*HLT 362 Week 3 Understanding Independent Samples t-Test*

##### HLT 362 Week 3 Understanding Independent Samples t-Test

There are a few different ways to conduct an ANOVA test in SPSS. The first way is to use the “ANOVA” command. To do this, go to “Statistics” and then select “ANOVA.” After selecting this option, a dialog box will appear. Next, select the variable that you want to use as the dependent variable and click “OK.” Another way to conduct an ANOVA test in SPSS is to use the “Regression” command (van den Bergh et al., 2020). To do this, go to “Statistics” and then select “Regression.” After selecting this option, a dialog box will appear. Next, select the variable that you want to use as the dependent variable and click ” OK. When conducting an ANOVA to see if there is a statistically significant difference in the Interval Depression Score among 3 groups of shift workers, one may want to first ensure that the data meets the assumption of normality. This can be done by running a goodness-of-fit test, such as the Kolmogorov-Smirnov test (Liu & Wang, 2021). If the data meet the assumption of normality, one can proceed with conducting the ANOVA. The null hypothesis for this test is that there is no difference in the Interval Depression Scores among the three groups of shift workers. The alternative hypothesis is that there is a difference in at least one of the group means. The purpose of this assignment is to conduct an ANOVA to see if there is a statistically significant difference in the Interval Depression Score among 3 groups of shift workers.

**Part One**

**Identify the independent and dependent variables.**

While conducting ANOVA test, it is necessary to determine both the dependent and independent variables. In this case, the

independent variable is Shift Worked while the dependent variable is Depression Score (Interval).

**Write a null hypothesis.**

**H0: **There is no statistical significance between the depression score and the shift worked.

**Write an alternative non-directional hypothesis.**

**H1:** There is a statistical significance between the depression score and the shift worked.

- Interpret your results. Guidelines for interpreting ANOVA results can be found in

Table 1: ANOVA |
|||||

Shift Worked (nominal) 1=first, 2=second, 3=third | |||||

Sum of Squares | df | Mean Square | F | Sig. | |

Between Groups | 10.267 | 12 | .856 | 1.672 | .162 |

Within Groups | 8.700 | 17 | .512 | ||

Total | 18.967 | 29 |

Table 1 shows ANOVA output between the dependent and independent variables identified in the study. The significant value generated is 0.162 which is greater than 0.05 level of significance i.e., 0.162> 0.05, as a result, we fail to reject the null hypothesis. We therefore conclude that There is no statistical significance between the depression score and the shift worked.

**Part Two**

ANOVA is a statistical technique that is used to test for differences between groups. In this case, we are looking at the Interval Depression Score (IDS) among three groups of shift workers. The ANOVA analysis will help us determine if there are any significant differences between the IDS scores of the different groups (Akbay et al., 2019). To carry out the ANOVA analysis, we first need to gather data from each of the three groups of shift workers. We will need to know the mean IDS score for each group, as well as the number of people in each group. Once we have this information, we can plug it into an ANOVA calculator (there are many freely available online).

The significant value generated is 0.162 which is greater than 0.05 level of significance i.e., 0.162> 0.05, as a result, we fail to reject the null hypothesis. We therefore conclude that There is no statistical significance between the depression score and the shift worked. There are a number of possible explanations for why there is no statistical significance between the depression score and the number of shifts worked. It could be that the sample size is too small to detect a difference, or that the relationship between depression and shift work is more complex than a simple linear relationship. Another possibility is that other factors, such as job satisfaction or social support, play a larger role in determining depression among workers who do shift work.

**Conclusion**

The significant value generated is 0.162 which is greater than 0.05 level of significance i.e., 0.162> 0.05, as a result, we fail to reject the null hypothesis. From the study conducted, there is no statistical significance between the depression score and the shift worked. There are a few different ways to conduct an ANOVA test in SPSS. The first way is to use the “ANOVA” command. To do this, go to “Statistics” and then select “ANOVA.” After selecting this option, a dialog box will appear.

**References**

Akbay, L. O. K. M. A. N., Akbay, T., Osman, E. R. O. L., & Kilinc, M. (2019). Inadvertent Use of ANOVA in Educational Research: ANOVA is not A Surrogate for MANOVA. *Journal of Measurement and Evaluation in Education and Psychology*, *10*(3), 302-314. https://doi.org/10.21031/epod.524511

Liu, Q., & Wang, L. (2021). t-Test and ANOVA for data with ceiling and/or floor effects. *Behavior Research Methods*, *53*(1), 264-277. https://link.springer.com/article/10.3758/s13428-020-01407-2

Van den Bergh, D., Van Doorn, J., Marsman, M., Draws, T., Van Kesteren, E. J., Derks, K., … & Wagenmakers, E. J. (2020). A tutorial on conducting and interpreting a Bayesian ANOVA in JASP. *LAnnee psychologique*, *120*(1), 73-96. https://www.cairn.info/revue-l-annee-psychologique-2020-1-page-73.htm?ref=doi

**Topic 3 DQ 1**

Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis. Discuss why this is important in your practice and with patient interactions.

