# files (1)TechniquesandInterpretationforStatisticalAnalysis-PROVIDEARESPONSETOPEER.docxTechniquesandI

files (1)TechniquesandInterpretationforStatisticalAnalysis-PROVIDEARESPONSETOPEER.docxTechniquesandInterpretationforStatisticalAnalysis-PROVIDEARESPONSETOPEER.docx
PROVIDE A RESPONSE TO PEER. CONTINUE TO ENGAGE AND PROVIDE MORE INSIGHT. MUST BE AT LEAST 200WORDS FOR EACH RESPONSE.

1st Post: T-test is a widely used statistical method to compare group means (Vicky, 2009). Using t-test requires multiple assumptions: normal distribution of values, dependent variable measured using continuous or interval rations, random sampling of data, independent observations, and homogeneity of variance. T-test is used to test null or alternative hypotheses to find out whether the difference between two populations is real or by chance. Independent samples t-test is used with two independent groups. When a sample is paired or dependent it is called paired samples t-test.

In a research study about the effectiveness of nursing board games in psychiatric nursing courses for undergraduate students, Chia-Shan et al. (2023) used parallel two-arm experimental design. They recruited 106 participants, 53 in each group. Nurses in the investigation group used board games during the eight-week course while nurses in the control group received traditional instruction. After the intervention, nurses in the intervention group (those who used board games) scored significantly higher than the control group in psychiatric knowledge, attitude, and self-reported learning satisfaction. Researchers administered three scales to measure nursing knowledge, attitude, and satisfaction. Nurses in both groups received pre- and post-tests. Researchers used two-sample t-tests and paired t-tests to determine differences in the learning outcomes between two groups with a p-value smaller than 0.05.  During pre-test, there were no significant differences between two groups (t = -1.60, p = .112). After the intervention there was a significant difference between two groups in the psychiatric nursing knowledge (t = ??2.06, p = .042). In this study, researchers use t-test to determine differences between means of control and investigation group and to determine differences in pre- and post-test within each group.
References:
Chia-Shan, W., Mei-Fang, C., Hwang, H., & Lee, B. (2023). Effectiveness of a nursing board games in psychiatric nursing course for undergraduate nursing students: An experimental design.
Nurse Education in Practice, 70, 103657.
https://doi.org/10.1016/j.nepr.2023.103657

2nd Post: T-scores and z-scores (z-values and t-values) show how many standard deviations from the mean the statistical estimate is located (What are z-scores and t-scores? n.d.). If the t-score is 2.5, the estimate is 2.5 standard deviations from the predicted mean. The more standard deviations from the mean, the less likely the estimate occurred due to chance (under the null hypothesis). The z-scores are used when the population standard deviation and mean are known (rare), and the sample size is 30 or above (Foltz, 2012; Glen, 2014). In other cases, t-scores are used. T-scores are more conservative and lead to less certainty with an increased margin of error. When using t-scores, researchers must use degrees of freedom to account for a small sample size. When the sample size is above 30 and definitely above 100, the difference between t-distribution and standard normal z-distribution becomes indistinguishable.

3rd Post: Analysis of variance (ANOVA) is a statistical method to assess the variances between group means (Frost, 2023). Researchers use the null hypothesis to determine whether two or more populations have significant differences. ANOVA requires that at least one variable is categorical independent, and another is continuous dependent. In one-way ANOVA, only one factor is investigated (Surbhi, 2017). In two-way ANOVA, two factors are investigated concurrently. In other words, the main difference between one-way and two-way ANOVA is the number of independent variables: one or two (Bevans, 2020). An example of one-way ANOVA is to test the relationship between show brands (Nike, Sketchers, Adidas) and finish times in the 5K race. For example, two-way ANOVA tests the relationship between shoe brands (Nike, Sketchers, Adidas), runners age groups (18-40, 41-65, and above 65), and finishing times in the 5K race. The independent variables are shoe brand and runners’ age group; the dependent variable is finishing times in the 5K race.

Categorical independent variables in ANOVA are named factors that could have several levels. For example, an independent variable in the experiment could have three levels (control, treatment 1, and treatment 2). The ANOVA test could help determine whether the mean outcomes for those three levels are different. Two-way ANOVA deals with two factors, each of those with different levels. For example, we could add another factor ?? gender to the previously mentioned experiment. Then, the gender factor would have two levels ?? male and female. In two-way ANOVA, each factor could interact with dependent variable but also interact with each other.

4th: One-way ANOVA could be considered an extension of a t-test for independent variables (Mishra et al., 2019). T-test helps determine statistically significant differences or the lack of between the means of two groups. ANOVA helps to do the same but for three or more groups. If the groups could be determined using one factor (independent variable), we would use one-way ANOVA. If the groups could be split using two factors, we should use two-way ANOVA.

Ravid (2020) explained that comparing multiple groups using a t-test would require comparing the means of each group to the means of other groups one by one. To compare the means of the three groups, we would have to run three t-tests. To compare the means of five groups, we would have to run ten t-tests. Such a process is cumbersome. In addition, each t-test comes with a particular error level, and the errors would get compounded for multiple t-tests. ANOVA allows the comparison of means for multiple groups, with the error kept at 0.05.
At my work, I could use a t-test to determine whether the handwashing rate differs between nurses and nursing assistants. I would randomly choose a group of nurses and a group of nursing assistants and compare the means of their handwashing rates. I could also use a t-test to determine whether work satisfaction differs between male and female nurses. Conversely, I could use one-way ANOVA to determine the relationship between nursing seniority (new graduate, staff nurse, and senior nurse) and work satisfaction. If I decide to investigate an additional factor, such as gender, on work satisfaction, I will use two-way ANOVA because I have two factors (gender and nursing seniority), each with multiple levels, that could affect the dependent variable (staff satisfaction).

