The analysis of univariate data
Univariate data analysis involves the analysis of a single variable, usually with descriptive
statistics such as the calculation of:
•
frequencies
• percentages
• means (the arithmetic ‘average’ of data)
•
median and mode
• standard deviations.
The analysis of bivariate data
In
most research, the investigation frequently requests the analysis of the connection
between two factors. One helpful way of doing this is to apply recurrence analysis and then
put data in a matrix or table.
For instance, a specialist may wish to determine the
connection among gender and advancement to senior positions in the school management
in Astana. Prior examination demonstrated that there are respondents in this example with
no missing information for either factor. The table data on
these two factors might be
introduced as follows:
Rank
Male
Female
Row rates
Principals/ Deputies
10
6
16
HoD’s
50
40
90
Teaching staff
98
104
202
Column totals
158
150
308
In such tables, which present the data for two variables through a process referred to as
cross-tabulation, it is possible to examine if the variables are significantly associated. The
degree of fit can be statistically tested by using the Chi-squared (x
2
) test, a non-parametric
test that can be used with any data type. The degree of fit between two variables can also
be measured with correlation coefficients.
Another common procedure in the analysis of two variables is to test the significance of a
difference between the means of two groups for the same variable. A number of statistical
procedures can be used for this purpose: “students’” t-test, analysis of variance (ANOVA)
and the Mann Whitney U-test,
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