REPLY TO DISCUSSION

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What is a hypothesis?

A hypothesis is a proposed explanation for a phenomenon. For an idea to be a scientific hypothesis, the scientific method requires that one can test it. (Hypothesis, 2021)

The hypothesis is an educated summation of the outcome between independent and dependent variables and the research process that will test the validity by analyzing the data. (Ambrose, 2018) Healthcare is the evolving study on the improvement of the quality of care. Questioning how to improve the quality is the 1st step. The prediction of the outcome between the two variables of dependent and independent is the hypothesis. With the variables identified, data collection is based on the variables. The research data will reflect the correlation but cannot prove causation that indicates a direct correlation between the cause and effect.

The null hypothesis is essentially the “devil’s advocate” position. That is, it assumes that whatever you are trying to prove did not happen (Hypothesis Testing, 2018)

The increased prevalence of cannabidiol oil (CBD) for pain control leads to a hypothesis. There has not been a study to reinforce nor disprove the statement that CBD use decreases pain. The theory of CBD use will reduce pain with regular consumption. The null hypothesis lacks the data for the relationship between the variables, or there is no effect on them. The data will reflect the patients that utilize CBD and have no pain relief. The alternative hypothesis indicates a relationship between the variables stating the decrease in pain by the subject that takes CBD. This data pattern can be interpreted to reject the null hypothesis, and if rejected, the alternative is accepted.

The use of alternative medicines has increased aromatherapy to reduce or prevent nausea in emergency room patients. There is no data to reinforce or disprove the hypothesis that the arbitrary resolves or decreases nausea in ER patients. The null hypothesis lacks subjective data to correlate the relationship between the variables and the direct effect. The data would be objective that using aromatherapy decreased or relieving nausea. An alternative hypothesis will show a connection between the variables reflecting the decrease or relief of sickness in the subject group. What is an idea?

A hypothesis is a proposed explanation for a phenomenon. For an idea to be a scientific hypothesis, the scientific method requires that one can test it. (Hypothesis, 2021)

The hypothesis is an educated summation of the outcome between independent and dependent variables and the research process that will test the validity by analyzing the data. (Ambrose, 2018) Healthcare is the evolving study on the improvement of the quality of care. Questioning how to improve the quality is the 1st step. The prediction of the outcome between the two variables of dependent and independent is the hypothesis. With the variables identified, data collection is based on the variables. The research data will reflect the correlation but cannot prove causation that indicates a direct correlation between the cause and effect.

The null hypothesis is essentially the “devil’s advocate” position. That is, it assumes that whatever you are trying to prove did not happen (Hypothesis Testing, 2018)

The increased prevalence of cannabidiol oil (CBD) for pain control leads to a hypothesis. There has not been a study to reinforce nor disprove the statement that CBD use decreases pain. The theory of CBD use will reduce pain with regular consumption. The null hypothesis lacks the data for the relationship between the variables, or there is no effect on them. The data will reflect the patients that utilize CBD and have no pain relief. The alternative hypothesis indicates a relationship between the variables stating the decrease in pain by the subject that takes CBD. This data pattern can be interpreted to reject the null hypothesis, and if rejected, the alternative is accepted.

The use of alternative medicines has increased aromatherapy to reduce or prevent nausea in emergency room patients. There is no data to reinforce or disprove the hypothesis that the arbitrary resolves or decreases nausea in ER patients. The null hypothesis lacks subjective data to correlate the relationship between the variables and the direct effect. The data would be objective that using aromatherapy decreased or relieving nausea. An alternative hypothesis will show a connection between the variables reflecting the decrease or relief of sickness in the subject group.

References

Ambrose, J. (2018). Applied Statistics for Health Care. Grand Canyon University.

https://doi.org/https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-

care/v1.1/#/chapter/3

Hypothesis. (2021). Wikipedia. Retrieved April 28, 2021, from https://en.wikipedia.org/wiki/Hypothesis

Hypothesis Testing. (2018). Laerd Statistics. Retrieved April 28, 2021, from https://statistics.laerd.com/statistical-guides/hypothesis-testing-3.php