Factor Analysis OF Splendor Plus

Title:- Factor Analysis Of Product (HERO Splendor Plus)

 Authors:-

Adil (021330024021)

N Vaishnavi P (021330024521)

Kartik Dodiya (021330024551)

Introduction:- Factor analysis is a statistical method used to identify the underlying relationships between variables and reduce data into smaller, interpretable factors. In this study, a factor analysis was performed on 10 characteristics of the Hero Splendor Plus bike to explore how these characteristics cluster into meaningful groups or components.

The Hero Splendor Plus is a popular motorcycle known for its fuel efficiency, reliability, and affordability. Understanding how these and other features are perceived by users can help in designing better marketing strategies and product improvements. The characteristics analyzed include:

• Reliable Engine
• Fuel Efficiency
• Comfortable Seating
• i3S Technology
• Integrated Braking System (IBS)
• Durable Build
• Suspension System
• Analog Instrument Console
• Bright Lighting
• Affordability

Objective:- To reduce the dimension

Data Collection:- Data was collected using a Google Form, which was circulated among classmates. The respondents were asked to rate each of the 10 characteristics using a 5-point Likert scale:

• 1: Strongly Disagree
• 2: Disagree
• 3: Neutral
• 4: Agree
• 5: Strongly Agree

A total of 58 responses were obtained. The responses were then analyzed using SPSS to perform a factor analysis. Principal Component Analysis (PCA) with Varimax rotation was used to identify the underlying factors.

Data Analysis:- The analysis consists of two main components: the Cluster Membership Table and Anova Table.

• Cluster Membership Table

 

Cluster Membership
Case Number	Cluster	Distance
1	.	.
2	1	1.935
3	1	2.948
4	1	1.220
5	4	2.778
6	2	2.835
7	2	1.356
8	3	.000
9	1	1.468
10	1	2.276
11	1	1.561
12	1	2.491
13	4	1.474
14	.	.
15	1	2.259
16	1	1.649
17	1	1.776
18	1	2.304
19	1	2.842
20	1	2.375
21	1	3.130
22	1	2.537
23	1	2.631
24	4	1.535
25	4	.900
26	1	1.840
27	1	1.754
28	1	1.220
29	4	1.832
30	4	1.443
31	4	3.450
32	1	1.502
33	1	1.902
34	1	1.593
35	1	1.220
36	4	1.379
37	4	1.345
38	4	1.832
39	4	1.164
40	4	.949
41	1	1.861
42	1	1.725
43	1	1.740
44	1	1.847
45	1	1.657
46	4	1.928
47	1	1.086
48	1	1.536
49	4	1.345
50	2	2.691
51	1	1.649
52	1	1.220
53	1	2.276
54	2	1.356
55	1	2.703
56	4	1.621
57	2	1.356
58	1	2.851

 

The Cluster Membership Table shows the assignment of each case (participant) to a cluster and the corresponding distance value. Here’s a breakdown:

1. Cluster Distribution:
   • There are four clusters (1, 2, 3, and 4).
   • The majority of cases are in Cluster 1, indicating that a significant portion of the participants have similar views or perceptions regarding the bike’s characteristics.
   • Cluster 3 has only one participant (Case Number 8) with a distance of 0, indicating a clear fit within this cluster.
2. Distance Values:
   • Distance values represent how close each case is to the center of its assigned cluster.
   • Lower distance values indicate that a case closely aligns with its cluster’s profile, while higher values suggest a weaker fit.
   • Cases with higher distances (e.g., Case 31 in Cluster 4 with a distance of 3.450) are farther from the cluster center, suggesting they may have unique perceptions compared to other members in the cluster.

• Anova Table

 

ANOVA
	Cluster		Error		F	Sig.
	Mean Square	df	Mean Square	df
Reliable Engine	5.456	3	.503	52	10.855	<.001
Fuel Efficiency	4.376	3	.257	52	17.019	<.001
Comfortable Seating	8.521	3	.486	52	17.530	<.001
i3S Technology	6.998	3	.447	52	15.673	<.001
Integrated Braking System (IBS)	4.340	3	.323	52	13.418	<.001
Durable Build	5.740	3	.267	52	21.466	<.001
Suspension System	2.432	3	.414	52	5.871	.002
Analog Instrument Console	5.441	3	.395	52	13.780	<.001
Bright Lighting	4.408	3	.563	52	7.829	<.001
Affordability	1.079	3	.394	52	2.741	.052
The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal.

 

The ANOVA Table provides insights into how the different characteristics vary across clusters. Here’s an interpretation of the significant values:

1. Significant Differences (p < 0.05):
   • Most characteristics show significant differences between clusters (p-values < 0.001), suggesting that clusters are distinguished based on participants’ perceptions of these characteristics.
   • For example, Comfortable Seating (F = 17.530) and Durable Build (F = 21.466) have high F-values, indicating these characteristics vary notably across clusters and are key factors differentiating the clusters.
2. Marginally Significant Difference:
   • Affordability has a p-value of 0.052, indicating a borderline level of significance. This suggests affordability may vary less distinctly across clusters than other characteristics.
3. Cluster Means:
   • Higher mean square values for certain characteristics (e.g., Comfortable Seating with a mean square of 8.521) imply that these characteristics contribute significantly to the cluster differences. Clusters might be structured based on high variation in responses to these particular features.

 

Conclusion

The analysis indicates that the clusters were created based on distinct perceptions of specific bike characteristics. The significant F-values in the ANOVA table reveal that attributes like Comfortable Seating, Durable Build, Fuel Efficiency, and i3S Technology are key factors that differentiate the clusters, highlighting their importance to customers. The marginally significant result for Affordability suggests that it may be less critical in distinguishing between user groups.

This clustering and factor analysis provide valuable insights for Hero Splendor Plus, as the identified clusters can inform targeted marketing strategies. For instance, focusing on key features like comfort and durability may attract customers in specific clusters.