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This 6-page paper discusses some statistical terms and when they are used such as bivariate and univariate analysis and regression versus crosstabulation. Bibliography lists 3 sources.
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6 pages (~225 words per page)
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for statistical process and analysis). The main difference is that univariate only measures one aspect of a variable, where as bivariate measures many aspects of a variable.
Univariate statistics can include a weight number, or a weight variable number (which can be zero, if a weight variable isnt specified) (CDS/ISIS, 2003). Another
univariate statistic is the Wtsum, or in other words, the number of cases if the weight variable isnt specified (or weighted number of cases, which is also classified as sum
of weights) (CDS/ISIS, 2003). Other univariate statistics include the mode (first category containing the maximum frequency), the median (distribution, with one average),
mean, (average of all variables), variance (distance between the variables), and standard deviation (CDS/ISIS, 2003). Bivariate statistics, as mentioned before, tests many
aspects of a variable (CDS/ISIS, 2003). Some bivariate statistics include chi-square (typically used to test significance of differences of distributions among independent, or separate, groups), or Cramers V (CDS/ISIS, 2003).
Cramvers V is a description of the strength of association in a sample, lying between 0.0 (which reflects total independence from one another) to 1.0 (which demonstrates complete dependence of
the attributes) (CDS/ISIS, 2003). Another bivariate statistic is contingency coefficient - and this is also used to describe the strength of association
in a sample (CDS/ISIS, 2003). Adjust N - this is the number of cases with valid codes, and is weighted if a weight variable was specified (CDS/ISIS, 2003).
In short, people generally will use bivariate vs. univariate analysis, depending on how complicated a particular research project might be. Sometimes, if only one
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