{"id":57415,"date":"2018-05-21T23:17:45","date_gmt":"2018-05-22T06:17:45","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=57415"},"modified":"2024-08-13T20:59:54","modified_gmt":"2024-08-14T03:59:54","slug":"nominal-ordinal-interval-ratio","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/nominal-ordinal-interval-ratio\/","title":{"rendered":"Levels of Measurement: Nominal, Ordinal, Interval &#038; Ratio"},"content":{"rendered":"\n<p>The nominal, ordinal, interval &amp; ratio levels of measurement are scales that allow us to measure and classify gathered data in well-defined variables to be used for different purposes.<span class=\"Apple-converted-space\">&nbsp;<\/span><\/p>\n\n\n\n<p>Mainly used for these four scales are:<\/p>\n\n\n\n<ul>\n<li><strong>Nominal<\/strong>: Used to categorize data into mutually exclusive categories or groups.<\/li>\n\n\n\n<li><strong>Ordinal<\/strong>: Used to measure variables in a natural order, such as rating or ranking. They provide meaningful insights into attitudes, preferences, and behaviors by understanding the order of responses.<\/li>\n\n\n\n<li><strong>Interval<\/strong>: Used to measure variables with equal intervals between values. Temperature and time often make use of this type of measurement, enabling precise comparisons and calculations.<\/li>\n\n\n\n<li><strong>Ratio<\/strong>: Allows for comparisons and computations such as ratios, percentages, and averages. Great for research in fields like science, engineering, and finance, where you need to use ratios, percentages, and averages to understand the data.<\/li>\n<\/ul>\n\n\n\n<p>Below, we\u2019ll discuss everything you need to know about these measurement levels, characteristics, examples, and how to use them.<\/p>\n\n\n\n\n\n<h2 class=\"wp-block-heading\">Levels of Measurement in Statistics<\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">To perform statistical <a href=\"https:\/\/www.questionpro.com\/blog\/what-is-data-analysis\/\">data analysis<\/a>, it is important first to understand variables and what should be measured using them. <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">There are different levels of measurement in statistics, and data measured using them can be broadly classified into qualitative and quantitative data. Let&#8217;s discuss the Nominal, Ordinal, Interval, and ratio scales.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">First, let\u2019s understand what a variable is. You can measure a variable, which is a quantity that changes across the population. For instance, consider a sample of employed individuals. <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">The variables for this set of the population can be industry, location, gender, age, skills, job type, <a href=\"https:\/\/www.questionpro.com\/blog\/paid-time-off-pto-what-it-is-pros-cons\/#:~:text=QuestionPro%20Products%20Workforce-,Paid%20Time%20Off%20(PTO)%3A%20What%20it%20is%2C%20Pros%20%26,a%20certain%20amount%20of%20time.\">paid time off<\/a>, etc. The value of the variables will differ with each <a href=\"https:\/\/www.questionpro.com\/blog\/employee-spotlight\/\">employee spotlight<\/a>.&nbsp;<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">For example, it is practically impossible to calculate the average hourly rate of a worker in the US. So, a sample audience is randomly selected to represent the larger population appropriately. <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Then, we calculate the average hourly rate of this sample audience. Using statistical tests, you can conclude the average hourly rate of a larger population.<\/span> In statistical analysis, distinguishing between <a href=\"https:\/\/www.questionpro.com\/blog\/categorical-data-vs-numerical-data\/\">categorical data and numerical data<\/a> is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.<\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">A variable&#8217;s measurement level decides the statistical test type to be used. The mathematical nature of a variable, or in other words, how a variable is measured, is considered the level of measurement.<\/span><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What are Nominal, Ordinal, Interval, and ratio?<\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">Nominal, Ordinal, Interval, and ratio are defined as the four fundamental measurement scales used to capture data in the form of <a href=\"https:\/\/www.questionpro.com\/blog\/surveys\/\">surveys<\/a> and <a href=\"https:\/\/www.questionpro.com\/blog\/what-is-a-questionnaire\/\">questionnaires<\/a>, each being a <a href=\"https:\/\/www.questionpro.com\/article\/multiple-choice-questions.html\">multiple-choice question<\/a>.&nbsp;<\/span><\/p>\n\n\n\n<p>Each scale is an incremental level of measurement, meaning each scale fulfills the function of the previous scale, and all survey question scales, such as <a href=\"https:\/\/www.questionpro.com\/article\/likert-scale-survey-questions.html\">Likert<\/a>, <a href=\"https:\/\/www.questionpro.com\/semantic-differential-scale.html\">Semantic Differential<\/a>, <a href=\"https:\/\/www.questionpro.com\/blog\/what-is-a-dichotomous-question\/\">Dichotomous<\/a>, etc, are the derivation of these four fundamental levels of variable measurement.