Advanced and Multivariate Statistical Methods: Practical by Craig A. Mertler, Rachel Vannatta Reinhart

By Craig A. Mertler, Rachel Vannatta Reinhart

Ideal for non-math majors, Advanced and Multivariate Statistical Methods teaches scholars to interpret, current, and write up effects for every statistical strategy with out overemphasizing complicated math. This hugely utilized method covers the why, what, whilst and the way of complex and multivariate statistics in a manner that's neither too technical nor too mathematical. scholars additionally how one can compute every one procedure utilizing SPSS software program.

New to the 6th Edition

  • Instructor ancillaries at the moment are on hand with the 6th edition.
  • All SPSS instructions and screenshots were up to date to model 23 of the software.
  • Student studying pursuits were extra as a way for college kids to focus on their studying and for teachers to concentration their instruction.
  • Key phrases are reviewed and bolstered finally of bankruptcy fabric to make sure that scholars comprehend the vocabulary of complex and multivariate statistics.

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Extra info for Advanced and Multivariate Statistical Methods: Practical Application and Interpretation

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Two decision-making tools are provided to assist in identifying which statistical method to utilize— the Table of Statistical Tests and the Decision-Making Tree for Statistical Tests. The Table of Statistical Tests is organized by the type and number of IVs and DVs, while the Decision-Making Tree for Statistical Tests is organized by the purpose of the research question. 1. Table of Statistical Tests. 2. Decision-Making Tree for Statistical Tests. Research Question Number & Type of DVs Number & Type of IVs Covariates Test Goal of Analysis Bivariate Correlation and/or Regression Determine relationship and prediction 2+ quantitative Multiple Regression Create linear combination that best predicts DV 2+ quantitative Path Analysis 1 categorical (2 categories) t Test 1 quantitative 1 quantitative Degree of Relationship 1+ quantitative 1 quantitative 1 categorical (2+ categories) 2+ categorical Group Differences 1 categorical None One-way ANOVA Some One-way ANCOVA None Factorial ANOVA Some Factorial ANCOVA None One-way MANOVA Some One-way MANCOVA None Factorial MANOVA Some Factorial MANCOVA 2+ quantitative 2+ categorical 1 categorical (2 categories) 2+ mixed 1 categorical (2+ categories) 2+ quantitative Logistic Regression Prediction of Group Membership Discriminant Analysis Factor Analysis (theoretical) Structure 3+ quantitative Principal Components (empirical) 24 Estimate causal relations among variables in a hypothesized model Determine significance of mean group differences Create linear combo of DVs to maximize mean group differences Create linear combo of IVs of the log of odds of being in one group Create best linear combo to predict group membership Create linear combinations of observed variables to represent latent variable Chapter 2 A Guide to Multivariate Techniques Exercises for Chapter 2 Directions: The research questions that follow are used as examples throughout this chapter.

Homoscedasticity is the assumption that the variability in scores for one continuous variable is roughly the same at all values of another continuous variable. , the variability in a continuous dependent variable is expected to be roughly consistent at all levels of the independent, or discrete grouping, variable). In the univariate case, homogeneity of variances is assessed statistically with Levene’s test. This statistic provides a test of the hypothesis that the samples come from populations 35 Chapter 3 Pre-Analysis Data Screening with the same variances.

5 and 3 box lengths from the upper or lower edges of the box are outliers and are designated by a small circle (o). The specific case number is also listed next to the symbol. Although not depicted in the figure, cases with values greater than 3 box lengths from the edges are also identified and designated with asterisks (*) and the specific case number. It should be noted that these designations are conventions within SPSS. 2. Sample Box Plot Indicating One Outlier. 5 and 3 box lengths: they are indicated with o.

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