The t test for nursing

T Tests And ANOVA In Clinical Practice

t Tests and ANOVA in Clinical Practice

Inferential statistics enable researchers to apply the data they gather and the conclusions they draw from a particular sample to a larger population. As the name implies, inferential statistics focus on inferring whether there is a relationship between two or more variables. These statistical analyses include t tests and analysis of variance (ANOVA). t Tests are part of a group of statistical tests that test hypotheses; in fact, it is necessary to formulate a hypothesis in order to use a t test, because the results of the test can only be interpreted in the context of a scientific hypothesis.

Inferential statistics such as t tests work well for comparing two groups. Although mathematically equivalent to the t test, ANOVA allows for the comparison of more than two groups. Therefore, when three or more groups are involved, the ANOVA should be used.

In this week’s Discussion, you are asked to locate a current research article that utilizes either a t

test or ANOVA analysis. You provide a summary of the research study and of the study’s application to evidence-based practice. You also examine the article’s use of a t test or ANOVA and how either of those statistical analysis tools helped to inform the article’s conclusions and recommendations.

To prepare:

Consider some of the important issues in health care delivery or nursing practice today. Bring to mind the topics to which you have been exposed through previous courses in your program of study, as well as any news items that have caught your attention recently. Select one topic to consider for this Discussion.
Next, review journal, newspaper, and Internet articles that provide credible information on your topic (you can choose any nursing topic from Confidentiality or Work Place Bullying or any other nursing related issue that will be easy to locate a scholarly research article on which uses a t test or ANOVA)
Then, select one research article on which to focus that used inferential statistical analysis (either a t test or ANOVA) to study the topic.
With information from the Learning Resources in mind, evaluate the purpose and value of the research study discussed in your selected article and consider the following questions:

Who comprised the sample in this study?
What were the sources of data?
What inferential statistic was used to analyze the data collected (t test or ANOVA)?
What were the findings?
Ask yourself: How did using an inferential statistic bring value to the research study? Did it increase the study’s application to evidence-based practice?
By tomorrow Wednesday 09/27/17, 8 pm, write a minimum of 550 words essay in APA format with a minimum of 3 references from the list in the instructions area. Include the level one headings as numbered below:

Post a cohesive response that addresses the following:

1) Identify the topic you selected in the first line of your posting. (you can choose any nursing topic from Confidentiality or Work Place Bullying or any other nursing related issue that will be easy to locate a scholarly research article on which uses a t test or ANOVA)

2) Summarize the study discussed in your selected research article and provide a complete APA citation. Include in your summary the sample, data sources, inferential statistic utilized, and findings.

3) Evaluate the purpose and value of this particular research study to the topic.

4) Did using inferential statistics strengthen or weaken the study’s application to evidence-based practice?

Required Readings

Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns, and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). St. Louis, MO: Saunders Elsevier.

Chapter 25, “Using Statistics to Determine Differences”

This excerpt elaborates on how statistics are used to examine causality using procedures such as contingency tables, chi-squares, t tests, and analysis of variance (ANOVA).

Statistics and Data Analysis for Nursing Research

Chapter 5, “Statistical Inference”

This chapter discusses inferential statistics, sampling error, sampling distributions, and the laws of probability. The chapter also introduces key terms such as standard error of mean, hypothesis testing, and parametric test.

Chapter 6, “t Tests: Testing Two Mean Differences”

This chapter considers the various forms of the t test, including the two-sample t test, Kolmogrov-Smirnov test, independent groups t test, and dependent groups t test. The chapter also discusses the many variables involved in these tests such as effect size, meta-analysis, and Cohen’s d.

Chapter 7, “Analysis of Variance” (pp. 137–146 and 155–158)

The first part of this chapter introduces the basic assumptions, requirements, general logic, and terminology surrounding analysis of variance (ANOVA). The second excerpt focuses on sampling distribution of the F ratio and the null and alternative hypotheses.

Jadcherla, S. R., Wang, M., Vijayapal, A. S., & Leuthner, S. R. (2010). Impact of prematurity and co-morbidities on feeding milestones in neonates: A retrospective study. Journal of Perinatology, 30(3), 201–208. doi:10.1038/jp.2009.149

This article outlines the procedures and results of a retrospective study of how perinatal and comorbidity factors affect the rate at which infants meet feeding milestones. The article also includes an application of inferential statistics to the results of the study.

Optional Resources

Shin, J. H. (2009). Application of repeated-measures analysis of variance and hierarchical linear model in nursing research. Nursing Research, 58(3), 211–217. doi:10.1097/NNR.0b013e318199b5ae

Walden University. (n.d.). Analysis of variance. Retrieved August 1, 2011, from http://streaming.waldenu.edu/hdp/researchtutorials/educ8106_player/educ8106_analysis_of_variance_anova.html

Walden University. (n.d.). Inferential statistics. Retrieved August 1, 2011, from http://streaming.waldenu.edu/hdp/researchtutorials/educ8106_player/educ8106_inferential_stats_and_hypothesis_testing.html

Walden University. (n.d.). t-Tests. Retrieved August 1, 2011, from http://streaming.waldenu.edu/hdp/researchtutorials/educ8106_player/educ8106_ttests.html

Identify the Topic you Selected in the First Line of your Posting

The topic I selected is nursing burnout. I attempted to select a t-test study for nursing burnout and EHR; however, I could not find a study covering these key words, so I settled for nursing burnout. The DNP project I wish to implement is to create an educational training for the new EHR start-up to decrease nurse stress and burnout.

