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 process covers the why, what, while and the way of complicated and multivariate records in a manner that's neither too technical nor too mathematical. scholars additionally methods to compute each one process utilizing SPSS software program.
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Extra resources for Advanced and Multivariate Statistical Methods
Example text
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.
Although the Decision-Making Tree begins with the purpose of the research question, we recommend first identifying the number and types of variables, as this will guide the process of determining the purpose. The steps for using the Decision-Making Tree are as follows: 1. 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.
Among the statistical options for assessing univariate normality are the use of skewness and kurtosis coefficients. Skewness is a quantitative measure of the degree of symmetry of a distribution about the mean. Kurtosis is a quantitative measure of the degree of peakedness of a distribution. A variable can have significant skewness, kurtosis, or both. , a skewness value 0), there is a clustering of cases to the left, and the right tail is extended with only a small number of cases. , a skewness value 0), there is a clustering of cases to the right, and the left tail is extended with only a small number of cases.