Random sample of data from the population.Linearity can be assessed visually using a scatterplot of the data. This assumption ensures that the variables are linearly related violations of this assumption may indicate that non-linear relationships among variables exist.

Each pair of variables is bivariately normally distributed at all levels of the other variable(s).Each pair of variables is bivariately normally distributed.The biviariate Pearson correlation coefficient and corresponding significance test are not robust when independence is violated.no case can influence another case on any variable.for any case, the value for any variable cannot influence the value of any variable for other cases.the values for all variables across cases are unrelated.There is no relationship between the values of variables between cases.Independent cases (i.e., independence of observations).Linear relationship between the variables.Cases must have non-missing values on both variables.Two or more continuous variables (i.e., interval or ratio level).To use Pearson correlation, your data must meet the following requirements: The bivariate Pearson Correlation does not provide any inferences about causation, no matter how large the correlation coefficient is.

Note: The bivariate Pearson Correlation only reveals associations among continuous variables. If you wish to understand relationships that involve categorical variables and/or non-linear relationships, you will need to choose another measure of association. Note: The bivariate Pearson Correlation cannot address non-linear relationships or relationships among categorical variables.

The direction of a linear relationship (increasing or decreasing).

The strength of a linear relationship (i.e., how close the relationship is to being a perfectly straight line).

Whether a statistically significant linear relationship exists between two continuous variables.

The bivariate Pearson correlation indicates the following:

Correlations within and between sets of variables.

The bivariate Pearson Correlation is commonly used to measure the following:

Working with 'Check All That Apply' Survey Data (Multiple Response Sets).

Descriptive Stats by Group (Compare Means).

Descriptive Stats for Many Numeric Variables (Descriptives).

Descriptive Stats for One Numeric Variable (Frequencies).

Descriptive Stats for One Numeric Variable (Explore).

Computing Variables: Recoding String Variables into Coded Categories (Automatic Recode).

Computing Variables: Recoding Categorical Variables.

Computing Variables: Rank Transforms (Rank Cases).

Getting Started with SPSS Toggle Dropdown.

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