Ratio Scale of Measurement: Definition, Characteristics & Examples

What Is Ratio Scale: Definition

Ratio scale of measurement is a measurement level that has absolute zero and can compare ratios of values. This creates conditions not only to compare values but also to calculate ratios, for example, height, weight, and distance. The difference between ratio scale and other levels of measurement is that here values can be added, subtracted, multiplied, and divided. As for the results, they have a clear and meaningful interpretation. 

Where do we encounter ratio scale of measurement in real life? Let’s start with simple examples of ratio variables that can be measured using this scale. For example, weight.

Ratio Level of Measurement Example

A person's weight is a variable with which ratio scale can be used. There is a true zero point which means no weight at all, and weight can be measured in kilograms or pounds. For example, a person weighing 100 pounds will be twice as heavy as a person weighing 50. That is, this allows researchers to calculate the ratio between weights and make comparisons.

Common Uses of Ratio Scales

So, when can you apply this method? Scientists use it in many industries for measurement and statistical analysis. Here are some popular uses of ratio scale:

• Physical measurements: height, weight, length, and temperature.
• Scientific research: allows you to evaluate and analyze data that relates to experiments, observations, and survey questions.
• Business and finance: allows you to evaluate financial data, and calculate ratios that will allow you to make the right and informed decisions.
• Quality control: enables you to evaluate quality and analyze the product.

Ratio Scale Characteristics

Ratio scale is universal. As noted earlier, it is most suitable for measuring most quality indicators.

Here are the key characteristics of ratio level of measurement that you should know:

• True zero point Analyzed data has a true zero point, which means that a zero on scale represents complete absence of a measured attribute.
• Equal intervals Intervals between values on a scale are equal. This means that the difference between any two values is completely the same.
• Continuous data Analyzed data is continuous, which means that there are an infinite number of possible values between any two points on the scale.
• Mathematical operations Mathematical operations such as addition, subtraction, multiplication, and division can be performed on analyzed data.
• Meaningful rations Since analyzed data has a true zero point, meaningful ratios can be formed between values on the scale. For example, if one value is twice as large as another, this means that the first value is twice as large as an attribute being measured.

Understanding a True Zero on a Ratio Scale

Understanding how  true zero on a ratio scale works is crucial in statistical analysis. The presence of a true zero allows researchers to gain meaningful comparisons between ratio scale units. Ratios can be calculated between measurements that create meaningful comparisons of the magnitude of a measured attribute. 

Example

For example, we can go back to the above ratio scale example of a person's weight, where one weighs 100 pounds and another weighs 50, which allows you to establish that the second object weighs half as much as the first.

There are also other types of scales, and this can be best seen using temperature scale as an example. 

Example

When it comes to ratios of temperatures, an indicator of zero does not mean the absence of temperature but only the absence of heat and the possibility of a negative temperature. This allows us to make meaningful comparisons between measurements using ratios.

For this reason, understanding true zero comes first for accurate and meaningful statistical analysis.

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Ratio Scale Examples

So, for a better understanding of ratio scale, it is worth considering examples of ratio level of measurement. This will give the best understanding of how it can be used and for what. Understanding this concept enables scientists to use ratio scale in practice.

Ratio Variable Examples

Discrete Ratio Variables:

• Number of children in a family (accordingly, here it cannot be a fraction or a negative number, but only a quantitative indicator).
• Number of dogs that belong to one owner (as you understand, this cannot be a fractional part or a negative number).
• Number of stores runned by the same owner (cannot be a fraction or a negative number).

Continuous Ratio Variables:

• Human height (here can be any value in the range depending on physiological characteristics of a thin person. For example, 1 meter 83 centimeters).
• Weight of a person (here can be any value in the range depending on physiological characteristics of a thin person. For example, 55.5 kg).
• Time spent on the task (can take any value in range, for example, 14.5 minutes).

Examples of Ratio Scale:

• Length can be measured using ratios. For example, using a ruler, you can measure the length of a sheet in centimeters.
• Weight is also possible to measure as a ratio. For example, you can determine the weight of apples or flour in kilograms.
• Temperature can also be measured using this indicator when zero would mean no heat but not the absence of temperature at all.
• We measure time in seconds, minutes, hours, and so on. For example, an event that lasts 2 hours is twice as long as an event that lasts 1 hour.
• We measure money in monetary units such as dollars, pounds sterling, euros, etc. A person who has $200 is twice as rich as a person who has $100.

