Using numbered scales is one of the best ways to get accurate data from your surveys. Scales like interval scales, nominal scales, ratio scales, and ordinal scales make it easier to analyze survey data.
In this blog, we’ll explore how to make the most of the interval scale in your survey and learn how to use it most effectively.
An interval scale can be defined as a quantitative measurement scale where variables have an order, the difference between two variables is equal, and the presence of zero is arbitrary. It can be used to measure variables that exist along a common scale in equal intervals.
These are a few characteristics of an interval scale:
There are four fundamental levels of quantitative measurement scales:
Nominal scales involve variables that are simply named but have no specific order or defined interval.
On the other hand, Ordinal scales do have an order, but the difference between the two variables is not defined. In Interval scales, there is order and a defined distance between variables.
The final scale, known as the Ratio scale, produces the order of variables, the difference between variables, and (unlike the Interval scale) also provides information on the value of true zero.
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Interval scales are best suited in surveys where respondents must enter values regarding temperature, time, and dates. Interval scales can be easily integrated into multiple-choice questions or rating scale questions by asking respondents to use a numerical scale to make a rating.
A popular example of the use of Interval scales within surveys is within NPS® surveys. Net Promoter Score® surveys measure the likelihood of customers recommending a company’s products or services to others. It does so by asking them to rate their likelihood to do so on a numeric scale from 0 to 10, where 0 indicates they are not likely at all, and 10 indicates they are very likely.
Certain survey questions, such as those used in NPS® and CSAT surveys, have specific methods and formulas for interpreting their results.
Interval scales may not always help generate useful data, especially if the measurement system used is highly arbitrary. However, they may be used to calculate the mean and median values of a set of aggregate data.
The more arbitrary your interval scale is, the less useful the data produced by it will be. Although a constant interval variable is necessary for interval scales, it may be difficult to keep the interval variable constant while measuring variables such as opinions and feelings. Most interval scales, even those widely used, tend to have only modest accuracy and are not fully reliable.
When appropriately used, interval scales can be used to extract useful and powerful data from survey responses, as they can add mathematical significance to quantify opinion and feedback surveys.
These are a few reasons you should choose Voxco for your survey software needs:
While creating surveys with Voxco, you can use any kind of scale, whether that be nominal, ordinal, interval, or ratio.
Voxco’s powerful dashboards analyse survey responses as and when they come in. With our dashboard, Interval data can be measured and analyzed in the most efficient way.
Voxco survey software has survey templates ready for any kind of survey you may need. Our NPS® , CSAT, and CES survey templates can be used to make your surveying process quick and easy as our software analyses the responses for you.
With Voxco, you can create your survey once and send it to respondents across all channels. This saves time as it eliminates the need for the reprogramming of the same questionnaire.
An interval question is one of the types of survey questions that ask respondents to choose from intervals on a scale as their response to the survey questions. The difference between any two points on the interval scale is consistent.
Examples of scales commonly used for interval scale questions include temperature measured in degrees Celsius or Fahrenheit, time measured in minutes or hours, and age measured in years.
The three most common types of scaling questions used in surveys are
An interval scale survey is a type of survey that uses questions or statements presented on an interval scale. In an interval scale survey, respondents are asked to rate or evaluate something based on a numerical scale where the intervals between the scale points are equal. This scale allows for the measurement of the magnitude of differences between responses.
The main difference between ordinal and interval surveys lies in the nature of the scale used and the level of measurement they provide. Here are the key distinctions:
Ordinal scale | Interval scale | |
Nature of Scale | In an ordinal survey, the scale used is an ordinal scale. The responses are ranked or ordered based on a particular characteristic or attribute. The scale indicates the relative position or preference of the responses but does not provide information about the magnitude of differences between them. | In an interval survey, the scale used is an interval scale. The responses are measured on a scale where the intervals between the points are equal. The scale not only provides relative ranking but also allows for the measurement of the magnitude of differences between responses. |
Level of Measurement | The level of measurement in an ordinal survey is ordinal. It categorizes or orders responses without quantifying the exact differences between them. The responses are ranked or ordered based on their relative position or preference. | The level of measurement in an interval survey is an interval. It provides a meaningful measurement of the differences between responses, as the intervals between scale points are equal. The scale allows for quantifying and comparing the magnitude of differences between responses. |
Data Analysis | In an ordinal survey, the statistical analysis focuses on non-parametric methods that consider the order or rank of responses, such as calculating medians and percentiles or using rank-based tests like the the Mann-Whitney U test or Kruskal-Wallis test. | In an interval survey, statistical analysis can include both non-parametric and parametric methods. Parametric methods, such as calculating means and standard deviations or performing t-tests or analysis of variance (ANOVA), can be employed due to the availability of equal intervals and a meaningful zero point. |
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