{"id":3785,"date":"2021-03-12T08:21:00","date_gmt":"2021-03-12T07:21:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/data-levels-of-measurement\/"},"modified":"2023-11-21T14:13:27","modified_gmt":"2023-11-21T13:13:27","slug":"data-levels-of-measurement","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-levels-of-measurement\/","title":{"rendered":"4 Levels of Measurement: Nominal, Ordinal, Interval &#038; Ratio"},"content":{"rendered":"<p><strong>When carrying out any kind of data collection or analysis, it\u2019s essential to understand the nature of the data you\u2019re dealing with.<\/strong><\/p>\n<p>Within your dataset, you\u2019ll have different variables\u2014and these variables can be recorded to varying degrees of precision. This is what\u2019s known as the level of measurement.<\/p>\n<p>There are four main levels of measurement: <strong>nominal<\/strong>, <strong>ordinal<\/strong>, <strong>interval<\/strong>, and <strong>ratio<\/strong>. In this guide, we\u2019ll explain exactly what is meant by levels (also known as types or scales) of measurement within the realm of data and statistics\u2014and why it matters. We\u2019ll then introduce you to the four types of measurements, providing a few examples of each.<\/p>\n<p>If you&#8217;d like to get your hands on some datasets already, try this <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/short-courses\/become-a-data-analyst\/\"><strong>free data analytics short course<\/strong><\/a> to get started.<\/p>\n<p>Here&#8217;s what we&#8217;ll cover:<\/p>\n<ol>\n<li><strong><a href=\"#what-are-levels-of-measurement-in-data-and-statistics\">What are levels of measurement in data and statistics?<\/a><\/strong>\n<ul>\n<li><a href=\"#levels-of-measurement-importance\"><span style=\"font-weight: 400;\">Why are levels of measurement important?<\/span><\/a><\/li>\n<\/ul>\n<\/li>\n<li><strong><a href=\"#what-are-the-four-levels-of-measurement\">What are the four levels of measurement?\u00a0<\/a><\/strong>\n<ul>\n<li><a href=\"#nominal\"><span style=\"font-weight: 400;\">Nominal<\/span><\/a><\/li>\n<li><a href=\"#ordinal\"><span style=\"font-weight: 400;\">Ordinal<\/span><\/a><\/li>\n<li><a href=\"#interval\"><span style=\"font-weight: 400;\">Interval<\/span><\/a><\/li>\n<li><a href=\"#ratio\"><span style=\"font-weight: 400;\">Ratio<\/span><\/a><\/li>\n<\/ul>\n<\/li>\n<li><strong><a href=\"#faq\">Levels of measurement: FAQ<\/a><\/strong><\/li>\n<li><strong><a href=\"#key-takeaways\">Key takeaways<\/a><\/strong><\/li>\n<\/ol>\n<p>Let\u2019s get started!<\/p>\n<h2 id=\"what-are-levels-of-measurement-in-data-and-statistics\">1. What are levels of measurement in data and statistics?<\/h2>\n<p>Level of measurement refers to how precisely a variable has been measured. When gathering data, you collect different types of information, depending on what you hope to investigate or find out.<\/p>\n<p>For example, if you wanted to analyze the spending habits of people living in Tokyo, you might send out a survey to 500 people asking questions about their income, their exact location, their age, and how much they spend on various products and services. These are your variables: data that can be measured and recorded, and whose values will differ from one individual to the next.<\/p>\n<p>When we talk about levels of measurement, we\u2019re talking about how each variable is measured, and the mathematical nature of the values assigned to each variable. This, in turn, determines what type of analysis can be carried out.<\/p>\n<p>Let\u2019s imagine you want to gather data relating to people\u2019s income. There are various levels of measurement you could use for this variable. You could ask people to provide an exact figure, or you could ask them to select their answer from a variety of ranges\u2014for example:<\/p>\n<ul>\n<li>(a) 10-19k<\/li>\n<li>(b) 20-29<\/li>\n<li>(c) 30-39k<\/li>\n<\/ul>\n<p>You could ask them to simply categorize their income as \u201chigh,\u201d \u201cmedium,\u201d or \u201clow.\u201d<\/p>\n<p>Can you see how these levels vary in their precision? If you ask participants for an exact figure, you can calculate just how much the incomes vary across your entire dataset (for example).<\/p>\n<p>However, if you only have classifications of \u201chigh,\u201d \u201cmedium,\u201d and \u201clow,\u201d you can\u2019t see exactly how much one participant earns compared to another. You also have no concept of what salary counts as \u201chigh\u201d and what counts as \u201clow\u201d\u2014these classifications have no numerical value. As a result, the latter is a less precise level of measurement.<\/p>\n<h3 id=\"levels-of-measurement-importance\">Why are levels of measurement important?<\/h3>\n<p>Level of measurement is important, as it determines the type of statistical analysis you can carry out. As a result, it affects both the nature and the depth of insights you\u2019re able to glean from your data.<\/p>\n<p>Certain statistical tests can only be performed where more precise levels of measurement have been used, so it\u2019s essential to plan in advance how you\u2019ll gather and measure your data.