Rating scale in research

The selection of rating scales in survey questions is very important. It is a tool that allows quantification of respondents' opinions, attitudes, behaviors and preferences. These scales can range from simple binary scales to rating scales to more complex summative scales. A well-designed rating scale can provide valuable data that is both reliable and statistically analyzable. An example of a frequently used scale is the Likert scale.

Why use a rating scale

The main purpose of rating scales is to allow normalization of the responses received, which greatly facilitates statistical analysis and inference. With a rating scale, it is also possible to compare results over time or between different study groups by assigning numerical or categorical values.

Types of rating scales

There are several basic types of rating scales that are used in research. These are:

Nominal scale

When a variable operates within a nominal scale, it adopts labels or values that do not show a natural order derived from the characteristics of the observed phenomenon. Numbers used as values of a nominal variable serve only as symbolic designations and are not suitable for performing mathematical operations on them or comparing them. Examples of attributes that are measured using a nominal scale include names of towns and cities, iris color, gender or "yes" or "no" responses.

Ordinal scale

When we talk about ordinal scale variables, we are referring to values that are ordered or sequential, but without the ability to precisely determine the differences or relationships between these values.

Examples of variables that operate on an ordinal scale include education level or the place occupied by participants in a competition.

In contrast, variables such as gender, age, or temperature are not examples of ordinal variables because they do not meet the criteria of this scale.

Interval scale

A variable is on an interval scale, when it is possible to calculate and interpret the differences between its values in a real-world context, but dividing one value of a variable by another is not a meaningful operation. This means that although the unit of measurement is defined, the zero point is set arbitrarily.

Examples of attributes that are measured using the interval scale are temperature expressed in degrees Celsius and date of birth.

Quotient scale

The quotient scale represents the most precise type of measurement scales. It provides the ability to perform all mathematical operations on measured values, including division and multiplication. Its distinguishing feature is the existence of a natural zero point, below which there are no real values. Variables that can be measured using the quotient scale are, for example, age expressed in years, income in currency, height in centimeters, or temperature in Kelvin.

Unlike less sophisticated scales, the quotient scale allows unlimited use of mathematical operations and statistical methods. However, unlike the absolute scale, it does not imply a natural unit of measurement for the phenomena being measured.

Absolute scale

The absolute scale is the most advanced type of measurement scale, characterized by the fact that the zero point and the unit of measurement are naturally determined by the properties of the phenomenon being measured. Analogous to a quotient scale, for a variable measured on an absolute scale, both the quotient and the difference between two measurements have a meaningful interpretation.

An example of a variable that is measured using an absolute scale is the number of pears.

Each of these types of scales is appropriate for specific types of data and research purposes.

Design an appropriate rating scale

When designing a rating scale, researchers need to define clear criteria and choose the appropriate measurement accuracy so that the results are reliable and useful based on the responses obtained.

Determining the criteria

The first step in designing an evaluation scale is to determine the criteria that will form the basis for the evaluation. It is necessary to decide what characteristics or behaviors will be measured and how they will be categorized. Examples include the nominal scale, which classifies data without specifying their order, and the ordinal scale, which allows data to be ranked according to a specific hierarchy or intensity.

Which rating scale to choose? Choose the right type of scale

Next, researchers must choose a scaling method appropriate to the type and purpose of the study. A popular tool is the Likert scale, used to measure the degree of agreement or disagreement. Another example is the interval scale, which measures differences between responses. Remember that the choice of scale affects the quality of the survey questions collected. It is especially important that it is not an unbalanced scale, that is, one where the number of positive ratings does not equal the number of negative ratings. Using such a scale can make the data unbiased.

Incorrect grading scale

The use of an incorrect rating scale can result in later problems. It can lead to misinterpretation of the results, as respondents may have difficulty determining their opinions accurately. The use of too limited a rating scale will significantly limit the possibilities for statistical analysis , making it difficult to detect subtle differences or trends.

When the scale is not tailored to respondents' cultural context, responses may be unintentionally subject to systematic error. Using a scale that is too complex or unintuitive can increase the risk of errors in the data collection process itself.

Popular rating scales in research

In the research process, rating scales allow standardization of responses, making them easier to analyze and interpret. Three of the commonly used scales are the Likert Scale, the Point Scale and the Percentage Scale, each of which is applicable depending on the specifics of the survey.

Likert Scale

The Likert scale is a methodical tool used to measure attitudes, opinions or perceptions. It is typically constructed from a set of statements to which respondents express the degree of their agreement or disagreement by selecting one of several options, most often 5 or 7. Each option is assigned a numerical value, allowing quantification of responses. Also important here is the use of a middle option expressing undecidedness.

The Likert scale is one of the most widely used scales in sociology and psychology. Developed by Rensis Likert, this scale allows respondents to express their degree of agreement or disagreement with a statement in the form of a symmetrical, usually odd number of options.

An example of a 7-point Likert scale

Do you agree with the following statement.

The trip was very successful.

1. Strongly disagree

2. I disagree

3. I rather disagree

4. I have no opinion

5. I rather agree

6. I agree

7. Strongly agree

Point scale

The point scale used to evaluate attitudes, opinions, phenomena or objects is based on a system where the respondent marks a point corresponding to his/her evaluation on a scale from the lowest to the highest value. A scale of 1 to 5 (Likert scale) or 1 to 10 is often used, where a lower value means a worse rating and a higher value means a better rating.

Percentage scale

The percentage scale is mainly used in the context of evaluating performance results, for example in education. It presents results in the form of percentages from 0% to 100%, where 0% means no correct answers and 100% means fully correct. It is an absolute scale, allowing easy comparison of results between individuals.

Application of the rating scale

In scientific research, the rating scale plays a key role in accurately determining and interpreting data. The detailed choice of the scale directly affects the quality and reliability of the results.

Quantitative research

In quantitative research, where data are expressed numerically, interval and ratio scales are often used. An interval scale makes it possible not only to compare values, but also to accurately identify differences between them. A relative scale, on the other hand, has a defined unit and a zero point, which makes it possible to perform all mathematical operations, including the determination of ratios.

Qualitative research

In qualitative research, nominal and ordinal scales are used to group data and organize categories according to a certain characteristic. Nominal scale allows you to categorize data without indicating value relationships. Examples include gender or type of housing. An ordinal scale allows you to create a hierarchy, an example of which is the degree of agreement in a survey.

Data analysis

Data analysis in survey research is crucial for understanding the information collected and obtaining reliable conclusions. Appropriate statistical methods allow raw data to be processed to extract useful insights.

Statistical processing

In statistical processing, it is essential to use appropriate techniques to analyze data extracted using various scales, such as nominal, ordinal, interval or relative. Using Likert scales, analysis can include the calculation of descriptive statistics, such as means or medians, as well as advanced techniques.

Interpretation of results

Interpretation of results is the step during which the results of statistical processing are analyzed in the context of the research problem. It is important that any interpretation be based on a rigid methodological framework and appropriate to the measurement scale used. For example, when interpreting data from a nominal scale, researchers focus on the frequency of occurrence of each category, while the analysis of a relative scale will enable the exact proportions or relationships between variables to be determined.