The type of questions a researcher poses ultimately informs the choice of a research method
Running head: CULTURAL COMPETENCY ANALYSIS 0
Cultural Competency Analysis
Data analysis provides a statistical interpretation of the findings in a manner that the results can be easily interpreted and a conclusion made based on the data collected. The variables that are considered in the study are evaluated where it is possible to develop a better understanding on important concepts, which define a greater level of accuracy under which important decisions can be undertaken. The key objective that is being investigated in this case is to determine whether studying abroad has a significant influence on cultural competence among nurses due to the interaction with individuals from other cultural settings.
The variables that are being investigated in this case provide a greater focus and understanding on cultural competence among nurses. Therefore there is need to determine whether there is relationship between nurses cultural competence and their opinions and experiences abroad. Therefore, the dependent variable that is evaluated in this case is cultural competence, which depends on the other specific variables that have been incorporated within the research.
Dependent variable = cultural competence
Independent variable = individual opinion and experiences abroad.
Analysis methods that are integrated in this case focus on determining the exact relationship between cultural competences and studying abroad which influences a number of factors such as individual opinion and experiences. Therefore, the integration of both descriptive and inferential analysis methods will be essential in determining a better understanding on the existing relationship between the dependent and independent variables. The qualitative and quantitative data that has been organized in this case will provide the basis under which better decisions will be undertaken to ensure that the study objectives is achieved.
The descriptive statistical analysis techniques have been integrated in the analysis to provide a greater understanding on important concepts, which influence the existing analytical elements. Descriptive statistics in will incorporate qualitative data. Qualitative research is a significant statistical research technique that is more concerned with developing explanations of social phenomena and has a non-positivist perspective. Thus, emphasis of the qualitative technique is more on general characteristics of certain phenomena that is being considered by the researcher. Qualitative research provides a significant visual understanding of major characteristics that are considered in a given study and thus it provides a better environment where quantitative research can be developed effectively and ensure that the interpretations that are made from quantitative research make sense when compared to the qualitative interpretation made.
Inferential statistics is usually incorporated into the study to test hypothesis where there is need to have scientific understanding of the issues that are being considered in a given study. Inferential statistics therefore focuses on sample data to infer about the total population. Therefore, the results that are obtained represent the total population that is being represented. Inferential statistics therefore focuses on a number of statistical tests, which are considered regarding the elements considered. Therefore, important considerations that need to be made is to ensure that the data in place conforms to a given statistical tool that is being considered. In every inferential statistical tool that is considered, there are a number of assumptions that are put in place to ensure that the test that is employed is valid.
Correlation and bivariate analysis are inferential statistical tests that are used to test inferences based on hypothesis that have been developed. They are used together in order to provide a clear understanding on the key elements which are investigated within a given research process. Correlation analysis provides an understanding on the existing strength and relationship between the variables that are being investigated while bivariate analysis provides a predictive understanding where it is possible to understand the effect of independent variables on the underlying dependent variable that is being developed. The regression model that is developed in this case can be used to provide a predictive understanding on the existing relationship between dependent and independent variables.
The study therefore aims at understanding the relationship between the independent variables and the dependent variable, which is cultural competence. Therefore, correlation and regression will be able to provide a better environment where the relationship between the dependent and independent variables can be based. Regression analysis will be able to provide a significant model, which will include all the variables, and thus it will provide a better environment where the company will be able to consider in seeking understanding the future patterns in order to make better plans for the future, which will have positive engagement.
Therefore, critical evaluation of the underlying variables, which are being considered in the analysis, is essential in determining the best results, which will provide a greater understanding on how the independent variables that have been included in the analysis influence the outcomes. Therefore, the correlation and regression methods will be integral in understand whether studying abroad has a statistical significant influence on the cultural competence among nurses.
Null hypothesis (Ho): Opinion and experiences on studying abroad do not have a statistically significant relationship with nursing cultural competence.
Alternative hypothesis (Ha): Opinion and experiences on studying abroad have a statistically significant relationship with nursing cultural competence.
|**. Correlation is significant at the 0.01 level (2-tailed).|
A Pearson correlation analysis was conducted to determine whether there is a statistically strong relationship between Opinion and experiences that could influence cultural competence. The analysis was able to determine that there was a positive moderate relationship between opinion and experiences of studying abroad. The variables were found to be statistically significant (P<0.05).
Bivariate regression analysis
|Model||R Square||Adjusted R Square||Std. Error of the Estimate|
|a. Predictors: (Constant), T1 Scale of student’s mathematics utility, T2 Scale of student’s mathematics utility|
The model summary analysis shows that Opinion and experiences provide a very minimal explanation on the cultural competence among nurses. The coefficient of determination( r2 = 0.001) which means that the predictor variables that are considered in this case can only predict 0.1% of the dependent variable that was being investigated.
|Model||Sum of Squares||df||Mean Square||Sig.|
|a. Dependent Variable: cultural competence|
|b. Predictors: (Constant), Opinion, experiences|
The analysis of variance as obtained in this case shows that there is significance considering the underlying consideration that the p value = 0.027 which is much less than 0.05. This based on a finding we are able to conclude that the predictor model that is being considered in this case is significant hence can be used to predict the dependent variable that is cultural competence among nurses.
|Model||Unstandardized Coefficients||Standardized Coefficients||Sig.|
|a. Dependent Variable: Years math teacher has taught high school math|
A bivariate linear regression analysis was conducted to evaluate whether opinion and experiences of students studying abroad have a statistically significant effect on cultural competence. The results found that Opinion and experiences have a statistically significant effect on the Years math teacher has taught high school math (p<0.05)
The coefficient analysis provides a better understanding under which it is possible to predict the dependent variable as well as being in a position to understand the relationship between all the variables that were included in the analysis. The predictive model in this case would be y = 10.344 + 0.0136x – 0.203×2
x = Experiences
x2 = Opinion
The model shows that an increase in the positive experiences results into varying outcome in the cultural competence. An increase positive opinion results into an increased cultural competence.
Cultural engagement forms an important environment where delivery of quality nursing care services can be enhanced. There are important elements within the cultural focus in every cultural setting, which has a significant influence on the overall process under how healthcare is delivered. It is important to understand that the current healthcare industry has been significantly hampered in providing quality healthcare to the standard developed due to the inability of healthcare providers to adopt a more diverse focus in delivery of healthcare services.
Asadoorian, M. O., & Kantarelis, D. (2005). Essentials of inferential statistics. Lanham: University Press of America.
Blank, S. S. (1968). Descriptive statistics. New York, NY: Appleton-Century-Crofts.
Greene, J., & D’Oliveira, M. (2005). Learning to use statistical tests in psychology. Maidenhead: Open University Press.
Mendenhall, W., Beaver, R. J., & Beaver, B. M. (2009). Introduction to probability and statistics. Belmont, CA: Brooks/Cole, Cengage Learning.