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Nominal Data Analysis for Statistics

What Is Nominal Data Analysis?

Not all data investigated by researchers are hard numbers. It is actual for inductive data analysis. Nominal data is one of the four levels of measurement commonly used. It is also the weakest form because of its nature. Nominal data are qualities. The analysis will require having a data set with no less than two categories involved. The data set is what is held in common, and the categories are the qualities. Nominal scales will label variables and not provide quantitative value. Examples would be where a person lives, what their gender is, and what the color of their eyes is.

statistical data analysis schema

How to Perform Nominal Data Statistical Analysis

A spreadsheet like Excel is very good for doing nominal data statistical analysis. Tables can be set up with dependent variables listed as headings for rows and independent variables for columns. For example, Female can be used as independent variable heading and Eye Color to be used as the dependent variable. The proper data set can be placed in the appropriate cell (e.g. Brown, 10%). Researchers then able to take a look at the cross tabular frequency of Female by Eye Color. This can be used for inferential analysis.

Statistical Tests for Nominal Data

Cross tabulation on Excel can permit a number of the statistical tests to be used for nominal data. Chi-square statistical tests for nominal data can be used to analyze relationship between two data variables, and logistics regression can model responses using independent variables as predictors. Percentages and mode can be used, to analyze this form of information. Nominal data is well-suited for graphical analysis using bar charts and pie charts. The use of scattershot graphs, however, is totally ineffective.

Nominal data analysis is exceptionally good for social science research. Social science is a discipline that collects data in any number of forms. These can be cognitions or opinions among other possibilities. Nominal data can be analyzed with analysis of variance (ANOVA) as it allows one category to be compared against another. The coding is very simple because labels or any symbol to represent the category can be used.

Nominal data can also be used in survey research. The data can be coded with a variable given a number (e.g., the city of Boston receives a “02”, Baltimore gets a “01”, etc.). It is relatively easy to profile respondents to a survey with the use of nominal data. Nominal data analysis has also been used in economics, particularly attempting to determine real value with price index.

This is a type of analysis is very well-suited for spreadsheets and software packages such as SPSS. Labeling has helped in doing evaluation in areas of school justice, and nominal data analysis is not very difficult to perform. As with any data analysis instrument, the important thing is to determine how it is going to be used. Chi-square and logistics regression have to be set up properly, and nominal data analysis does require following a set process to arrange information. Nevertheless, this is a form of analysis that is especially useful in the areas of social science research and is used quite frequently in those areas to derive usable information.

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