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PSY FPX 7864 Assessment 4 Data Analysis & Application

PSY FPX 7864 Assessment 4 Data Analysis & Application Free Sample Papers Anxiety (1) BS Psychology (119) Depression (11) Essay (2) MS Psychology (108) Need writer for your Psychology Papers? Get your paper in 24 Hours. We have a team of Psychology Academic Writers who can help you quickly write plagiarism-free papers, essays, and research articles. Hire Writer PSY FPX 7864 Assessment 4 Data Analysis & Application Name Capella University PSY FPX 7864 Quantitative Design and Analysis Prof. Name Date Also Read PSY FPX 7864 Assessment 4 Data Analysis & Application Read More PSY FPX 7864 Assessment 3 ANOVA Application and Interpretation Read More PSY FPX 7864 Assessment 2 Correlation Application and Interpretation Read More PSY FPX 7864 Assessment 1 Descriptive Statistics Read More Load More

PSY FPX 7864 Assessment 3 ANOVA Application and Interpretation

PSY FPX 7864 Assessment 3 ANOVA Application and Interpretation Free Sample Papers Anxiety (1) BS Psychology (119) Depression (11) Essay (2) MS Psychology (107) Need writer for your Psychology Papers? Get your paper in 24 Hours. We have a team of Psychology Academic Writers who can help you quickly write plagiarism-free papers, essays, and research articles. Hire Writer PSY FPX 7864 Assessment 3 ANOVA Application and Interpretation Name Capella University PSY FPX 7864 Quantitative Design and Analysis Prof. Name Date Introduction & Data File Overview The Analysis of Variance (ANOVA) method is employed to examine differences across multiple groups. This study aims to identify disparities among sections and Quiz 3 scores within a cohort of 105 students. The independent variable is the student’s section, while the dependent variable is the Quiz 3 score. The section variable is categorical and divided into subgroups, whereas Quiz 3 scores are continuous. The total sample size, or N size, comprises 105 students. The research question is: Is there a significant difference among the mean scores of different sections on Quiz 3? The null hypothesis posits no significant differences, whereas the alternative hypothesis suggests substantial differences between section and Quiz 3 scores. ANOVA will be utilized to test these hypotheses, assuming that Y follows a normal distribution or that Y is consistent across all factor levels. Test of Normality Normality within the dataset is assessed using the Shapiro-Wilk test, which yields a p-value of 0.000. A p-value below 0.05 in the Shapiro-Wilk test indicates a non-normal distribution or differences. Therefore, based on this data, the null hypothesis is rejected, indicating a lack of normal distribution. Results and Analysis Descriptive Statistics The skewness of the dataset is 0.00, suggesting a normal distribution, while the kurtosis is -1.419, which is beyond the expected range.   N Mean SD SE Coefficient of Variation Section 1 3 7.273 1.153 0.201 0.159 Section 2 3 6.333 1.611 0.258 0.254 Section 3 3 7.939 1.560 0.272 0.196 The table above presents data for all three sections. Mean scores are shown in the third column, with Section 1 averaging 7.27, Section 2 averaging 6.33, and Section 3 averaging 7.94. Standard deviation is also depicted in column 4. ANOVA The table below illustrates a one-way ANOVA test, identifying the significance of differences among the sections. Degrees of freedom are 2 between sections and 102 within groups. The F-value of 10.951 indicates significant differences among the sections. Moreover, the p-value of 0.000 rejects the null hypothesis. The effect size, at 0.246, is relatively large. PSY FPX 7864 Assessment 3 ANOVA Application and Interpretation   Cases Sum of Squares df Mean Square F p Section   47.042 2 23.521 10.951 < .001 Residuals   219.091 102 2.148   Comparisons The table below shows the mean difference between each section. Notably, Sections 1 and 2 exhibit a mean difference of 0.939, while Sections 1 and 3 show a mean difference of -0.667. All values exceeding 0.05 indicate significant differences regardless of section. Post hoc analysis reveals that Section 3’s performance significantly surpasses the other two sections. Comparison Mean Difference SE t p (Tukey) 1 vs 2 0.939 0.347 2.710 0.021 1 vs 3 -0.667 0.361 -1.848 0.159 2 vs 3 -1.606 0.347 -4.633 < .001 Conclusion ANOVA identifies substantial differences among the sections. The null hypothesis is rejected, affirming the alternative hypothesis. While ANOVA facilitates comparison across multiple variables and is user-friendly, it lacks a mechanism for identifying the most significant variable. Application This test is useful in various real-life scenarios, including education, as demonstrated in this study. Additionally, it can aid in optimizing outcomes in healthcare settings, such as medication therapies and treatment methodologies. PSY FPX 7864 Assessment 3 ANOVA Application and Interpretation References Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE Publications Ltd. Howell, D. C. (2012). Statistical Methods for Psychology (8th ed.). Wadsworth. McDonald, J. H. (2014). Handbook of Biological Statistics (3rd ed.). Sparky House Publishing. Also Read PSY FPX 7864 Assessment 3 ANOVA Application and Interpretation Read More PSY FPX 7864 Assessment 2 Correlation Application and Interpretation Read More PSY FPX 7864 Assessment 1 Descriptive Statistics Read More PSY FPX 7543 Assessment 4 Cultural and Ethical Issues: Combined Case Study Read More Load More