<\/p>\n<h2 id=\"what-are-the-four-levels-of-measurement\">3. What are the four levels of measurement? Nominal, ordinal, interval, and ratio scales explained<\/h2>\n<p>There are four types of measurement (or scales) to be aware of: <strong>nominal<\/strong>, <strong>ordinal<\/strong>, <strong>interval<\/strong>, and <strong>ratio<\/strong>.<\/p>\n<p>Each scale builds on the previous, meaning that each scale not only \u201cticks the same boxes\u201d as the previous scale, but also adds another level of precision.<\/p>\n<figure style=\"width: 1200px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" title=\"The four levels of measurement displayed in a table: Nominal, ordinal, interval, and ratio\" src=\"\/en\/wp-content\/uploads\/old-blog-uploads\/four-levels-of-measurement-data.jpg\" alt=\"The four levels of measurement displayed in a table: Nominal, ordinal, interval, and ratio\" width=\"1200\" height=\"780\" \/><figcaption class=\"wp-caption-text\">The four levels of measurement displayed in a table: Nominal, ordinal, interval, and ratio<\/figcaption><\/figure>\n<p>Let&#8217;s go through each in turn to give you an idea of what they are, and how they interact.<\/p>\n<h3 id=\"nominal\">Nominal<\/h3>\n<p>The <strong>nominal scale<\/strong> simply categorizes variables according to qualitative labels (or names). These labels and groupings don\u2019t have any order or hierarchy to them, nor do they convey any numerical value.<\/p>\n<p><img decoding=\"async\" title=\"A definition of nominal data with examples and a brief summary of how it's analyzed\" src=\"\/en\/wp-content\/uploads\/old-blog-uploads\/nominal-data.jpg\" alt=\"A definition of nominal data with examples and a brief summary of how it's analyzed\" \/><\/p>\n<p>For example, the variable \u201chair color\u201d could be measured on a nominal scale according to the following categories: blonde hair, brown hair, gray hair, and so on.<\/p>\n<p>You can learn more in <a href=\"\/en\/blog\/data-analytics\/what-is-nominal-data\/\">this complete guide to nominal data<\/a>.<\/p>\n<h3 id=\"ordinal\">Ordinal<\/h3>\n<p>The <strong>ordinal scale<\/strong> also categorizes variables into labeled groups, and these categories have an order or hierarchy to them.<\/p>\n<p><img decoding=\"async\" title=\"A definition of ordinal data with examples and how it's analyzed\" src=\"\/en\/wp-content\/uploads\/old-blog-uploads\/ordinal-data.jpg\" alt=\"A definition of ordinal data with examples and how it's analyzed\" \/><\/p>\n<p>For example, you could measure the variable \u201cincome\u201d on an ordinal scale as follows:<\/p>\n<ul>\n<li>low income<\/li>\n<li>medium income<\/li>\n<li>high income.<\/li>\n<\/ul>\n<p>Another example could be level of education, classified as follows:<\/p>\n<ul>\n<li>high school<\/li>\n<li>master\u2019s degree<\/li>\n<li>doctorate<\/li>\n<\/ul>\n<p>These are still qualitative labels (as with the nominal scale), but you can see that they follow a hierarchical order.<\/p>\n<p>Learn more in <a href=\"\/en\/blog\/data-analytics\/what-is-ordinal-data\/\">our guide to ordinal data<\/a>.<\/p>\n<h3 id=\"interval\">Interval<\/h3>\n<p>The <strong>interval scale<\/strong> is a numerical scale which labels and orders variables, with a known, evenly spaced interval between each of the values.<\/p>\n<p><img decoding=\"async\" title=\"A definition of interval data and how it's analyzed, with examples\" src=\"\/en\/wp-content\/uploads\/old-blog-uploads\/interval-data.jpg\" alt=\"A definition of interval data and how it's analyzed, with examples\" \/><\/p>\n<p>A commonly-cited example of interval data is temperature in Fahrenheit, where the difference between 10 and 20 degrees Fahrenheit is exactly the same as the difference between, say, 50 and 60 degrees Fahrenheit.<\/p>\n<p>Find out more about <a href=\"\/en\/blog\/data-analytics\/what-is-interval-data\/\">interval data in our full guide<\/a>.<\/p>\n<h3 id=\"ratio\">Ratio<\/h3>\n<p>The <strong>ratio scale<\/strong> is exactly the same as the interval scale, with one key difference: The ratio scale has what\u2019s known as a \u201ctrue zero.\u201d<\/p>\n<p><img decoding=\"async\" title=\"A definition of ratio data and how it's analyzed, with examples\" src=\"\/en\/wp-content\/uploads\/old-blog-uploads\/ratio-data.jpg\" alt=\"A definition of ratio data and how it's analyzed, with examples\" \/><\/p>\n<p>A good example of ratio data is weight in kilograms. If something weighs zero kilograms, it truly weighs nothing\u2014compared to temperature (interval data), where a value of zero degrees doesn\u2019t mean there is \u201cno temperature,\u201d it simply means it\u2019s extremely cold!<\/p>\n<p>You\u2019ll find we&#8217;ve made <a href=\"\/en\/blog\/data-analytics\/what-is-ratio-data\/\" target=\"_blank\" rel=\"noopener\">a full guide to ratio data<\/a> if you want to dive deeper.<\/p>\n<h2>4. Another way of thinking about the levels of measurement<\/h2>\n<p>Another way to think about levels of measurement is in terms of <strong>the relationship between the values assigned to a given variable<\/strong>.