A STATISTICAL COMPARISON OF FOUR CLOTHING BRANDS USING ONE-WAY ANOVA
RAHUL PAWAR
1. INTRODUCTION
The clothing and apparel industry is one of the most competitive and rapidly evolving sectors in India. Consumers today have access to a wide range of clothing brands offering varied styles, quality levels, price ranges, and brand images. Purchasing decisions are influenced by factors such as fabric quality, comfort, durability, design, affordability, and overall brand reputation.
With increasing competition, it becomes essential to understand whether consumers perceive meaningful differences among popular clothing brands. Statistical tools help transform consumer opinions into measurable insights. One-Way Analysis of Variance (ANOVA) is an effective method used to compare the mean ratings of multiple groups and determine whether observed differences are statistically significant or occur by chance.
This study applies One-Way ANOVA to compare consumer satisfaction ratings of four well-known Indian clothing brands— FabIndia, Biba, Manyavar, and Louis Philippe—to examine whether significant differences exist in their mean ratings.
2. OBJECTIVES
The objectives of the study are:
- To analyze consumer satisfaction ratings of selected clothing brands.
- To compare the mean ratings of four clothing brands using One-Way ANOVA.
- To determine whether there is a statistically significant difference in consumer perception among the selected brands.
- To understand overall consumer preference patterns in the Indian clothing market.
3. LITERATURE REVIEW
Nie (2019)
Nie’s research emphasizes how external market conditions, consumer behaviour, and organizational performance influence variations within and across product categories. Although the study focuses on technological organizations, the findings are relevant to the clothing industry, where changing fashion trends, pricing strategies, and consumer expectations directly affect brand performance and customer ratings.
Zhang & Chen (2014)
Zhang and Chen highlight the importance of efficient resource allocation, product quality, and innovation in maintaining competitive advantage. In the clothing sector, differences in fabric quality, design innovation, pricing, and branding strategies can lead to variations in consumer satisfaction, which can be measured statistically using tools such as ANOVA.
4. DATA COLLECTION
The data for this study was collected through a structured consumer survey. Respondents were asked to rate four Indian clothing brands—FabIndia, Biba, Manyavar, and Louis Philippe—on a standardized 1–10 scale based on overall satisfaction.
Each brand received ratings from 40 respondents, resulting in a total of 160 observations.
Details of Data Collection
- Sample Size (N): 160
- Number of Brands: 4
- Responses per Brand: 40
- Rating Scale: 1 (Very Poor) to 10 (Excellent)
- Criteria Considered: Quality, comfort, durability, design, and overall satisfaction
One-Way ANOVA was applied to determine whether differences in consumer ratings among the four brands are statistically significant.
5. DATA ANALYSIS
Summary Statistics
|
Brand |
Count |
Sum |
Average |
Variance |
|
FabIndia |
40 |
321 |
8.025 |
2.18 |
|
Biba |
40 |
322 |
8.05 |
1.89 |
|
Manyavar |
40 |
333 |
8.325 |
2.33 |
|
Louis Philippe |
40 |
328 |
8.20 |
1.60 |
The average ratings of all four brands are above 8, indicating high overall consumer satisfaction.
ANOVA: Single Factor
|
Source of Variation |
SS |
df |
MS |
F |
P-value |
F crit |
|
Between Groups |
2.35 |
3 |
0.78 |
0.39 |
0.76 |
2.66 |
|
Within Groups |
312.05 |
156 |
2.00 |
|||
|
Total |
314.40 |
159 |
Hypotheses
- Null Hypothesis (H₀): The mean ratings of all four clothing brands are equal.
- Alternative Hypothesis (H₁): At least one brand has a different mean rating.
Decision Rule
Since the p-value (0.76) is greater than 0.05, and the F-value (0.39) is less than the F-critical value (2.66), the null hypothesis is accepted.
Interpretation
The statistical analysis indicates that there is no significant difference in the mean consumer ratings of FabIndia, Biba, Manyavar, and Louis Philippe. Although minor numerical differences exist, these differences are not statistically meaningful.
6. CONCLUSION
The One-Way ANOVA analysis reveals that consumer satisfaction levels across the four selected clothing brands are statistically similar. All brands received consistently high ratings, reflecting strong brand acceptance and positive consumer perception.
Since the p-value exceeds the level of significance, the variation in mean ratings is likely due to random factors rather than actual differences in brand performance. Therefore, it can be concluded that none of the selected clothing brands significantly outperforms the others in terms of overall consumer satisfaction based on the collected data.
This study highlights the competitive nature of the Indian clothing market, where multiple brands successfully meet consumer expectations.
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.