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#REDIRECT ] |
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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 ] and its attributes 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. |
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{{rcatsh| |
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Univariate analysis contrasts with ] – the analysis of two variables simultaneously – or multivariate analysis – the analysis of multiple variables simultaneously.<ref name=babbie/> Univariate analysis is also used primarily for descriptive purposes, while bivariate and multivariate analysis are geared more towards explanatory purposes.<ref name=babbie/> Univariate analysis is commonly used in the first stages of research, in analyzing the data at hand, before being supplemented by more advance, inferential bivariate or ].<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> |
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{{r sec}} |
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{{r hist}} |
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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.Thank you |
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{{r wikidata}} |
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] chart (ranked from lowest to highest) comparing international ] rates in 2002.]] |
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{| border=1 cellspacing=0 cellpadding=2 |
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!Age range |
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!Frequency |
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!Percent |
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| under 18 |
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|10 |
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|5 |
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|18–29 |
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|50 |
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|25 |
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|29–45 |
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|40 |
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|20 |
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|45–65 |
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|40 |
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|20 |
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|over 65 |
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|60 |
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|30 |
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| Valid cases: 200 <br> Missing cases: 0 |
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There are several tools used in univariate analysis data; their applicability depends on whether we are dealing with a ] (such as age) or a ] (such as gender).<ref name=babbie/> |
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In addition to frequency distribution, univariate analysis commonly involves reporting measures of ] (location).<ref name=babbie/> This involves describing the way in which ] tend to cluster around some value.<ref>Dodge, Y. (2003) , OUP. ISBN 0-19-920613-9, p. 61</ref> In the univariate analysis, the measure of central tendency is an ] of a set of ], the word average being variously construed as ], ], ] or other measure of location, depending on the context.<ref name=babbie/> |
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Another set of measures used in the univariate analysis, that's complementing the study of the central tendency, involves studying the ].<ref name=babbie/> Those measurements look at how the values are distributed around values of central tendency.<ref name=babbie/> The dispersion measures most often involve studying the ], ], and the ].<ref name=babbie/> |
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==See also== |
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* ] |
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* ] |
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* ] |
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==References== |
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{{reflist}} |
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] |
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