{"id":195136,"date":"2020-08-07T09:52:29","date_gmt":"2020-08-07T07:52:29","guid":{"rendered":"https:\/\/www.scribbr.nl\/?p=195136"},"modified":"2022-06-09T09:09:51","modified_gmt":"2022-06-09T07:09:51","slug":"nominal-data","status":"publish","type":"post","link":"https:\/\/www.scribbr.com\/statistics\/nominal-data\/","title":{"rendered":"Nominal Data | Definition, Examples, Data Collection &#038; Analysis"},"content":{"rendered":"<p><strong>Nominal data <\/strong>is labelled into mutually exclusive categories within a variable. These categories cannot be ordered in a meaningful way.<\/p>\n<p>For example, pref<span style=\"font-size: 1rem;\">erred mode of transportation is a nominal variable, because the data is sorted into categories: car, bus, train, tram, bicycle, etc.<\/span><\/p>\n<p><!--more--><\/p>\n<h2>Levels of measurement<\/h2>\n<p>The <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//levels-of-measurement///">level of measurement<\/a> indicates how precisely data is recorded. There are 4 hierarchical levels: nominal, <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//ordinal-data///">ordinal<\/a>, <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//interval-data///">interval<\/a>, and <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//ratio-data///">ratio<\/a>. The higher the level, the more complex the measurement.<\/p>\n<p>&nbsp;<\/p>\n<p><img src=https://www.scribbr.com/"https:////www.scribbr.com//wp-content//uploads//2020//08//levels-of-measurement-1.png/" alt=\"The 4 levels of measurement: nominal, ordinal, interval, and ratio\" width=\"660\" class=\"aligncenter wp-image-195883\" srcset=\"https:\/\/www.scribbr.com\/wp-content\/uploads\/2020\/08\/levels-of-measurement-1.png 1423w, https:\/\/www.scribbr.com\/wp-content\/uploads\/2020\/08\/levels-of-measurement-1-300x182.png 300w, https:\/\/www.scribbr.com\/wp-content\/uploads\/2020\/08\/levels-of-measurement-1-1024x622.png 1024w, https:\/\/www.scribbr.com\/wp-content\/uploads\/2020\/08\/levels-of-measurement-1-768x467.png 768w\" sizes=\"(max-width: 1423px) 100vw, 1423px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p>Nominal data is the least precise and complex level. The word nominal means \u201cin name,\u201d so this kind of data can only be labelled. It does not have a rank order, equal spacing between values, or a true zero value.<\/p>\n<h2>Examples of nominal data<\/h2>\n<p>At a nominal level, each response or observation fits only into one category.<\/p>\n<p>Nominal data can be expressed in words or in numbers. But even if there are numerical labels for your data, you can\u2019t order the labels in a meaningful way or perform arithmetic operations with them.<\/p>\n<p>In social scientific research, nominal variables often include gender, ethnicity, political preferences or student identity number.<\/p>\n<table class=\"kb-table responsive full-width auto-th alternate\">\n<caption>Examples of nominal variables<\/caption>\n<thead>\n<tr>\n<th>Variable<\/th>\n<th>Categories<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Zip code<\/th>\n<td>\n<ul>\n<li>2138<\/li>\n<li>90210<\/li>\n<li>1007<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<th>Political preferences<\/th>\n<td>\n<ul>\n<li>Republican<\/li>\n<li>Democrat<\/li>\n<li>Independent<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<th>Employment status<\/th>\n<td>\n<ul>\n<li>Employed<\/li>\n<li>Unemployed<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<th>Literary genre<\/th>\n<td>\n<ul>\n<li>Comedy<\/li>\n<li>Drama<\/li>\n<li>Satire<\/li>\n<li>Epic<\/li>\n<li>Tragedy<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Variables that can be coded in only 2 ways (e.g. yes\/no or employed\/unemployed) are called binary or dichotomous. Since the order of the labels within those variables doesn\u2019t matter, they are types of nominal variable.<\/p>\n<h2>How to collect nominal data<\/h2>\n<p>Nominal data can be collected through open- or closed-ended survey questions.<\/p>\n<p>If the variable you are interested in has only a few possible labels that capture all of the data, use closed-ended questions.<\/p>\n<figure class=\"kb-textbox border-left\"><figcaption>Examples of closed-ended questions<\/figcaption><table class=\"kb-table responsive full-width auto-th\">\n<tbody>\n<tr>\n<th>What is your gender?<\/th>\n<td><i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Male<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Female<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Other<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Prefer not to answer<\/td>\n<\/tr>\n<tr>\n<th>Do you own a smartphone?<\/th>\n<td><i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Yes<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> No<\/td>\n<\/tr>\n<tr>\n<th>What is your favorite movie genre?