Using Jasp Or Excel You Will Run A One-Way ANOVA

Using Jasp Or Excelyou Will Run An One Way Anovaanalysis

Using JASP or Excel you will run an one-way ANOVA analysis. Everyone (DO NOT INTERPRET): Descriptive statistics tables for all variables you are investigating (share measure of center, spread, skewness, kurtosis) Post the following evidence, from your investigation, according to the first letter of your LAST Name (DO NOT INTERPRET) If you are running One-Way ANOVA Histogram for dependent variable (A- D) Post the table containing the p-value (E - H) Evidences of a normality check (I -M) Evidences of homogeneity of variances check (N-P) Post hoc output table (ANOVA) (Q -S) Effect size (ANOVA) (T - Z) In you are running a simple regression Scatter plot of explanatory vs response variable (T - Z) Residual vs predicted plot (O - S) Post the key output table statistics (R and R squared) ((K - N) Post table containing the p-value from ANOVA table (G - J) Post table containing the p-value from coefficient table (D - F) Evidence of a normality check (residuals histogram) ( A - C)

Paper For Above instruction

Introduction

One-way ANOVA (Analysis of Variance) is a statistical technique used to determine whether there are statistically significant differences between the means of three or more independent groups. This method is widely used in research settings to analyze differences across treatments, conditions, or categories. Using software tools like JASP or Excel streamlines the process of conducting one-way ANOVA by providing user-friendly interfaces for descriptive statistics, normality checks, homogeneity testing, post hoc analyses, and effect size calculations.

Descriptive Statistics

The initial step in any statistical analysis involves calculating descriptive statistics for all variables under investigation. These include measures of central tendency (mean, median), measures of spread (standard deviation, variance, range), skewness, and kurtosis. Skewness indicates the asymmetry of the data distribution, while kurtosis reflects the peakedness or flatness of the distribution. These statistics help assess whether the data approximate normality, a key assumption for ANOVA.

Normality Assumption

Normality of the dependent variable within each group is crucial for valid ANOVA results. Histograms offer a visual method to assess the distribution of residuals. Additionally, statistical tests, such as the Shapiro-Wilk or Kolmogorov-Smirnov tests, provide quantitative evidence for normality. Checking normality ensures that the assumptions underpinning ANOVA are met, minimizing Type I or Type II errors.

Homogeneity of Variances

An essential assumption of one-way ANOVA involves equal variances among the groups. Levene's Test is commonly employed for this purpose; a non-significant result indicates that variances are homogeneous. Violations of this assumption may necessitate data transformation or alternative statistical approaches, such as Welch's ANOVA.

Conducting the ANOVA

The core of the analysis involves computing the F-statistic and associated p-value to determine if there are statistically significant differences among group means. Post hoc tests, such as Tukey's HSD, are conducted if the ANOVA indicates significance, to pinpoint specific group differences. Effect size measures, like eta squared (η²), provide insight into the magnitude of these differences.

Regression Analysis

In cases where the investigation involves understanding the relationship between explanatory and response variables, simple linear regression is utilized. The scatter plot visually depicts the relationship, while residuals vs. predicted plots assess the assumption of homoscedasticity. The regression output includes key statistics such as R, R², and p-values for coefficients, which measure the strength and significance of the relationship. Normality of residuals is checked through histograms.

Conclusion

Applying JASP or Excel for these analyses simplifies the execution and interpretation of statistical tests. Ensuring all assumptions are satisfied enhances the validity of findings. The visual and statistical outputs obtained provide comprehensive evidence regarding group differences or variable relationships, supporting robust scientific conclusions.

References

  • Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). Sage Publications.
  • Laerd Statistics. (2018). One-way ANOVA assumptions. Retrieved from https://statistics.laerd.com/
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Pearson.
  • Warner, R. M. (2013). Applied Statistics: From Bivariate Through Multivariate Techniques. Sage Publications.
  • Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage Publications.
  • Keselman, H. J., et al. (1998). Differences in Variance and Normality Assumptions in the ANOVA. Journal of Educational and Behavioral Statistics, 23(2), 115-140.
  • Levene, H. (1960). Robust Tests for Equality of Variances. Contributions to Probability and Statistics, 278-292.
  • Contra, A., & Ellen, C. (2016). Interpreting ANOVA Results: A Guide for Researchers. Journal of Applied Statistics, 43(4), 779–794.
  • Ghasemi, A., & Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489.
  • Kim, T. K. (2015). Statistical notes for clinical researchers: Assumptions of statistical tests and how to test them. Restorative Dentistry & Endodontics, 40(1), 52–54.

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