<\/p>\n\n\n\n<p>Before we discuss all four levels of <a href=\"https:\/\/www.questionpro.com\/blog\/5-and-7-point-measurement-scales\/\">measurement scales<\/a> in detail, with examples, let&#8217;s briefly look at what these scales represent.<\/p>\n\n\n\n<p>A nominal scale is a naming scale where variables are simply &#8220;named&#8221; or labeled with no specific order. The ordinal scale has all its variables in a specific order, beyond just naming them. Interval scale offers labels, order, as well as a specific interval between each of its variable options.<\/p>\n\n\n\n<p>The ratio scale bears all the characteristics of an interval scale. In addition to that, it can also accommodate the value of &#8220;zero&#8221; on any of its variables.<\/p>\n\n\n\n<p>Here&#8217;s more of the four levels of measurement in research and statistics: Nominal, Ordinal, Interval, Ratio.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"992\" height=\"925\" src=\"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2018\/05\/Types-of-measurements-scales.jpg\" alt=\"Types of measurements scales innfographic\" class=\"wp-image-118268\"\/><\/figure><\/div>\n\n\n<p class=\"has-text-align-center\"><strong>LEARN ABOUT:<\/strong> <a href=\"https:\/\/www.questionpro.com\/blog\/graphic-rating-scale\/\">Graphic Rating Scale<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Nominal Scale: 1<sup>st<\/sup> Level of Measurement<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.questionpro.com\/blog\/nominal-scale\/\"><span style=\"font-weight: 400;\">Nominal Scale,<\/span><\/a><span style=\"font-weight: 400;\"> also called the categorical variable scale, is defined as a scale that labels variables into distinct classifications and doesn\u2019t involve a quantitative value or order. This scale is the simplest of the four variable measurement scales. Calculations done on these variables will be futile as the options have no numerical value.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">There are cases where this scale is used for the purpose of classification &#8211; the numbers associated with variables of this scale are only tags for categorization or division. Calculations done on these numbers will be futile as they have no <a href=\"https:\/\/www.questionpro.com\/blog\/quantitative-research\/\">quantitative research<\/a> significance.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">For a question such as: <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Where do you live?<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">1- Suburbs<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">2- City<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">3- Town<\/span><\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">A nominal scale is often used in research surveys and questionnaires where only variable labels hold significance. <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">For instance, a customer survey asking &#8220;Which brand of smartphones do you prefer?&#8221; Options<\/span>: &#8220;Apple&#8221;- 1<span style=\"font-weight: 400;\">, &#8220;Samsung&#8221;-2, &#8220;OnePlus&#8221;-3.<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">In this <\/span><a href=\"https:\/\/www.questionpro.com\/article\/survey-question-answer-type.html\"><span style=\"font-weight: 400;\">survey question<\/span><\/a><span style=\"font-weight: 400;\">, only the names of the brands are significant for the researcher conducting consumer research or <a href=\"https:\/\/www.questionpro.com\/blog\/netnography\/\">netnography<\/a>. There is no need for any specific order for these brands. However, while capturing nominal data, researchers conduct analysis based on the associated labels.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">In the above example, when a survey respondent selects Apple as their preferred brand, the data entered and associated will be &#8220;1&#8221;. This helped in quantifying and answering the final question &#8211; How many respondents selected Apple, how many selected Samsung, and how many went for OnePlus &#8211; and which one is the highest? <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">This is the fundamental of quantitative research, and the nominal scale is the most fundamental research scale.<\/span><\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Nominal Scale Data and Analysis<\/h4>\n\n\n\n<p><span style=\"font-weight: 400;\">There are two primary ways in which <\/span><a href=\"https:\/\/www.questionpro.com\/blog\/nominal-data\/\"><span style=\"font-weight: 400;\">nominal scale data<\/span><\/a><span style=\"font-weight: 400;\"> can be collected: <\/span><\/p>\n\n\n\n<ol>\n<li><span style=\"font-weight: 400;\">By asking an <\/span><a href=\"https:\/\/www.questionpro.com\/open-ended-questions.html\"><span style=\"font-weight: 400;\">open-ended question<\/span><\/a><span style=\"font-weight: 400;\">, the answers of which can be coded to a respective number of labels decided by the researcher. