Capella University

RSCH FPX 7864 Assessment 4 ANOVA Application and Interpretation
Capella University, DNP, RSCH-FPX7864

RSCH FPX 7864 Assessment 4 ANOVA Application and Interpretation

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RSCH FPX 7864 Assessment 4 ANOVA Application and Interpretation Data Analysis Plan The statistical method known as analysis of variance (ANOVA) can be used to compare and test differences between two or more means. The ANOVA test is considered vital for using comparative as well as experimental research designs (Koli, 2024). In the following assessment, the results for correct answers within Quiz 3 are assessed for correct frequency, which shows if there is any divergence. Section and Quiz 3 A variable that a student is assigned to, and is a type of variable, is the classroom group, which is the variable section. Quiz 3, however, is a continuous variable and measures students’ performance as determined by the number of correct answers given. Research Question Is there a statistically significant difference in Quiz 3 scores among students from different classroom sections? Null Hypothesis (Ho) There is no statistically significant difference in Quiz 3 scores among students from different classroom sections. Alternative Hypothesis (HA) There are statistically significant differences in Quiz 3 scores among students from different classroom sections. Testing Assumptions Levene’s test was used to ascertain whether the assumption of homogeneity was met. The test is a conventional practice in checking the equal variance assumption in analysis. Assumption of homogeneity means that the variance is the same for all groups. By performing Levene’s test, following a conventional analysis in checking homogeneity in analysis of variance, F(2, 102) = 2.690, p = 0.073, where df1 = 2 and df2 = 102 were yielded. The p-value is already large, which means that there is not enough evidence to reject the null hypothesis, so homogeneity is achieved. Based on the evidence, Levene’s test result was not significant, meaning the assumption of homogeneity of variances in ANOVA was valid, and it is appropriate to interpret the differences in group means (Najafi & Nasiri, 2023). However, violation of assumptions has little effect on results in the analysis of variance but can impact precision and/or statistical power. Results and Interpretation The values for mean (M) and standard deviation (SD) for Quiz 3 outcomes are presented below for all groups, organized according to the section variable. Section 1: M = 7.242, SD= 1.173 Section 2: M = 6.179, SD= 1.537 Section 3: M = 7.545, SD= 1.734 The analysis of the scores from Quiz 3 in the classrooms using one-way ANOVA revealed significant differences in the mean scores of the three sections of the classrooms. The highest average score was obtained by students in Section 3 (M = 7.545, SD = 1.734) followed by students in Section 1 and in Section 2 which obtained a mean of 7.242 and 1.173 and a mean of 7.325 and 1.275, respectively. The mean of the scores of students in Section 2 was 6.179 with a std deviation of 1.537, which was the lowest. A one-way ANOVA indicated that F(2, 102) = 8.354, p < .001, which means that the performance on Quiz 