35 relationship in the sample data is strong enough to use to model the relationship in
the population. The correlation coefficient, r, tells about the strength of the linear relationship between x and y.
b. Characteristics of Correlational Survey Research
Correlational research is aimed at determining the nature, degree and direction of relationships between variables or using these relationships to make
predictions. Correlational studies typically investigate a number of variables expected to be related to a major, complex variable.
According to Butler, R.
2014, the characteristics of Correlational Survey research are:
1 Correlational studies are carried out to explain important human behavior or to
predict likely outcomes identify relations among variables. 2
If a relation of sufficient magnitude exists between two variables, it becomes possible to predict a score on either variable if a score on the other variable is
known Prediction Studies. 3
The variable that is used to make the prediction is called the predictor variable. 4
The variable about which the prediction is made is called the criterion variable predicted variable.
5 Both scatterplots and regression lines are used in correlational studies to
predict a score on a criterion variable 6
A predicted score is never exact. Through a prediction equation see p. 585, researchers use a predicted score and an index of prediction error standard
error of estimate to conclude if the score is likely to be incorrect. PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
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c. Types of Correlational Design
There are many types of correlational research. The commonality among all types of correlational research is that they explore relationships between
variables. Where descriptive research only described what was going on, correlational research talks about the link between different things. It is important
to understand that correlational research does not tell us that variable A caused Variable B, but rather that they are somehow related Alston, 2004. The design
has two forms, they are.
1 Relational design
The first is called a relational design because the basic intent is to explain the relation
– or association – between two or more variables. An examination of the relation between students’ attitudes toward math and their math achievement
is an example of a relational design. In fact, this is a specific design known as a simple bivariate correlational design. This design can be extended to include more
variables to more precisely describe the relations among variables. There are five general characteristics associated with relational designs. These are as follows:
a Two or more variables are collected from each individual in the sample.
b Data is collected at a single point in time.
c The data is analyzed as a single group.
d A correlational coefficient e.g., Pearson Product Moment, multiple R is
reported and discussed in terms of strength, direction, and statistical significance.
e Interpretations from the statistical results are drawn about the relation.
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2 Prediction Design
A second type of correlational design is called a prediction design. The purpose of this design is to identify variables that can effectively predict some
outcome or criterion. The variable being predicted is called the criterion variable, and the variable or variables being used to predict the criterion are called
predictors. When a study involves only two variables, the predictive relation is estimated with a statistical procedure called simple linear regression. When more
than one predictor variable is used to predict a criterion, the analysis is called multiple regression. This is an extremely powerful statistical procedure that can
estimate the collective as well as the individual contributions of all predictor variables. This analysis makes it is possible to develop not only an effective
prediction, but also an efficient one. While the general characteristics of prediction designs are similar to those
for relational designs, they differ several important ways. These are as follows. a
Two or more variables are collected from each individual in the sample. b
Data for the predictor variables is typically collected prior to that of the criterion variable.
c The data is analyzed as a single group.
d A correlation coefficient – typically a multiple R – is reported and discussed in
terms of its strength, direction, and statistical significance. e
The contribution of each predictor variable is examined in terms of its unique contribution to the prediction of the criterion variable.
A regression coefficient – b
i
– is reported for each predictor variable and is discussed in terms of its significance to the prediction of the criterion variable. In
