Levels of Measurement: Nominal, Ordinal, Interval and Ratio

Examples of ratio scales include concentration, length, weight, duration, and more. Because there’s a zero position, it opens up the doors for inferential and descriptive analysis techniques. Use ratio scales to understand the size of the market, market share, revenue, pricing, etc. To recap, nominal scales only take into consideration the label of the options while ignoring order. Ordinal scales take the label of the options into consideration as well as the order of those options.

It is easy to remember the primary role of this scale too, ‘Interval’ indicates ‘distance between two entities’, which is what Interval scale helps in achieving. 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. While in the Kruskal–Wallis H test, researchers can analyze whether two or more ordinal groups have the same median or not. 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 as it is possible for two common favorites can exist in a target population.

Following Coefficient alpha provides a solution to this problem. Measurement of Sea Level is another example of an interval scale. With each of these scales there is direct, measurable quantity with equality of units. In addition, zero does not represent the absolute lowest value. Rather, it is point on the scale with numbers both above and below it (for example, -10 degrees Fahrenheit).

Internal consistency of a nominal scale meaning-item measure can be measured by correlating scores on subsets of items making up a scale. Using multiple variables to measure one concept can often provide a more complete account of some concept than could any single variable. Even in the physical sciences, multiple measurements are often used to make sure an accurate representation is obtained.

The mean uses all values to give you a single number for the central tendency of your data. The median is the value exactly in the middle of your data set. To find the middle position, take the value at position (n+1)/2 where n is the total number of values.

The Interval scale quantifies the difference between two variables whereas the other two scales are solely capable of associating qualitative values with variables. A variable’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. In nominal scale a variable is divided into two or more categories, for example, agree/disagree, yes or no etc. It’s is a measurement mechanism in which answer to a particular question can fall into either category.

Characteristics of interval scale

If they intend to obtain more information than what they would collect using a nominal scale, they can use the ordinal scale. This is where ordinal scale is a step above nominal scale – the order is relevant to the results and so is their naming. Status at 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 market research to gather and evaluate relative feedback about product satisfaction, changing perceptions with product upgrades, etc. Can be presented in tabular or graphical formats for a researcher to conduct a convenient analysis of collected data. Also, methods such as Mann-Whitney U test and Kruskal–Wallis H test can also be used to analyze ordinal data.

A deviation from the mean is the difference between a value in your data set and the mean. The range, standard deviation and variance describe how spread your data is. The range is the easiest to compute; the standard deviation and variance are more complicated, but also more informative.

Learn with CareerFoundry

Range, standard deviation, and variance are all measures of variability within your dataset. You can calculate the range by subtracting the lowest value in your dataset from the highest. Standard deviation calculates, on average, how much each individual score deviates from the mean, allowing you to gauge how your data are distributed. The mode, median, and mean are all measures of central tendency. The mode is the most frequently occurring value; the median is the middle value , and the mean is an average of all values. So, to calculate the mean, add all values together and then divide by the total number of values.

People indicate which of the statements with which they agree and the average response is computed. First, you must be very clear about exactly what it is you’re trying to measure. Then, collect statements on the topic ranging from attitudes that are favorable to unfavorable.

How Much Can You Earn on a Data Architect Salary?

Let’s look at an example where the difference between values isn’t quantifiable. Introduction to Statistics is our premier online video course that teaches you all of the topics covered in introductory statistics. Statology is a site that makes learning statistics easy by explaining topics in simple and straightforward ways. Open-ended question, the answers of which can be coded to a respective number of label decided by the researcher.

Nominal scales can, to an extent, overlap with ordinal scales because a few of them have order. For example, very short, short, tall, very tall could be considered a nominal scale with an order. In the nominal scale examples above, only the names of options hold any significance to the researcher. For the survey question presented, it wouldn’t matter if Samsung, Apple, or Nokia were first or last on the scale.

Remember that the assessment of reliability and validity is an ongoing process. For example, consider an experimenter taking a reading of the time period of a pendulum’s full swing. If their stopwatch or timer starts with 1 second on the clock, then all of their results will be off by 1 second. Types of systematic errors include personal errors and instrumental errors.

