pare a 10-minute presentation (10-15 slides, not including title or reference slide) on organizational culture and values.
- Describe how alignment between the values of an organization and the values of the nurse impact nurse engagement and patient outcomes.
- Discuss how an individual can use effective communication techniques to overcome workplace challenges, encourage collaboration across groups, and promote effective problem solving. Incorporate how system needs and the culture of health may influence the outcomes. How does this relate to health promotion and disease prevention in the larger picture?
- Identify a specific instance from your own professional experience in which the values of the organization and the values of the individual nurses did or did not align. Describe the impact this had on nurse engagement and patient outcomes.
While APA style format is not required,solid academic writing is expected and in-text citations and references should be presented using APA documentation guidelines, which can be found in the APA Style Guide.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment.
Understanding Independent Samples t-Test
Statistical Technique in Review
The independent samples t-test is a parametric statistical technique used to determine significant differences between the scores obtained from two samples or groups. Since the t-test is considered fairly easy to calculate, researchers often use it in determining differences between two groups. The t-test examines the differences between the means of the two groups in a study and adjusts that difference for the variability (computed by the standard error) among the data. When interpreting the results of t-tests, the larger the calculated t ratio, in absolute value, the greater the difference between the two groups. The significance of a t ratio can be determined by comparison with the critical values in a statistical table for the t distribution using the degrees of freedom (df) for the study (see Appendix A Critical Values for Student’s t Distribution at the back of this text). The formula for df for an independent t-test is as follows:
The t-test should be conducted only once to examine differences between two groups in a study, because conducting multiple t-tests on study data can result in an inflated Type 1 error rate. A Type I error occurs when the researcher rejects the null hypothesis when it is in actuality true. Researchers need to consider other statistical analysis options for their study data rather than conducting multiple t-tests. However, if multiple t-tests are conducted, researchers can perform a Bonferroni procedure or more conservative post hoc tests like Tukey’s honestly significant difference (HSD), Student-Newman-Keuls, or Scheffé test to reduce the risk of a Type I error. Only the Bonferroni procedure is covered in this text; details about the other, more stringent post hoc tests can be found in Plichta and Kelvin (2013) and Zar (2010).
The Bonferroni procedure is a simple calculation in which the alpha is divided by the number of t-tests conducted on different aspects of the study data. The resulting number is used as the alpha or level of significance for each of the t-tests conducted. The Bonferroni procedure formula is as follows: alpha (α) ÷ number of t-tests performed on study data = more stringent study α to determine the significance of study results. For example, if a study’s α was set at 0.05 and the researcher planned on conducting five t-tests on the study data, the α would be divided by the five t-tests (0.05 ÷ 5 = 0.01), with a resulting α of 0.01 to be used to determine significant differences in the study.
The t-test for independent samples or groups includes the following assumptions:
The t-test is robust, meaning the results are reliable even if one of the assumptions has been violated. However, the t-test is not robust regarding between-samples or within-samples independence assumptions or with respect to extreme violation of the assumption of normality. Groups do not need to be of equal sizes but rather of equal variance. Groups are independent if the two sets of data were not taken from the same subjects and if the scores are not related (Grove, Burns, & Gray, 2013; Plichta & Kelvin, 2013). This exercise focuses on interpreting and critically appraising the t-tests results presented in research reports. Exercise 31 provides a step-by-step process for calculating the independent samples t-test.