PSY FPX 7864 Assessment 2 Correlation Application and Interpretation

PSY FPX 7864 Assessment 2 Correlation Application and Interpretation Free Sample Papers Anxiety (1) BS Psychology (119) Depression (11) Essay (2) MS Psychology (106) Need writer for your Psychology Papers? Get your paper in 24 Hours. We have a team of Psychology Academic Writers who can help you quickly write plagiarism-free papers, essays, and research articles. Hire Writer PSY FPX 7864 Assessment 2 Correlation Application and Interpretation Name Capella University PSY FPX 7864 Quantitative Design and Analysis Prof. Name Date Plan for Data Analysis Understanding the correlation between past academic performance and current achievements provides valuable insights into the trajectory of student learning. While numerous factors influence a student’s success, their previous Grade Point Average (GPA) serves as a broad indicator of their academic history and capabilities. This analysis focuses on four variables: Quiz 1, GPA, Final, and Total, all of which are considered continuous variables. Correlation Analysis Overall-Final Relationship:  Research Inquiry: Does a significant relationship exist between the overall points accumulated in the course and the number of correct responses on the final examination?  Null Proposition: There is no significant relationship between the overall points accumulated in the course and the number of correct responses on the final examination. Alternative Hypothesis: The alternative hypothesis suggests that there exists a meaningful relationship between the total points accumulated throughout the course and the number of accurate responses provided on the final examination. GPA-Quiz 1 Correlation: Investigation Focus: Does a student’s prior GPA correlate significantly with their performance on Quiz 1, measuring the number of correct responses? Null Assumption (H₀): The correlation between a student’s previous GPA and their Quiz 1 performance, in terms of correct answers, is not significant. Alternative Hypothesis (H₁): A notable link exists between a student’s prior GPA and their performance on Quiz 1, measured by the number of accurate responses. Assessing Assumptions In the provided descriptive statistics, the skewness and kurtosis measures for GPA and the final exam are displayed. Both GPA and final exam skewness values are within the -1 to 1 interval, indicating reasonably balanced distributions. This implies that the data is likely normally distributed.   Results & Interpretation Descriptive Statistics (Table 1):   GPA Total Quiz 1 Final Mean 2.862 100.086 7.467 61.838 Std. Dev 0.713 13.427 2.481 7.635 Skewness -0.220 -0.757 -0.851 -0.341 Kurtosis -0.688 1.146 0.162 -0.277 Correlation Matrix (Table 2): Table 2 reveals a slight uptrend of 0.152 between GPA and Quiz 1 scores. Nonetheless, the obtained P-value of 0.212 indicates that this correlation lacks statistical significance. Consequently, we uphold the null hypothesis. Correlations by Pearson:   Quiz 1 GPA Total Final Quiz 1 — 0.152 0.121 0.499 GPA 0.152 — 0.318 0.379 Total 0.121 0.318 — 0.875 Final 0.499 0.379 0.875 — The ‘Final’ and ‘Total’ are highly correlated, with a coefficient of 0.875, indicating a strong relationship between them. This correlation is statistically significant, suggesting that 76% of the variation in the ‘Total’ can be explained by the ‘Final.’ Consequently, we reject the null hypothesis. Likewise, there’s a modest association between GPA and the Final exam, indicated by a correlation coefficient of 0.379. This finding holds statistical importance, indicating that approximately 14% of the fluctuations in GPA can be clarified by variations in the Final exam scores. Statistical Conclusions Although there isn’t enough proof to suggest a strong link between GPA and Quiz 1 results, the connections between ‘Final’ and ‘Total’ scores, as well as between GPA and Final scores, show clear statistical significance. PSY FPX 7864 Assessment 2 Correlation Application and Interpretation Application Correlation analysis plays a crucial role in investigating relationships, such as those between military service experiences and specific medical conditions among veterans. Such analysis aids in identifying patterns in health outcomes, potentially leading to the recognition of conditions as “presumptive,” simplifying access to benefits and treatment for affected veterans. References Betancourt, J. A., et al. (2021). Exploring Health Outcomes for U.S. Veterans Compared to Non-Veterans from 2003 to 2019. Healthcare (Basel, Switzerland), 9(5), 604. doi:10.3390/healthcare9050604 Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE. Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences (10th ed.). Cengage Learning. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications. PSY FPX 7864 Assessment 2 Correlation Application and Interpretation McHugh, M. L. (2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143-149.   Also Read PSY FPX 7864 Assessment 2 Correlation Application and Interpretation Read More PSY FPX 7864 Assessment 1 Descriptive Statistics Read More PSY FPX 7543 Assessment 4 Cultural and Ethical Issues: Combined Case Study Read More PSY FPX 7543 Assessment 3 Ethical dilemma case study Read More Load More

