Examples of interval/ratio variables include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth. 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 3-pont scale explaining how much a customer liked a product, ranging from "Not very much", to "It is OK", to "Yes, a lot"). Assumption #1: Your two variables should be measured on an ordinal, interval or ratio scale.In practice, checking for these three 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. You need to do this because it is only appropriate to use a Spearman’s correlation if your data "passes" three assumptions that are required for Spearman’s correlation to give you a valid result. When you choose to analyse your data using Spearman’s correlation, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a Spearman’s correlation. First, we introduce you to the assumptions that you must consider when carrying out a Spearman’s correlation. We show you the main procedure to carry out a Spearman’s correlation in the Procedure section. This "quick start" guide shows you how to carry out a Spearman’s correlation using SPSS Statistics. Possible alternative tests to Spearman's correlation are Kendall's tau-b or Goodman and Kruskal's gamma. If you would like some more background information about this test, which does not include instructions for SPSS Statistics, see our more general statistical guide: Spearman's rank-order correlation. For example, you could use a Spearman’s correlation to understand whether there is an association between exam performance and time spent revising whether there is an association between depression and length of unemployment and so forth. The test is used for either ordinal variables or for continuous data that has failed the assumptions necessary for conducting the Pearson's product-moment correlation. It is denoted by the symbol r s (or the Greek letter ρ, pronounced rho). The Spearman rank-order correlation coefficient (Spearman’s correlation, for short) is a nonparametric measure of the strength and direction of association that exists between two variables measured on at least an ordinal scale. Spearman's Rank-Order Correlation using SPSS Statistics Introduction
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