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

By Craig A. Mertler, Rachel Vannatta Reinhart

ISBN-10: 113828971X

ISBN-13: 9781138289710

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

New to the 6th Edition

  • Instructor ancillaries are actually on hand with the 6th edition.
  • All SPSS instructions and screenshots were up to date to model 23 of the software.
  • Student studying goals were further 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 spite of everything of bankruptcy fabric to make sure that scholars comprehend the vocabulary of complicated and multivariate statistics.

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

Sample text

Identify the variables in the research question. 2. Indicate which variables are the independent and dependent variables and covariates. 3. Determine the type (categorical or quantitative) of all variables. If a variable is categorical, determine the number of categories. 4. Use the table to identify    the appropriate row for the IVs, the appropriate column for the DVs, and the row and column intersection that indicates the statistical test to be used. These steps are applied to the following research question: Does ethnicity significantly affect reading achievement, math achievement, and overall achievement among sixth grade students after adjusting for family income?

It is critical, then, to assess the extent to which the sample data meet the assumptions. The issue at hand is one of test robustness. Robustness refers to the relative insensitivity of a statistical test to violations of the underlying inferential assumptions. In other words, it is the degree to which a statistical test is still appropriate to apply when some of its assumptions are not met: If in the presence of marked departures from model assumptions, little or no discrepancy between nominal and actual levels of significance occurs, then the statistical test is said to be robust with respect to that particular violation (Kennedy & Bush, 1985, p.

Interpret the proper application of Mahalanobis distance when evaluating potential outliers. Compare and contrast various forms of departure from normality in a variable’s (or set of variables’) distribution. Discuss the purpose and use of data transformations. Describe the use of residuals in evaluating violations to the assumption of linearity. Explain how the assumption of homoscedasticity is assessed and violations are determined. Test data set(s) for various pre-analysis conditions by following the appropriate SPSS guidelines provided.

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


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