How to Measure Ratio Scale?

So, how can you measureratio scale data? Next, you will find out all necessary data to measure a ratio scale variablefor different purposes. Keep reading to discover various methods.

Measuring Ratio Scale With Descriptive Statistics 

What about ratio scale in statistics? When it comes to measuring a ratio scale with descriptive statistics, there are some tools and methods that can be used to gain an accurate understanding of data. You will learn more about these methods below.

Frequency Distribution

Frequency distribution is a method used to measure the distribution of data in a dataset. It involves organizing data into a table or graph, with each value or range of values being represented.

Example

Let's assume that a teacher grades a math test from 50 to 100. There are 30 people in the class. A teacher wants to get rations to see how many students get a certain mark. A teacher wanted to create a free-currency distribution to show how many students got a specific grade. In this frequency distribution, score range is divided into intervals of 10 points each. The number of students who scored within each interval is recorded.

Measures of Central Tendency 

Measures of central tendency are statistical measures that aim to provide the most popular value for a set of data. There are 3 main measures of central tendency: mean, median, and mode.

• Mean is the most commonly used measure of central tendency. It requires adding all values in a dataset and dividing by the number of values.
• Median is an average value in the dataset when ratios of values are in order.
• Mode is the value that occurs most frequently in the dataset. This is useful for categorical data such as the most common color or product type.

It is important to know that all 3 measures of central tendency have their uses and limitations.

Variability 

Variability is the degree to which data points in a given dataset differ from one another. That is, it measures the degree of variance or spread in a dataset. There are several ways to measure variability:

• Range: the simplest measure of variability, calculated by subtracting the smallest value in the data set from the largest value.
• Variance: a more complex measure that takes into account main differences between each data point and the mean of the dataset.
• Standard deviation: the square root of variance and is often used to describe the spread of a normal distribution.

Variations are used in many areas, including statistics, finance, and quality control. In statistical analysis, this can help identify outliers or unusual data points that may skew results. In finance, it is used to measure the risk that is associated with an investment. It can also be used for quality control to make sure that products or processes are consistent and within acceptable limits.

Standard Deviation 

Standard deviation as a statistical measure is the degree of deviation or dispersion of a set of data values from the mean or average value. It indicates how much individual data points deviate from the mean of the set.

In other words, standard deviation gives an idea of the spread of data around the mean. If standard deviation is low, it means that data points are clustered close to the mean. Whereas a high standard deviation indicates that data points are spread over a wider range ratio of values.

You can calculate the standard deviation by taking the square root of the variance, which is an average of the squared differences of each data point from the mean. Researchers often apply it to different studies, including those on finance research topics or economy research topics and science to analyze ratio data.

Measuring Ratio Scales with Inferential Statistics 

Inferential statistics involves using sample data to draw inferences about a larger population. Inferential statistics are used to test hypotheses and predict and estimate population parameters based on sample data.

When it comes to measuring data of a ratio scaleusing inferential statistics, there are different methods that can be used.

Statistical Tests

Statistical tests are analytical tools that are used to analyze and interpret ratio data. They help determine the significance of the results and whether they are random or not. Statistical tests are used in a variety of fields, including science, medicine, economics, psychology, and social sciences.

There are various types of statistical tests:

• T-tests
• ANOVA
• Regression
• Chi-square
• Correlation
• Mann-Whitney U test
• Kruskal-Wallis H test.

Each test type comes to answer specific research questions and depends on data being analyzed. Interpreting the results of a statistical test requires knowledge of basic statistics and a test type understanding. Tests help provide evidence for validity of a research hypothesis and can be used to make informed decisions based on data analyzed.

Other Ratio Scale Data Analysis Methods

What about other ratio scale data analysis methods? There is a need to highlight some other methods that can be handy in offering a clear picture regarding some research. Let’s talk about SWOT analysis, conjoint analysis, trend analysis, TURF, and cross-tabs.

SWOT Analysis

This is strategic planning method, and its essence is to influence internal and external environment factors and divide them into 4 categories:

• Strengths
• Weaknesses
• Opportunities
• Threats.

It is important to understand that SWOT analysis consists of internal and external analysis. Internal analysis allows you to study the company's activities, products, and internal processes. The external analysis allows you to study the factors that affect the company from the outside. For example, competition in the market, inflation, or crisis. As such, SWOT analysis can be used to enhance both internal and external validity. 