PSY FPX 7864 Assessment 1 Descriptive Statistics

PSY FPX 7864 Assessment 1 Descriptive Statistics Free Sample Papers Anxiety (1) BS Psychology (119) Depression (11) Essay (2) MS Psychology (105) Need writer for your Psychology Papers? Get your paper in 24 Hours. We have a team of Psychology Academic Writers who can help you quickly write plagiarism-free papers, essays, and research articles. Hire Writer PSY FPX 7864 Assessment 1 Descriptive Statistics Name Capella University PSY FPX 7864 Quantitative Design and Analysis Prof. Name Date Introduction Descriptive statistics serve as a crucial tool in data analysis, allowing for the summarization of datasets that represent either an entire population or a sample. This analytical approach includes measures of central tendency—such as the mean, median, and mode—alongside measures of variability like standard deviation, variance, and range. Additionally, it considers kurtosis and skewness to understand the distribution shape (Hayes, 2023). Histograms, visual representations of frequency distributions, facilitate the comprehension of a variable’s distribution (What is a Histogram Chart, 2023). Section 1: Histograms for Visual Interpretation Distribution Plots This section presents two histograms that display student scores across various ranges. The height of each bar indicates the count of students within each score range, offering a clear visual representation of the final exam score distribution (What is a Histogram Chart, 2023). Section 2: Calculate Measures of Central Tendency and Dispersion Descriptive Statistics The table below summarizes the descriptive statistics for GPA and Quiz 3 scores:   GPA Quiz 3 Mean 2.862 7.133 Std. Dev. 0.713 1.600 Skewness -0.220 -0.078 Std. Error of Skewness 0.236 0.236 Kurtosis -0.688 0.149 Std. Error of Kurtosis 0.467 0.467 PSY FPX 7864 Assessment 1 Descriptive Statistics The mean, a common measure of central tendency, represents the average values for GPA (M = 2.862) and Quiz 3 scores (M = 7.133). Standard deviation, which provides insight into the dispersion of scores relative to the mean, is also reported: GPA (SD = 0.713) and Quiz 3 (SD = 1.600). Both skewness and kurtosis values fall within the acceptable range of -1 to +1, indicating a relatively normal distribution for both datasets (Quality Advisor, 2023). References Hayes, A. (2023, March 21). Descriptive statistics: Definition, Overview, types, example. Investopedia. Retrieved April 27, 2023, from https://www.investopedia.com/terms/d/descriptive_statistics.asp What is a histogram chart? TIBCO Software. (n.d.). Retrieved April 28, 2023, from https://www.tibco.com/reference-center/what-is-a-histogram-chart Trochim, P. W. M. K. (n.d.). Descriptive statistics. Research Methods Knowledge Base. Retrieved April 28, 2023, from https://conjointly.com/kb/descriptive-statistics/ PSY FPX 7864 Assessment 1 Descriptive Statistics Quality Advisor. Pqsystems.com. (n.d.). Retrieved April 28, 2023, from https://www.pqsystems.com/qualityadvisor/DataAnalysisTools/interpretation/histogram_stats.php Also Read PSY FPX 7864 Assessment 1 Descriptive Statistics Read More PSY FPX 7543 Assessment 4 Cultural and Ethical Issues: Combined Case Study Read More PSY FPX 7543 Assessment 3 Ethical dilemma case study Read More PSY FPX 7543 Assessment 2 Read More Load More

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