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">The other alternative to collect nominal data is to include a <\/span><a href=\"https:\/\/www.questionpro.com\/multiple-choice-questions.html\"><span style=\"font-weight: 400;\">multiple-choice question<\/span><\/a><span style=\"font-weight: 400;\"> in which the answers will be labeled. <\/span><\/li>\n<\/ol>\n\n\n\n<p><span style=\"font-weight: 400;\">In both cases, the analysis of gathered data will happen using percentages or mode,i.e., the most common answer received for the question. It is possible for a single question to have more than one mode,<\/span> as it is possible for two common favorites to<span style=\"font-weight: 400;\"> exist in a target population. &nbsp;<\/span><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Nominal Scale Examples<\/h4>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">Gender<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Political preferences <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Place of residence<\/span><\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><span style=\"font-weight: 400;\">What is your Gender?<\/span><\/td><td><span style=\"font-weight: 400;\">What is your Political preference?<\/span><\/td><td><span style=\"font-weight: 400;\">Where do you live?<\/span><\/td><\/tr><tr><td>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">M- Male<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">F- Female <\/span><\/li>\n<\/ul>\n<\/td><td>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">1- Independent<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">2- Democrat<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">3- Republican<\/span><\/li>\n<\/ul>\n<\/td><td>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">1- Suburbs<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">2- City <\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">3- Town <\/span><\/li>\n<\/ul>\n<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"has-text-align-center\"><strong>LEARN ABOUT:<\/strong> <a href=\"https:\/\/www.questionpro.com\/blog\/average-order-value\/\">Average Order Value<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Nominal Scale SPSS<\/h4>\n\n\n\n<p><span style=\"font-weight: 400;\">In SPSS, you can specify the level of measurement as scale (numeric data on an interval or ratio scale), ordinal, or nominal. Nominal and ordinal data can be either string alphanumeric or numeric.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Upon importing the data for any variable into the SPSS input file, it takes it as a scale variable by default since the data essentially contains numeric values. It is important to change it to either nominal or ordinal or keep it as scale depending on the variable the data represents.<\/span><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ordinal Scale: 2<sup>nd<\/sup> Level of Measurement<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.questionpro.com\/blog\/ordinal-scale\/\"><span style=\"font-weight: 400;\">Ordinal Scale<\/span><\/a><span style=\"font-weight: 400;\"> is defined as a variable measurement scale used to simply depict the order of variables and not the difference between each variable<\/span>. These scales generally depict non-mathematical ideas such as frequency, satisfaction, happiness, a degree of pain, etc. It is quite straightforward to remember the implementation of this scale as \u2018Ordinal\u2019 sounds similar to \u2018Order,\u2019<span style=\"font-weight: 400;\"> which is exactly the purpose of this scale.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Ordinal Scale maintains descriptional qualities along with an intrinsic order but is void of an origin of scale,<\/span> and thus, the distance between variables can\u2019t be calculated. Descriptional qualities indicate tagging properties similar to the nominal scale, in addition to which the ordinal scale also has a relative position of variables. This scale&#8217;s origin is absent, so there is no fixed start or \u201ctrue zero.\u201d<span style=\"font-weight: 400;\"><br><\/span><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Ordinal Data and Analysis<span style=\"font-weight: 400;\">&nbsp;&nbsp;<\/span><\/h4>\n\n\n\n<p><a href=\"https:\/\/www.questionpro.com\/blog\/ordinal-data\/\"><span style=\"font-weight: 400;\">Ordinal scale data<\/span><\/a><span style=\"font-weight: 400;\"> can be presented in tabular or graphical formats for a researcher to conduct a convenient analysis of collected data. Also, methods such as the <\/span>Mann-Whitney U test and the Kruskal\u2013Wallis<span style=\"font-weight: 400;\"> H test can also be used to analyze ordinal data. These methods are generally implemented to compare two or more ordinal groups. <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">In the Mann-Whitney U test, researchers can conclude which variable of one group is bigger or smaller than another variable of a randomly selected group. In the Kruskal\u2013Wallis H test, researchers can analyze whether two or more ordinal groups have the same median or not.