The document discusses the classification of variables in research, detailing types such as numeric, categorical, experimental, and non-experimental variables. It includes definitions and examples of continuous and discrete variables, as well as ordinal, nominal, dichotomous, and polychotomous variables. The significance of understanding these classifications is emphasized for effective research design and analysis.

1. CLASSIFICATION OF VARIABLES

2. OBJECTIVES
1. Determine the classifications of variables
2. Differentiate the classifications variables from
one another.
Identify the types of variables used in a
research study.

3. ACTIVITY:
JEEPNEY RIDE

4. VARIABLE
 Any property or factor that a researcher measures, controls, and /or
manipulates.
 Any changing quantity or measure of any factor, trait, or condition
that can exist in differing amount or types.
 It is a logical set of characteristics, numbers, or quantities that can be
measured or counted.
 Anything that has quantity or quality that varies.

5. EXAMPLE:
Age, sex, business income and expenses,
country of birth, capital expenditure, class
grades, eye color and vehicle type.

6. KINDS OF VARIABLES:
1. Numeric Variable:
- variables with values that normally describe a measurable
numerical quantity.
- values that are considered as quantitative data.
- variables that answer the questions “how many “or “how
much”

7. a. Continuous variables:
- these are quantitative or numeric variables
- these can be obtained by measuring or
computation
- also called as interval variables
Examples: income, time, age, electric or water bills, height,
weight, length, general average

8. b. Discrete Variables:
- these are quantitative or numeric variables
- these can be obtained by counting
- these assume any whole value within the limits of given
variables
Examples: number of family members, number of registered cars,
number of votes, number of respondents

9. 2. Categorical Variables:
- these are variables that describe a quality or
characteristic of a data
- variables that answer the questions “ what type,
which category”

10. a. Ordinal Variables:
- these are values that can be logically arranged or ranked
- these can be expressed through sequential and numerical
order
- these allow comparison of degree
Examples: academic grades such as A, B, C, rank such as first,
second, third, clothing size such as X,L,M,S,

11. b. Nominal Variables:
- these are values that cannot be arranged in a logical order or
sequence
- these are only concerned with names and categories of responses
- these cannot quantify data
Examples: nationality, hair and eye color, business type, kinds of
religion, kinds of languages,

12. c. Dichotomous Variables:
- these variables represent only two categories when
observed and measured
- this value is most often a representation for a measured
variables
Example: age (under 56 or more), gender (male or female),
answer (yes or no), correctness ( true or false)

13. d. Polychotomous Variables:
- these are variables that can have more than two values
- these are variables that have many categories
Examples: educational attainment ( elementary, high
school, college, graduate, post graduate)
: level of performance (excellent, very good,
good, satisfactory, poor)

14. 3. Experimental Variables
- anything that can change or be changed
a. Independent Variables
- these are manipulated variables that cause a change in another variable
- also known as manipulated or explanatory variable
b. Dependent Variables
- these variables are usually affected by the manipulation of the independent
variables
- these are responses or effects that result from the treatment or condition
employed
- also known as response or predicted variable

15. c. Extraneous Variables
- these variables are already existing during the conduct of an experiment
- these could influence the result of the experiment
- variables that are minimized to lessen the impact on the responses
- these are not included in the study but may affect the dependent
variable
- also known as mediating, intervening, or covariate variables

16. Examples:
a. Title of Research: AN EXPERIMENT ON THE METHODS OF
TEACHING AND LANGUAGE ACHIEVEMENT
AMONG ELEMENTARY PUPILS
Independent Variable : METHODS OF TEACHING
Dependent Variable : LANGUAGE ACHIEVEMENT
Extraneous Variables : VENTILATION; PHYSICAL AMBIANCE

17. b. Title of Research: USE OF GARDENING TOOLS AND TYPES OF
FERTILIZER: THEIR EFFECTS ON THE AMOUNT
OF HARVEST
Independent Variables: USE OF GARDENING TOOLS, TYPES
OF FERTILIZER
Dependent Variable : AMOUNT OF HARVEST
Extraneous Variables : HUMIDITY LEVEL, TYPES OF
SEEDS/PLANTS

18. 4.Non- experimental Variables:
a. Predictor Variables
- these are used in regression analyses
- these provide information on an associated dependent
variable regarding a particular outcome
- these change the other variable/s in a non-experimental
study

20. b. Criterion Variables
- these are usually affected by predictor variables (outcome)
- these are usually used in making predictions
- also known as the dependent variable but are not exactly
interchangeable

21. Examples:
a. Title of Research: COMPETENCIES OF TEACHERS AND STUDENTS’
BEHAVIOR IN SELECTED PRIVATE SCHOOLS.
Predictor Variable : COMPETENCIES OF TEACHERS
Criterion Variable : STUDENTS’ BEHAVIOR

22. b. Title of Research: CONDUCT OF GUIDANCE AND COUNSELING
PROGRAMS AND DEGREE OF ABSENTEEISM AND
DROP-OUT RATE AMONG GRADE 8 CLASSES
Predictor Variable : CONDUCT OF GUIDANCE COUNSELING PROGRAMS
Criterion Variable : DEGREE OF ABSENTEEISM AND DROP-OUT RATE

23. c. Title of Research: THE TYPES OF FACILITIES, ADMINISTRATOR’S
PROFILE, AND PARENTS’ SUPPORT TOWARDS
SCHOOL EFFECTIVENESS AMONG PUBLIC SENIOR
HIGH SCHOOL
Predictor Variable : TYPES OF FACILITIES; ADMINISTRATOR’S PROFILE;
PARENT’S SUPPORT
Criterion Variable : SCHOOL EFFECTIVENESS

24. THANK YOU

25. TOPIC EVALUATION
ORDINAL VARIABLE
NOMINAL VARIABLE
DICHOTOMOUS VARIABLE

26. WHY VARIABLES ARE
IMPORTANT IN
RESEARCH?