Understanding 'Nominal': Definition and Explanation
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Nominal data is a fundamental type of data used in various fields, including education, business, psychology, and healthcare. This data categorises items into distinct groups without any inherent order or ranking among the categories [1][3][5].
Characteristics of Nominal Data
Nominal data is qualitative rather than quantitative, and its categories are mutually exclusive and exhaustive. This means that an item fits in only one category, and no hierarchy or ordering exists between categories. The data are labels or names without numeric meaning [1][2][5].
Examples of nominal data variables include gender (male, female), eye color (blue, green, brown), types of cuisine (Italian, Chinese, Mexican), or favourite ice cream flavours [1][2][5].
Methods of Collecting Nominal Data
Nominal data can be collected through several methods, such as surveys and questionnaires with predefined categorical options, structured or semi-structured interviews gathering categorical responses, observations of behaviours or preferences, and analysis of administrative records [2]. For instance, asking for a preferred mobile phone brand in a survey is an example of collecting nominal data.
Analysis of Nominal Data
For analysis, nominal data are typically summarised using counts and percentages for each category and visualised in bar charts or pie charts. The mode, or most common category, is a common measure. Statistical tests for nominal data include chi-square tests to examine relationships between categorical variables [1][3][2].
To create a frequency distribution table, set up a spreadsheet with the important information and use Microsoft Excel to create a pivot table. It is also essential to use multiple choice questions to ensure responses fit the expected categories [4].
In conclusion, nominal data classifies items into distinct, non-ordered categories and is primarily analysed through counts and mode, with data collection relying on surveys, interviews, observations, or records. Understanding nominal data provides valuable insights into a population or sample, making it a useful tool in various fields.
References
[1] Statistical Analysis and Data Mining Using Python. Raschka, S., & Mirjalili, Y.
[2] Introduction to Statistical Learning. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
[3] Data Analysis Using SQL and Excel. James M. Griffin, John W. Selby.
[4] Excel 2013: Pivot Tables and Charts. John Wiley & Sons.
[5] Introduction to Statistics and Probability. J. Paul Ross.
- In the field of education and self-development, learning about different types of data, such as nominal data, is essential as it helps categorize items without any inherent order or ranking.
- To gain deeper insights into a population's preferences or characteristics, media outlets often collect nominal data through various methods like surveys, interviews, or analyzing administrative records and present the findings in forms of bar charts or pie charts for easier understanding.
- As part of the learning process, it's crucial to understand the statistical analysis techniques specifically designed for nominal data, such as chi-square tests, which help identify relationships between categorical variables to provide valuable information for further decision making and analyzing data trends.
