This document discusses different types of variables that can be measured in educational research. It defines quantitative variables as numerical variables that represent a measurable quantity, like test scores. Quantitative variables can be discrete, like number of children in a family, or continuous, like age or height. Qualitative variables represent categorical variables and can be nominal, with no implied ordering, or ordinal, with a clear ordering. The document also discusses independent and dependent variables in a study, how to develop a good hypothesis, and the steps to test a hypothesis.

1. Educational
Research and
Statistics
Course Code 6461
Asima shahzadi
asimashahzadi7@gmail.com

3. A variable in research simply refers to a
person, place, thing, or phenomenon that
you are trying to measure in some way.
Intelligence
Height
Test score
Variable

5.  Quantitative variables are numerical. They represent a measurable
quantity.
1. High school Grade Point Average (e.g. 4.0, 3.2, 2.1).
2. Bank account balance (e.g. $100, $987, $-42.
3. Average number of lottery tickets sold (e.g. 25, 2,789, 2 million).
4. How many cousins you have (e.g. 0, 12, 22).
5. The amount in your paycheck (e.g. $200, $1,457, $2,222).
Quantitative variable
(Numeric Variable)

6. A discrete quantitative variable is one that
can only take specific numeric values (rather
than any value in an interval), but those
numeric values have a clear quantitative
interpretation
Quantitative discrete variables

7. 1. Number of children per family
2. Number of students in a class
3. Number of citizens of a country
 Even if it would take a long time to count the citizens of a
large country, it is still technically doable. Moreover, for all
examples, the number of possibilities is finite. Whatever the
number of children in a family, it will never be 3.58 or 7.912 so
the number of possibilities is a finite number and thus
countable.
some examples of discrete variables

8. quantitative continuous variables are
variables for which the values are not
countable and have an infinite number of
possibilities.
Quantitative continuous variables

9. 1. Age
2. Weight
3. Height
 For simplicity, we usually referred to years, kilograms (or
pounds) and centimeters (or feet and inches) for age,
weight and height respectively. However, a 28-year-old
man could actually be 28 years, 7 months, 16 days, 3
hours, 4 minutes, 5 seconds, 31 milliseconds, 9
nanoseconds old.
Examples

10.  qualitative variables (also referred as categorical variables
or factors in R) are variables that are not numerical and
which values fits into categories.
 In other words, a qualitative variable is a variable which
takes as its values modalities, categories or even levels, in
contrast to quantitative variables which measure
a quantity on each individual.
Qualitative variables

11. Qualitative variables are divided into
two types:
1. nominal
2. ordinal.

12. A qualitative nominal variable is a qualitative
variable where no ordering is possible or implied in
the levels.
For example, the variable gender is nominal because
there is no order in the levels female/male. Eye color
is another example of a nominal variable because
there is no order among blue, brown or green eyes.
Qualitative nominal variable

13. qualitative ordinal variable not only classify persons or
objects, it also ranks them.
For instance, if the severity of road accidents has been
measured on a scale such as light, moderate and fatal
accidents, this variable is a qualitative ordinal variable
because there is a clear order in the levels.
Qualitative ordinal variable

14. The independent variable is the cause. Its
value is independent of other variables in
your study.
The dependent variable is the effect. Its
value depends on changes in the
independent variable.
The variables in a study of a cause-and-effect relationship are
calledthe independent and dependent variables

16.  “A hypothesis can be defined as a tentative explanation of the
research problem, a possible outcome of the research, or an
educated guess about the research outcome.” (Sarantakos,
1993: 1991)
 Examples of a hypothesis are:
1. Newspapers affect people's voting pattern.
2. Attendance at lectures influences exam marks.
Hypothesis

17. 1. A good hypothesis is consistent with previous research
2. A good hypothesis provides suitable explanation
3. A good hypothesis states expected relationship
between variables
4. A good hypothesis is testable with the help of data
5. A good hypothesis should be testable within time
6. A good hypothesis defines what is relevant and
irrelevant
Characteristics of good hypothesis

18. Two types of Hypothesis

19.  A directional hypothesis is a prediction made by a researcher regarding
a positive or negative change, relationship, or difference between two
variables of a population.
 This prediction is typically based on past research, accepted theory,
extensive experience, or literature on the topic.
 Key words that distinguish a directional hypothesis are: higher, lower,
more, less, increase, decrease, positive, and negative.
 A researcher typically develops a directional hypothesis from research
questions and uses statistical methods to check the validity of the
hypothesis.
Directional hypothesis

22. A hypothesis is tested through the determination of:
1. Sample
2. Instrument
3. Design
4. Procedure
5. Collection of data
6. Analysis of data
Testing the Hypothesis

24. 1. Identify key variables (concepts)
2. Generate a hypothesis to answer the research
question
3. Specify the model
Test the actual theory using specific data and
measurements
Once you get a clear research question