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Paperback. Etat : new. Paperback. Reveal hidden patterns in your data using multivariate descriptive analysis Many researchers believe multivariate statistics belong only to inferential research, leaving powerful analytical tools unused in descriptive studies. How to Use Multivariate Statistics in Descriptive Research: Making the Invisible Visible challenges this assumption directly, demonstrating how factor analysis, cluster analysis, and discriminant analysis can expose patterns and relationships that simpler methods overlook transforming how social and behavioral scientists understand their data. Written in clear, practical language, this book provides step-by-step instructions for conducting multivariate analyses using SPSS, R, and Excel. Each chapter features real-world illustrations that ground abstract concepts in concrete applications. Reflective sections titled Revealing the Opening Quote connect statistical insights to broader understanding, helping readers see beyond numbers to meaningful interpretation. Readers will also find: Detailed guidance on applying factor analysis to identify underlying constructs within complex descriptive datasets and research questionsCluster analysis techniques that group observations based on shared characteristics, revealing natural patterns invisible to univariate approachesDiscriminant analysis methods that classify cases and predict group membership using multiple variables simultaneously for clearer interpretationPractical software tutorials walking through each statistical procedure in SPSS, R, and Excel with reproducible examplesChapter-ending reflections that bridge statistical technique to conceptual understanding, reinforcing both mechanical skill and interpretive insight Designed for educators, graduate students, and researchers in the social and behavioral sciences, this book empowers readers to move beyond basic descriptive statistics. By mastering multivariate techniques, researchers gain the ability to detect hidden structures in their data and communicate findings with greater precision and confidence. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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paperback. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback. Etat : New. Reveal hidden patterns in your data using multivariate descriptive analysis Many researchers believe multivariate statistics belong only to inferential research, leaving powerful analytical tools unused in descriptive studies. How to Use Multivariate Statistics in Descriptive Research: Making the Invisible Visible challenges this assumption directly, demonstrating how factor analysis, cluster analysis, and discriminant analysis can expose patterns and relationships that simpler methods overlook - transforming how social and behavioral scientists understand their data. Written in clear, practical language, this book provides step-by-step instructions for conducting multivariate analyses using SPSS, R, and Excel. Each chapter features real-world illustrations that ground abstract concepts in concrete applications. Reflective sections titled "Revealing the Opening Quote" connect statistical insights to broader understanding, helping readers see beyond numbers to meaningful interpretation. Readers will also find: Detailed guidance on applying factor analysis to identify underlying constructs within complex descriptive datasets and research questionsCluster analysis techniques that group observations based on shared characteristics, revealing natural patterns invisible to univariate approachesDiscriminant analysis methods that classify cases and predict group membership using multiple variables simultaneously for clearer interpretationPractical software tutorials walking through each statistical procedure in SPSS, R, and Excel with reproducible examplesChapter-ending reflections that bridge statistical technique to conceptual understanding, reinforcing both mechanical skill and interpretive insight Designed for educators, graduate students, and researchers in the social and behavioral sciences, this book empowers readers to move beyond basic descriptive statistics. By mastering multivariate techniques, researchers gain the ability to detect hidden structures in their data and communicate findings with greater precision and confidence.…

Paperback. Etat : New. Reveal hidden patterns in your data using multivariate descriptive analysis Many researchers believe multivariate statistics belong only to inferential research, leaving powerful analytical tools unused in descriptive studies. How to Use Multivariate Statistics in Descriptive Research: Making the Invisible Visible challenges this assumption directly, demonstrating how factor analysis, cluster analysis, and discriminant analysis can expose patterns and relationships that simpler methods overlook - transforming how social and behavioral scientists understand their data. Written in clear, practical language, this book provides step-by-step instructions for conducting multivariate analyses using SPSS, R, and Excel. Each chapter features real-world illustrations that ground abstract concepts in concrete applications. Reflective sections titled "Revealing the Opening Quote" connect statistical insights to broader understanding, helping readers see beyond numbers to meaningful interpretation. Readers will also find: Detailed guidance on applying factor analysis to identify underlying constructs within complex descriptive datasets and research questionsCluster analysis techniques that group observations based on shared characteristics, revealing natural patterns invisible to univariate approachesDiscriminant analysis methods that classify cases and predict group membership using multiple variables simultaneously for clearer interpretationPractical software tutorials walking through each statistical procedure in SPSS, R, and Excel with reproducible examplesChapter-ending reflections that bridge statistical technique to conceptual understanding, reinforcing both mechanical skill and interpretive insight Designed for educators, graduate students, and researchers in the social and behavioral sciences, this book empowers readers to move beyond basic descriptive statistics. By mastering multivariate techniques, researchers gain the ability to detect hidden structures in their data and communicate findings with greater precision and confidence.…

