Cor relational research in second language classroom

1. Correlational Research
M. Ed Third Semester
PNC

2. Introduction
• the type of research in which the researcher tries to find out the
relationship between variables or data.
• If you want to find out the relationship between blood pressure level and
cholesterol, it is a correlational research.
• It mainly focuses its attention of how the two variables (independent and
dependent) used in the study are related to each other.
• In correlational studies the researcher examines the strength of
relationships between variables by determining how change in one variable
is correlated with change in the other variable.
• Correlational research is a type of non-experimental research method, in
which a researcher measures two variables, understands and assess the
statistical relationship between them with no influence from any
extraneous variable. Our mind can do some brilliant things.

3. • The purpose of correlational research is to determine the relations
among two or more variables.
• Hatch and Farhady (1992, p 195) opine that the correlational analysis
is a tool of analysing the collected data to find out the relationship
between two variables used in the study.
• The data is collected and coded statistically to find out the co-
relationship between the variables. The statistics that describes the
relationship between two variables is called a correlation coefficient.
This coefficient indicates how closely the two variables are related.
• Example of Occurrence of Cancer and marriage….
• Let us say marriage has a negative correlation with cancer. This means
that people who are married are less likely to develop cancer.

4. Naturalistic Observation
• involves observing people’s behaviour in the environment in
which it typically occurs.
• Researchers engaged in naturalistic observation usually make
their observations as unobtrusively as possible so that
participants are often not aware that they are being studied.
• Ethically, this method is considered to be acceptable if the
participants remain anonymous and the behaviour occurs in
a public setting where people would not normally have an
expectation of privacy

5. Archival Data
• It refers to the data that have already been collected for
some other purpose but used in this research by the
researcher.
• Relevancy is the primary concern in using archival data.
• measurement can be more or less straightforward when
working with archival data
• For example – Implicit egoism

6. Correlation Coefficient
• The correlation coefficient is a quantitative measure that represents
the degree of relationship between the pairs of variables. It results in
the value that ranges from [+1] to [-1]. To put it into other words, a
correlation coefficient is a decimal number between [+1] to [-1]
indicating the degree to which the two variables are related
• There are three types of correlation coefficient:
a. Positive Relationship [from 0 to +1]
b. Negative Relationship [from o to -1]
c. Zero relationship [only 0]

7. Positive Relationship [0 to +1]
• A positive correlation between two variables is when an
increase in one variable leads to an increase in the other
variable and a decrease in one variable will see a decrease in
the other variable. For example, the amount of money a
person has might positively correlate with the number of
cars he has.

8. Negative Correlation [from 0 to -1]
• A negative correlation is quite literally the opposite of
positive correlation. This means, if there is an increase in one
variable, the second variable will show a decrease and vice
versa.
• For example: the level of being educated might negatively
correlate with the crime rate when an increase in one
variable leads to a decrease in another and vice versa. This
means if in some ways the level of education in a country is
improved, it can lead to lowering the crime rates. Please
note, that this doesn’t mean that lack of education leads to
crimes. This means lack of education and crime is believed to
have a common reason: poverty.

9. Zero relationship [only 0]
• In this third type, two variables are not correlated. This
means a change in one variable may not necessarily see a
change in the other variable. For example, being a millionaire
and happiness is not correlated. This means an increase in
money doesn’t lead to happiness.