Advanced and Multivariate Statistical Methods 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 no overemphasizing complex math. This hugely utilized process covers the why, what, while and the way of complicated and multivariate information in a manner that's neither too technical nor too mathematical. scholars additionally compute every one method 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 additional as a way for college students to focus on their studying and for teachers to concentration their instruction.
  • Key phrases are reviewed and strengthened after all of bankruptcy fabric to make sure that scholars comprehend the vocabulary of complex and multivariate statistics.
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    Additional info for Advanced and Multivariate Statistical Methods

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    In regression, several IVs are used to develop an equation that can be used to predict the value of a DV. For missing data, the variable with missing values becomes the DV. Cases with complete data are used to develop this prediction equation. The equation is then used to predict missing values of the DV for incomplete cases. An advantage to this procedure is that it is more objective than a researcher’s guess and factors in more information than simply inserting the overall mean. One disadvantage of regression is that the predicted scores are better than they actually would be.

    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.

    3. Once variables have been transformed, it is important to reevaluate the normality assumption. Following the confirmation of a normal or near-normal distribution, the analysis may proceed typically, resulting in vastly improved results (Tabachnick & Fidell, 2007). In addition, the researcher should be cognizant 33 Chapter 3 Pre-Analysis Data Screening of the fact that any transformations performed on the data must be discussed in the Methods section of any research report. It should be understood that the topic of data transformation is much too broad to be adequately addressed here.

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