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, while and the way of complicated and multivariate facts in a manner that's neither too technical nor too mathematical. scholars additionally tips on how to compute each 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 ambitions were additional as a method for college kids to focus on their studying and for teachers to concentration their instruction.
- Key phrases are reviewed and bolstered in any case of bankruptcy fabric to make sure that scholars comprehend the vocabulary of complicated and multivariate statistics.
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Additional resources for Advanced and Multivariate Statistical Methods: Practical Application and Interpretation
If a variable is categorical, determine the number of categories. 4. Determine the purpose of the research question: degree of relationship, group differences, prediction of group membership, or structure. Here are a few helpful hints in using the variable information to determine the research question purpose. When the IVs and DVs are all quantitative, the purpose is degree of relationship. When the IVs are categorical and the DVs are quantitative, the purpose is group differences. When the DVs are categorical, the purpose is predicting group membership.
The direction of the deviation must also be considered. The above transformations are appropriate for distributions with positive skewness. If the distribution has a negative skew, the appropriate strategy is to reflect the variable and then apply the transformation procedure listed above. Reflection involves finding the largest score in the distribution and adding 1 to it to form a constant that is larger than any score in the distribution. A new variable is then created by subtracting each score from the constant.
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.
Advanced and Multivariate Statistical Methods: Practical Application and Interpretation by Craig A. Mertler, Rachel Vannatta Reinhart