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44873602
Principal Components Analysis (Quantitative Applications in the Social Sciences)
84% of respondents would recommend this to a friend
Item #:
44873602
INR 3791
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What Stands Out
Comprehensive Methodology
Provides a thorough explanation of Principal Components Analysis, catering to both novices and experts, ensuring users can grasp complex concepts for practical applications in social sciences.
User-Friendly Approach
Utilizes clear examples and intuitive illustrations, making it accessible for researchers and students to apply PCA effectively, enhancing their analytical skills in real-world data analysis.
Highly Practical
Focuses on practical applications in the social sciences, addressing common user challenges, and offering step-by-step guidance that empowers users to interpret results confidently.
Product Details
| Publisher | SAGE Publications, Inc |
| Publication date | May 1, 1989 |
| Edition | 1st |
| Language | English |
| Print length | 96 pages |
| ISBN-10 | 0803931042 |
| ISBN-13 | 978-0803931046 |
| Item Weight | 4.9 ounces (138.92 grams) |
| Dimensions | 5.5 x 0.22 x 8.5 inches (14 x 0.6 x 21.6 cm) |
| Part of series | Quantitative Applications in the Social Sciences |
Who Should Buy?
-
Social Science Researchers
Ideal for researchers analyzing survey data to uncover patterns and relationships within large datasets.
-
Graduate Students
Beneficial for graduate-level students learning statistical techniques, particularly those focusing on multivariate analysis.
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Data Analysts
Suitable for analysts seeking to simplify complex data and enhance visualization through dimensionality reduction methods.
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Beginner Statisticians
May be overwhelming for beginners lacking fundamental statistics knowledge or experience with PCA.
Product Description
Principal Components Analysis (Quantitative Applications in the Social Sciences)
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Probability & Statistics Editorial Review
Principal Components Analysis (Quantitative Applications in the Social Sciences) is a solid introduction to PCA, although it comes with some challenges. Readers appreciate the author’s effort in explaining complex concepts without heavy reliance on matrix algebra, though many still find that some familiarity with it is necessary to fully grasp the material. The book also effectively discusses inputs to PCA, including correlation versus covariance matrices, making it beneficial for those in applied settings. Although the author covers important concepts, some reviews mention that the lack of clear algorithms for extracting principal components can be frustrating for tool-makers. Overall, it's valuable for understanding PCA's role in multivariate analysis, albeit with some caveats regarding its dense content and terminological choices.
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Pros
- Thorough discussion on PCA fundamentals
- Useful tips for selecting principal components
- Strong foundation for related analyses
- Accessible for those with varying quantitative backgrounds
- Examples relate PCA to real-world scenarios
Cons
- Terminology may be a bit dated and confusing
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