Gives readers the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data.Starts with a formulation of the population models, delineates the corresponding sample results, and liberally illustrates everything with examples.
Offers an abundance of examples and exercises based on real data. Appropriate for experimental scientists in a variety of disciplines. Please note you need to add our email km0bookmail.org to approved e-mail addresses. Other readers will always be interested in your opinion of the books youve read. Whether youve loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. Because of this generality, canonical correlation is probably the least used of the multivariate procedures. Factor Analysis, Principal Components Analysis (PCA), and Multivariate Analysis of Variance (MANOVA) are all well-known multivariate analysis techniques and all are available in NCSS, along with several other multivariate analysis procedures as outlined below. Multivariate Analysis Trial Of NCSSTo see how these tools can benefit you, we recommend you download and install the free trial of NCSS. Multivariate analysis techniques are used to understand how the set of outcome variables as a combined whole are influenced by other factors, how the outcome variables relate to each other, or what underlying factors produce the results observed in the dependent variables. If you would like to examine the formulas and technical details relating to a specific NCSS procedure, click on the corresponding Documentation PDF link under each heading to load the complete procedure documentation. There you will find formulas, references, discussions, and examples or tutorials describing the procedure in detail. For example, an individuals response to the questions on an exam is influenced by underlying variables such as intelligence, years in school, age, emotional state on the day of the test, amount of practice taking tests, and so on. The answers to the questions are the observed or outcome variables. The factor analyst hopes to find a few factors from which the original correlation matrix may be generated. The factor analyst hopes to identify each factor as representing a specific theoretical factor. Another goal of factor analysis is to reduce the number of variables. The analyst hopes to reduce the interpretation of a 200-question test to the study of 4 or 5 factors. The results may be rotated using varimax or quartimax rotation and the factor scores may be stored for further analysis. PCA calculates an uncorrelated set of variables known as factors or principal components. These factors are ordered so that the first few retain most of the variation present in all of the original variables. Unlike its cousin Factor Analysis, PCA always yields the same solution from the same data. NCSS performs PCA on either a correlation or a covariance matrix. The analysis may be carried out using robust estimation techniques. By way of illustration, suppose a group of students is each given two tests of ten questions each and you wish to determine the overall correlation between these two tests. Weights are constructed to maximize the correlation between these two averages. This correlation is called the first canonical correlation coefficient. You can then create another set of weighted averages unrelated to the first and calculate their correlation. This correlation is the second canonical correlation coefficient. The process continues until the number of canonical correlations equals the number of variables in the smallest group.
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