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Near East University
Department of English Language
Teaching
Advanced Research Techniques
Correlational Studies
Abdalmonam H. Elkorbow
Topics to discuss
- Definition , purposes, types of correlational
study
- Relationship study
- Prediction study
- The process
- Correlation coefficient
- Conducting of Relationship Studies
- Conducting of prediction Studies
- Independent and dependent variables
Definitions of correlational studies
A correlational study is a type of study in which two (or more) are measured and
compared in a large group of individuals.
- The results of a correlational study allow us to determine whether or not the
two variables “go together” — that is, to determine the degree to which they
change together, on average. If two variables change together in the same
direction, such as height and weight (taller people tend to be heavier, on
average, and vice versa), we say that the variables are positively correlated.
If two variables change together in the opposite direction, such as alcohol intake
and driving ability (the more alcohol one drinks, the less one is able to drive well,
on average, and vice versa), we say that the variables are negatively correlated.
The major strength of correlational studies is that they allow us to quickly
discover general relationships among variables (or, at least, more quickly than if
we compared a large number of case studies).
purpose correlational studies :
Two basic purposes
1- Help explain important human behaviors
-(Relationship Studies)
2-Predict likely outcomes
-(Prediction Studies)
Relationship STUDIES
- Researchers often investigate a number of
variables they believe are related to a more
complex variable.
- Unrelated variables dropped from further
consideration
- Most researchers most probably trying to
gain some ideas about cause and effect
-However it does not establish cause and
effect
PREDICTION STUDIES
- Predict a score on one variable if a score on
the other variable is known
- Determine the predictive validity of
measuring instruments
- Predictor Variable; variable that is used to
make the prediction
- Criterion Variable; variable about which
the prediction is made
The process
1-select the problem
- Variables to be correlated are selected on the basis of some rationale
- Increases the ability to meaningfully interpret results
- Inefficiency and difficulty interpreting the results from a shotgun approach
2- select participants and instrument
- Participant and instrument selection
* Minimum of 30 subjects
* Instruments must be valid and reliable
* Higher validity and reliability requires smaller samples
* Lower validity and reliability requires larger samples
3- Design and procedures
Collect data on two or more variables for each subject. two or more scores
are obtained for each member of the sample, one score for each variable of
interest, and the paired scores are then correlated …the result is expressed
as a correlation coefficient.
The process
4- Data analysis
Compute the appropriate correlation coefficient. …the two or more
scores are obtained for each member of the sample, one score for each
variable of interest, and the paired scores are then correlated …the
correlation coefficient indicates the degree of relationship between the
variables of interest.
correlation coefficients
A correlation coefficient identifies the size and
direction of a relationship
- Size /Ranges from 0.00 – 1.00
- Directions
*Positive or negative
-Interpreting the size of correlations
-General rule
* Less than .35 is a low correlation
* Between .36 and .65 is a moderate
correlation
*Above .66 is a high correlation
Correlational coefficient
- Predictions
* Between .60 and .70 are adequate for group
predictions
* Above .80 is adequate for individual predictions
- Interpreting the size of correlations
Criterion-related validity
* Above .60 for affective scales is adequate
* Above .80 for tests is minimally acceptable
- Inter-rater reliability
* Above .90 is very good
* Between .80 and .89 is acceptable
* Between .70 and .79 is minimally acceptable
* Lower than .69 is problematic
Correlation coefficient
Interpreting the direction of correlations
Direction
Positive
High scores on the predictor are associated with high
scores on the criterion
Low scores on the predictor are associated with low
scores on the criterion
Negative
High scores on the predictor are associated with low
scores on the criterion
Low scores on the predictor are associated with high
scores on the criterion
Positive or negative does not mean good or bad
Correlation coefficient
- Interpreting the size and direction of correlations using the
general rule
* +.95 is a strong positive correlation
* +.50 is a moderate positive correlation
* +.20 is a low positive correlation
* -.26 is a low negative correlation
* -.49 is a moderate negative correlation
* -.95 is a strong negative correlation
Conducting Relationship Studies
- Identify a set of variables
1- Limit to those variables logically related to the
criterion
2- Avoid the shotgun approach
* Possibility of erroneous relationships
* Issues related to determining statistical
significance
- Identify a population and select a sample
- Identify appropriate instruments for measuring each
variable
- Collect data for each instrument from each subject
- Compute the appropriate correlation coefficient
Conducting a predictions studies
- Identify a set of variables
*Limit to those variables logically related
to the criterion
- Identify a population and select a sample
- Identify appropriate instruments for
measuring each variable
- Ensure appropriate levels of validity and
reliability
- Collect data for each instrument from each
subject
* Typically data is collected at different
points in time
- Compute the results
INDEPENDENT AND DEPENDENT
VARIABLES:
* As I said before A variable is an object, event, idea, feeling, time
period, or any other type of category you are trying to measure.
Independent variable: It is a variable that stands alone and isn't
changed by the other variables you are trying to measure. For
example, someone's age might be an independent variable. Other
factors (such as what they eat, how much they go to school, how
much television they watch) aren't going to change a person's age. In
fact, when you are looking for some kind of relationship between
variables you are trying to see if the independent variable causes
some kind of change in the other variables, or dependent variables
INDEPENDENT AND DEPENDENT
VARIABLES:
* dependent variable: means something that
depends on other factors. For example, a test
score could be a dependent variable because
it could change depending on several factors
such as how much you studied, how much
sleep you got the night before you took the
test, or even how hungry you were when you
took it. Usually when you are looking for a
relationship between two things you are
trying to find out what makes the dependent
variable change the way it does.
INDEPENDENT AND DEPENDENT
VARIABLES:
Many people have trouble remembering which is the independent
variable and which is the dependent variable. An easy way to
remember is to insert the names of the two variables you are using in
this sentence in they way that makes the most sense. Then you can
figure out which is the independent variable and which is the
dependent variable:
(Independent variable) causes a change in (Dependent Variable) and it
isn't possible that (Dependent Variable) could cause a change in
(Independent Variable).
INDEPENDENT AND DEPENDENT
VARIABLES:
For example:
(Time Spent Studying) causes a change in (Test
Score) and it isn't possible that (Test Score) could
cause a change in (Time Spent Studying).
References
Kendall, M. G. (1955) "Rank Correlation Methods", Charles
Griffin & Co
Székely, G. J. Rizzo, M. L. and Bakirov, N. K. (2007). "Measuring and
testing independence by correlation ofdistances“,
. doi:10.1214/009053607000000505