What is Interval Data?
Interval data is defined as a type of quantitative data where there are mathematically precise equal intervals between the data points, without a true zero to represent absence.
For example, temperature is interval data because it can be plotted in a scale with mathematically equal intervals, and zero point does not indicate an absence of temperature itself. It simply means it’s colder. This is applicable for any temperature scale be it Celsius or Fahrenheit.
This is unlike nominal and ordinal data, which are qualitative in nature and cannot have measurable equal intervals in between the data points.
Interval data also differs in this regard from ratio data, where a true zero point exists. Example here would be weight, where zero weight does indicate a weight-less state. This is unlike temperature where zero does not indicate a temperature-less state.
Age is a unique data because it is debatable if it is to be considered strictly interval data or ratio data. We would side with it being interval data because zero age cannot be a data point on an age scale, as that would simply mean the person or being has not been born and does not represent a true zero value.
Key Characteristics of Interval Data with Examples
Let us understand interval data in more depth with clarity on each of its characteristics:
- Quantitative in nature:
Amongst the four basic data types for measurement (nominal, ordinal, interval and ratio), interval is the first to have quantitative characteristic, prior to that nominal and ordinal are both qualitative in character.
This means interval data allows for true quantitative mathematical evaluations from data collection.
For example, collecting data on the ‘height’ of people in a city, state or country gives us the ability to perform calculations to find median, average, mean etc.
- Mathematically equal intervals between data points:
When you see height, time, temperature etc mentioned on a scale, you will always see equal intervals between them.
For example, an online form that asks ‘adult’ respondents to select ‘year’ of birth on an option scale. Here, all years between starting and ending dates are mentioned in equal intervals. Such as, dates starting from 1925, 1926, 1927…… to 2008.
- No True Zero Point:
When we say that interval data does not have a true zero point, it is important to note that zero can exist on a scale, but it doesn’t mean ‘absence’ which is what zero means. For example, a temperature scale does have a zero point, it just doesn’t mean no temperature.
However, on certain interval data ranges, zero itself cannot exist. Like in the case of height. No matter how small an object or being, the height can never be truly zero.
Interval Data Examples and Sources
Here are some more examples and common sources of interval data capture:
- Survey questions
Most surveys tend to use interval data in one form or another. For example, any data collection regarding dates, formula based number sequences, temperature etc, are all interval data on a scale.
- Census data
Census data on age, birth date, number of people in a household etc are all interval data.
Here, interval data is also contextual. For example, when asked about the number of years a person has stayed in a State or country that they are voting from, it cannot be zero in this context else they wouldn’t have voting rights there. But without this context, a person may have spent zero days in a particular state or country.
- Meteorological information
All data regarding weather and temperature are interval data. For example, humidity can be represented with mathematically accurate equal intervals but humidity cannot be completely zero anywhere on Earth.
Even lab created environments on Earth cannot have a true zero humidity, some water vapour, no matter how tiny, is always present. In fact, even the Sun has water vapour.
- Tailor measurements
Any tailoring house is using interval data. Height, breath and length measurements create our clothing, and all three are interval data.
Best Practices for Collecting and Managing Interval Data
- Ensure accurate calibration of measurement:
Since interval data is mathematical, the final data points need to be fully accurate. Whether your interval data source is a survey or a measuring equipment, it is important to validate all mathematical formulas and equipment accuracy to avoid broad errors. For example, if you are using a sequence like the Fibonacci for research studies, an error in the formula can render the research error prone and useless.
- Standardize data collection procedures:
To maintain consistency and comparability, standardize the procedures used for data collection. This includes using the same methods, tools, and protocols across all data collection efforts.
For example, when administering standardized tests or surveys, ensure that all respondents are given the same instructions and that the testing conditions are uniform. This helps in reducing variability that can arise from different collection methods.
- Implement data validation and quality checks:
Incorporate data validation and quality checks throughout the data collection process. This involves setting up procedures to detect and correct errors or inconsistencies in the data. Regular audits, automated validation rules, and data cleaning protocols can help in identifying outliers, missing values, or inaccuracies.
For example, implementing software that flags outlier temperatures that fall outside expected ranges can help in catching errors early.
- Ensure data security and privacy:
Protect the integrity and confidentiality of interval data by implementing robust security measures. This includes using encryption, secure access controls, and regular backups.
For example, when managing sensitive data from standardized tests, ensure that personal information is anonymized and that data is stored in secure, encrypted databases to prevent unauthorized access.
- Promote Transparency and Reproducibility:
Ensure that data management practices promote transparency and reproducibility. This involves maintaining clear documentation of data collection and analysis processes, making it easier for others to understand, replicate, and validate your findings. Sharing detailed methodologies and data sources in research publications or reports helps in fostering trust and credibility in the data and its analyses.
