Different type of data ...Statistics in education

DATA TYPES / MASUREMENT

       Variable can take on many different forms and levels sophistication.      

         The relationship between what is being measured and the number that represent what is being measured known as the levels measurement. Broadly speaking, variable can be categorical or continuous, and  can have different levels of measurement.

    I.            Categorical variable:

A categorical variable is made up categories.

The entities are different categories this known as categorical variables which includes,

• Ø         Binary variable
• Ø         Nominal variable
• Ø       Ordinal variable         

Ø Binary :  A categorical variable is one that name distinct entities. In its simplest form it name just two distinct types things, and this is known as Binary variables.

eg: male or female, alive or dead, pregnant or not, and responding “Yes” or” No” to a question.

Ø  Nominal :  When two things that are equivalent in some sense are given the same name (or number),but there are more than two possibilities ,the variable is said to be a nominal variables.

It should be obvious that if the variable is made up of names , it is pointless to do arithmetic on them(if you multiply a human by a cat,  you do not get a hat ). However, sometimes numbers are used to denote categories.

Eg: The numbers worn by players in a football team. In football, the numbers of shifts denote specific field positions, so the number 10 is always worn by the fly-half and the number 1 is always the hooker. These numbers do not tell us anything other than what position the player plays. We could equally have shirts with FH and H instead of 10 and 1.A number 10 players is not necessarily better than a number 1 .It is equally as daft to try to do arithmetic with nominal scales.

Ø  Ordinal :  When categories are ordered, the variables is known as ordinal variable. However, these data tell us nothing about the differences between values. Ordinal scale, numbers reflect their rank order or merits position within their own group or class with respect to some quality, property or performance.

 The ordinal scale places events in order .The defect in such scale lice in the fact that the units along the scale are unequal in size.

The difference in the achievement scores between the first and the second merit position holder is not necessarily equal to the difference between the second and third.

      Eg: The beauty contest winners are three. The names of the winners don’t    provide any information about where they came in the contest; however labeling them according to their performance does-first, second and third. These categories are ordered. In using ordered categories we now know that the woman who won was better than the women who came second and third. Ordinal data, therefore, tell us more than normal data but they still do not tell us about the difference between points on a scale.



   

            II.            Continuous Variables :

      A continuous variable is one that gives us a score for each person and can take on any value on the measurement scale that we are using. Continuous variables which includes,

Ø        Interval variable

Ø        Ratio variable

Ø   Interval variable :  Interval data are considerably the statistical tests in this book rely on having data measured at this level. To say that data are interval, we must be certain that equal intervals on the scale represent equal differences in the property being measured.  Interval scales can have an arbitrary zero but it is not possible to determine for them what may be called on absolute zero or the unique origin. The primary limitation of the interval scale is the lack of a true zero. It does not have the capacity to measure the complete absence of a trait or characteristic.

         Eg: On www.ratemyprofessors.com students are encouraged to rate their lecturers on several dimension (some of the lectures’ rebuttals of their negative evaluations are worth a look). Each dimension(i.e helpfulness,clarity,etc.)is evaluated using a 5-point scale. For this scale to be interval it must be the case that the difference between helpfulness ratings of 1 & 2 is the same as the difference between say 3 & 4, or 4 & 5. Similarly, the difference in helpfulness between ratings of 1 & 3 should be  identical to the difference between ratings of 3  & 5. Variables like this that look interval (and are treated as interval) are often ordinary.

Ø  Ratio variable :  It constitutes the find and highest type of scale in terms of measurement. Ratio scales have a absolute or true zero of measurement. So the Ratios value of values along the scale should be meaningful. Here measures are not only expressed in equal units but are also taken from a true zero. The zero on such scales essentially means an absence of quality or attributes being assessed.

Eg; All physical measurement are example of ratio scale, such as length, width, weight, capacity etc… temperature

 In the measurement of all these attributes all the concerned measuring scales start from a true zero.  These scales easily permit statements regarding the comparative ratio in relation to some quality or property existing among the different individual or objects.

CONCLUSION       

 

    These proceding from the nominal scale (the least precise type of scale) to ratio scale (the most precise) relevant information is obtained increasingly. If the nature of the variables permits, the researcher should use the scale that provides the most precise description.  Researchers in physical science have the advantage to describe variables in ratio scale form but the behavioral science are generally limited to describe variable in interval scale form a less precise type of measurement.

REFERENCES

v DISCOVERING STATISTICS USING SPSS, ANDY FIELD,III EDITION.

v KOTTARI C.R, RESEARCH METHADOLOGY METHODS AND TECHNIQUES, II EDITION, NEW AGE INTERNATIONAL PUBLISHERS.

v MANGAL S.K, STATISTICS IN PSYCHOLOGY AND EDUCATION, II EDITION, ASOKE K GHOSH.