Relationship between Two Research Tests in Nursing - Case Study Example

Running head: Scenario for Nursing Relationship between GPA Points and Successful Completion of a PhD Introductory Quantitative Research for Nursing [Student’s Name] [Course Title] [Instructor’s Name] [Date] Why were the tests requested? The main reason why the tests were requested was to ascertain the relationship that exists between the GPA and the completion level of PhD introductory research course for nursing. The key problem is that it is appropriate to understand the relationship between the GPA and the successful completion of a rigorous PhD nursing quantitative research course of nursing. The necessity of reliable and valid research which is built on a stable scientific foundation as well as further generates evidence that is based on the practice foundation is seen to be so critical to the professional nursing. The major problem is a firm foundation is built for a successful research career that will demand a highly built skill of rigor and logic scholarship. The tests carried out were meant to answer the research question that includes; what is the correlation between the average GPA points and successful completion of a PhD introductory quantitative research course for nursing? In relation to the research question, the null hypothesis is: There is no significant relationship between average GPA points and successful completion of a PhD introductory quantitative research course for nursing The alternative hypothesis is: There is a significant relationship between the average GPA points and successful completion of a PhD introductory quantitative research course for nursing Since this data is quantitative and continuous, I considered to use correlation analysis and regression analysis in order to enable me understand the relationship between the two variables (average GPA points and successful completion of a PhD introductory quantitative research course for nursing).The Nominal data represented by ABCD (level of PhD completion) was converted to scale format, that is 1,2,3,4 respectively. A being the lowest level and D being the highest level of PhD completion of nursing quantitative research. Results and Discussion The results of the correlation analysis have been presented in the table as follows: Correlations GPA Completion Level of PhD GPA Pearson Correlation 1 .111 Sig. (2-tailed) .443 N 50 50 Completion Level of PhD Pearson Correlation .111 1 Sig. (2-tailed) .443 N 50 50 The results presented above from correlation analysis show that there exists a positive relationship between the GPA and the completion level of PhD introductory research course for nursing. The positive relationship is depicted by the coefficient value of 0.111 between the two variables. Apart from this, it is also pertinent to note above that there exists a no significant 2 - tailed correlation between the GPA and the completion level of PhD introductory research course for nursing. This implies that there was no sufficient evidence to conclude that there was a positive correlation between the two variables. Apart from the correlation analysis above, regression analysis between the variables (GPA and the completion level of PhD introductory research course for nursing) included in dataset was also performed as shown in the tables below to understand how GPA and the completion level of PhD introductory research course for nursing interact with each other. In this regard, following regression analysis assumes GPA as the dependent variable and completion level of PhD introductory research course for nursing as independent variable. These considerations of variables imply that the completion level of PhD introductory research course for nursing and GPA are interrelated through regression analysis. The model used for this purpose, does not show satisfactory result since the value of R square is too small (-0.08) to represent variations in the data. However, the relationship between two variables is concluded to be negative and it is not a significant one, as indicated by negative values for coefficient and p value, which is greater than 0.05.This implies that, there is no enough evidence to conclude that there is a relationship between the two variables and one of the factors that contributes to such is may be the sampling was not done properly, the confounding factors, less sample size etc. The regression analysis shows a relatively weak model. The coefficient of Determination(R squared) shows that only 8% of the total variation is explained by the one factor that includes GPA. The standard error is 0.767, determined by the low R squared. In regards to the hypothesis testing, by considering an alpha of 0.05, the results indicate that the variable GPA is to be rejected because the p value is greater than 0.05.This independent variable that include GPA influenced negatively the dependent variable which completion level of PhD introductory research course for nursing. This demonstrated that when GPA was high the completion level of PhD introductory research course for nursing was low. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .111a .012 -.008 .767 a. Predictors: (Constant), GPA ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression .352 1 .352 .599 .443b Residual 28.228 48 .588 Total 28.580 49 a. Dependent Variable: Completion Level of PhD b. Predictors: (Constant), GPA Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.017 .992 1.026 .310 GPA .226 .292 .111 .774 .443 a. Dependent Variable: Completion Level of PhD The challenges The most challenging part when is that the PhD level was nominal and it’s very hard to deal with the nominal data in inferential statistics in this case. Therefore I first had to convert it into a scale of 1, 2, 3 and 4 in order to be able to do the regression analysis as well as correlation. Another challenge is that the P values are greater than 0.05 indicating that the statistics is not significant. Exploring the influence of GPA on completion level of PhD introductory research course for nursing is the matter of future research. There were some high p values in the analysis indicating that some of the data was not good enough to infer and majorly it could be due to small sample size, confounding factors and outliers (Davidson et al, 2004). Specifically, whether such negative influence is frequent among the completion level of PhD introductory research course for nursing, this work is led to another level despite the availability of data is expected to be a major challenge (Dey, 1993). Small sample size may result to higher P values than 0.05 and also it can result to wrong estimates which can give wrong interpretation (Snee and Marquardt, 1984).In order to get satisfactory results, it is recommended that the above problems be put into consideration. In conclusion, according to the data output indicated above, there is no conclusion that can be made in regards to the relationship that exists between between the GPA and the completion level of PhD introductory research course for nursing. This is because the data is not statistically significant and it is clearly indicated by the p value which is greater than 0.05.as mentioned it could be due to a number of reasons. Bibliography Snee, R. D. & Marquardt, D. W. (1984). Collinearity diagnostics depend on the domain of prediction, and model, and the data. The American Statistician 38: 83–87. Davidson, Russell and James G. MacKinnon (2004), Econometric Theory and Methods, Ox-ford University Press, New York. Dey, I. (1993).Qualitative Data Analysis: A User-friendly Guide for Social Scientists. London, New York: Routledge.. Print Read More