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| '''Univariate analysis''' is the simplest form of ].<ref name=babbie>], , 12th edition, Wadsworth Publishing, 2009, ISBN 0-495-59841-0, p. 426-433</ref> The analysis is carried out with the description of a single ] |
'''Univariate analysis''' is the simplest form of ].<ref name=babbie>], , 12th edition, Wadsworth Publishing, 2009, ISBN 0-495-59841-0, p. 426-433</ref> The analysis is carried out with the description of a single ] in terms of the applicable ].<ref name=babbie/> For example, if the variable "age" was the subject of the analysis, the researcher would look at how many subjects fall into a given age attribute categories. | ||
| Univariate analysis contrasts with ] – the analysis of two variables simultaneously – or multivariate analysis – the analysis of multiple variables simultaneously |
Univariate analysis contrasts with ] – the analysis of two variables simultaneously – or ] – the analysis of multiple variables simultaneously.<ref name=babbie/> Univariate analysis is commonly used in the first, ] stages of research, before being supplemented by more advanced, ] bivariate or multivariate analysis.<ref>Harvey Russell Bernard, , Rowman Altamira, 2006, ISBN 0-7591-0869-2, p. 549</ref><ref>A. Cooper, Tony J. Weekes, , Rowman & Littlefield, 1983, ISBN 0-389-20383-1, pp. 50–51</ref> | ||
| A basic way of presenting univariate data is to create a ] of the individual cases, which involves presenting the number of attributes of the variable studied for each case observed in the ].<ref name=babbie/> This can be done in a table format, with a ] or a similar form of graphical representation.<ref name=babbie/> A sample distribution table and a bar chart for an univariate analysis are presented below (the table shows the frequency distribution for a variable "age" and the bar chart, for a variable "] rate"): - this is an edit of the previous as the chart is an example of bivariate, not univariate analysis - as stated above, bivariate analysis is that of two variables and there are 2 variables compared in this graph: incarceration and country. | A basic way of presenting univariate data is to create a ] of the individual cases, which involves presenting the number of attributes of the variable studied for each case observed in the ].<ref name=babbie/> This can be done in a table format, with a ] or a similar form of graphical representation.<ref name=babbie/> A sample distribution table and a bar chart for an univariate analysis are presented below (the table shows the frequency distribution for a variable "age" and the bar chart, for a variable "] rate"): - this is an edit of the previous as the chart is an example of bivariate, not univariate analysis - as stated above, bivariate analysis is that of two variables and there are 2 variables compared in this graph: incarceration and country. | ||
Revision as of 23:36, 18 May 2013
Univariate analysis is the simplest form of quantitative (statistical) analysis. The analysis is carried out with the description of a single variable in terms of the applicable unit of analysis. For example, if the variable "age" was the subject of the analysis, the researcher would look at how many subjects fall into a given age attribute categories.
Univariate analysis contrasts with bivariate analysis – the analysis of two variables simultaneously – or multivariate analysis – the analysis of multiple variables simultaneously. Univariate analysis is commonly used in the first, descriptive stages of research, before being supplemented by more advanced, inferential bivariate or multivariate analysis.
A basic way of presenting univariate data is to create a frequency distribution of the individual cases, which involves presenting the number of attributes of the variable studied for each case observed in the sample. This can be done in a table format, with a bar chart or a similar form of graphical representation. A sample distribution table and a bar chart for an univariate analysis are presented below (the table shows the frequency distribution for a variable "age" and the bar chart, for a variable "incarceration rate"): - this is an edit of the previous as the chart is an example of bivariate, not univariate analysis - as stated above, bivariate analysis is that of two variables and there are 2 variables compared in this graph: incarceration and country.
| Age range | Frequency | Percent |
|---|---|---|
| under 18 | 10 | 5 |
| 18–29 | 50 | 25 |
| 29–45 | 40 | 20 |
| 45–65 | 40 | 20 |
| over 65 | 60 | 30 |
| Valid cases: 200 Missing cases: 0 |
There are several tools used in univariate analysis; their applicability depends on whether we are dealing with a continuous variable (such as age) or a discrete variable (such as gender).
In addition to frequency distribution, univariate analysis commonly involves reporting measures of central tendency (location). This involves describing the way in which quantitative data tend to cluster around some value. In the univariate analysis, the measure of central tendency is an average of a set of measurements, the word average being variously construed as (arithmetic) mean, median, mode or other measure of location, depending on the context.
Another set of measures used in the univariate analysis, complementing the study of the central tendency, involves studying the statistical dispersion. Those measurements look at how the values are distributed around values of central tendency. The dispersion measures most often involve studying the range, interquartile range, and the standard deviation.
See also
References
- ^ Earl R. Babbie, The Practice of Social Research", 12th edition, Wadsworth Publishing, 2009, ISBN 0-495-59841-0, p. 426-433
- Harvey Russell Bernard, Research methods in anthropology: qualitative and quantitative approaches, Rowman Altamira, 2006, ISBN 0-7591-0869-2, p. 549
- A. Cooper, Tony J. Weekes, Data, models, and statistical analysis, Rowman & Littlefield, 1983, ISBN 0-389-20383-1, pp. 50–51
- Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9, p. 61