NURS 8201 t-Tests and ANOVA



  • Summarize your interpretation of the ANOVA statistics provided in the Week 5 ANOVA Exercises SPSS Output document.
    • Note: Interpretation of the ANOVA output should include identification of the -value to determine whether the differences between the group means are statistically significant.
    • Be sure to accurately evaluate each of the results presented (descriptives, ANOVA results, and multiple comparisons using post-hoc analysis)

Reminder: The College of Nursing requires that all papers submitted include a title page, introduction, summary, and references. The Sample Paper provided at the Walden Writing Center provides an example of those required elements (available at to an external site.). All papers submitted must use this formatting.

NURS 8201 t-Tests and ANOVA

NURS 8201 t-Tests and ANOVA


The topic in which I focused on as a discussion, highlighted the point that there is an increasing number of nurse prescribers (NIP), however, there is little evidence that exists about their antibiotic prescribing practices (Ness, Currie, Reilly, McAloney-Kocaman, & Price, 2021). The article reports that the objective of the study was to measure nurse independent prescribers, who are managing the care of patients presenting with an upper respiratory tract infection for the first time, without prescribing an antibiotic, and assess what are the determinants for not prescribing antibiotics (2021). The literature shared that the inferential analysis was carried out using Spearman’s correlation to explore the relationship of the direct and indirect measures (independent variables) with intention (dependent variable) (2021). The analysis identified the significant predictors of intention for the multiple linear regression model, which was used due to the ordinal nature of the data and lack of normality. Also, the researchers used the Test-retest reliability to carry out the study by asking participants to complete the questionnaire again two weeks later and Spearman’s ρ correlation coefficient was used to check for stability of indirect measures. In my opinion, this strengthened the research (2021).  In my opinion, this standard of practice strengthened. 

Study Findings

The findings from 184 participants it was found that NIPs intended to manage their patients who presented with a URTI for the first time, without prescribing an antibiotic (2021). Key determinants were perceived norm, perceived behavioral control, and moral norm for this study (2021).


Calculating statistical data to obtain the mean is often a long and tedious process (Rosenfeld, 2021). The t-test and the one-way analysis of variance (ANOVA) are the two most common tests used for this purpose (2021). The literature reports that in the first decades of the twentieth century, an Englishman by the name of Ronald Aylmer Fisher radically changed the use of statistics in research (2021). He invented the technique called Analysis of Variance and founded the entire modern field called Design of Experiments (2021). ANOVA is a test that provides a global assessment of a statistical difference in more than two independent means (Sullivan, 2021).

ANOVA Example

A clinical trial is run to compare weight loss programs and the participants are randomly assigned to one of the comparison programs and these individuals are counseled on the details of the assigned program (Sullivan, 2021). Partakers in the test followed the assigned program for 8 weeks. The outcome of interest focused on is weight loss, in which is defined for the study as the difference in weight measured at the start of the study (baseline) and weight measured at the end of the study (8 weeks), measured in pounds. The trial included three popular weight loss programs. The first one is a low-calorie diet. The second is a low-fat diet and the third is a low carbohydrate diet. For comparison purposes, the team built in a fourth group as a control lineup (2021). The trial implemented the ANOVA by using the following five-step approach.

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 Set up hypotheses and determine the level of significance

Select the appropriate test statistic

Set up decision rule.

Compute the test statistic



William Sealy Gosset introduced the t-statistic in 1908 (Peckinpaugh, 2018). The t-test is a statistical hypothesis test where it follows a Student’s t – distribution if the null hypothesis is supported (2018). The t-test conveys how significant the differences between groups are; In other words, the t-test identifies if the differences (measured in means) could have happened by chance (2018). With that being said, both the t-test as well as the ANOVA looks at the difference in means and the spread of the distributions (i.e., variance) across groups; however, the ways that they determine the statistical significance are different (Peckinpaugh, 2018). The t-test is used when determining whether two averages or means are the same or different (2018). A t-test has more odds of committing an error when more means are used, which is why ANOVA is used when comparing two or more means (2018).

