New Trend of Educational Variable -Data Organization.
- Types of variables.
- Practical illustration of variables.
- Definition of data.
- Data organization.
A variable is a characteristic or trait of a person, object or a group under investigation that can vary or change.
TYPES OF VARIABLES
They are several types of educational variables
INDEPENDENT VARIABLE: One can also refer to the independent variable as a predictor or an explanatory variable and it is the variable the researcher manipulate to have an effect on the dependent variable.
For example, if a researcher is interested in determining the effect of projector on student’s academic achievement in mathematics.
The variable the researcher is manipulating here is the “project” which is the independent variable and “achievement” is the dependent variable. Now, what is this dependent variable?
DEPENDENT VARIABLE: Dependent variable can also be called criterion or outcome variable. It is a variable whose changes the researcher is interested in. The dependent variable is a variable that receive the effect of the independent variable. E.g achievement score.
There are also independent variables that can sweeten or change the taste of relationships between other variables. Such variables include;
This is a third independent variable that is capable of strengthening or weakening, and even change the direction of the relationship between the dependent and independent variable.
For example, if a researcher is interested in investigating the influence of gender on student’s interest and academic achievement in physics, the moderator variable in this case is the “gender”.
Moderator variable can either be;
Qualitative moderator variable are mostly used for categorization or classification. These are gender, location, ethnic group, school type, and so on.
Quantitative moderator variable are those with numerical values and sometimes there are more involved in a research work than just being part of the demographic data. E.g age, weight, length, and so on.
EXTRANEOUS VARIABLE: It is an independent variable that the researcher is not interested in, however it is capable of invalidating or nullifying the outcome of the result of an investigation. Examples include noise, environmental factor, air pressure and so on.
This variable is often found in experimental studies. It can also be called covariate or concomitant variable.
INTERVEING/MEDIATING VARIABLE: An abstract concept that facilitate or enhances the relationship between the dependent and independent variable e.g intelligence, likeness, interest, hate and so on.
A student making head way in mathematics may be as a result of the likeness he/she has for the teacher and vice versa.
New Trend of Educational Variable -Data Organization
There is also an educational variable that one can refer to as ‘busy body’, most times it creates a problem without taking part in it. An example of such is the suppressor variable.
SUPPRESSOR VARIABLE: This is a predictor variable that has no business or relationship with the dependent variable but has an unholy effect on the independent variable such that it influences the relationship between the dependent and independent variable.
In order to truly understand the concept of this suppressor variable and its effect, we will be using a real life example involving a married man(X1) and his wife(Y1) taking a walk together and a passer-by girl with big a**(X2). Take a look this picture below
X2 is the suppressor variable
X1 is the predicator
Y1 is the dependent variable.
Notice that X1 and Y1 are the married couple and X2 (suppressor) is the passer-by girl that has no relationship with the Y1 (dependent variable) i.e the wife, but has an effect on X1(predicator) I.e the husband.
Now this effect caused by this X2(suppressor variable) can either strengthen, weaken, and even change the nature of the relationship between X1(the Husband) and Y1(the wife). Mainly looking at the picture above, it is obvious that X1(Husband) is already in a big trouble from Y1(wife).
So that is what suppressor variable does, when added to a regression model, it can either impact it negatively, positively or no impact at all.
One might ask, what is a data? Data is an observable or measureable characteristic of variable. For a data to make sense, it has to be properly organized.
Data organization deals with presentation of data in a meaningful way so that it can be easily interpreted and used for further operations. This can be done with the help of the frequency table.
The frequency table is a table that shows a given value and the number of times the value occurs in a given distribution.
Example 1: Using these hypothetical values, 2,5,3,2,6,6,7,5,6,2,3,5,4,4,4,4,4,6,6,6. These data will be organized using the frequency table.
X = raw scores
F = frequency (number of occurrence)
CF = Cumulative frequency
%CF = percentage cumulative frequency
‘X’ column contain the respective distinct number in the distribution or the raw scores
The ‘F’ column is gotten by just making simple count of the number of times each value appears. E.g from the distribution (2 appeared three times, that’s why it takes the value ‘3’ in the F column). 3 appeared twice, 4 appeared six times and so on.
The “tally” column is also a form of representing or showing the number of times each value occurs but in a tally form. You cross a tally when the number counted is up to five. You just use the last stroke to cross it. E.g ||||
New Trend of Educational Variable -Data Organization
How to calculate cumulative Frequency
To get the values for the ‘CF’ column, the first frequency is always picked as the baseline (constant), follow this below addition method to get subsequent ones.
Note: The addition is done diagonally to get the cumulative frequency (CF). The last cumulative frequency (CF) should equal to the total sum of the frequency (F), otherwise you are wrong
Determining the percentage cumulative frequency (%CF) column, is by taking each frequency and dividing it by the last cumulative frequency value (20), then multiply it by 100.
first %CF = 3/20 *100 =15%
second %CF = 2/20 *100 =25%
third %CF = 6/20*100 =55%
Task: complete the rest in the table 1.
How to group and organize data
The above example is on ungroup data. Supposing you have several or a large data distribution, organizing the data in the way we did above becomes tedious and cumbersome. Hence there is a need to group the data.
Example 2: given these data distribution below
2, 5, 4, 8,10,12,7,15,20,25,12,8,6,7,14,18,19,17,22,,11,9,13, and 15
To prepare or organize these data, the table will comprise of
- Class interval,
- True limit, and
- Class mark(x).
|Class interval||True limit||Frequency(f)||Class mark(x)|
|1 – 5||0.5 – 5.5||3||3|
|6 – 10||5.5 – 10.5||7||8|
|11 – 15||10.5 – 15.5||7||13|
|16 – 20||15.5 – 20.5||4||–|
|21 – 25||20.5 – 25.5||2||–|
‘Class interval’ column is gotten by choosing range of values you want the data to fall in. to know the right range to choose, you just have to inspect the data distribution you were given.
Note: always use odd numbers when creating the class interval in order not to have decimals when calculating the class mark(x)
‘True limit’ column is gotten by subtracting 0.5 from the lower class interval and adding 0.5 to the upper class interval. E.g lower class interval (1- 0.5 = 0.5) and upper class interval (5 + 0.5 = 5.5), so it will be 0.5 – 5.5 and so on.
After choosing the intervals, inspect the data distribution you were given and count how many values belongs to that interval, this makes up for your frequency (F).
The ‘class mark(x)’ is obtained by taking sum of each class interval and dividing it by 2. E.g for the class mark,
First Class mark(x) = (1+5)/2 = 3
Second Class mark(x) =(6+10)/2 = 8
Third Class mark(x) = (11+15)/2 = 13
Task: complete the remaining ones in the table 3 above.