2. A VARIABLE is anything that has a quantity or
quality that varies.
For instance, during the quarantine period, your
mother planted tomato seedlings in pots. Now
common understanding from science tells you
that several factors are affecting the growth of
tomatoes: sunlight, water, kind of soil, and
nutrients in soil.
3. The growth of tomatoes and the number of
fruits produced are examples of the
Dependent Variables. The amount of
water, and nutrients in the soil are the
Independent Variables.
4. The independent variable is also identified
as the presumed cause while the dependent
variable is the presumed effect. In an
experimental quantitative design, the
independent variable is pre-defined and
manipulated by the researcher while the
dependent variable is observed and
measured.
5. It is important to note other factors that may
influence the outcome (dependent variable)
which are not manipulated or pre-defined by the
researcher. These factors are called Extraneous
Variables. In our example , the presence of pests
and environmental stressors (e.g. pets, extreme
weather) are the extraneous variables.
6. When the researcher fails to control the
extraneous variable that it caused
considerable effect to the outcome, the
extraneous variable becomes a Confounding
Variable.
8. Quantitative Variables, also called numerical variables
are the type of variables used in quantitative research
because they are numeric and can be measured. Under
this category are discrete and continuous variables.
Qualitative Variables are also referred to as categorical
variables are not expressed in numbers but are
descriptions or categories. It can be further divided into
nominal, ordinal or dichotomous.
9. A discrete variable can assume a countable number of
values or whole numbers. It does not take negative values or
values between fixed points.
o Number of steps to the top of the Eiffel Tower*
A continuous variable can assume any value along a given
interval of a number line. It takes fractional (non-whole
number) values that can either be a positive or a negative.
o The time a tourist stays at the top
once s/he gets there
10. Discrete variables
o Number of sales
o Number of calls
o Shares of stock
o People in line
o Mistakes per page
Continuous variables
o Length
o Depth
o Volume
o Time
o Weight
11. Levels of
Measurement
It was the American psychologist Stanley Smith
Stevens who proposed the levels of measurement
or scales of measure.
12. Nominal Level
Data in this level are classified into categories. Names are
used as labels. Numbers and letters are used to represent
variables.
Examples:
a. Marital Status: Single, Married, Divorce
b. Sex: Male, Female
1. M maybe used instead of Male
F maybe used instead of Female
2. 1 maybe used instead of Male
2 maybe used instead of Female
13. Ordinal Level
Data in this level are ranked but the degree of difference
between them are not determined. Items are ordered when
sorted out.
Example:
a. Size – small, medium, large, extra large
b. Rank – 1st, 2nd, 3rd, etc.
14. Interval Level
The degree of difference between the data can be
specified but not the ratio between them. In this
level, zero (0) does not mean total absence of what
being measured.
Example:
a. Temperature in Celsius Scale
15. Ratio Level
In this level, data such as scores can be expressed as ratio.
In ratio level, zero (0) has no actual or true value. Most
measurement in engineering and physical sciences are in
ratio level.
Examples:
a. Mass is measured on ratio scale.
b. Length is also measured on ratio scale.
19. B. Tell what kind of measurement scale is
appropriate for the following information.
Nominal – Ordinal – Interval – Ratio - Dichotomous
1) Eye color
2) Gender
3) Race
4) Religious affiliation
5) Weights of basketball
players
6) Temperature
7) Speed of sound
8) Top ten in the nursing board
examination
9) Height
10)Sizes of shoes