NURS 8201 t-Tests and ANOVA
NURS 8201 t-Tests and ANOVA

 T-Test Example

T-tests can be used in real life to compare averages (Glen, 2021). For example, a drug company may want to test a new cancer drug to find out if it improves life expectancy. In an experiment, there’s always a control group (Glen, 2021). The control group may show an average life expectancy of +5 years, while the group taking the new drug might have a life expectancy of +6 years (2021). It would seem that the drug might work. But it could be due to a fluke. To test this, researchers would use a t-test to find out if the results are repeatable for an entire population. There are three main types of t-test:

An Independent Samples t-test compares the means for two groups.

A Paired sample t-test compares means from the same group at different times (say, one year apart).

A-One sample t-test tests the mean of a single group against a known mean.

Use the following tools to calculate the t-test:

How to do a T-test in Excel.

T-test in SPSS.

T distribution on the TI 89.

T distribution on the TI 83.


 Circulation (2008). Analysis of variance Retrieved From 117:115–121

 Glen, S., (2021). T-Test (Student’s T-Test): Definition and Examples From Elementary Statistics for the rest of us!

 Franscisco, (2017). Difference Between T-TEST and ANOVA. Difference Between Similar Terms and Objects.

 Ness, V., Currie, K., Reilly, J., McAloney-Kocaman, K., & Price, L. (2021). Factors associated with independent nurse prescribers’ antibiotic prescribing practice: a mixed-methods study using the Reasoned Action Approach. Journal of Hospital Infection, 113, 22–29.

 Rosenfeld, B., Vermont Mathematics Initiative (2021)

 Sullivan, L., (2021). Hypothesis Testing – Analysis of Variance (ANOVA).

Inferential statistics facilitates the researcher to analyze data and draw inferences and conclusions from the respective data. They are statistical procedures used to reach conclusions about associations between variables and are explicitly designed to test hypotheses. The selected topic investigates the application of MI (motivational interviewing) and whether it increases referrals and promotes compliance with appointments to treatment programs. It examines the effects of motivational interviewing to determine if patients afflicted with an opioid use disorder accepted referrals to MAT clinics and adherence to scheduled appointments.

APA Citation

Morgenstern et al. (2017). Dismantling motivational interviewing: Effects on initiation of behavior change among problem drinkers seeking treatment. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 31(7), 751–762.

Morgenstern et al. (2017) examined MI’s hypothesized active ingredients using a dismantling design. Problem drinkers (N=139) seeking treatment were randomized to one of three conditions: MI, relational MI without the directional elements labeled spirit-only MI (SOMI), or a non-therapy control (NTC) condition and followed for eight weeks. On average, participants were middle-aged, well-educated (70% college graduates), employed (78%), Caucasian (76%), and female (57%). Those assigned to MI or SOMI received four sessions of treatment over eight weeks. Condition equivalence on demographics, drinking, and other problem severity at baseline were determined using chi-square tests, t-tests, and one-way ANOVAs.

There were no significantly different effects of the average of MI and SOMI compared to NTC on either TLFB drinking outcome (MI + SOMI vs NTC: SSD: B = .11, SE = .10, p = .22; HDD: B = −.04, SE = 0.18, p = .73). In addition, there were no significantly different effects on drinking between MI and SOMI (MI vs SOMI: SSD: B = −.00, SE = .09, p = .97; HDD: B = .13, SE = 0.12, p = .27). The study investigated the mechanism of behavior change associated with motivational interviewing, contrasting MI and SOMI to test whether strategies that selectively identify and reinforce change talk led to improved outcomes relative to a client-centered therapy that did not include directional strategies. In addition, the contrast between SOMI and NTC was designed to test whether client-centered therapy strategies alone improved outcomes relative to a non-therapy condition in which participants were offered normative feedback and encouragement to change on their own. Neither hypothesis was supported.

Inferential statistics used t-tests and one-way ANOVAs, among other statistics. No significant differences were found in attrition across conditions. All participants significantly reduced their drinking by week 8, but reductions were equivalent across conditions. The hypothesis that baseline motivation would significantly moderate condition effects on outcome was generally not supported.


Morgenstern, J., Kuerbis, A., Houser, J., Levak, S., Amrhein, P., Shao, S., & McKay, J. R. (2017). Dismantling motivational interviewing: Effects on initiation of behavior change among problem drinkers seeking treatment. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 31(7), 751–762.

Polit, D. F., & Beck, C. T. (2020). Essentials of nursing research: Appraising evidence for nursing practice. Lippincott Williams & Wilkins.

