STATISTICAL COMPARISON OF FOUR CLOTHING BRANDS USING ONE-WAY ANOVA
STUTI BALID
1. INTRODUCTION
The clothing and fashion industry is one of the most dynamic and competitive consumer markets worldwide. Consumers evaluate clothing brands based on factors such as fabric quality, comfort, durability, design, price, and overall satisfaction. With increasing brand choices available in the market, it becomes important to understand whether consumers perceive significant differences among popular clothing brands.
To analyze whether consumer ratings differ significantly among four selected clothing brands, a One-Way Analysis of Variance (ANOVA) test was conducted. This statistical technique helps determine whether observed differences in mean ratings are statistically significant or occur due to random variation.
2. OBJECTIVE
The objective of this study is to apply One-Way ANOVA to compare consumer satisfaction ratings of four different clothing brands and to determine whether there is a statistically significant difference among their mean ratings.
3. LITERATURE REVIEW
Nie (2019)
Nie’s study highlights how external market forces, consumer preferences, and organizational strategies influence performance differences across brands. In the clothing industry, changing fashion trends and consumer expectations significantly affect brand perception and customer ratings.
Zhang & Chen (2014)
This research emphasizes the role of innovation, quality consistency, and effective resource allocation in sustaining competitive advantage. Clothing brands vary in fabric quality, design innovation, pricing strategies, and brand loyalty, which may result in measurable differences in consumer satisfaction.
4. DATA COLLECTION
Data was collected through consumer surveys evaluating four clothing brands (Brand A, Brand B, Brand C, and Brand D). Respondents rated the brands on a 1–10 scale based on various performance attributes.
Key details:
- Sample Size (N): 160 respondents
- Number of Brands: 4
- Ratings included: fabric quality, comfort, durability, design, fit, and overall satisfaction
Each brand received ratings from an equal number of respondents. One-Way ANOVA was used to determine whether differences in mean ratings among the brands were statistically significant.
5. DATA ANALYSIS
The following ANOVA results were obtained:
ANOVA Table
|
ANOVA |
||||||
|
Source of Variation |
SS |
df |
MS |
F |
P-value |
F crit |
|
Between Groups |
64.62 |
3.00 |
21.54 |
40.32 |
0.00 |
2.66 |
|
Within Groups |
83.35 |
156.00 |
0.53 |
|||
|
Total |
147.98 |
159.00 |
|
|
|
|
Hypotheses:
- Null Hypothesis (H₀): The mean satisfaction ratings of all clothing brands are equal.
- Alternative Hypothesis (H₁): At least one clothing brand has a different mean satisfaction rating.
Since the p-value (0.00) is less than the significance level of 0.05, the null hypothesis is rejected. This indicates that there is a statistically significant difference in consumer satisfaction among the four clothing brands.
6. CONCLUSION
The One-Way ANOVA results clearly show that consumer ratings differ significantly among the four selected clothing brands. The calculated F-value (40.32) is much higher than the critical F-value (2.66), and the p-value is far below 0.05. This confirms that the variation in consumer ratings is not due to chance but reflects genuine differences in brand performance.
Therefore, the study concludes that at least one clothing brand is perceived differently by consumers in terms of quality and overall satisfaction.
7. REFERENCES
Nie, X. (2019). Legal challenges of the Asia-Pacific Space Cooperation Organization in the context of the Belt and Road Initiative. Space Policy, 47, 1–7.
Zhang, H., & Chen, Y. (2014). Role of the Asia-Pacific Space Cooperation Organization in advancing space technology and its applications in the Asia-Pacific region. Advances in Space Research, 54(3), 403–409.