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| '''Univariate analysis''' is the simplest form of ] |
'''Univariate analysis''' is the simplest form of ]<ref name=babbie>], , 12th edition, Wadsworth Publishing, 2009, ISBN 0-495-59841-0, p. 426-433</ref> known as ]. 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 given age attribute categories. | ||
| 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> | 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> | ||
Revision as of 22:50, 18 July 2015
Univariate analysis is the simplest form of quantitative (statistical) analysis known as descriptive statistics. 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 given age attribute categories.
Univariate analysis contrasts with bivariate analysis – the analysis of two variables simultaneously – or multivariable 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.
Methods
A basic way of presenting univariate data is to create a frequency distribution of the individual cases, which involves presenting the number of cases in the sample that fall into each category of values of the variable. This can be done in a table format or with a bar chart or a similar form of graphical representation. A sample distribution table is presented below, showing the frequency distribution for a variable "age".
| Age range | Number of cases | 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 |
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 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 another measure of location, depending on the context. For a categorical variable, such as preferred brand of cereal, only the mode can serve this purpose. For a variable measured on an interval scale, such as temperature on the Celsius scale, or on a ratio scale, such as temperature on the Kelvin scale, the median or mean can also be used.
Further descriptors include the variable's skewness and kurtosis.
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