Ethical

Methodology

Extraneous variables

In this research, in finding that extraneous variable are likely to have some negative impacts on my research through the distortion of the findings. The general inferences made from the findings in this work would not be I awe of the pattern that was initially expected at the time of planning this research. The participants in this research would be the main source of the extraneous variables. Any variation in the answers given by the participants of the surveys in the population sample is to be the main source of the errors observed in this research work. To limit these errors, it would be essential to use the random allocation of the issues to the responses that are already predetermined for data collection (Brown, 2002).

Personal perception of what constitutes to be a challenge is another important factor that is deemed to be an influencer of extraneous variables. In this manner the perception of what constitutes to a problem for one person may be different from another person, for instance, one nurse may report lack of proper planning in the healthcare is a problem while another one may not report such as a problem. This hurts the findings, and the inferences made. However, this can be reduced by making the environment as natural as possible to have the best responses.

Description of instrument

For this research, the major instrument that I intend to use is the questionnaire. This instrument is appropriate in gathering the best responses from the nurses on the problem experienced while conducting their mandate. However, this instrument will not be used in isolation; it will be supplemented by another instrument, especially in areas where it cannot give the best data.

Validity and reliability estimates

For this research, the validity of this approach is ascertained through the use of the exact questions that are presented to the nurses across all the strata in Miami where this research seeks to garner data from the nurses as well as other population that may have crucial information that could help in answering the research question. Here there are underlying factors through which the findings of from the research findings that may be correlated to establish whether there is any relationship between them (Maltby et al., 2014).To ascertain the reliability of the estimate, the consistency score would be very important as more consistency findings will indicate that the estimates were indeed made precisely. This is a good indicator that shows that the results or the conclusions made will help answer the research questions.

Description intervention.

For this research, the intervention that is deemed preferable is the purposeful implementation of the change strategies. To get reliable findings, it is essential to use simple approaches in the standing of the stepwise through involving all the subjects. This is an essential tool that enhances inclusivity and therefore giving a wide range of data that is important for answering the research question.

Data collection procedures

For this research, the primary data collection methods will be through questionnaires that will be sent to participating nurses in the survey to provide the needed data that will be processed to answer the question at hand. Through this data collection method, the nurses will have an opportunity to give information about the issues that they may be facing in their profession.

Data Analysis Plans

A data analysis is a layout of how you are going to manage and analyze your research data. A data analysis plan is meant to show what your proposal is all about. A qualitative data analysis is essentially about detection, and the tasks of defining, categorizing, theorizing, explaining, exploring and mapping are fundamental to the analyst’s role (Ritchie, & Spencer, 2002). An excellent analysis plan is an indication that your research proposal is well formulated. A data analysis should show;

· The data you will collect – the outcomes, the independent variables and the correlated clusters.

· How to classify data, how data will be measured and show the missing data.

· Analysis of the data – hypothesis and research design

· Sample size and the possible findings and

· Data resources such as hardware and software.

Data Analysis for Descriptive Statistics

Descriptive statistics as the name suggests give descriptions of a variable. These statistics provide a summary of the chosen data through graphing of the chosen data. This data may include, the difference between values, the mean of the values and the category of the values. Data analysis for descriptive statistics includes numeric and graphic data summaries.

Pie charts, bar charts and histograms are good examples of descriptive statistics. They help visualize and analyze the data in a simple way. The tools of descriptive statistics include;

· Central tendency – mean and median

· Dispersion – cluster of the values

· Skewedness - distribution of values

Data Analysis for Inferential Statistics

Inferential simply implies making inferences from the data collected. Inferential statistics measure the existing difference between two variables or existing two subgroups of variables. Inferential statistics are used to make comparisons and draw conclusions from the study data (Simpson, 2015). These statistics allows the researcher come up with conclusions regarding another variable following the data acquired from the research. Tools for inferential statistics include;

· Hypothesis testing

· Regression analysis and

· Confidence interval