Correlational Research Designs

2 Correlational Research Refers to studies in which the purpose is to discover relationships between variables through the use of correlational statistics Correlational statistics are used extensively in test construction and analysis

3 Correlational Research Designs Scattergram Is a pictorial representation of the correlation between two variables: The scores of individuals on one variable are plotted on the x-axis of a graph and the scores of the same individuals on another variable are plotted on the y-axis. Each point on the graph contains two pieces of information, the individual’s position with respect to the x-axis and with respect to the y-axis Line of best fit: indicates that each unit of increment in the x-axis variable is accompanied by a unit of increment on the y-axis variable

4 Purpose of Correlation Coefficient Is to express in mathematical terms the degree and direction of relationship between two (or more) variables. If the the relationship between two variables is perfectly positive, the correlation coefficient will be If the relationship between two variables is perfectly negative, the correlation coefficient will be –1.00. If there is no relationship, the coefficient will be 0. If two variables are somewhat related, the coefficients will have a value between 0 and 1.00 (for a positive relationship) or between 0 and –1.00 (for a negative relationship)

5 Purpose of Correlation Coefficient The correlation coefficient is a precise way of stating the degree to which one variable is related to another, and the direction of the relationship (positive or negative) Another way, the CC tells us how effectively individuals’ scores on one measure (e.g., an intelligence test) can be used to predict their scores on another measure (e.g., an achievement test) If predictions can be made, this suggests (DOES NOT PROVE) that the variable measured by the predictor instrument has a causal influence on the variable measured by the other instrument

6 Correlation and Causality The correlational approach to analyzing relationships between variables is subject to the same limitations to causal inference as the causal-comparative approach Is there a third variable that might determine a relationship? A correlational relationship between two variables occasionally is the result of an ARTIFACT If a significant relationship between variables is found, their causality can be tested more definitively by using the experimental method

7 Advantages and Uses Advantage over causal-comparative or experimental methods is that it permits one to analyze the relationships among a large number of variables in a single study (e.g., correlation matrix) It provides information concerning the degree of the relationship between the variables being studied Used for two purposes: 1.to explore relationships between variables; and 2.to predict scores on one variable from subjects’ scores on other variables

8 Limitations of Relationships Studies Correlations obtained in a relationship study cannot establish cause-and-effect relationship between the variables that are correlated Many researchers have criticized relationship studies because this type of study breaks down complex abilities and behaviors into simpler components Success in many of the complex activities that interest us probably can be achieved in different ways (e.g., successful in being a principal – lack of any set of characteristics common to all successful principals)