The data can be qualitative or quantitative in nature. The statistical investigations provide robust results when the data is quantitative in nature. However, there are modern scales of qualitative data measurement that conveniently convert qualitative information or data into quantitative information or data. One of such example is Likert Scale of agreement. In social sciences, whenever the investigator trying to measure the attributes such as level of satisfaction, agreement, happiness etc. the recorded data can be measured at four levels of measurement namely; Nominal, Ordinal, Interval and Ratio. All statistical tools can be used to analyze the data measured on ratio level, while the scope of application of statistical tools get narrower from interval to ordinal to nominal level of measurements due to restriction of mathematical laws. Following diagram can explain the elevation of levels of data measurement.
figure - 1
It can be observed from the diagram that ordinal level of measurement has all properties of nominal level and certain additional properties, while interval level of data has all properties of ordinal level of data measurement and certain additional properties. The ratio level of measurement has all the properties of nominal, ordinal and interval level and certain additional properties.
Nominal Level
Also known as dichotomous or categorical level. Certain questions of the data collecting instrument produce data that can be measured at nominal level only. For instance; if a question is asked what is your gender? and the respondent has to choose from either, "Male" or "Female". This data will only help in categorizing the entire data into two groups i.e., "Males" and "Females". Apart from categorization there is no other significant information is communicated at this level of measurement e.g., which group is having a higher professional experience, which group has a higher average weight or height. Also there is a very limited scope of statistics that can be applied on this data. At-most we can understand the percentage representation of two groups through a table of via pie chart.
Ordinal Level
The ordinal level of measurement contains all the properties of Nominal data and in additional the data can also be layered. For Instance; Consider following example; The question asked from the respondent is
What is your qualification?
Below High School
Intermediate
Graduate
Post Graduate
Above PG
The answer to the particular question will capture the data with ordinal level of measurement. Apart from categorizing the data into 5 categories we also understand that the group having PG is more qualified then graduates and below. We also come to know that PG qualified people have some kind of specialization. Hence, we can order that data apart from categorizing. We can statistically do the cross tabulation comparison of the categories also apply median if the data can be quantified or already in quantitative format.
Interval Level
When the data is measurable in such a way that the '0' is not absolute, the data is considered as interval level of data. This simply means that the captured attribute is not absent at '0'. For example; if the temperature is 0 degree Celsius it doesn't mean that the temperature is absent at that point. Most of the statistical calculations make sense at interval level of data. Taking ratio of two numbers does not make any sense at this level. For instance; we can not say that the 6 degree Celsius is twice hotter than 3 degree Celsius.
Ratio Level
This is the highest level of data measurement. This level of data measurement contains all properties of data measurement till interval level and also considers '0' as absolute i.e., at '0' the attribute of the data is absent. For example; zero money in the bank account means there is no money in the bank account. Also all possible statistical analysis can be performed on ratio level of data.
However, researcher shall not confuse the measurement of data with the quality of data. The measurement of data shall be as per the need of the study. Capturing data on either of the scale does not dilute the importance of the variable.
For example in order to compare the level of satisfaction from the particular product w.r.t. gender needs the data to be captured on nominal level as well.
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A Systematic Approach in Understanding the levels of Data Management , The Four Levels of Data Management - nominal , Ordinal , interval and Ratio.
Thank You Sir
Aditya Narayan
Section A
924368
Thank you sir for sharing this with us.
It can help us to differentiate the level of data management
Shalni gaud
924107
Section A
To perform statistical data analysisis important first to understand variables and what should be measured using them.For example, it is practically impossible to calculate the average hourly rate of a worker in the US. So, a sample audience is randomly selected to represent the larger population appropriately.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. By understanding the differences between nominal, ordinal, interval, and ratio data, researchers can make more informed decisions about the appropriate statistical tests to use and how to interpret their results.
Statistics provide a systematic approach to analyzing data and making informed decisions based on evidence. some reasons highlighting the importance of statistical decision-making:
1.Identifying relationships and trends
2.Identifying relationships and trends
3.Evaluating claims and conclusions
statistics play a vital role in decision-making across various aspects of our daily lives. By utilizing statistical methods and tools, we can make more informed, evidence-based decisions, evaluate claims and conclusions
Thank you sir for giving us the valuable information about the levels of data measurement