![]() ![]() However, don't worry because even when your data fails certain assumptions, there is often a solution to overcome this (e.g., transforming your data or using another statistical test instead). In fact, do not be surprised if your data fails one or more of these assumptions since this is fairly typical when working with real-world data rather than textbook examples, which often only show you how to carry out a Pearson's correlation when everything goes well. When moving on to assumptions #2, #3 and #4, we suggest testing them in this order because it represents an order where, if a violation to the assumption is not correctable, you will no longer be able to use a Pearson's correlation. Examples of ordinal variables include Likert scales (e.g., a 7-point scale from "strongly agree" through to "strongly disagree"), amongst other ways of ranking categories (e.g., a 5-point scale for measuring job satisfaction, ranging from "most satisfied" to "least satisfied" a 4-point scale determining how easy it was to navigate a new website, ranging from "very easy" to "very difficult or a 3-point scale explaining how much a customer liked a product, ranging from "Not very much", to "It is OK", to "Yes, a lot").įortunately, you can check assumptions #2, #3 and #4 using Stata. Note: If either of your two variables were measured on an ordinal scale, you need to use Spearman's correlation instead of Pearson's correlation. If you are unsure whether your two variables are continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide. Examples of such continuous variables include height (measured in feet and inches), temperature (measured in ☌), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), reaction time (measured in milliseconds), test performance (measured from 0 to 100), sales (measured in number of transactions per month), and so forth. Assumption #1: Your two variables should be measured at the continuous level.However, you should decide whether your study meets this assumption before moving on. Since assumption #1 relates to your choice of variables, it cannot be tested for using Stata. If any of these four assumptions are not met, analysing your data using a Pearson's correlation might not lead to a valid result. ![]() There are four "assumptions" that underpin a Pearson's correlation. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a Pearson's correlation to give you a valid result. In this guide, we show you how to carry out a Pearson's correlation using Stata, as well as interpret and report the results from this test. If there was a strong, negative association, we could say that the longer the length of unemployment, the greater the unhappiness. Alternately, you could use a Pearson's correlation to understand whether there is an association between length of unemployment and happiness (i.e., your two variables would be "length of unemployment", measured in days, and "happiness", measured using a continuous scale). If there was a moderate, positive association, we could say that more time spent revising was associated with better exam performance. A value of 0 (zero) indicates no relationship between two variables.įor example, you could use a Pearson's correlation to understand whether there is an association between exam performance and time spent revising (i.e., your two variables would be "exam performance", measured from 0-100 marks, and "revision time", measured in hours). Its value can range from -1 for a perfect negative linear relationship to +1 for a perfect positive linear relationship. A Pearson's correlation attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, r, indicates how far away all these data points are to this line of best fit (i.e., how well the data points fit this new model/line of best fit). The Pearson correlation generates a coefficient called the Pearson correlation coefficient, denoted as r. The Pearson product-moment correlation coefficient, often shortened to Pearson correlation or Pearson's correlation, is a measure of the strength and direction of association that exists between two continuous variables. Pearson's Correlation using Stata Introduction ![]()
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