Summarize the Study Discussed in your Selected Research Article and Provide a Complete APA Citation. Include in your Summary the Sample, Data Sources, Inferential Statistic Utilized, and Findings

Malliarou, M. M., Moustaka, E. C., & Konstantinidis, T. C. (2008). Burnout of nursing personnel in a regional university hospital. Health Science Journal, 2(3), 140-152.

The authors attempted through this study to determine whether burnout has various levels as correlated with demographic, education level, and professional indices. The study was conducted at a regional hospital with two questionnaires: a demographic questionnaire and the Maslach Burnout Inventory. Descriptive statistical analysis was completed with One Way Variance Analysis (ANOVA) and a t-test. The ANOVA was used to determine the statistical significance of the levels of burnout and the levels of demographic and education level data. The t-test was then used to compare the means of the two groups and the ANOVA was used to compare the means in multiple groups. The authors discovered that demographic and education level data did not have statistical significance for burnout prediction, and that the higher the level of perceived burnout, the more the nurse is likely to quit their position, leave the facility, or retire.

Evaluate the Purpose and Value of this Particular Research Study to the Topic.

The purpose of this research study was to determine if there are different levels of burnout and if burnout is correlated to demographic and educational level status. The t-test is a test that seeks to reject the null hypothesis and show that there is statistical significance between the variables (Laerd, 2013). The study question is an intriguing one! Attempting to look at different levels of burnout and if these levels can correlate to staff nurses demographics/education is one that has not been broached before. In this case, discovering that there is not a statistical significance is great news as well – this information can be used when constructing additional studies. This information can also be used when creating policies in facilities for preventing burnout, understanding what burn out is, what it is not, and factors that create burnout and those that do not.

Did Using Inferential Statistics Strengthen or Weaken the Study’s Application to Evidence-Based Practice?

In this case, the use of inferential statistics strengthened the study and the information gained from the study that can be generalized to the population at large. Being able to statistically calculate that there is not a significance between demographics and education level and levels of burnout with staff nurses. If this were a qualitative study, this valuable piece of information would not have been revealed.

References

Laerd Statistics. (2013). Independent t-test for two samples. Retrieved from https://statistics.laerd.com/statistical-guides/independent-t-test-statistical-guide.php

Malliarou, M. M., Moustaka, E. C., & Konstantinidis, T. C. (2008). Burnout of nursing personnel in a regional university hospital. Health Science Journal, 2(3), 140-152.

/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

BELOW ARE THE CORRECTIONS SHE MADE AFTER THE TEACHER REQUESTED!

As requested - expanded discussion board answer detailing the t-test and ANOVA measurements. NOTE: The information listed here is the extent of the information given in the original study article.

The study determined demographics and education level, and levels of burnout in an attempt to determine a correlation between the two.

This table shows the levels of burnout:

Scores of subscales of burnout

Subscale low median high

Emotional exhaustion <=20 21-30 >=31

Personal accomplishments >=42 41-30 <=35

Depersonalisation <=5 6-10 >=11

This table shows the demographic information – NOTE: This is a European study and has been recorded in the article as the European notations of a comma instead of a decimal poin

Table 1

Demographic information of nursing staff

Characteristics n=64

Mean age ±SD (year) 37,17 ± 7,38

Mean duration of nursing ±SD(year) 13,6 ±8,9

Gender male 6

female 58

Educational level

Technological Educational

Institutions 39

2 years nursing school 25

Marital status:

Married 43

Single or divorced 20

Additional education

nursing specialization title 3

none 61

Working role

Head nurse 11

Clinical registered nurse 30

Nurse assistant 23

Working place in hospital

ICUs, emergency or

operating rooms 35

Inpatient services 20

Outpatient clinics,

laboratories or

administrative units 9

Working experience

1-5 years 20

6-15 years 14

>16years 30

Shifts worked

Only days 16

Days and/or nights 48

Mean number of night shifts

per person in a month 3,47 (±2,75)

Table 2

Mean– SD Burnout Subscales

subscales mean SD

EMOTIONAL EXHAUSTION 26,77 12,64

DEPERSONALIZATION 10,09 6,40

PERSONAL ACCOMPLISHMENTS 37,98 7,10

Putting this information together:

Page 146 of the article shows the ANOVA results as the burnout questionnaire with very little correlation between the demographic data and the burnout levels that is visualized in Table 4 (p. 148). The t-test was performed on the demographic data and working conditions Table 3 (p. 146). Again, there was little statistical significance with this measurement. The authors then ran an analysis using a Chi-Square test for the variables of education level and the three burnout dimensions revealed through the ANOVA analysis.

The use of a Chi-Square will test if there is a statistical significance between two variables with the same sample, and whether there is a statistical significance between the variables (StatTrek, 2017). The Chi Square analysis showed that additional education had a statistical significance with the level of burnout of depersonalization. In addition, this analysis revealed that emotional exhaustion level was found to be influenced by shift work and willingness to retire. Lastly, the Chi Square correlated the burnout level of depersonalization with hindrance of collaboration (p.145.