To put it more simply, it is an opportunity to determine what mistakes the business makes, how to correct them, and what risks exist. Often, business analysts use a five-point scale to evaluate factors.

>> Learn more: Internal Validity vs External Validity 

Conjoint Analysis

Conjoint analysis is a way to find the optimal combination between product characteristics by mathematically analyzing the ratings given by buyers for each combination of these characteristics. Often, marketers use this analytical method to develop new products and services. Conjoint analysis can be applied to different types of data (nominal, ordinal, interval). Still, it is most often conducted using ratio scale data, which allows for more precise measurements and comparisons of attribute levels. 

The purpose of this analysis is to determine the optimal combination of new or existing product’s characteristics. Mathematical analysis of preferences determines the hierarchy of values that underlie the choice. In such an analysis, the assumption is made that the properties of the goods must be independent.

Trend Analysis

Trend analysis is a method that uses mathematical models to predict future results based on historical data. This method determines the variance from the baseline in terms of cost, schedule, or scope using data from previous reporting periods and predicting the amount of variance in that parameter at some point in the future.

Ratio scale data is often used in trend analysis, as it allows for precise measurement and comparisons of values over time. For example, financial ratios such as return on investment (ROI) or debt-to-equity ratio are commonly used in trend analysis to identify progressive changes in financial performance.

TURF Analysis

Turf analysis is a statistical model and is most commonly used in marketing and PR. It is a market research method used to determine the optimal combination of products/services to achieve maximum customer coverage.

TURF analysis frequently utilizes ratio scale data to assess the significance of various products or services, and to compute scores for reach and frequency. For instance, in a media campaign TURF analysis, ratio scale data could be utilized to gauge the audience reach and frequency of diverse media channels, including TV, radio, and social media.

Cross-Tabs

This statistical method can be used to summarize and analyze the relationship between two or more categorical variables. It is presented in a table format where the rows represent one variable, and the columns represent another variable. The cells in the table contain the frequency or number of times two variables appear together.

The goal is to identify patterns or ratios between variables. This data analysis allows you to determine whether there is a significant relationship between the variables or whether they are independent of each other. This information can be useful in many areas such as market research, social sciences, and healthcare.

>> Read more: Difference Between Independent and Dependent Variables

Ratio Scale Advantages

Ratio scale has many advantages over other scales. Here are main benefits of using a ratio scale:

• Ability to make meaningful comparisons Ratio scale has a true zero point, which means that measurements can be compared and analyzed in a meaningful way. For example, if you are measuring weight, you can say that one object is twice as heavy as another.
• Ability to get accurate measurements Proportion scales provide a high measurement accuracy. This makes it easier to analyze data and draw conclusions.
• Opportunity to get many statistical analysis insights Ratio scales allow for a wide range of statistical analysis, including mean, standard deviation, and regression analysis. This makes it easier to extract information from the data.
• Applicability and scientific research Ratio scale is the preferred measurement scale in scientific research, especially in fields such as physics, engineering, and biology. This is because it offers accurate and meaningful measurements that can be used to make accurate predictions and conclusions.

Overall, ratio scale is a powerful tool for researchers to make accurate measurements and meaningful comparisons. Its benefits make it a popular choice in scientific research and analysis.

Disadvantages of Ratio Scale of Measurement 

While this type of scale offers many advantages, there are also some notable disadvantages of this method:

• Limited applicability The measurement scale is not suitable for all types of data, as it requires a fixed zero point.
• Complexity Ratio scale is more complex. This means that more time and effort are required to collect data and make analysis.
• Limited range The rational measurement scale has a limited range because it cannot measure values less than zero. This means that it is not suitable for measuring negative values such as debt or loss.
• Misinterpretation Ratio scale can be misinterpreted if zero point is not clearly understood.

Bottom Line on Ratio Level of Measurement 

So, ratio level understanding gives you many opportunities to get important data. The ratio level measurement can be used in many different areas. Need an example of a ratio scale? It is applicable in physics, engineering, and finance, where accurate measurements are important. It is also widely used in market research, where data is collected on things like income, age, and time spent on a particular activity. In addition, it can be used in psychology.

Thus, the ratio measurement level is the highest level of measurement in statistics, and it allows precise comparisons and formulations of ratios.