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In chapter 1 the section 1.1 explains what Multivariate statistics is which is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis.

Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the actual problem being studied.

In addition, multivariate statistics is concerned with
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The design of a study defines the study type (descriptive, correlational, semi-experimental, experimental, review, meta-analytic) and sub-type (e.g., descriptive-longitudinal case study), research problem, hypotheses, independent (IVs) and dependent variables (DVs), experimental design, and, if applicable, data collection methods and a statistical analysis plan. Research design is the framework that has been created to find answers to research
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The primary factors that are important in conducting statistical test are variables (categorical or quantitative) and the number of (IVs) independent variables and (DVs) dependant variables. To facilitate the identification process the chapter provides two decision- making tools so that it is easier to make a decision. The chapter presents the decision making tools and gives an overview of the statistical techniques addressed in this text as well as basic univariate test, all of which will be organized by the four types of research questions: degree of relationship, significance of group differences, prediction of group membership, and structure. Statistical test that analyze the degree of relationship include bivariate correlation and regression, multiple regression and path analysis. Research questions addressing degree of relationship all have quantitative variables. Methods that examine the significance of group differences are t test, one-way and factorial ANOVA, one-way and factorial ANCOVA, one- way and factorial MANOVA, and one-way and factorial MANCOVA. Research questions that address group differences have categorical IVs. Statistical tests that predict group membership are discriminate analysis and logistic regression. Research questions that address prediction of group membership have a categorical DV. Statistical

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