CHARACTERISTICS
OF CORRELATIONAL DESIGN
1.
Display of scores.(Scatterplot and
Metrics)
2.
Association between scores.(Direction,
Form and Strength)
3.
Multiple variable analysis.(Partial
correlation and Multiple regression)
Display
of scores
Plot
the scores on a graph (Scatterplot) or present the scores in a table (correlation
matrix.)
Scatterplot.
·
Researchers plot scores for two
variables on a graph to provide a visual picture of the form of the scores.
·
This allows researchers to identify the
type of association among variables and locate extreme scores.
·
Provide useful information about the
form of association. Eg: scores are linear (straight line) curvilinear (U
shaped).
·
Scatterplot are also called scatter diagram.
·
It
is a pictorial image on a graph of two sets of scores for participants.
Correlation
matrix.
·
A correlational matrix presents a visual
display of scores of the correlation coefficients for all variables in a study.
We list all variables on both a horizontal row and vertical column in the
table.
Association
between scores
After
correlation researchers graph scores and produce a correlation matrix. They can
then interpret the meaning of association between scores. This calls for
understanding the direction of the association, the form of the distribution,
the degree and strength of association.
Direction of association.
·
It is very important to identify the
intersection, or movement in a graph.
·
There is positive correlation and
negative correlation.
·
Positive correlation – the points move
the same direction. X increases Y also increases and X decreases Y also
decreases.
·
Negative correlation - the points
moves opposite direction. When X increase Y decrease; X decrease, Y increase.
Form
of the association.
Correlational
researchers identify the form of the plotted scores as linear or non linear.
(a) Positive
linear relationship
(b) Negative
linear relationship
(c) No
correlation
(d) and
(e) Curvilinear
Positive
linear relationship
Low/high
scores on one variable relate to low/high scores on the second variable.
Negative
linear relationship
Low
scores on one variable relate to high scores on the other variable.
No
correlation
Scores
on one variable does not tell us or predict any information about the possible
scores on other variable.
Curvilinear
An
increase, plateau, and decline in Y axis variable with the increasing values of
the X axis variable.
Degree
and strength of association.
The
association between two variables or sets of scores is a correlation
coefficient of -1 to +1, with 0.00 indicating no linear association at all.
This association between two sets of scores reflects whether there is constant,
predictable association between the scores.
If,
correlation= -1 or +1 = perfect
linear correlation, values between -1 and +1 = predictable or constant, values
are 0.00 = no linear or no relationship.
Multiple variable analysis
In
many correlational studies, researchers predict outcomes based on more than one
predictor variable. Thus they need to account for the impact of each variable.
Two multiple variable analysis approaches are partial correlation and multiple
regression. Here predictor variable means independent variable.
Partial correlation.
·
We study three, four or five variables
as predictors of outcomes.
·
The type of variable called a
‘mediating’ or ‘intervening’ variable.
·
Stands between independent and dependent
variable and influence them.
·
This variable is different from control
variable that influence the outcomes in an experiment.
·
We use partial correlation to determine
the amount of varience that an intervening variable explains in both in
independent and dependent variable.
Multiple
regression.
It
is a statistical procedure for examining the combined relationship of multiple
independent variable with a single dependent variable.
REFERENCE
John,
W., Creswell. Educational research: planning, conducting and evaluating quantitative
and qualitative research (4th ed.) 342-353
