Understanding the scales of the measurement of their data allows data scientists to determine the appropriate statistical test to perform. There are four primary scales of measurement in research: nominal, ordinal, interval, and ratio. These are also called the levels of measurement.
Scales of the Measurement
Nominal Scale
The nominal scale of measurement establishes the identity and attributes of data. This scale possesses specific attributes but, lacks any quantitative statistical significance. A nominal scale is one of the scales of measurement that categorizes data into distinct and separate groups without any inherent order or numerical value attached to the categories. For example, nominal data can encompass characteristics such as eye color and country of birth.
There are three categories in which nominal data can be further divided:
Nominal with Order
A nominal scale with an order, also referred to as an ordinal scale, is one of the scales of measurement that classifies data into distinct groups while also indicating the relative order or ranking of those groups. Unlike a basic nominal scale that simply categorizes data without any inherent order, a nominal with order scale assigns an order or position to the classes. There are different ways to categorize nominal data, such as using terms like “cold, warm, hot, and very hot.”
Nominal without Order
Variables are categorized into separate groups based on their unique characteristics or traits. Categories do not have a specific order attached to them. Categories are assigned numbers solely for the purpose of identification, not for any kind of ranking. For example, gender (male, female), marital status, religion, race, country, and eye color.
Dichotomous Scale
A dichotomous scale is a survey response scale that offers only two options positioned at opposite ends. Some examples of dichotomous scale questions are “Yes/No”, “True/False”, “Agree/Disagree”, and “Fair/Unfair”. Dichotomous scales are valuable tools for collecting precise and binary data. However, it is important to be aware of the potential for respondent fatigue, which may lead to a bias towards selecting the positive option.
When it comes to rating scales, dichotomous scales may offer greater reliability but may capture a different level of detail than scales with more response options. In education and other research, dichotomous questions are commonly employed to gather precise and unambiguous data.
Ordinal Scale
Data in the ordinal scale is organized in a specific order. It is one of the scales of measurement in which each value is ranked, the information provided does not clarify the distinctions between the categories. These values cannot be added or subtracted. An example of this type of data would involve satisfaction data points in a survey, where the responses are categorized as ‘one’ for happy, ‘two’ for neutral, and ‘three’ for unhappy.
Ordinal scales, such as Likert scales, are widely regarded as the most effective examples. A Likert scale is a rating scale that evaluates opinions, attitudes, or behaviors numerically. It consists of four or more questions that assess a single attitude or trait when the scores of the responses are combined. The Likert scale can be viewed as an interval scale if we assume that the distance between options is equal, even though it is technically an ordinal scale.
Likert Scale
A Likert scale, identified with its inventor, the American social scientist Rensis Likert, is a commonly used psychometric approach to gather opinions and feelings from survey participants. It typically offers a range of 5 or 7 answer options. A Likert scale is a unidimensional scale that researchers utilize to gather respondents’ attitudes and opinions. Various variations of Likert scales are designed to directly measure individuals’ opinions. Typically, multiple choice options for the Likert item include strongly agree, agree, neutral, disagree, and strongly disagree.
Rating Scales
Rating scales are a valuable tool for gathering data from respondents, allowing them to provide feedback on attributes, features, or performance. This type of survey question collects both quantitative and qualitative data, providing a comprehensive understanding of the subject. Rating scales are one of the scales of measurement that offer a variety of response options, including numbers (1-5), descriptors (poor to excellent), or visual scales to assess opinions, attitudes, or performance. Rating scales are commonly represented numerically, such as on a scale of 1-5.
However, it’s important to note that these numbers are simply labels and do not necessarily indicate equal intervals between them. They are frequently utilized in surveys, customer satisfaction assessments, and employee evaluations to collect structured feedback. Some researchers suggest that rating scales with a sufficient number of response options can resemble an interval scale designed in a way that the differences between values are approximately equivalent.
However, there is ongoing debate surrounding this issue and it is not universally accepted. A rating scale is a closed-ended survey question utilized to assess survey respondents’ feelings regarding a specific product or statement. Respondents are usually given a variety of options to choose from, ranging from Excellent to Terrible. For example, when you visit an online shopping site, you’ll come across a rating scale question that prompts you to rate your shopping experience.
Interval Scale
The interval scale is a quantitative measurement scale that incorporates order, meaningful and equal differences between variables, and an arbitrary zero point instead of a true zero. It quantifies variables that are measured consistently and at regular intervals. When someone is asked to rate their meal on a scale of one to ten, an interval scale is used. Other examples of interval scales include thermometers, temperature measurements (°C, °F), IQ scores, and calendar dates.
Ratio Scale
The ratio scale is a measurement scale that allows for the order of variables. Ratio scale is one of the scales of measurement that encompass characteristics from all four scales of measurement. The data is organized and categorized based on specific characteristics, allowing for easy classification and analysis.
Ratio scales also have a distinct characteristic that sets them apart from interval scales: the presence of a ‘true zero.’ Weight, height, and distance are all examples of variables ratio scale that can be measured and compared. How old are you? Or, may I ask about the number of children you have? Please indicate on a scale from zero to seven.
Conclusion
Nominal: the data can only be categorized. Ordinal: data can be classified and ranked. Interval: the data can be organized, ordered, and evenly distributed. Ratio: the data can be classified, ranked, evenly distributed, and has a natural zero, which allows for a comprehensive analysis.