3 was indeed statistically significantly different among the classroom sections. The fact is very strong evidence in favor of the alternative hypothesis that there were meaningful differences in Quiz 3 scores among sections. The value of the F statistic indicates that the variance among group means was significantly greater than the variance within the groups. The correlation indicated is fairly high between classroom sections and quiz scores. Combined with the results of one-way ANOVA, the researcher was able to make a statement that there were meaningful differences among group means, which means that the classification of groups was correlated with variability in the outcome measure (Hazar & Hazar, 2025). Moreover, the very small p-value indicates that the noted variations in Quiz 3 scores across the sections are very unlikely to be the product of chance. Therefore, the null hypothesis was rejected and the alternative hypothesis accepted as the findings indicated that there was a significant difference in the performance of Quiz 3 between the classroom sections. Other comparisons might be achieved by post hoc analysis with Tukey’s HSD, comparing the difference in Quiz 3 scores for any of these three sections. A p-value of < 0.05 was achieved by comparing the data in Section 1 and Section 2. So there is a significant difference between the Quiz 3 scores of Section 1 and Section 2. While the mean scores in Section 1 seemed high when compared with the mean scores in Section 2, it did not reach a 7.242 score. Furthermore, there was a comparison of Sections two and three. The obtained p-value was: 0.001, < 0.05, which means that the difference is statistically significant. The mean differences among the sections were determined, and it was found that Section 3 had the best mean. A comparison was, however, made between Section 1 and Section 3, and a high p-value was achieved. The value was well beyond the 0.05 value. Therefore, there is no statistically significant difference in cutoffs, and the cutoff in Section Three is slightly higher. The obtained values in the comparison include a t-statistic value of 2.993 in comparing Section 1 and Section 2, and -3.846 in comparing Section two with Section 3. Results indicated that there were meaningful differences between academic outcomes in specific sections of the classroom, suggesting that section-level resources, including instruction, peer relationships, and classroom climate, could be important factors to consider when examining academic outcomes (Rosen & Kelly, 2023). Overall, the data indicate that there were no significant. Sections 1 and 3 on Quiz 3, but that Section 2 had significantly lower performance compared to the other sections. Statistical Conclusions The performance of the different sections on Quiz 3 scores was evaluated using a one-way analysis of variance (ANOVA) statistical test. The results were statistically significant, F (2, 102) = 8.354, p < .001, indicating a significant difference in performance of the different sections of the classroom in Quiz 3. The null hypothesis was rejected. Also, the results revealed (F = 2.690, p = .073) that the data had equal variances as the results of Levene’s test were not significant.

RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation
Capella University, DNP, RSCH-FPX7864

RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation

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RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation t-Test Application and Interpretation Statistical testing is an important part of many research projects related to the healthcare sector. The t-test is a parametric test that is used to compare the means of two groups to see if the differences found are statistically significant (Qualtrics, 2020). In the current case, the variable “review-1” is a categorical variable that shows attendance at sessions with values labeled as 1 for no and 2 for yes, while the variable “final” is a continuous variable that shows the total number of correct answers to a test. The methodical process enables the development of accurate and evidence-based information regarding the effect of preparatory sessions on students’ performance. Data Analysis Plan Research Question Does a statistically significant difference exist in exam results between students who attend review sessions and those who do not? Null Hypothesis No statistically significant difference exists in exam results between students who attend review sessions and those who do not. Alternative Hypothesis A statistically significant difference exists in exam results between students who attend review sessions and those who do not. Testing Assumptions Levene’s Test Assumption Check The equality of variances test, which was carried out by Levene, obtained an F-statistic of 0.219 and a p-value of 0.641, indicating that there is no difference in the variance of scores between students who attended the review class and those who did not attend the review class. The p-value is larger than the commonly used threshold for significance, p<0.05, and the significance of the equality is confirmed. So the normal independent samples t-test can be performed. In particular, with degrees of freedom df₁ = 1 and df₂ = 103, the test fails to reject the null hypothesis of equal variances (Aslan et al., 2021). The number of students studied was 105, and the variance of both groups was comparable, so it was possible to easily compare the groups without having to use other tests like Welch’s t-test, since the mean scores were different. Results and Interpretations Descriptives Independent Samples T-Test A comparison of independent groups was done to see if there was any effect of participation in the preparatory courses on the results. It involved two groups in the sample: group 1 (55 students) who participated in the preparatory courses, and group 2 (50 students) who did not attend the preparatory courses or workshops. The descriptive analysis showed that the mean score of students who did not attend the supplementary courses (M = 60.420, SD = 8.680) was slightly higher than the mean score of students who attended (M = 60.182, SD = 7.930). Levene’s test was used to test the assumption of equal variances before proceeding with the hypothesis test. There was no significant difference for the test (F = 0.219, df = 103, p = 0.641) and thus the standard independent samples t-test was used. The scores were compared between the two groups with the t-test: −0.147 (103) < 0, p = 0.883 reported no significant difference between the two groups in scores on the final exam. The value of p is > 0.05; the null hypothesis is therefore not rejected. The fact suggests that there was no difference between the two groups, whether they took the additional preparatory courses or not. Moreover, the difference between the means was very small (0.238 points) and statistically and practically negligible, since the score represents less than 1% difference in the performance of each group. The standard deviations of 7.930 and 8.680 for the attending and non-attending groups, respectively, further support the fact that the variability in the two groups is similar. The overall findings suggest that the impact of the additional preparatory sessions on the students’ actual assessment scores was not significant, and thus, it is argued that the use of such supplemental sessions does not necessarily lead to improved student performance. Statistical Conclusion Analysis of the test data showed a less than 1% difference in the scores of students who attended the review sessions (n = 55, M = 60.182, SD = 7.930) and n = 50, M = 60.420, SD = 8.680 for those who did not. The results of Levene’s test of equality of variances were not significant (F = 0.219, p = 0.641), and therefore the standard independent samples t-test was appropriate. The t-test result showed t (103) = −0.147, p = 0.883, with a mean difference of 0.238 points between the groups. Therefore, there was no rejection of the null hypothesis of no difference in exam performance. Based on the results, it has been found that the current version of the review sessions is not enough to make a positive impact on performance, and there is a need for review of the teaching method, teaching design, or teaching modality. Limitations and Alternative Explanations There are a number of methodological issues that may have affected the results. While the independent samples t-test is a good test for comparing the means of two groups, the study does not take into account other factors that may influence the results, such as previous academic achievement or individual study practices. The small sample size (n = 105) may also result in diminished statistical power in detecting smaller but important differences between groups. Also, the yes/no nature of a variable for review session attendance fails to account for other important factors, such as how many times the students attended and the length of review session attendance. There is the possibility of selection bias: Students who participated in review sessions might have been different from students who didn’t participate in those review sessions with regard to motivation, availability, and other academic demands. The effectiveness of the review sessions may also be influenced by the timing of the review sessions in relation to the final examination. Future studies should use multivariate analysis methods to take into account the confounding variables and examine the effect of preparatory sessions on a wider range of students. Application Independent samples t-tests

