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Illustrated use of Normality Data Tests and the Factor Analysis Model - Softcover

 
9781511568548: Illustrated use of Normality Data Tests and the Factor Analysis Model

Synopsis

When carrying out Factor analysis, it is the procedure, to first appreciate the form of distribution of the variables set for analysis. While doing so it is necessary however to recognise that normality of such data though useful is not a required underpinning of the properties of Factor analysis. Nevertheless, since Principle Components are linear combinations of the original variables, it is not unreasonable to expect them to be nearly normal. Nevertheless, it is often necessary to verify that the first few components (the Principle Components) are approximately normally distributed when they are to be used as the input data for additional analysis. Further still, more meaning can generally be given to the components in cases where observations are assumed to be multivariate normal. Two methods of data reduction can be adopted for use and this is consistent with the principle of triangulation that seeks to confirm results using complementary methods. Such a twofold approach respects recommendations on the conduct of factor analysis that at least two methods be used to verify its effectiveness and the authenticity of results. Accordingly, the Principle Component Factor Analysis and the Maximum Likelihood Factor Analysis methods that replicate similar procedure for conducting Factor Analysis are selected for consideration here.

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About the Author

Paul Mwangi Maringa (PhD) is an Associate Professor of Architecture and Planning. He has taught variously in diploma, degree, and graduate theory and portfolio courses in the department architecture at the Jomo Kenyatta University of Agriculture and Technology (JKUAT) in Juja, Kenya for 14 years; and also in the department of civil and environmental engineering at the Kigali Institute of Science and Technology (KIST), Kigali, Rwanda, for 2 years. His academic and professional career has seen him take up positions as head of department, Ag., Vice Rector and Ag., Rector; as well as Editor-in-Chief, Associate Editor and referee for two, one and three peer reviewed academic journals respectively. He has also served variously as an Architect/Planner with the Nairobi Provincial office of the Ministry of Works, Githunguri & Collins International, and Ramani Consultants. He has considerable diverse consulting experience in TVET working variously as a technical expert & master trainer in building construction, an infrastructural planning & development expert, an associate project team leader, and senior expert for planning & project management. He is a registered architect with the Board of registration of Architects and Quantity Surveyors of Kenya (BORAQS); a corporate member of the Architectural Association of Kenya (AAK-Architects Chapter), a graduate member of the Architectural Association of Kenya (AAK-Town Planning Chapter), and a graduate member of the Kenya Institute of Planners (KIP). His professional and academic career, teaching in Universities that spans the last 29 years has covered Kenya, Uganda, The Kingdom of Swaziland, Tanzania and Rwanda. He pursues the growth of knowledge in the disciplines of architectural design and its behavioural underpinnings, urban growth management, sustainability as well as in TVET management reform.

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ISBN 10: 1511568542 ISBN 13: 9781511568548
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Paperback. Condition: new. Paperback. When carrying out Factor analysis, it is the procedure, to first appreciate the form of distribution of the variables set for analysis. While doing so it is necessary however to recognise that normality of such data though useful is not a required underpinning of the properties of Factor analysis. Nevertheless, since Principle Components are linear combinations of the original variables, it is not unreasonable to expect them to be nearly normal. Nevertheless, it is often necessary to verify that the first few components (the Principle Components) are approximately normally distributed when they are to be used as the input data for additional analysis. Further still, more meaning can generally be given to the components in cases where observations are assumed to be multivariate normal. Two methods of data reduction can be adopted for use and this is consistent with the principle of triangulation that seeks to confirm results using complementary methods. Such a twofold approach respects recommendations on the conduct of factor analysis that at least two methods be used to verify its effectiveness and the authenticity of results. Accordingly, the Principle Component Factor Analysis and the Maximum Likelihood Factor Analysis methods that replicate similar procedure for conducting Factor Analysis are selected for consideration here. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781511568548

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