Paperback. Etat : new. Paperback. Reveal hidden patterns in your data using multivariate descriptive analysis Many researchers believe multivariate statistics belong only to inferential research, leaving powerful analytical tools unused in descriptive studies. How to Use Multivariate Statistics in Descriptive Research: Making the Invisible Visible challenges this assumption directly, demonstrating how factor analysis, cluster analysis, and discriminant analysis can expose patterns and relationships that simpler methods overlook transforming how social and behavioral scientists understand their data. Written in clear, practical language, this book provides step-by-step instructions for conducting multivariate analyses using SPSS, R, and Excel. Each chapter features real-world illustrations that ground abstract concepts in concrete applications. Reflective sections titled Revealing the Opening Quote connect statistical insights to broader understanding, helping readers see beyond numbers to meaningful interpretation. Readers will also find: Detailed guidance on applying factor analysis to identify underlying constructs within complex descriptive datasets and research questionsCluster analysis techniques that group observations based on shared characteristics, revealing natural patterns invisible to univariate approachesDiscriminant analysis methods that classify cases and predict group membership using multiple variables simultaneously for clearer interpretationPractical software tutorials walking through each statistical procedure in SPSS, R, and Excel with reproducible examplesChapter-ending reflections that bridge statistical technique to conceptual understanding, reinforcing both mechanical skill and interpretive insight Designed for educators, graduate students, and researchers in the social and behavioral sciences, this book empowers readers to move beyond basic descriptive statistics. By mastering multivariate techniques, researchers gain the ability to detect hidden structures in their data and communicate findings with greater precision and confidence. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

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Paperback. Etat : Brand New. 272 pages. 7.00x0.60x9.90 inches. In Stock.

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Etat : New. pp. 272.

Paperback. Etat : Brand New. 272 pages. 7.00x0.60x9.90 inches. In Stock.

Paperback. Etat : new. Paperback. Reveal hidden patterns in your data using multivariate descriptive analysis Many researchers believe multivariate statistics belong only to inferential research, leaving powerful analytical tools unused in descriptive studies. How to Use Multivariate Statistics in Descriptive Research: Making the Invisible Visible challenges this assumption directly, demonstrating how factor analysis, cluster analysis, and discriminant analysis can expose patterns and relationships that simpler methods overlook transforming how social and behavioral scientists understand their data. Written in clear, practical language, this book provides step-by-step instructions for conducting multivariate analyses using SPSS, R, and Excel. Each chapter features real-world illustrations that ground abstract concepts in concrete applications. Reflective sections titled Revealing the Opening Quote connect statistical insights to broader understanding, helping readers see beyond numbers to meaningful interpretation. Readers will also find: Detailed guidance on applying factor analysis to identify underlying constructs within complex descriptive datasets and research questionsCluster analysis techniques that group observations based on shared characteristics, revealing natural patterns invisible to univariate approachesDiscriminant analysis methods that classify cases and predict group membership using multiple variables simultaneously for clearer interpretationPractical software tutorials walking through each statistical procedure in SPSS, R, and Excel with reproducible examplesChapter-ending reflections that bridge statistical technique to conceptual understanding, reinforcing both mechanical skill and interpretive insight Designed for educators, graduate students, and researchers in the social and behavioral sciences, this book empowers readers to move beyond basic descriptive statistics. By mastering multivariate techniques, researchers gain the ability to detect hidden structures in their data and communicate findings with greater precision and confidence. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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Taschenbuch. Etat : Neu. Neuware - Reveal hidden patterns in your data using multivariate descriptive analysis Many researchers believe multivariate statistics belong only to inferential research, leaving powerful analytical tools unused in descriptive studies. How to Use Multivariate Statistics in Descriptive Research: Making the Invisible Visible challenges this assumption directly, demonstrating how factor analysis, cluster analysis, and discriminant analysis can expose patterns and relationships that simpler methods overlook - transforming how social and behavioral scientists understand their data. Written in clear, practical language, this book provides step-by-step instructions for conducting multivariate analyses using SPSS, R, and Excel. Each chapter features real-world illustrations that ground abstract concepts in concrete applications. Reflective sections titled 'Revealing the Opening Quote' connect statistical insights to broader understanding, helping readers see beyond numbers to meaningful interpretation. Readers will also find: - Detailed guidance on applying factor analysis to identify underlying constructs within complex descriptive datasets and research questions - Cluster analysis techniques that group observations based on shared characteristics, revealing natural patterns invisible to univariate approaches - Discriminant analysis methods that classify cases and predict group membership using multiple variables simultaneously for clearer interpretation - Practical software tutorials walking through each statistical procedure in SPSS, R, and Excel with reproducible examples - Chapter-ending reflections that bridge statistical technique to conceptual understanding, reinforcing both mechanical skill and interpretive insight Designed for educators, graduate students, and researchers in the social and behavioral sciences, this book empowers readers to move beyond basic descriptive statistics. By mastering multivariate techniques, researchers gain the ability to detect hidden structures in their data and communicate findings with greater precision and confidence.…