• The Chi-square test of independence is used to explore the relationship between two nominal variables.
• In our example, there is no mode because each value only appears once.
• When the scale is used for identification purpose, then there is a strict one-to-one correspondence between the object and numerical value assigned to each object.
• The range is the easiest to compute; the standard deviation and variance are more complicated, but also more informative.

In that sense, there is an implied hierarchy to the four levels of measurement. 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’s generally preferable to work with interval and ratio data. You can only find mode with nominal scales, you can find median with ordinal scales, interval scales lend themselves to mean, mode, and median. Ratio scales can use all of that plus other methods such as geometric mean and coefficient of variation.

A true zero means there is an absence of the variable of interest. In ratio scales, zero does mean an absolute lack of the variable. 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. Even if interval scales are amazing, they do not calculate the “true zero” value which is why the next scale comes into the picture. Interval scale contains all the properties of the ordinal scale, in addition to which, it offers a calculation of the difference between variables.

Below easy-to-remember chart might help you in your statistics test. Also known as relative standard deviation , the coefficient of variation is a standardized measure of dispersion. It can only be used with ratio levels of measurement because it is a fraction. The coefficient of variation is the standard deviation divided by the mean.

BROWSE BY CATEGORY

Construct validity exists when a measure reliably measures and truthfully represents a unique concept. Construct validity consists of several components, including; Face validity, Content validity, Criterion validity, Convergent validity, and Discriminant validity. The test-retest method of determining reliability involves administering the same scale or measure to the same respondents at two separate times to test for stability. If the measure is stable over time, the test, administered under the same conditions each time, should obtain similar results. The three major criteria for evaluating measurements are reliability, validity, and sensitivity.

It is to be noted that the nominal scale does not determine the amount of characteristic possessed by the object and is only used for counting purposes. Going from lowest to highest, the 4 levels of measurement are cumulative. This means that they each take on the properties of lower levels and add new properties. Because of the existence of true zero value, the ratio scale doesn’t have negative values. Interval Scale is defined as a numerical scale where the order of the variables is known as well as the difference between these variables. Variables that have familiar, constant, and computable differences are classified using the Interval scale.

You can include numbered or unnumbered variables, but common survey examples include gender, location, political party, pets, and so on. 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. 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. The interval level is a numerical level of measurement which, like the ordinal scale, places variables in order.

• Status at 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.
• Ratio scales are the cream of the crop when it comes to statistical analysis because they have everything you need.
• The extent to which the scores from a measure represent the variable they are intended to.
• Questions that can be measured on the interval scale are the most commonly used question types in research studies.
• We could number them in any order we like either 1 to 5 or 5 to 1 in ascending or descending order.
• Ordinal scales have labels, the order matters, but the value doesn’t.

There is no true zero because temperature can go into the negatives. The most popular of which are graphs that break down the percentage of answers options selected. Additionally, these graphs can show the absolute number of respondents. That’s because, due to the nature of the options presented on the scale, there’s often no way of knowing the degree of difference between them. Even when the difference between options is quantifiable, it doesn’t yield much insight when compared to the order of the values. Pritha has an academic background in English, psychology and cognitive neuroscience.

The problem definition process should suggest the concepts that must be measured. A concept can be thought of as a generalized idea that represents something of meaning. Concepts like age, sex, education, and number of siblings are relatively concrete properties. Concepts such as loyalty, personality, trust, customer satisfaction, and so on are more difficult to both define and measure. For example, loyalty has been measured as a combination of customer share and commitment.

Central Tendency | Understanding the Mean, Median & Mode Measures of central tendency help you find the middle, or average, of a data set. If you have a choice, the ratio level is always preferable because you can analyze data in more ways. The higher the level of measurement, the more precise your data is. Depending on the level of measurement of the variable, what you can do to analyze your data may be limited. There is a hierarchy in the complexity and precision of the level of measurement, from low to high .

Measurement is done for the attributes of the units under study but not the units themselves. The height, weight, age or other such attributes of a person are measured. The nominal scale possesses only the description characteristic .

For example, if your two middle values were “agree” and “strongly agree,” it would not be possible to calculate the mean; so, in this case, you would have no median value. The interval variable has order and the difference between the variables have meaning but the ratio between them doesn’t have meaning. For example, if you increase the temperature from 10 to 20 degrees Celsius, it’s not twice as hot. With a ratio variable scale, the difference between the variables has meaning and the ratio between them does as well.