<\/p>\n<p>With the nominal scale, there&#8217;s no relationship between the values; there&#8217;s no relationship between the categories \u201cblonde hair\u201d and \u201cblack hair\u201d when looking at hair color, for example. The ratio scale, on the other hand, is very telling about the relationship between variable values.<\/p>\n<p>For example, if your variable is \u201cnumber of clients\u201d (which constitutes ratio data), you know that a value of four clients is double the value of two clients. As such, you can get a much more accurate and precise understanding of the relationship between the values in mathematical terms.<\/p>\n<p>In that sense, <strong>there&#8217;s an implied hierarchy to the four levels of measurement<\/strong>. Analysis of nominal and ordinal data tends to be less sensitive, while interval and ratio scales lend themselves to more complex statistical analysis. With that in mind, it\u2019s generally preferable to work with interval and ratio data.<\/p>\n<h2 id=\"faq\">5. Levels of measurement: FAQ<\/h2>\n<h3>What are the 4 levels of measurement?<\/h3>\n<p>The 4 levels of measurement, also known as measurement scales, are nominal, ordinal, interval, and ratio. These levels are used to categorize and describe data based on their characteristics and properties.<\/p>\n<h3>What is level of measurement in statistics?<\/h3>\n<p>Level of measurement, also known as scale of measurement, refers to the process of categorizing data based on the characteristics and properties of the data. It&#8217;s important in statistics because it helps determine the appropriate statistical methods and tests that can be used to analyze the data.<\/p>\n<h3>Is age an interval or ratio?<\/h3>\n<p>Age is typically considered to be measured on a ratio scale. This is because age has a true zero point, which means that a value of zero represents the absence of age. In addition, it&#8217;s possible to perform mathematical operations such as addition, subtraction, multiplication, and division on age values.<\/p>\n<h3>Is gender nominal or ordinal?<\/h3>\n<p>Gender is typically considered to be measured on a nominal scale. This is because gender is a categorical variable that has no inherent order or ranking. It&#8217;s not possible to perform mathematical operations on gender values.<\/p>\n<h2 id=\"key-takeaways\">5. Key takeaways<\/h2>\n<p>So there you have it: the four levels of data measurement and how they\u2019re analyzed. In this article, we\u2019ve learned the difference between the various levels of measurement, and introduced some of the different descriptive statistics and analyses that can be applied to each.<\/p>\n<p>If you&#8217;re looking to pursue a career in <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/difference-between-data-scientist-and-data-analyst\/\">data analytics<\/a>, or even just dabbling in statistics, this fundamental knowledge of the types of measurement will stand you in good stead.<\/p>\n<p>If you enjoyed learning about the different levels of measurement, why not get a <strong><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/short-courses\/become-a-data-analyst\/\">hands-on introduction to data analytics with this free, 5-day short course<\/a>?<\/strong><\/p>\n<p>At the same time, keep building on your knowledge with these guides:<\/p>\n<ul>\n<li><strong><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/inferential-vs-descriptive-statistics\/\">What\u2019s the difference between descriptive and inferential statistics?<\/a><\/strong><\/li>\n<li><strong><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/multivariate-analysis\/\">An introduction to multivariate analysis<\/a><\/strong><\/li>\n<li><strong><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-data-visualization\/\">What is data visualization and why is it important?<\/a><\/strong><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>When working with data, it&#8217;s important to understand the four levels of measurement: Nominal, ordinal, interval, and ratio. Learn all about them (with examples) in this guide.<\/p>\n","protected":false},"author":5,"featured_media":1286,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"yes","_lmt_disable":"","footnotes":""},"categories":[3],"tags":[],"class_list":["post-3785","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analytics"],"acf":{"homepage_category_featured":false},"modified_by":"Matthew Deery","_links":{"self":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3785","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/comments?post=3785"}],"version-history":[{"count":8,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3785\/revisions"}],"predecessor-version":[{"id":30399,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3785\/revisions\/30399"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media\/1286"}],"wp:attachment":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media?parent=3785"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/categories?post=3785"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/tags?post=3785"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}