Paperback. Etat : New. Reveal hidden patterns in your data using multivariate descriptive analysis Many researchers believe multivariate statistics belong only to inferential research, leaving powerful analytical tools unused in descriptive studies. How to Use Multivariate Statistics in Descriptive Research: Making the Invisible Visible challenges this assumption directly, demonstrating how factor analysis, cluster analysis, and discriminant analysis can expose patterns and relationships that simpler methods overlook - transforming how social and behavioral scientists understand their data. Written in clear, practical language, this book provides step-by-step instructions for conducting multivariate analyses using SPSS, R, and Excel. Each chapter features real-world illustrations that ground abstract concepts in concrete applications. Reflective sections titled "Revealing the Opening Quote" connect statistical insights to broader understanding, helping readers see beyond numbers to meaningful interpretation. Readers will also find: Detailed guidance on applying factor analysis to identify underlying constructs within complex descriptive datasets and research questionsCluster analysis techniques that group observations based on shared characteristics, revealing natural patterns invisible to univariate approachesDiscriminant analysis methods that classify cases and predict group membership using multiple variables simultaneously for clearer interpretationPractical software tutorials walking through each statistical procedure in SPSS, R, and Excel with reproducible examplesChapter-ending reflections that bridge statistical technique to conceptual understanding, reinforcing both mechanical skill and interpretive insight Designed for educators, graduate students, and researchers in the social and behavioral sciences, this book empowers readers to move beyond basic descriptive statistics. By mastering multivariate techniques, researchers gain the ability to detect hidden structures in their data and communicate findings with greater precision and confidence.…

Etat : New. 2026. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Etat : Neu. How to Use Multivariate Statistics in Descriptive Research | Making the Invisible Visible | Gary J Conti | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | Wiley | EAN 9781394362608 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.…

Etat : New. 2026. 1st Edition. paperback. . . . . .

Paperback. Etat : New. Reveal hidden patterns in your data using multivariate descriptive analysis Many researchers believe multivariate statistics belong only to inferential research, leaving powerful analytical tools unused in descriptive studies. How to Use Multivariate Statistics in Descriptive Research: Making the Invisible Visible challenges this assumption directly, demonstrating how factor analysis, cluster analysis, and discriminant analysis can expose patterns and relationships that simpler methods overlook - transforming how social and behavioral scientists understand their data. Written in clear, practical language, this book provides step-by-step instructions for conducting multivariate analyses using SPSS, R, and Excel. Each chapter features real-world illustrations that ground abstract concepts in concrete applications. Reflective sections titled "Revealing the Opening Quote" connect statistical insights to broader understanding, helping readers see beyond numbers to meaningful interpretation. Readers will also find: Detailed guidance on applying factor analysis to identify underlying constructs within complex descriptive datasets and research questionsCluster analysis techniques that group observations based on shared characteristics, revealing natural patterns invisible to univariate approachesDiscriminant analysis methods that classify cases and predict group membership using multiple variables simultaneously for clearer interpretationPractical software tutorials walking through each statistical procedure in SPSS, R, and Excel with reproducible examplesChapter-ending reflections that bridge statistical technique to conceptual understanding, reinforcing both mechanical skill and interpretive insight Designed for educators, graduate students, and researchers in the social and behavioral sciences, this book empowers readers to move beyond basic descriptive statistics. By mastering multivariate techniques, researchers gain the ability to detect hidden structures in their data and communicate findings with greater precision and confidence.…

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Paperback. Etat : new. Paperback. The National Institutes of Health Publication 09-6366, Phenotypes and Endophenotypes: Foundations for Genetic Studies of Nicotine Use and Dependence, NCI Tobacco Control Monograph 20, (the twentieth volume of the Tobacco Control Monograph series of the National Cancer Institute) reviews the scientific foundation for genetic studies of nicotine use and dependence. The authors and editors perform an admirable job synthesizing the expanding literature in the field and developing a scientific blueprint for the integration of genetic approaches into transdisciplinary studies of nicotine dependence. This seminal work should be examined in the context of global public health action on tobacco prevention and control as well as advances in genomics and related technologies. It is important to ask how genetic studies of nicotine use and dependence can contribute to the overall public health effort in tobacco control and prevention. For, despite public health efforts, an estimated 45 million people in the United States still smoke. Globally, one billion individuals smoke tobacco on a regular basis, and millions of individuals die yearly from illnesses related to tobacco. A "one size fits all" public health approach has not been fully successful. All available tools will be needed to meet the demand for effective and sustainable tobacco control, including pharmacogenetic-informed treatments and social policy interventions for smoking cessation. Clearly, tobacco use in a population is the product of the interaction of agent, genetic, and environmental factors. Government policies are important modifiable environmental influences that can alter how tobacco products are designed and marketed and how consumers respond. Understanding individual variation in responses to tobacco can help our approach to different programs, policies, and treatments for nicotine dependence. Synergy occurs when tobacco control and prevention interventions directed at agent, host, and environmental factors are implemented together. However, no studies have adequately addressed simultaneously genetic variation, quantitative measures of behavioral, social and cultural variation, and the interaction among these sources of variation. This gap reflects the disciplinary silos that were not uncommon in the 20th century scientific enterprise. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…