Educational Data or Levels of Measurement.
- Data definition.
- Broad classification of data.
- Educational data and classification.
- Implication of Educational data classification.
Educational data is also called levels or scales of measurement. Data are observable or measurable characteristics of a variable. Educational data are those observable or measurable characteristics of the variables under investigation in the field of education.
Broad classification of data: Data are broadly classified into;
Qualitative Data: There are data concerned with statement, responses, pictures that are not quantified.
Quantitative Data: This is a data that deals with numerical values which is a measurable data.
Educational Classification of Data
In education, data are classified into four levels
Nominal Data: Nominal data or scale or level of measurement is concerned with naming, categorization or classification of data. It focuses on identification.
For instance, gender and under it, we have male and female. We can’t say male is higher than female nor female higher than male, as such it lacks magnitude. If we are to code male = 0 and female =1, we can’t say female is higher than male just because we assigned female to 1 and male to 0. The numbers is just for categorization.
Another example is color, country, political parties. Etc in the nominal level of measurement; no mathematical operation is possible. It is the least refined level of measurement. In this level, we just do simple count. The measures of central tendency possible here is the mode.
in addition to having all the properties of nominal data, it posses ordering or rank. Here we can categorize and rank. That is in this level of measurement, we can say a thing is different or greater than the other, however we cannot say or tell how much is greater. E.g position in class (1st, 2nd, 3rd ).
In ordinal level of measurement, equal interval does not correspond to equal attribute being measured. However, there is order in magnitude. The measures of central tendency that can be performed her is mode and median.
Interval Data: It posses all the properties of nominal and ordinal data plus an equal interval characteristics. It has magnitude and equality of interval. Here equal interval corresponds to equal attribute being measured.
Group A ── 10 20
Group B ── 30 40
It however lacks the presence of absolute zero. Examples of this includes, temperature scale of degree Celsius, days in the calendar, numbers of hours, students’ scores etc. statistical operation here is median and mode. Mathematical operation here + – *
in addition to having all the properties of nominal, ordinal and interval data; it has the presence of absolute zero. Absolute zero entails the complete absence of a particular trait. Example of such measurement here is the temperature scale of degree Fahrenheit.
The only measurement in education that has an absolute zero is ‘deviation’. It is because the negative and the positive values will cancel each other. Example of ratio scale is mass, height, length. All other mathematical operation is possible because it is the most refined level of measurement.
Types of Data
Discrete Data: it is a level of measurement that we can measure specific values in essence between any given set of values. There is no intermediate value. It is called countable data. E.g 1 , 2 ,3 , 4 ,5 etc.
All examples of nominal and ordinal data fall under discrete data.
Continuous Data: this type of data falls under uncountable data. Here there are countless intermediate values in between a given set of values. All example of inter and ratio data fall under continuous data.
Note: data can be downgraded but data cannot be upgraded e.g
90 80 67 45 40
A B C D E
Implication of Data Classification
- Data classification gives a deeper understanding with the kind of data set under consideration as well as mathematical operation that can be performed on it.
- It enhances measurement.