This is insightful, inferential statistics facilitate the researcher analyzing data and drawing inferences and conclusions from the respective data. They are statistical procedures used to reach conclusions about associations between variables and are explicitly designed to test hypotheses. Some of the inferential statistics that can be applied in the quantitative study process include ANOVA, t-test, Pearson correlation, chi-square tests, and regression analysis (Franscisco, 2017). The choice of inferential statistics to use depend on the variables in the study. In most cases, the research variables need to have continuous data and sometimes categorical variables, as in the case of an independent sample t-test. Before considering the application of inferential statistics, there is always the need for data analysis to perform descriptive statistics to establish the attributes of each variable (Weissgerber et al., 2018).

The study that you have selected is quantitative; it has all the attributes of quantitative research. The sample size that has been used is appropriate and representative of the population under consideration. However, one of the questions that I would like to ask is: from the study selected, how was the data collected? Did the researcher consider the types of variables during the process of data collection? From the study, there were clear research outcomes. In other words, there were no significantly different effects of the average of MI and SOMI compared to NTC on either TLFB drinking outcome. The ANOVA and t-test were correctly applied given the variables that were under consideration (Kim, 2017). Finally, inferential statistics can be applied to compare the relationship between the variables under consideration. When Pearson correlation is used, the coefficient is normally considered in determining whether there is a correlation between the variables.


 Franscisco, (2017). Difference between T-TEST and ANOVA. Difference between Similar Terms and Objects.

Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean journal of anesthesiology70(1), 22.

Weissgerber, T. L., Garcia-Valencia, O., Garovic, V. D., Milic, N. M., & Winham, S. J. (2018). Meta-Research: Why we need to report more than’Data were Analyzed by t-tests or ANOVA’. Elife7, e36163.

Since entering the career of nursing, I believe that most nurses would like to gather as much experience as they can to become a proficient and well-rounded staff in this profession. Being a nurse for about six years now, I have spent the last two and a half years working my way up to become an intensive care unit (ICU) nurse. Being an ICU nurse is a specialty in itself that provides many nursing with the competitive pay, comprehensive benefits, and extensive learning experience in critical level of care. As the ICU can be a stressful environment for patients and families, with established long term consequences, the impact that this unique environment can have on healthcare professionals is increasingly being recognized.

            What I have noticed while being a nurse in the critical care environment, I have noticed a significant increase in our nurse turnover rates for both local and traveling nurse staff. For as long as I have been working in this hospital (in a different unit at the time), many nurses are either not trained properly and/or experiencing burnout early on in their career due particularly in the ICU unit. The exposure of  nurses within a high acuity nursing environment without the proper support from our management has led to burnout. Furthermore, I have noticed that the ICU unit is the only unit with the least amount of local nurses that stay employed for at least two years into their career life.

Most of the staff nurses that I have worked with have expressed the desires to leave off-island in search for better opportunities or change in nursing career. Our hospital is going through a constant battle with recruiting and retaining their nursing staff, specifically more significant in the ICU unit. Our medical director is currently working alongside the hospital administrators about looking for ways to address the increase burnout that the staff nurses are experiencing and construct a resilient healthcare system. For as long as I have been working in this hospital (in a different unit at the time), many nurses are either not trained properly with the advanced skills needed dealing with life threatening illnesses and/or lack the skills to tackle critically ill conditions. As a result overall, burnout causes decrease in quality of care, poor performances, increase mortality in patients, and errors in the healthcare environment.

The impact that this unique environment can have on healthcare professionals is increasing therefore, as a DNP prepared nurse, to gain a more complete understanding of critical care well-being requires commitment to measure, develops interventions, and re-measure them. An analysis variation or ANOVA tests done for each survey or experimental results are significant and help us figure out if the studies prove our hypothesis. Inferential statistics takes data from samples and make generalizations about a population. Experimental analysis using t-test, to compare the means of two groups or ANOVA (analysis of variance) to analyze the difference between the means of more than two groups, would help make estimates about the population at study (nurses) and testing hypothesis to draw conclusions (Bhandari, 2020).

