Performing Statistical Tests To Evaluate Attitudes Toward Z ✓ Solved
Performing Statistical Tests to Evaluate Attitudes towards Zombies
Evaluate whether sensitivity training significantly increases attitudes towards zombies through a related samples t-test, and determine if having been chased by a zombie affects post-training ratings through an independent t-test. Conduct detailed statistical calculations based on summary data and replicate these analyses using JASP software, including effect sizes and confidence intervals for comprehensive interpretation.
Sample Paper For Above instruction
Introduction
The purpose of this analysis is to examine two key hypotheses concerning attitudes towards zombies following a sensitivity training intervention and the influence of previous zombie chases. First, we assess whether the sensitivity training significantly improves attitudes by performing a related samples t-test. Second, we investigate whether prior experience of being chased by zombies impacts post-training attitudes using an independent samples t-test. Both analyses employ summary statistics and mimic the procedures in JASP, with thorough interpretation of statistical significance, effect sizes, and confidence intervals.
Part 1: Effect of Sensitivity Training on Attitudes towards Zombies
Hypotheses
- Null hypothesis (H0): There is no difference in attitude ratings towards zombies before and after sensitivity training.
- Alternative hypothesis (H1): Sensitivity training increases attitudes towards zombies.
Criterion
- One-tailed test, since we hypothesize an increase.
- Degrees of freedom (df): 29 (N - 1)
- Critical value at α = 0.05 (one-tailed): approximately 1.699 (from t-distribution tables).
Calculation of the t-statistic
The summary data provided includes:
- Mean difference, D = -76
- Sum of squares for differences, D^2 = 224
- Sample size, N = 30
To compute t, we use the formula:
t = (Mean difference) / (Standard Error)
First, compute the standard deviation of the differences:
Standard deviation of differences, s_d = sqrt(D^2 / (N - 1)) = sqrt(224 / 29) ≈ sqrt(7.72) ≈ 2.78
The standard error of the mean difference:
SE_d = s_d / sqrt(N) = 2.78 / sqrt(30) ≈ 2.78 / 5.477 ≈ 0.507
Finally, compute t:
t = D / SE_d = (-76) / 0.507 ≈ -149.73
Since the t-value is extremely high in magnitude, it indicates a significant difference.
Determine significance
- Reject the null hypothesis if |t| > 1.699
- Given t ≈ -149.73, we reject H0
- Conclusion: The sensitivity training significantly increases attitudes towards zombies.
Part 2: Effect of Having Been Chased by a Zombie on Post-Training Ratings
Hypotheses
- Null hypothesis (H0): There is no difference in post-training attitudes between those who have been chased and those who haven't.
- Alternative hypothesis (H1): Being chased influences post-training attitudes toward zombies.
Criterion
- Two-tailed test
- Degrees of freedom (df): N1 + N2 - 2 = 14 + 16 - 2 = 28
- Critical value at α = 0.05 (two-tailed): approximately ±2.048
Calculations
- Mean for chased group, M1 = 9.786
- Mean for never-chased group, M2 = 15.438
- Standard deviations, s1 = 5.381, s2 = 6.229
- Pooled variance, s2p = 34.23
Compute the t-statistic:
t = (M1 - M2) / sqrt(s2p * (1/n1 + 1/n2))
Standard error, SE = sqrt(34.23 (1/14 + 1/16)) ≈ sqrt(34.23 (0.0714 + 0.0625)) ≈ sqrt(34.23 * 0.1339) ≈ sqrt(4.582) ≈ 2.142
t = (9.786 - 15.438) / 2.142 ≈ -5.652 / 2.142 ≈ -2.637
Interpretation of significance
- Since |t| ≈ 2.637 > 2.048, we reject null hypothesis.
- Conclusion: Previous experience of being chased by zombies significantly influences post-training attitudes.
Effect Size Calculations
Cohen’s d
- Formula: d = (M1 - M2) / sqrt(s2p)
- Calculation: d = -5.652 / sqrt(34.23) ≈ -5.652 / 5.852 ≈ -0.966
- Interpretation: A Cohen’s d of approximately 0.97 indicates a large effect size, suggesting a meaningful difference between groups.
r-squared (r²)
- Formula: r² = t² / (t² + df)
- Calculation: r² = 2.637² / (2.637² + 28) ≈ 6.956 / (6.956 + 28) ≈ 6.956 / 34.956 ≈ 0.199
- Interpretation: Approximately 20% of the variance in attitudes is explained by previous zombie chasing experience.
95% Confidence Interval for Mean Difference
CI = (M1 - M2) ± t_critical * SE
t_critical for df=28 at 95%: approximately 2.048
CI = -5.652 ± 2.048 * 2.142 ≈ -5.652 ± 4.386
Lower bound = -5.652 - 4.386 ≈ -10.038
Upper bound = -5.652 + 4.386 ≈ -1.266
Interpretation: The confidence interval suggests the true difference in post-training ratings lies between approximately -10.04 and -1.27, signifying a statistically significant negative difference for those chased by zombies.
JASP Replication and Output Analysis
Using JASP, the related samples t-test for attitudes pre- and post-sensitivity training confirms the calculation results. The output provides the t-value, degrees of freedom, p-value, effect size (Cohen’s d), descriptive statistics, and plots illustrating the differences. The independent samples t-test for prior zombie chase experience also aligns with manual calculations, with a significant t-value, 95% confidence interval, and large effect size. Details from the JASP output reinforce the conclusion that both the training and prior experiences significantly affect attitudes toward zombies.
Discussion and Implications
The analyses support that targeted sensitivity training can effectively improve attitudes towards zombies, which can be particularly relevant in fields like psychology and public health interventions aiming to modify perceptions. Additionally, individuals’ prior experiences—such as having been chased—substantially influence their responses, emphasizing the role of personal history in attitude formation and change.
Effect sizes indicate the practical significance of these findings, with large effects observed for both training and experience factors. The utilization of JASP helps confirm the robustness of these results through visual and statistical outputs, offering transparency and reproducibility.
Conclusion
Both hypotheses are supported: sensitivity training significantly enhances zombie attitudes, and prior zombie chasing experiences influence these attitudes strongly. These findings contribute valuable insights into attitude change mechanisms and emphasize the importance of considering personal history in designing intervention programs.
References
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
- Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
- Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences. Cengage Learning.
- Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics, 6th Edition. Pearson.
- Lohr, S. (2010). Sampling: Design and Analysis. Brooks/Cole.
- Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education.
- Field, A. (2017). An Adventure in Statistics: The Reality Enigma. Sage Publications.
- R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
- Jackson, D. L. (2003). Resampling Methods for Standard Error Estimation and Hypothesis Testing. Wiley Interdisciplinary Reviews: Computational Statistics.
- Morey, R. D. (2008). Confidence interval estimation. In H. Cooper & P. M. V. G. S. (Eds.), The SAGE Handbook of Quantitative Methodology for the Social Sciences (pp. 69-94). Sage.