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 method with out overemphasizing complicated math. This hugely utilized technique covers the why, what, whilst and the way of complicated and multivariate records in a manner that's neither too technical nor too mathematical. scholars additionally the way to compute each one process utilizing SPSS software program.

New to the 6th Edition

  • Instructor ancillaries are actually to be had with the 6th edition.
  • All SPSS instructions and screenshots were up to date to model 23 of the software.
  • Student studying pursuits 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 complex and multivariate statistics.
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    Extra info for Advanced and Multivariate Statistical Methods

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    Errors in measurement, which are greater at some levels of the independent variable than at others, may also cause a lack of homoscedasticity. Heteroscedasticity, or the violation of the assumption of homoscedasticity, can be assessed through the examination of bivariate scatterplots. Within the scatterplot, the collection of points between variables should be approximately the same width across all values with some bulging toward the middle. Although subjective in nature, homoscedasticity is best assessed through the examination of bivariate scatterplots.

    Another method of estimating missing values involves the calculation of the means, using available data, for variables with missing values. Those mean values are then used to replace the missing values prior to the main analysis. When no other information is available to the researcher, the mean is the best estimate for the value on a given variable. This is a somewhat conservative procedure because the overall mean does not change by inserting the mean value for a case and no guessing on the part of the researcher is required.

    Using data that are available, a researcher should conduct tests to see if patterns exist in the missing data. To do so, one could create a dichotomous dummy variable, coded so that one group includes cases with values on a given variable and the other group contains cases with missing values on that variable. For instance, if respondents on an attitudinal survey are asked to provide their income and many do not supply that information (for reasons unknown to us at this time), those who provided an income level would be coded 0 and those who did not would be coded 1.

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