<\/span><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Ordinal Scale Examples<\/h4>\n\n\n\n<p><span style=\"font-weight: 400;\">Status at the workplace, tournament team rankings, order of product quality, and order of agreement or satisfaction are some of the most common examples of the ordinal Scale. These scales are generally used in <a href=\"https:\/\/www.questionpro.com\/blog\/what-is-market-research\/\">market research<\/a> to gather and evaluate relative feedback about product satisfaction, changing perceptions with product upgrades, etc. <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">For example, a semantic differential scale question such as: <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">How satisfied are you with our services?<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">Very Unsatisfied &#8211; 1<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Unsatisfied &#8211; 2<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Neutral &#8211; 3 <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Satisfied &#8211; 4<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Very Satisfied &#8211; 5<\/span><\/li>\n<\/ul>\n\n\n\n<ol>\n<li><span style=\"font-weight: 400;\">Here, the order of variables is of prime importance,<\/span> and so is the labeling. Very unsatisfied will always be worse than unsatisfied,<span style=\"font-weight: 400;\"> and satisfied will be worse than very satisfied. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">This is where the <\/span>ordinal scale is a step above the nominal scale &#8211; the order is relevant to the results,<span style=\"font-weight: 400;\"> and so is their naming. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Analyzing results based on the order along with the name becomes a convenient process for the researcher. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">If they intend to obtain more information than what they would collect using a nominal scale, they can use the ordinal scale. <\/span><\/li>\n<\/ol>\n\n\n\n<p><span style=\"font-weight: 400;\">This scale not only assigns values to the variables but also measures the rank or order of the variables, such as: <\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">Grades<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Satisfaction <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Happiness <\/span><\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">How satisfied are you with our services?<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">1- Very Unsatisfied <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">2- Unsatisfied <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">3- Neural <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">4- Satisfied <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">5- Very Satisfied<\/span><\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center\"><strong>LEARN ABOUT:<\/strong> <a href=\"https:\/\/www.questionpro.com\/blog\/nominal-vs-ordinal-scale\/\">Nominal vs. Ordinal Scale<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Interval Scale:&nbsp;3<sup>rd<\/sup> Level of Measurement<\/h3>\n\n\n\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.questionpro.com\/blog\/interval-scale\/\">Interval Scale<\/a> is defined as a numerical scale where the variables&#8217; order is known<\/span> and the difference between these variables. Variables that have familiar, constant, and computable differences are classified using the Interval scale. It is easy to remember the primary role of this scale, too, \u2018Interval\u2019 indicates \u2018distance between two entities,\u2019 which is what the Interval scale helps achieve<span style=\"font-weight: 400;\">. &nbsp; <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">These scales are effective as they open doors for the <a href=\"https:\/\/www.questionpro.com\/blog\/statistical-analysis-methods\/\">statistical analysis<\/a> of provided data. Mean, median, or mode can be used to calculate the central tendency in this scale. The only drawback of this scale is that there is no pre-decided starting point or a true zero value.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">The interval<\/span> scale contains all the properties of the ordinal scale and<span style=\"font-weight: 400;\"> offers a calculation of the difference between variables. The main characteristic of this scale is the equidistant difference between objects. &nbsp;<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">For instance, consider a Celsius\/Fahrenheit temperature scale &#8211; <\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">Eighty<\/span> degrees is always higher than 50 degrees,<span style=\"font-weight: 400;\"> and the difference between these two temperatures is the same as the difference between 70 degrees and 40 degrees. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Also, the value of 0 is arbitrary because negative temperature values exist &#8211; which makes the Celsius\/Fahrenheit temperature scale a classic example of an interval scale. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Interval scale is often chosen in research cases where the difference between variables is a mandate &#8211; which can\u2019t be achieved using a nominal or ordinal scale. The Interval scale quantifies the difference between two variables, whereas the other two scales can solely associate <a href=\"https:\/\/www.questionpro.com\/blog\/qualitative-observation\/\">qualitative observation<\/a><\/span> <span style=\"font-weight: 400;\">values with variables. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Unlike the previous two scales, an ordinal scale&#8217;s mean and median values can be evaluated. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">In statistics, interval scale is frequently used as a numerical value that can not only be assigned to variables<\/span> but calculations based on<span style=\"font-weight: 400;\"> those values can also be carried out. <\/span><\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">Even if interval scales are amazing, they do not calculate the \u201ctrue zero\u201d value, which is why the next scale comes into the picture.<\/span><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Interval Data and Analysis<\/h4>\n\n\n\n<p><span style=\"font-weight: 400;\">All the techniques applicable to nominal and ordinal data analysis are applicable to <a href=\"https:\/\/www.questionpro.com\/blog\/interval-data\/\">Interval Data<\/a> as well. Apart from those techniques, there are a few analysis methods,<\/span> such as descriptive statistics correlation regression analysis, which is extensively used <span style=\"font-weight: 400;\">for analyzing interval data.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.questionpro.com\/blog\/descriptive-analysis\/\">Descriptive analysis<\/a> statistics is the term given to the analysis of numerical data. It<\/span> helps to describe, depict, or summarize data in a meaningful manner, and it helps in the <span style=\"font-weight: 400;\">calculation of mean, median, and mode.<\/span><\/p>\n\n\n\n<p class=\"has-text-align-center\"><strong>LEARN MORE: <\/strong><a href=\"https:\/\/www.questionpro.com\/blog\/descriptive-research-vs-correlational-research\/\">Descriptive Research vs Correlational Research<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Interval Scale Examples<\/h4>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">There are situations where attitude scales are considered to be interval scales.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Apart from the temperature scale, time is also a very common example of an interval scale, as the values are already established, constant, and measurable.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Calendar years and times also fall under this category of measurement scales.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Likert scale, <\/span><a href=\"https:\/\/www.questionpro.com\/features\/net-promoter-score.html\"><span style=\"font-weight: 400;\">Net Promoter Score<\/span><\/a><span style=\"font-weight: 400;\">, Semantic Differential Scale, <a href=\"https:\/\/www.questionpro.com\/help\/bipolar-likert-scale.html\">Bipolar Matrix Table<\/a>, etc., are the most-used interval scale examples. <\/span><\/li>\n<\/ul>\n\n\n\n<p>The following questions fall under the Interval Scale category:<\/p>\n\n\n\n<ul>\n<li>What is your family income?<\/li>\n\n\n\n<li>What is the temperature in your city?<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Ratio Scale: 4<sup>th<\/sup> Level of Measurement<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.questionpro.com\/blog\/ratio-scale\/\"><span style=\"font-weight: 400;\">Ratio Scale<\/span><\/a><span style=\"font-weight: 400;\"> is defined as a variable measurement scale that not only produces the order of variables but also makes the difference between variables known,<\/span> along with information on the value of true zero. It is calculated by assuming that the variables have an option for zero, the difference between the two variables is the same,<span style=\"font-weight: 400;\"> and there is a specific order between the options.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">With the option of true zero, varied <a href=\"https:\/\/www.questionpro.com\/blog\/inferential-statistics\/\">inferential statistics<\/a> and descriptive analysis techniques can be applied to the variables. In addition to the fact that the ratio scale does everything that a nominal, ordinal, and interval scale can do, it can also establish the value of absolute zero. The best examples of ratio scales are weight and height. In market research, a ratio scale is used to calculate market share, annual sales, the price of an upcoming product, the number of consumers, etc. <\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">The ratio scale provides the most detailed information as researchers and statisticians can calculate the central tendency using statistical techniques such as mean, median, and <\/span>mode, and methods such as geometric mean, <span style=\"font-weight: 400;\">coefficient of variation, or harmonic mean can also be used on this scale.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">The ratio<\/span> scale accommodates the characteristics of three other variable measurement scales, i.e.,<span style=\"font-weight: 400;\"> labeling the variables, the significance of the order of variables, and a calculable difference between variables (which are usually equidistant).<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Because of the existence of a true zero value, the ratio scale doesn\u2019t have negative values. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">To decide when to use a ratio scale, the researcher must observe whether the variables have all the characteristics of an interval scale along with the presence of the absolute zero value. <\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">The rati<\/span>o scale can calculate the mean, mode, and median<span style=\"font-weight: 400;\">.<\/span><\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Ratio Data and Analysis<\/h4>\n\n\n\n<p><span style=\"font-weight: 400;\">At a fundamental level, <a href=\"https:\/\/www.questionpro.com\/blog\/ratio-data\/\">Ratio scale data<\/a> is quantitative in nature,<\/span> due to which all quantitative analysis techniques, such as SWOT, TURF, Cross-tabulation, Conjoint, etc., can be used to calculate ratio data. While some techniques, such as SWOT and TURF, will analyze ratio data in such a<span style=\"font-weight: 400;\"> manner that researchers can create roadmaps of how to improve products or services and <a href=\"https:\/\/www.questionpro.com\/cross-tabulation.html\">Cross-tabulation<\/a> will be useful in understanding whether new features will be helpful to the target market or not.<\/span><\/p>\n\n\n\n<p class=\"has-text-align-center\"><strong>LEARN ABOUT:<\/strong> <a href=\"https:\/\/www.questionpro.com\/blog\/level-of-analysis\/\">Level of Analysis<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Ratio Scale Examples<\/h4>\n\n\n\n<p><span style=\"font-weight: 400;\">The following questions fall under the Ratio Scale category:<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">What is your daughter&#8217;s current height?<\/span>\n<ul>\n<li><span style=\"font-weight: 400;\">Less than 5 feet.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">5 feet 1 inch \u2013 5 feet 5 inches<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">5 feet 6 inches- 6 feet<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">More than 6 feet<\/span><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">What is your weight in kilograms?<\/span>\n<ul>\n<li><span style=\"font-weight: 400;\">Less than 50 kilograms<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">51- 70 kilograms<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">71- 90 kilograms<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">91-110 kilograms<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">More than 110 kilograms<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Nominal, Ordinal, Interval, Ratio: Main Characteristics<\/h3>\n\n\n\n<p>The four data measurement scales &#8211; nominal, ordinal, interval, and ratio &#8211;&nbsp; are quite often discussed in academic teaching. The easy-to-remember chart might help you in your statistics test.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><b>Offers: <\/b><\/td><td><b>Nominal<\/b><\/td><td><b>Ordinal <\/b><\/td><td><b>Interval <\/b><\/td><td><b>Ratio<\/b><\/td><\/tr><tr><td><span style=\"font-weight: 400;\">The sequence of variables is established<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><\/tr><tr><td><span style=\"font-weight: 400;\">Mode<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><\/tr><tr><td><span style=\"font-weight: 400;\">Median<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><\/tr><tr><td><span style=\"font-weight: 400;\">Mean <\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><\/tr><tr><td><span style=\"font-weight: 400;\">Difference between variables can be evaluated <\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><\/tr><tr><td><span style=\"font-weight: 400;\">Addition and Subtraction of variables<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><\/tr><tr><td><span style=\"font-weight: 400;\">Multiplication and Division of variables<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><\/tr><tr><td><span style=\"font-weight: 400;\">Absolute zero <\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">&#8211;<\/span><\/td><td><span style=\"font-weight: 400;\">Yes<\/span><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"has-text-align-center\"><strong>LEARN ABOUT:<\/strong> <a href=\"https:\/\/www.questionpro.com\/blog\/ratio-scale-vs-interval-scale\/\">Interval vs. Ratio Scale<\/a> &amp; <a href=\"https:\/\/www.questionpro.com\/blog\/population-vs-sample\/\">Population vs Sample<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>Understanding the levels of measurement is crucial in research, as it affects the type of analysis that can be performed and the conclusions that can be drawn from the data. By understanding the differences between nominal, ordinal, interval, and ratio data, researchers can make more informed decisions about the appropriate statistical tests to use and how to interpret their results. <\/p>\n\n\n\n<p>Remember that selecting the appropriate level of measurement is a critical step in designing a research study, so take the time to carefully consider the measurement level most appropriate for your research question and data.<\/p>\n\n\n\n<p>QuestionPro offers various types of questions that will allow you to collect data for any variable, as well as powerful data analysis tools and data management platforms to harness the full potential of your studies.