# Point-Biserial Correlation

Statistics Solutions provides a data analysis plan template for the point-biserial correlation analysis.  You can use this template to develop the data analysis section of your dissertation or research proposal.

The template includes research questions stated in statistical language, analysis justification and assumptions of the analysis.  Simply edit the blue text to reflect your research information and you will have the data analysis plan for your dissertation or research proposal.

Data Analysis Plan: Point-Biserial Correlation

Copy and paste the following into a word document to use as your data analysis plan template.

Research Question:

RQ: What is the relationship between variable 1 and variable 2 (level 1 vs. level 2)?

Ho: There is no the relationship between variable 1 and variable 2 (level 1 vs. level 2).

Ha: There is a relationship between variable 1 and variable 2 (level 1 vs. level 2).

Data Analysis

To examine the research question, a point-biserial correlation will be conducted to assess if a relationship exists between variable 1 and variable 2 (level 1 vs. level 2).  Point-biserial correlations are appropriate when the research purpose is to evaluate if a relationship exists between a continuous variable and a dichotomous variable, and to find the magnitude of that correlation.  The point-biserial determines the strength of the relationship between two variables and how the distribution of the z scores varies.  A correlation coefficient, r, can range from 0 (no relationship) to -1(perfect negative linear relationship) to 1 (perfect positive linear relationship).  In a positive relationship, there is a direct relationship; that is, as the continuous variable increases, the other variable also increases.  A negative correlation is described as an inverse relationship; as the continuous variable increases, the other variable decreases.  Cohen’s standard will be used to assess the correlation coefficient on a scale where .10 indicates a weak association between the two variables, .30 indicates a reasonable association, and .50 represents a strong association.

Reference