Market Research Assignment Report
Title: Factor analysis of “Bose QuietComfort 45 Headphone”
Author: Nivedita Sawale – 021330624123
Pranali Chaudhari – 021330624035
Vaidehi Mehta – 021330024456
Introduction:
The Bose QuietComfort 45 headphones are premium over-ear noise-canceling headphones designed for comfort and superior sound quality. They feature advanced noise-canceling technology, a long battery life of up to 24 hours, and a lightweight design, making them ideal for long listening sessions. With Bluetooth connectivity and intuitive touch controls, they offer a seamless audio experience, perfect for music lovers and travelers alike.
Objective:
The objective of the market research on Bose QuietComfort 45 headphones is to understand consumer preferences and competitive positioning. This will inform marketing strategies and optimize product offerings to enhance market share.
Data Collection:
The data for Bose QuietComfort 45 headphone was collected from survey of questionnaire filled by 105 student and they grade they give to dimensions out of 10.
Data Analysis
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KMO and Bartlett’s Test |
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Kaiser-Meyer-Olkin Measure of Sampling Adequacy. |
.475 |
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Bartlett’s Test of Sphericity |
Approx. Chi-Square |
47.283 |
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df |
45 |
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Sig. |
.380 |
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Interpretation of KMO and Bartlett’s Test:
- KMO Measure: 0.475 indicates inadequate sampling (below the acceptable threshold of 0.6).
- Bartlett’s Test: Chi-Square = 47.283, Sig. = 0.380 suggests no significant correlations among variables (p > 0.05).
Conclusion:
Both tests indicate that the data may not be suitable for factor analysis due to insufficient sampling and lack of significant relationships.
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Component Matrix |
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Component |
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1 |
2 |
3 |
4 |
5 |
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Batteries life |
.708 |
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Type |
.551 |
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Noise cancelation |
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.624 |
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AI Feature |
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.516 |
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Connectivity |
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Price range |
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Colow option |
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Weight |
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Frequency Response |
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-.522 |
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Wireless range |
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-.521 |
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Extraction Method: Principal Component Analysis.
Interpretation Component Matrix Summary: 1. Component 1: Strong correlations with Batteries life (0.708) and Type (0.551). 2. Component 2: Moderate correlations with Noise cancelation (0.624) and AI Feature (0.516). 3. Component 3: Moderate negative correlations with Frequency Response (-0.522) and Wireless range (-0.521). 4. No significant loadings for Connectivity, Price range, Color option, Weight. Conclusion: Key features are identified in the first two components, while some variables show no significant impact. |
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a. 5 components extracted. |
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Total Variance Explained |
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Component |
Extraction Sums of Squared Loadings |
Rotation Sums of Squared Loadings |
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Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
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1 |
1.490 |
14.899 |
14.899 |
1.357 |
13.568 |
13.568 |
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2 |
1.335 |
13.352 |
28.252 |
1.313 |
13.132 |
26.700 |
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3 |
1.243 |
12.432 |
40.683 |
1.292 |
12.918 |
39.618 |
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4 |
1.169 |
11.692 |
52.375 |
1.203 |
12.025 |
51.643 |
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5 |
1.101 |
11.006 |
63.381 |
1.174 |
11.738 |
63.381 |
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Extraction Method: Principal Component Analysis. Interpretation Total Variance Explained Summary:
1. Extraction Sums of Squared Loadings: – The first five components explain a cumulative total of 63.381%of the variance in the data. – Each component contributes between 11.006%and 14.899% to the total variance.
2. Rotation Sums of Squared Loadings: – After rotation, the first five components also explain a cumulative total of 63.381%. – The contributions per component are slightly adjusted but remain similar, with each component explaining between 11.738% and 13.568%. Conclusion: The analysis indicates that a significant portion of the variance in the dataset is captured by the first five components, suggesting they are key factors in explaining the underlying data structure. |
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Rotated Component Matrix |
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Component |
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1 |
2 |
3 |
4 |
5 |
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AI Feature |
.745 |
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Batteries life |
-.690 |
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Noise cancelation |
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.777 |
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Colow option |
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.584 |
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Price range |
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-.666 |
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Type |
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.640 |
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Connectivity |
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.534 |
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Weight |
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.729 |
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Wireless range |
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.875 |
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Frequency Response |
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.507 |
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Rotated Component Matrix Summary: 1. Component 1: – AI Feature (0.745): Strong positive correlation. – Batteries life (-0.690): Strong negative correlation. 2. Component 2: – Noise cancelation (0.777): Strong positive correlation. – Color option (0.584): Moderate positive correlation. 3. Component 3: – Price range (-0.666): Strong negative correlation. – Type (0.640): Moderate positive correlation. – Connectivity (0.534): Moderate positive correlation.
4. Component 4: – Weight (0.729): Strong positive correlation.
5. Component 5: – Wireless range (0.875): Strong positive correlation. – Frequency Response (0.507): Moderate positive correlation. Conclusion: The matrix highlights distinct groupings of features, with strong correlations indicating key areas of focus for analysis and product differentiation. Extraction Method: Principal Component Analysis. Rotation Method: Varimax without Kaiser Normalization. |
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a. Rotation converged in 7 iterations. |
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Conclusion:
The analysis reveals weak sampling adequacy (KMO = .475) and five extracted components explaining 63.38% variance, with Key factors influencing these components include battery life, noise cancellation, AI features, wireless range, and price range, among others, with rotation refining each component’s loading.