<\/th>\n<td><i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Romance<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Action<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Mystery<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Animation<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Musical<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Comedy<br \/>\n<i class=\"fa fa-square-o\" aria-hidden=\"true\"><\/i> Thriller<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>If your variable of interest has many possible labels, or labels that you cannot generate a complete list for, use open-ended questions.<\/p>\n<figure class=\"kb-textbox border-left\"><figcaption>Examples of open-ended questions<\/figcaption><ol>\n<li>What is your student ID number?<\/li>\n<li>What is your zip code?<\/li>\n<li>What is your native language?<\/li>\n<\/ol>\n<\/figure>\n<h2>How to analyze nominal data<\/h2>\n<p>To analyze nominal data, you can organize and visualize your data in tables and charts.<\/p>\n<p>Then, you can gather some <a href=https://www.scribbr.com/"https:////en.wikipedia.org//wiki//Descriptive_statistics/">descriptive statistics<\/a> about your data set. These help you assess the <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//frequency-distributions///">frequency distribution<\/a> and find the central tendency of your data. But not all measures of central tendency or variability are applicable to nominal data.<\/p>\n<figure class=\"kb-textbox border-left green\"><figcaption>Example: Nominal data set<\/figcaption>You distribute a survey with a question asking respondents to select their political preferences from a list. Your data set is a list of response values.<\/p>\n<table class=\"kb-table responsive full-width auto-th\">\n<caption>Data set<\/caption>\n<tbody>\n<tr>\n<td>Republican<br \/>\nDemocrat<br \/>\nIndependent<br \/>\nIndependent<br \/>\nRepublican<br \/>\nRepublican<br \/>\nRepublican<br \/>\nDemocrat<br \/>\nIndependent<\/td>\n<td>Independent<br \/>\nRepublican<br \/>\nDemocrat<br \/>\nDemocrat<br \/>\nDemocrat<br \/>\nDemocrat<br \/>\nRepublican<br \/>\nDemocrat<br \/>\nDemocrat<\/td>\n<td>Democrat<br \/>\nRepublican<br \/>\nDemocrat<br \/>\nDemocrat<br \/>\nIndependent<br \/>\nRepublican<br \/>\nRepublican<br \/>\nDemocrat<br \/>\nDemocrat<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<h3>Distribution<\/h3>\n<p style=\"text-align: left;\">To organize this data set, you can create a frequency distribution table to show you the number of responses for each category of political preference.<\/p>\n<div class=\"scribbrTabs\" style=\"text-align: center;\">\n<ul class=\"scribbrTabs-menu\">\n<li class=\"current\"><a href=https://www.scribbr.com/"#\">Simple frequency distribution<\/a><\/li>\n<li><a href=https://www.scribbr.com/"#\">Percentage frequency distribution<\/a><\/li>\n<\/ul>\n<div class=\"scribbrTabs-tabs\">\n<div style=\"text-align: left;\" class=\"scribbrTabs-content\">\n<figure class=\"kb-textbox border-left green\">To create a simple frequency distribution table, list all possible categories in the variable in the left hand column and the number of responses for each category in the right hand column.<\/p>\n<table class=\"kb-table responsive full-width auto-th\">\n<thead>\n<tr>\n<th>Political preference<\/th>\n<th>Frequency<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Democrat<\/th>\n<td>13<\/td>\n<\/tr>\n<tr>\n<th>Republican<\/th>\n<td>9<\/td>\n<\/tr>\n<tr>\n<th>Independent<\/th>\n<td>5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<\/div>\n<div style=\"text-align: left;\" class=\"scribbrTabs-content\">\n<figure class=\"kb-textbox border-left green\">You can also convert the frequencies to percentages. To do so, divide each frequency by the total number of values and multiply this by 100.<\/p>\n<table class=\"kb-table responsive full-width auto-th\">\n<thead>\n<tr>\n<th>Political preference<\/th>\n<th>Percent<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Democrat<\/th>\n<td>48.1%<\/td>\n<\/tr>\n<tr>\n<th>Republican<\/th>\n<td>33.3%<\/td>\n<\/tr>\n<tr>\n<th>Independent<\/th>\n<td>18.5%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\n<p>&nbsp;<\/p>\n<p>Using these tables, you can also visualize the distribution of your data set in graphs and charts.<\/p>\n<div class=\"scribbrTabs\" style=\"text-align: center;\">\n<ul class=\"scribbrTabs-menu\">\n<li class=\"current\" style=\"text-align: center;\"><a href=https://www.scribbr.com/"#\">Bar graph<\/a><\/li>\n<li style=\"text-align: center;\"><a href=https://www.scribbr.com/"#\">Pie chart<\/a><\/li>\n<\/ul>\n<div class=\"scribbrTabs-tabs\" style=\"text-align: left;\">\n<div class=\"scribbrTabs-content\">\n<figure class=\"kb-textbox border-left blue\">You can use your simple frequency distribution table to create a bar graph. Plot the categories on the x-axis and the frequencies on the y-axis.