimal statistical deviation.
Plans for Data Cleaning
While analyzing the data, cleaning is an important step to ensure that the outcome is accurate. During the process of entering the data into the statistical package, SPSS, errors might occur, which the researcher should eliminate to enhance the accuracy (Kupzyk & Cohen, 2015). At the same time, the researcher might realize missing data or “invalid” responses that should be addressed. Therefore, I will carefully clean the data to remove such errors and present accurate findings. I will use Possible-Code Cleaning for the particular set of answer choices as well as the matching codes (especially since I will be using Likert items) (Abu-Bader, 2016). For example, for gender as a variable, I will use three codes, 2 for female, 1 for male, and 0 for no answer. Therefore, in case someone has indicated a code 6, it means that the code is wrong and should be eliminated.
Furthermore, I will use a data analysis package with the capability to check the common mistakes during data entry. The process will involve monitoring of all types of errors to ensure that the outcome of final process is “clean.” However, I should define the potential codes for every question before data entry. Therefore, any number that is outside the pre-defined possibility will be revealed through an error message. For example, the program will not give any response to code 6 for gender but will indicate an error. Through a careful process of cleaning, I will get only the relevant results from the analyzed data. The process is critical to ensure that the researcher does not present results with significant errors that might affect its validity.
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