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation
Capella University, DNP, RSCH-FPX7864

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

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RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation Data Analysis Plan For effective data analysis, there should be a systematic process for the collection of data and an analytical method for the analysis of the data. The approach will allow for a comprehensive review of the gathered data, which will help in creating valid research results (Tumiran, 2024). This research will try to investigate the possible relationship among the important factors affecting the students’ academic performances, which are the first quiz, final exams, cumulative points of the students in the course, and the previous GPA. The variables of the research are: In Test 1, A continuous variable that represents the number of correct responses obtained in the first test, with a scale of 0 up to the maximum possible score. Final Exam Score: the number of items correct on the final examination, on a scale of zero to the maximum possible, is a continuous variable. Total Points Earned: A continuous variable that is used to represent the total of all possible points that have been accumulated in an academic year. A continuous variable that represents a student’s past academic performance in a quantitative manner, ranging from 0.00 to 4.00 points. (Grade Point Average) Total-Final Correlation Research Question Is there a statistically significant relationship present between the total number of points a student earns during a semester and performance in the final examination? Hypotheses Null Hypothesis (H0) There is no statistically significant relationship present between the total points a student earns during a semester and performance in the final examination. H₀: ρ = 0. Alternative Hypothesis (Ha) There is a statistically significant relationship present between the total points a student earns during a semester and performance on the final examination. Hₐ: ρ ≠ 0. Quiz 1 and GPA Correlation Research Question Is students’ prior academic achievement, as measured by GPA, statistically related to performance in the first quiz? Hypotheses Null Hypothesis (H0): Students’ prior academic achievement, as measured by GPA, is not statistically related to performance in the first quiz. H₀: ρ = 0. Alternative Hypothesis (Ha): Students’ prior academic achievement, as measured by GPA, is statistically related to performance in the first quiz. Hₐ: ρ ≠ 0. Testing Assumptions Table 1: Descriptive Statistics Making the assumption test in statistical analysis is one of the important steps to ensure that the results of the research are valid and reliable. Descriptive statistics show that approximately 73.82% of the students did better in the cumulative semester points and final exam results. Among the variables included, Quiz 1 had the most negative skewness (-0.826) while the GPA had very low negative skewness (-0.096), which is near symmetry. The skewness and standard deviation of all the variables considered were within acceptable levels. The values for the variables ranged from −0.832 for GPA to 0.657 for total points earned, which were close to a normal distribution. There was no alarming trend in the measures of variability, and overall,l the data met the assumptions of being approximately normally distributed. In particular, the values of Skewness were less than the high limit of ±2.0 value as follows: total points earned: −0.758; GPA: −0.096; final exam performance: −0.606; Quiz 1 performance: −0.826. The skewness and Kurtosis values of the data between -2.0 and +2.0 are considered as approximately normal, which allows performing parametric statistical tests (Demir, 2022). Likewise, the values of kurtosis did not exceed ±3.0, further confirming the normal assumptions. The results of the correlation analysis showed that there was a strong and statistically significant positive association between total semester points and the performance of the students in the final examination, with r = 0.659 and p < 0.001, thus the null hypothesis was rejected. Conversely, Quiz 1 performance and GPA had a weak correlation that was not statistically significant (r = 0.142, p = 0.149), and the null hypothesis was maintained. Results & Interpretation Table 2: Pearson’s Correlations between Academic Performance Variables Pearson’s correlation coefficient (r) was used to measure the correlation between two continuous variables, where r is between −1 and +1, and the closer r is to +1 or −1 the stronger the correlation. Some interesting findings came from the correlation test. Cumulative semester points were significantly correlated with performance on Quiz 1 (r = 0.601, p < 0.001). Furthermore, Quiz 1 was moderately and significantly correlated with the final exam, with r = 0.422, p < 0.001. In contrast, grade point average (GPA) was also weakly correlated with performance on Quiz 1 (r = 0.142, p = 0.149), indicating that performance was not related to academic achievement on the initial assessments of the course. Based on the classic definition of correlation coefficients, 0.10, 0.30, and 0.50 are small, medium, and large effect sizes, respectively (Zieliński, 2025). Also, the correlation between the GPA and the cumulative points won during the semester was weak (r = 0.137, p = 0.164), but it was not significant. Nevertheless, a weak but significant correlation (r = 0.233, p = 0.017) was found between GPA and performance on the final examination. The highest correlation that was observed in the analysis was the total points earned in the semester and performance in the final examination (r = 0.659, p < 0.001). The above p-value is very small, meaning that the null hypothesis stating that there is no correlation between the performance in the semester and the result of the final exam can be rejected. Conversely, the null hypothesis with respect to the Quiz 1 score and the GPA was not rejected as it is not statistically significant. As a general rule, the findings of this study indicate that performance during the semester is a good guide to success at semester examinations, whereas that of previous semesters has little influence on the initial returns and appears to be of secondary importance towards the end of the course. Statistical Conclusions Some of the hypotheses were supported by the correlation analysis, which showed that a number of the academic performance measures collected at various assessment

RSCH FPX 7864 Assessment 1 Descriptive Statistics
Capella University, DNP, RSCH-FPX7864