<img src=https://www.scribbr.com/"https:////www.scribbr.com//wp-content//uploads//2020//08//Displaying-nominal-data-in-a-bar-chart.svg/" alt=\"Displaying nominal data in a bar chart\" width=\"600\" height=\"371\" class=\"aligncenter size-full wp-image-195138\" role=\"img\" \/><\/figure>\n<\/div>\n<div class=\"scribbrTabs-content\">\n<figure class=\"kb-textbox border-left blue\">You can use your percentage frequency distribution table to create in a pie chart. Each slice of the pie represents the proportion of the data set of a category. <img src=https://www.scribbr.com/"https:////www.scribbr.com//wp-content//uploads//2020//08//Displaying-nominal-data-in-a-pie-chart.svg/" alt=\"Displaying nominal data in a pie chart\" width=\"600\" height=\"371\" class=\"size-full wp-image-195151 aligncenter\" role=\"img\" \/><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<h3>Central tendency<\/h3>\n<p>The <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//central-tendency///">central tendency <\/a>of your data set tells you where most of your values lie.<\/p>\n<p>The <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//mode///">mode<\/a>, <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//mean///">mean<\/a>, and <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//median///">median<\/a> are three most commonly used measures of central tendency. However, only the mode can be used with nominal data.<\/p>\n<p>To get the median of a data set, you have to be able to order values from low to high. For the mean, you need to be able to perform arithmetic operations like addition and division on the values in the data set. While nominal data can be grouped by category, it cannot be ordered nor summed up.<\/p>\n<p>Therefore, the central tendency of nominal data can only be expressed by the mode &#8211; the most frequently recurring value.<\/p>\n<figure class=\"kb-textbox border-left green\"><figcaption>Mode<\/figcaption>To find the mode of your nominal data set, look for the most frequently appearing value in your frequency table.<\/p>\n<p>Since most participants in your study identify as Democrat, the mode is Democrat.<\/figure>\n<h3>Statistical tests for nominal data<\/h3>\n<p><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//inferential-statistics///">Inferential statistics<\/a> help you test scientific hypotheses about your data. Nonparametric statistical tests are used with nominal data.<\/p>\n<p>While parametric tests assume certain characteristics about a data set, like a <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//normal-distribution///">normal distribution<\/a> of scores, these do not apply to nominal data because the data cannot be ordered in any meaningful way.<\/p>\n<p><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//chi-square-tests///"><strong>Chi-square tests<\/strong><\/a> are nonparametric <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//statistical-tests///">statistical tests<\/a> for categorical variables. The goodness of fit chi-square test can be used on a data set with one variable, while the chi-square test of independence is used on a data set with two variables.<\/p>\n<p>The <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//chi-square-goodness-of-fit///">chi-square goodness of fit test<\/a> is used when you have gathered data from a single population through random sampling. To measure how representative your sample is, you can use this test to assess whether the frequency distribution of your sample matches what you would expect from the broader population.<\/p>\n<figure class=\"kb-textbox border-left green\"><figcaption>Chi-square test for goodness of fit<\/figcaption>Based on current data about your population, you expect 30% of your sample to identify as Democrat, 30% as Republican and 40% as Independent. Instead, your observed data show that 48% of your sample are Democrat, 33% Republican and 19% Independent.<\/p>\n<p>The goodness of fit <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//test-statistic///">test statistic<\/a> tells you how different what you observe is from what you would expect by chance. If the test statistic is zero, there is no difference between what you expect and what you observe.<\/figure>\n<p>With the chi-square test of independence, you can find out whether a relationship between two categorical variables is significant.<\/p>\n<figure class=\"kb-textbox border-left green\"><figcaption>Chi-square test of independence<\/figcaption>If you collect data on employment status as well as political preferences for each participant, you can test whether there is a relationship between the two variables in your sample. Using <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//hypothesis-testing///">hypothesis testing<\/a>, you can formally assess whether two nominal variables from a single sample are independent of each other.<\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Nominal data is labelled into mutually exclusive categories within a variable. These categories cannot be ordered in a meaningful way. For example, preferred mode of transportation is a nominal variable, because the data is sorted into categories: car, bus, train, tram, bicycle, etc.<\/p>\n","protected":false},"author":115,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":""},"categories":[39099],"tags":[],"acf":[],"yoast_head":"<title>Nominal Data | Definition, Examples, Data Collection &amp; Analysis<\/title>\n<!-- Added by HTTrack --><meta http-equiv="content-type" content="text/html;charset=UTF-8" /><!-- /Added by HTTrack -->
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