### Social Research Methods - Knowledge Base - Types of Relationships

The correlation coefficient is This is a weak positive relationship. This scatterplot depicts the relationship between the Number of Sports Events and the . Correlation analysis helps determine the direction and strength of a while a value of + represents a perfect positive relationship, meaning. This is called a causal relationship. The relationship is therefore causal. The positive correlation between the number of churches and the number of deaths.

Next, we calculate the correlation coefficient for the sample. As always, if the significance, p, is less than or equal to 0. We can reject the null hypothesis and interpret the correlation coefficient. The number of artists in a community is positively related to the amount of grant funding it received. Communities with more artists tended to receive more grant funding.

Partial Correlations Based on the data from our sample, we concluded that there is a positive relationship between number of artists and amount of grant funding. We assumed that the number of artists in a community is the causal factor, or, in other words, that the presence of more artists in the community leads to more grant funding.

This makes logical sense: We must be careful in our interpretation, however. Correlation does not imply causation. We cannot be certain that the number of artists is causally related to the amount of grant funding. It may be that both variables are caused by a third, unspecified variable.

What other factors might influence both the number of artists in a community and the amount of grant funding it received? The most likely factor is simply community size. We want to know if there is a relationship between number of artists and amount of grant funding when we control for community size. Community size is a control variable in this example. We will use a partial correlation to determine the effect of number of artists on amount of grant funding, independent of community size.

We will let SPSS calculate the partial correlation. Remember, the original correlation was: This tells us that there is no independent effect of number of artists on amount of arts funding. Community size, not number of artists, appears to be the true causal factor.

## Introduction to Sociology/Sociological Methods

To know for certain that community size is the causal factor we would need to control for other potential causal factors. No infringement is intended or implied. Half of the accounts that become overdrawn in one week are randomly selected and the manager telephones the customer to offer advice. Any difference between the mean account balances after two months of the overdrawn accounts that did and did not receive advice can be causally attributed to the phone calls.

If two variables are causally related, it is possible to conclude that changes to the explanatory variable, X, will have a direct impact on Y.

Non-causal relationships Not all relationships are causal. In non-causal relationships, the relationship that is evident between the two variables is not completely the result of one variable directly affecting the other.

### Introduction to Sociology/Sociological Methods - Wikibooks, open books for an open world

In the most extreme case, Two variables can be related to each other without either variable directly affecting the values of the other.

The two diagrams below illustrate mechanisms that result in non-causal relationships between X and Y. This is the type of relationship political scientists want to discover. A relationship between variables, however, does not necessarily mean that a causal relationship exists.

Remember, correlation does not necessarily mean, or guarantee, causation. In other words, the observed relationship may be a coincidence.

As a reminder, the direction of a relationship refers to positive or negative relations between variables. A positive relation means that as values of one variable increase, or decrease, values of the other variable also increase, or decrease.

A negative relationship means that as values of one variable increase, or decrease, values of the other variable change in the opposite direction. The magnitude of a relationship between variables is also important when considering causality.