Correlational research designs are founded on the assumption that reality is best described as a network of interacting and mutually-causal relationships. Everything affects--and is affected by--everything else. This web of relationships in not linear, as in experimental research.

Thus, the dynamics of a system--how each parts of the whole system affects each other part--is more important than causality. As a rule, correlational designs do not indicate causality. however, some correlational designs such as path analysis and cross-lagged panel designs, do permit causal statements. Correlational research is quantitative.

TYPES OF CORRELATIONAL RESEARCH DESIGNS

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• Bivariate Correlation
• Regression and Prediction
• Multiple Regression
• Factor Analysis
• Correlational Designs Used to Make Causal Conclusions
• Systems Analysis
  
  

1. BIVARIATE CORRELATION

The relationship between two variables is measured. The relationship has a degree and a direction.

The degree of relationship (how closely they are related) is usually expressed as a number between -1 and +1, the so-called correlation coefficient. A zero correlation indicates no relationship. As the correlation coefficient moves toward either -1 or +1, the relationship gets stronger until there is a "perfect correlation" at either extreme.

The direction of the relationship is indicated by the "-" and "+" signs. A negative correlation means that as scores on one variable rise, scores on the other decrease. A positive correlation indicates that the scores move together, both increasing or both decreasing.

A student's grade and the amount of studying done, for example, are generally positively correlated. Stress and health, on the other hand, are generally negatively correlated.

2. REGRESSION AND PREDICTION

If there is a correlation between two variables, and we know the score on one, the second score can be predicted. Regression refers to how well we can make this prediction. As the correlation coefficients approach either -1 or +1, our predictions get better.

For example, there is a relationship between stress and health. If we know my stress score, we can predict my future health status score.

3. MULTIPLE REGRESSION

This extends regression and prediction by adding several more variables. The combination gives us more power to make accurate predictions.

What we are trying to predict is called the CRITERION VARIABLE.

What we use to make the prediction, the known variables, are called PREDICTOR VARIABLES.

If we know not only my stress score, but also a health behavior score (how well I take care of myself) and how my health has been in the past (whether I am generally healthy or ill), we can more closely predict my health status. Thus, there are 3 predictors--stress, health behavior, and previous health status--and one criterion--future health.

4. FACTOR ANALYSIS

This statistical procedure identifies underlying patterns of variables. A large number of variables are correlated and the presence of high inter-correlations indicates a common underlying factor.

For example, we could measure a great many aspects of physical, emotional, mental, and spiritual health. Each questions would give us a score. High correlations (either positive or negative) among several of these scores would indicate a common underlying factor. Many diferent questions might all be measuring an "emotional health" factor, in which case there would high correlations between questions about anger, anxiety, depression, etc. Or, on the other hand, if these are each separate factors, there would be little correlation between the questions relating to anger, anxiety, and so on.

5. CORRELATIONAL DESIGNS USED TO MAKE CAUSAL CONCLUSIONS

Two designs used to make statements of cause and effect use correlational methods. These are PATH ANALYSIS and CROSS-LAGGED PANEL DESIGNS. I won't go into much detail on them here, but it is important to know that there are some times when correlational designs can be used to determine causality.

PATH ANALYSIS is used to determine which of a number of pathways connects one variable with another. For instance, we know there is a relationship between stress and health. Path analysis has been used to show that while there is a small path that "goes through" physiology, the predominate path connecting stress and health goes through health behaviors. That is, we know stress affects physiological factors such as coronary and immune functions. We also know that when we are stressed, we stop taking good care of ourselves, we sleep less, eat less well, fail to get proper exercises, etc. Research has shown that there is a stronger connection between stress, health behaviors, and health than there is between stress, physiology, and health. And this research used correlational statistics to draw this conclusion.

CROSS-LAGGED PANEL DESIGNS measures 2 variables at two points in time. It has been used, for example, to show that watching violence on TV leads to violent behavior, more than the other way around.

6. SYSTEMS ANALYSIS

This involves the use of complex mathematical procedures to determine dynamic processes, i.e., changes over time, feedback loops, and the elements and flow of relationships.

It has been used, for example, to diagram the differences between successful and unsuccessful elementary schools. Some of the elements in these systems are teachers' expectations of student performance, teaching effort, and student performance. Each of these affects the other and changes over time.