<\/p>\n\n\n\n\t<div class=\"banner-section wf-section\" lang=\"\" >\n\t\t<div class=\"right-column-container\">\n\t\t\t<div class=\"bannerbg white\">\n\t\t\t\t<span class=\"h1-2\">Create memorable experiences based on real-time data, insights and advanced analysis.<\/span>\n\t\t\t\t<a href=\"#userliteForm\" data-toggle=\"modal\" class=\"button w-button\">Request Demo<\/a>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\t<div class=\"userlite-modal modal fade\" id=\"userliteForm\" tabindex=\"-1\" role=\"dialog\" style=\"display: none;\">\n\t\t<div class=\"modal-dialog\" role=\"document\">\n\t\t\t<div class=\"modal-content\" role=\"document\">\n\t\t\t\t<div class=\"modal-body\">\n\t\t\t\t\t<div class=\"modal-header\">\n\t\t\t\t\t\t<button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\">\n\t\t\t\t\t\t\t<i class=\"material-icons\">close<\/i>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div class=\"contact-us-form-wrapper contact-box\">\n\t\t\t\t\t\t<div class=\"userlite-form-wrapper\">\n\t\t\t\t\t\t\t<iframe src=\"https:\/\/www.questionpro.com\/userlite-form-blog-en.html?product=Research&amp;referralurl=https:\/\/www.questionpro.com\/blog\/wp-json\/wp\/v2\/posts\/57415&amp;lang=en&amp;cat=market-research|research-tools-and-apps\" style=\"display: block;\" ><\/iframe>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"demo-form-wrapper success-message-div\" style=\"display:none\">\n\t\t\t\t\t\t\t<p class=\"success-message-para\"><\/p>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-1723605365911\"><strong class=\"schema-faq-question\">Q1. What are the 4 levels of measurement?<\/strong> <p class=\"schema-faq-answer\">The four levels of measurement are:<br\/><strong>Nominal Level<\/strong>: This is the most basic level of measurement, where data is categorized without any quantitative value. <br\/><strong>Ordinal Level<\/strong>: In this level, data can be categorized and ranked in a meaningful order, but the intervals between the ranks are not necessarily equal. <br\/> <strong>Interval Level<\/strong>: This level involves numerical data where the intervals between values are meaningful and equal, but there is no true zero point. <br\/><strong>Ratio Level<\/strong>: This is the highest level of measurement, where data can be categorized, ranked, and the intervals are equal, with a true zero point that indicates the absence of the quantity being measured. <\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1723605553411\"><strong class=\"schema-faq-question\">Q2. What are the 4 types of measurement scales?<\/strong> <p class=\"schema-faq-answer\">The four types of measurement scales are:<br\/><strong>Nominal<\/strong>: Categorical data without any order (e.g., gender, colors).<br\/><strong>Ordinal<\/strong>: Categorical data with a meaningful order but no equal intervals (e.g., satisfaction ratings).<br\/><strong>Interval<\/strong>: Numeric data with equal intervals but no true zero (e.g., temperature in Celsius).<br\/><strong>Ratio<\/strong>: Numeric data with equal intervals and a true zero (e.g., weight, height).<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1723606050715\"><strong class=\"schema-faq-question\">Q3. What is an example of a ratio and interval?<\/strong> <p class=\"schema-faq-answer\"><strong>Example of Ratio:<\/strong><br\/><strong>Weight<\/strong>: A weight of 0 kg indicates no weight, and you can compare weights (e.g., 10 kg is twice as heavy as 5 kg).<br\/><strong>Example of Interval:<\/strong><br\/><strong>Temperature (Celsius)<\/strong>: The temperature of 0\u00b0C does not mean there is no temperature; it\u2019s just a point on the scale. The difference between 10\u00b0C and 20\u00b0C is the same as between 20\u00b0C and 30\u00b0C, but you cannot say that 20\u00b0C is twice as hot as 10\u00b0C.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1723606272992\"><strong class=\"schema-faq-question\">Q4. What is an example of nominal and ordinal data?<\/strong> <p class=\"schema-faq-answer\">Here are examples of both nominal and ordinal data:<br\/>Nominal Data:<br\/><strong>Example<\/strong>: Types of fruit (e.g., apples, bananas, oranges)<br\/><strong>Explanation<\/strong>: Nominal data is categorical and does not have a specific order. Each category is distinct and cannot be ranked.<br\/>Ordinal Data:<br\/><strong>Example<\/strong>: Customer satisfaction ratings (e.g., poor, fair, good, excellent)<br\/><strong>Explanation<\/strong>: Ordinal data has a clear order or ranking. The categories indicate a level of satisfaction, but the intervals between the categories are not necessarily equal.<\/p> <\/div> <\/div>\n","protected":false},"excerpt":{"rendered":"<p>The nominal, ordinal, interval &amp; ratio levels of measurement are scales that allow us to measure and classify gathered data [&hellip;]<\/p>\n","protected":false},"author":86,"featured_media":102530,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[203,174],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Levels of Measurement: &quot;Nominal Ordinal Interval Ratio&quot; Scales<\/title>\n<meta name=\"description\" content=\"Nominal, Ordinal, Interval &amp; 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