One of the chosen inferential articles that describe the prevalence of burnout in the ICU healthcare assessed in the included analysis of variance study (ANOVA) through PubMed, Medline, and a web of sciences article reviews and observational study designs. Within the articles, the most commonly used instruments for data collection include the Maslach burnout inventory (MBI), professional quality of life scale, work related behavior, and experience patterns. According to a 4 large scale research study reported that the burnout prevalence rates ranges between 28%-61%; this study suggests that ICU workers were slightly (about 20%) more prone to burnout than the average healthcare (Chuang, Tseng, Lin, Lin & Chen, 2016). The following risk factors reported include: age, sex, marital status, personality traits, work experiences, work environment, workload, shift work, ethical issues, and decision making choices.

In another article review done by Kerlin, McPeake & Mikkelsen (2020), being that ICU can be a stress environment for both patients and families; the impact that this environment can have on healthcare environment is being increasingly recognized. Challenging situations, exposure to high mortality and daily difficult workloads can lead to excessive stress and resultant in moral distress, leading to burnout syndrome. This cross-sectional study, most critical care nurses experience about 81% of one or more burnout symptoms. The framework presented in this article implies that multidisciplinary and coordinated cares are essential components to high quality critical care delivery. The publications are assessed for relevance to using data to support observational study designs that examine associations between any risk factors and burnout in the ICU setting.

In a systematic meta-analysis done by Ramirez-Elvira (2021), the ANOVA is  carried out with different articles and journals from Medline and CINAHL following the PRISMA (preferred reporting item for systematic reviews and meta-analysis), with a sampling of N= 1989; there was an estimate of about 31% prevalence for high emotional exhaustion, 18% high depersonalization, and 49% low personal achievement (p.2). Furthermore, in an inferential statistical cross-sectional total population study among N=60 nurses using a self-administered MBI questionnaire resulted into a high burnout percentage of about 62% (Cishahayo, Nankundwa, Sego, & Bhengu, 2017). Burnout is measured through high emotional exhaustion (48%), high depersonalization (25%), and low personal accomplishment (50%).

On a much larger and international scale, in study done by Bhagavathula (2018), an institution based teaching hospital with a cross-sectional study conduced among healthcare providers N=500 serving about >50,000 population in Ethiopia; a questionnaire with sociodemographic details using descriptive analysis using correlation and multivariate logistic regression studied ANOVA using survey questionnaires of MBI scale. The overall prevalence of burnout is about 14%, respondents with debility was 53%, increase self criticism of about 56%, and depressive symptoms of about 46%. As a result, the nursing profession was a significant determinant for emotional exhaustion and burnout.

In conclusion, most inferential studies summarized above strengthens the application of evidenced based practices in promoting recruitment and retention policies in decreasing the risk of burnouts. Critical care courses and educational programs should be established by the support faculty to meet the needs of critical care assessments and criteria. Practice variability that necessitates changing for better conditions in a resource limited setting may excavate the underlying factors associated with nursing burnout.


Bhagavathula, A., Abegaz, T., Belachew, S., Gebreyohannes, E., Gebresillassie, B., & Chattu, V.

(2018). Prevalence of burnout syndrome among healthcare professionals working at Gondar University Hospital, Ethiopia. Journal of Educational Health Promotion 7(145). Retrieved from;year=2018;volume=7;issue=1;spage=145;epage=145;aulast=Bhagavathula

Bhandari, P. (2020). An introduction to inferential statistics. Scribbr Statistics. Retrieved

Chuang, C., Tseng, P., Lin, C., Lin, K. & Chen, Y. (2016). Burnout in the intensive care unit

professionals. Medicine Baltimore Journal. 95(50). Retrieved from

Cishahayo, E., Nankundwa, E., Sego, R., & Bhengu, B. (2017). Burnout among nurses working

in critical care settings: A case of a selected tertiary hospital in Rwanda. International Journal of Research in Medical Sciences. 5(12). Retrieved from

Kerlin, M., Mc Peake, J., & Mikkelsen, M. (2020). Burnout and joy in the profession of critical

care medicine. BMC Critical Care Journal. 24(98). Retrieved from

Ramirez-Elvira, S., Romero-Bejar, J., Suleiman-Martos, N., Gomez-Urquiza, J., Monsalve-

Reyes, C., Canadas-Delafuentes, G…Albendin-Garcia, L. (2021). Prevalence, risk factors, and burnout levels in intensive care unit: A systematic review and meta-analysis. International Journal of Environmental Research and Public Health. 18.Retrieved from