RSCH FPX 7864 Assessment 1 Descriptive Statistics

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RSCH FPX 7864 Assessment 1 Descriptive Statistics Student Name Capella University RSCH-FPX7864 Quantitative Design and Analysis Professor Name Submission Date Descriptive Statistics Part 1: Histogram Figure 1 Lower Division All of the lower-division student grades at the end of their exams have been plotted in a histogram. Histograms are among the most useful and readily available tools for investigating a data set and for discovering patterns in that data, according to Shreffler & Huecker (2023). The exam scores of lower division students are aligned with respect to the number of students who received a grade within each 5- point range between 40 and 75 points. The most frequent score numerically is between 60 and 65 (13 students). The overall frequency distribution is negatively skewed; that is a greater number of students have a score less than the median score of about 60, compared with students who have a score higher than the median score of about 60. The left-skewed frequency distribution shows that most of the scores are more common in the higher levels of the scoring categories and only become less common as scores get lower in the scoring categories. Figure 2 Upper Division The histogram represents 5-point intervals from 30 to 80 and the number of students who scored on the final exam. The two variables shown in the histogram are the independent variable, the final exam score, and the dependent variable, the upper division classification of a student. The intervals are therefore set up to make it easy to determine the hierarchy among students’ performance and, at the same time, evaluate the number of students included in each interval. Sixty-five to 70 was the interval with the highest number of students sampled (14 students). Histograms are a very effective visual format and widely used in various disciplines to show the distribution of a number of incidents over a range of possible values of a variable, as mentioned by Scheer et al. (2022). The histogram has a bell-shaped, symmetrical distribution, suggesting that the data distribution closely resembles the normal distribution. Part 02: Descriptive Statistics Table 1 Descriptive Statistics Descriptive statistics such as mean, standard deviation, skewness, and kurtosis are key in establishing the trends of data distribution and central elements of a data set. Descriptive statistics give an overview of the data and the main features of the sample (Fulk, 2023). The closer the mean and the SD are to each other, the closer the data will be to the mean, and the bigger the difference between the mean and SD, the bigger the difference between the spread of the data. In the case of the GPA variable, the average and standard deviation were M = 2.864, SD = 0.692, respectively. The total value of the values is 3.556, which gives a high level of concentration and high central tendency. The values obtained in the Quiz 3 variable are: M = 6.943 and SD = 1.604, and the highest value is 8.547, also indicating concentrated values and a high centrality level. Skew and kurtosis are other measures of normality in the distribution of descriptive statistics, in addition to the central tendency (Hatem et al., 2022). The skew value of the GPA variable was determined to be -0.096, and the Kurtosis was -0.832, both of which are within acceptable limits of -2 and +2, and therefore are normally distributed. On the same note, Quiz 3 variable gave out skewness and kurtosis of -0.333 and 0.662, respectively, all of which lie well within the acceptable boundaries of normality. Conclusion Descriptive statistics were used to analyse the frequencies and measures of central tendency of the two groups of students. The first-year students showed a negative skew, and the upper or second-year students were more nearly normal and peaked between 65 and 70. The data for both variables were normally distributed enough to be demonstrated by mean, standard deviation, skewness, and kurtosis values; none of which were outside the range of -2 to +2 as defined for skewness and kurtosis, providing additional support to the assertion that both variables were approximately centered at their means and were normally distributed. Step-By-Step Instructions to writeRSCH FPX 7864 Assessment 1 For step-by-step instructions on RSCH FPX 7864 Assessment 1, visit rschfpx7864assessment.com. References forRSCH FPX 7864 Assessment 1 Fulk, G. (2023). Descriptive statistics, an important first step. The Journal of Neurologic Physical Therapy, 47(2), 63. https://doi.org/10.1097/NPT.0000000000000434 Hatem, G., Zeidan, J., Goossens, M., & Moreira, C. (2022). Normality testing methods and the importance of skewness and kurtosis in statistical analysis. Beirut Arab University Journal – Science and Technology, 3(2), 7. https://doi.org/10.54729/KTPE9512 Scheer, J., Volkert, A., Brich, N., Weinert, L., Santhanam, N., Krone, M., Ganslandt, T., Boeker, M., & Nagel, T. (2022). Visualization techniques of time-oriented data for the comparison of single patients with multiple patients or cohorts: Scoping review. Journal of Medical Internet Research, 24(10), e38041. https://doi.org/10.2196/38041 Shreffler, J., & Huecker, M. R. (2023). Exploratory data analysis: Frequencies, descriptive statistics, histograms, and boxplots. PubMed; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK557570/ Capella professors to choose from for RSCH-FPX7864 Class Nicole Aclin, DNP, RN, CNE. Adriane Stasurak, DNP, RN, ANP-BC. (FAQs) related to RSCH FPX 7864 Assessment 1 Question 1: What is RSCH FPX 7864 Assessment 1 about? Answer 1: Analysis of data using histograms and descriptive statistics methods.

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