SPSS inferential statistics (Part. II). Dwayne Age measured as relationships or associations between two or more relationships between variables include. Judging from the brief description of your study proposal, I believe you intend to measure the effect of two categorical variables (effect of product and effect of. The bivariate Pearson Correlation is commonly used to measure the following: The sample correlation coefficient between two variables x and y is . By default, SPSS marks statistical significance at the alpha = and.
Even when your data fails certain assumptions, there is often a solution to overcome this. Your two variables should be measured at the interval or ratio level i. Examples of variables that meet this criterion include revision time measured in hoursintelligence measured using IQ scoreexam performance measured from 0 toweight measured in kgand so forth.
You can learn more about interval and ratio variables in our Types of Variable guide.
There is a linear relationship between your two variables. Whilst there are a number of ways to check whether a linear relationship exists between your two variables, we suggest creating a scatterplot using SPSS Statistics, where you can plot the one variable against the other variable, and then visually inspect the scatterplot to check for linearity.
Your scatterplot may look something like one of the following: In our enhanced guides, we show you how to: Pearson's correlation determines the degree to which a relationship is linear. Put another way, it determines whether there is a linear component of association between two continuous variables.
As such, linearity is not actually an assumption of Pearson's correlation. However, you would not normally want to pursue a Pearson's correlation to determine the strength and direction of a linear relationship when you already know the relationship between your two variables is not linear.
Instead, the relationship between your two variables might be better described by another statistical measure.
Pearson's Product-Moment Correlation using SPSS Statistics
For this reason, it is not uncommon to view the relationship between your two variables in a scatterplot to see if running a Pearson's correlation is the best choice as a measure of association or whether another measure would be better.
There should be no significant outliers. Outliers are simply single data points within your data that do not follow the usual pattern e.
In practice, checking for these two assumptions just adds a little bit more time to your analysis, requiring you to click of few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task. These two assumptions are: Your two variables should be measured on an ordinal, interval or ratio scale.
- Spearman's Rank-Order Correlation using SPSS Statistics
Examples of ordinal variables include Likert scales e. You can learn more about ordinal, interval and ratio variables in our article: There is a monotonic relationship between the two variables.
A monotonic relationship exists when either the variables increase in value together, or as one variable value increases, the other variable value decreases.
Whilst there are a number of ways to check whether a monotonic relationship exists between your two variables, we suggest creating a scatterplot using SPSS Statistics, where you can plot one variable against the other, and then visually inspect the scatterplot to check for monotonicity.
Your scatterplot may look something like one of the following: The relationship displayed in your scatterplot should be monotonic.
In our enhanced guides, we show you how to: Just remember that if you do not test these assumptions correctly, the results you get when running a Spearman's correlation might not be valid. This is why we dedicate a number of sections of our enhanced Spearman's correlation guide to help you get this right.