TechniquesandInterpretationforStatisticalAnalysis-PROVIDEARESPONSETOPEER.docx
PROVIDE A RESPONSE TO PEER. CONTINUE TO ENGAGE AND PROVIDE MORE INSIGHT. MUST BE AT LEAST 200WORDS FOR EACH RESPONSE.

1st Post: T-test is a widely used statistical method to compare group means (Vicky, 2009). Using t-test requires multiple assumptions: normal distribution of values, dependent variable measured using continuous or interval rations, random sampling of data, independent observations, and homogeneity of variance. T-test is used to test null or alternative hypotheses to find out whether the difference between two populations is real or by chance. Independent samples t-test is used with two independent groups. When a sample is paired or dependent it is called paired samples t-test.

In a research study about the effectiveness of nursing board games in psychiatric nursing courses for undergraduate students, Chia-Shan et al. (2023) used parallel two-arm experimental design. They recruited 106 participants, 53 in each group. Nurses in the investigation group used board games during the eight-week course while nurses in the control group received traditional instruction. After the intervention, nurses in the intervention group (those who used board games) scored significantly higher than the control group in psychiatric knowledge, attitude, and self-reported learning satisfaction. Researchers administered three scales to measure nursing knowledge, attitude, and satisfaction. Nurses in both groups received pre- and post-tests. Researchers used two-sample t-tests and paired t-tests to determine differences in the learning outcomes between two groups with a p-value smaller than 0.05.  During pre-test, there were no significant differences between two groups (t = -1.60, p = .112). After the intervention there was a significant difference between two groups in the psychiatric nursing knowledge (t = ??2.06, p = .042). In this study, researchers use t-test to determine differences between means of control and investigation group and to determine differences in pre- and post-test within each group.
References:
Chia-Shan, W., Mei-Fang, C., Hwang, H., & Lee, B. (2023). Effectiveness of a nursing board games in psychiatric nursing course for undergraduate nursing students: An experimental design.
Nurse Education in Practice, 70, 103657.
https://doi.org/10.1016/j.nepr.2023.103657

2nd Post: T-scores and z-scores (z-values and t-values) show how many standard deviations from the mean the statistical estimate is located (What are z-scores and t-scores? n.d.). If the t-score is 2.5, the estimate is 2.5 standard deviations from the predicted mean. The more standard deviations from the mean, the less likely the estimate occurred due to chance (under the null hypothesis). The z-scores are used when the population standard deviation and mean are known (rare), and the sample size is 30 or above (Foltz, 2012; Glen, 2014). In other cases, t-scores are used. T-scores are more conservative and lead to less certainty with an increased margin of error. When using t-scores, researchers must use degrees of freedom to account for a small sample size. When the sample size is above 30 and definitely above 100, the difference between t-distribution and standard normal z-distribution becomes indistinguishable.

3rd Post: Analysis of variance (ANOVA) is a statistical method to assess the variances between group means (Frost, 2023). Researchers use the null hypothesis to determine whether two or more populations have significant differences. ANOVA requires that at least one variable is categorical independent, and another is continuous dependent. In one-way ANOVA, only one factor is investigated (Surbhi, 2017). In two-way ANOVA, two factors are investigated concurrently. In other words, the main difference between one-way and two-way ANOVA is the number of independent variables: one or two (Bevans, 2020). An example of one-way ANOVA is to test the relationship between show brands (Nike, Sketchers, Adidas) and finish times in the 5K race. For example, two-way ANOVA tests the relationship between shoe brands (Nike, Sketchers, Adidas), runners age groups (18-40, 41-65, and above 65), and finishing times in the 5K race. The independent variables are shoe brand and runners’ age group; the dependent variable is finishing times in the 5K race.

Categorical independent variables in ANOVA are named factors that could have several levels. For example, an independent variable in the experiment could have three levels (control, treatment 1, and treatment 2). The ANOVA test could help determine whether the mean outcomes for those three levels are different. Two-way ANOVA deals with two factors, each of those with different levels. For example, we could add another factor ?? gender to the previously mentioned experiment. Then, the gender factor would have two levels ?? male and female. In two-way ANOVA, each factor could interact with dependent variable but also interact with each other.

4th: One-way ANOVA could be considered an extension of a t-test for independent variables (Mishra et al., 2019). T-test helps determine statistically significant differences or the lack of between the means of two groups. ANOVA helps to do the same but for three or more groups. If the groups could be determined using one factor (independent variable), we would use one-way ANOVA. If the groups could be split using two factors, we should use two-way ANOVA.

Ravid (2020) explained that comparing multiple groups using a t-test would require comparing the means of each group to the means of other groups one by one. To compare the means of the three groups, we would have to run three t-tests. To compare the means of five groups, we would have to run ten t-tests. Such a process is cumbersome. In addition, each t-test comes with a particular error level, and the errors would get compounded for multiple t-tests. ANOVA allows the comparison of means for multiple groups, with the error kept at 0.05.
At my work, I could use a t-test to determine whether the handwashing rate differs between nurses and nursing assistants. I would randomly choose a group of nurses and a group of nursing assistants and compare the means of their handwashing rates. I could also use a t-test to determine whether work satisfaction differs between male and female nurses. Conversely, I could use one-way ANOVA to determine the relationship between nursing seniority (new graduate, staff nurse, and senior nurse) and work satisfaction. If I decide to investigate an additional factor, such as gender, on work satisfaction, I will use two-way ANOVA because I have two factors (gender and nursing seniority), each with multiple levels, that could affect the dependent variable (staff satisfaction).

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