Spearman Correlation SPSS Step-By-Step Guide

hello guys it's Adam ala from SPSS Boskone in this lecture we're going to be looking at Spearman ranked order correlation just like the Pearson correlation Spearman is used to investigate the direction and strength of relationship between two variables difference between Spearman and Pearson correlation is that Spearman is the nonparametric alternative of the Pearson correlation which means if your data has violated the assumption of pearson then you will have to use the Spearman rank order correlation some of the assumptions of Pearson that you can ballot includes when your data is not normally distributed if you have significant outlier and one or both of your variables are ordinal you're going to use Spearman instead of Pearson correlation you have to note that Spearman correlation is not assumption free Spearman correlation also has assumptions one of the first assumption of Spearman is that two variables you're interested in correlating should either be measured on the ordinal level or skill level ordinal data is data that has a meaningful rank your ordinal data can be the presence or absence of particular characteristics for example it could be that whether a student graduated or not there's a minimum range between graduating and not graduating and your scale variable can be income or aging years or width or height in meters those are ratio data the second assumption is the assumption of monotonicity there must be a monotonic relationship between your two variables so when one increases the other increases as well and when one decreases the order decreases as well Spearman does not assume that there's a linear relationship it has assumed a monotonic or relationship and when we look at the scatter plot now to investigate this assumption you will see the difference between a monotonic oh and a linear relationship or this lecture we're going to be looking at these hypothesis the a penis of the children is associated with the cost of gift so we're looking at the association between a penis and gift to see if they are positively or negatively correlated or there is no relationship between them at all our hypothesis is that there will be an association between the two variables and the null hypothesis is that there will be no association between the two variables before you do a Spearman correlation first make sure that your data has indeed violated the assumption of Pearson now let's first check our data using the scatter plot we go into graph legacy dialogue a scatter plot we're going to get the simple scatter plot we define we put one of our variable in the y axis they are there in the x axis there is no preference for y or X because Spearman correlation does not consider which one is dependent or independent variable but in this case we're going to put a penis level in the y axis price of gift in the x axis you click on OK and you investigate the relationship using the scatter plot right as you can see if we try to find out if there's a linear relationship one of the assumption of Pearson is that it should be a linear relationship as we can see here there's no linear relationship let's draw the line of fit to see there's a linear relationship here's the line of fit right there and you can see it only goes through one of the points as you can see here we're violated one of the assumption of Pearson correlation there is no linear relationship as the line did not go through any of the point but what you will see here is that as a penis levels increasing this the cost of gift is also increasing and you can see that there is a monotonically increasing relationship between these two variable because there's a monotonic relationship we can use the Spearman correlation we also have to check if the variable is normally distributed or not and we can quickly check this using the chaperone work stairs or the comma graphs made of so going to analyze descriptive statistics and explore your data put in your two variables of interest select the plots click on normality plots with test and selects Instagram click on continue and then click on ok here's what we get from those tests we can see that happiness level is indeed normally distributed but price of gift is not normally distributed now we violated two assumptions of Pearson correlation and the third one let's check for outliers do we have outlier in any of the variables we check the box plot for outlier as you can see here we have two outliers and we may remove this outlier bow basically we have violated the assumption of Pearson we can go ahead and use the Spearman rank relation our first assumption of Spearman rank total correlation has been passed because we investigated the scatterplot and there's a monotonic or relationship between our two variables now the second assumption is that our two variables happiness level and cost of gift both ordinal and skill happiness level is measured on a scale of 1 to 10 so it's a like at skill and price of gift is a scale data it's a ratio data in SPSS now we are good to go to carry out the Spearman ranked order correlation in SPSS to do that you're going to analyze correlate and bivariate you put in your two variables of interest into the variables dialog box and instead of Pearson you select Spearman correlation and then you click on OK now you can see that there is a positive significant relationship between our two variables which means that as a penis level increases price of gift increases as well and as price of gifts increases a penis level increases now the difference between Spearman correlation and Pearson correlation when it comes to analysis is that Spearman correlation is actually done on the ranks of your score so SPSS carries out a Pearson correlation on the rank of a penis and the rank of price I'm going to show you how this works now let's get the rank of a penis and rank of price go to transform and rank your cases put in your two variables and click on ok so we're going to get the rank of these two variables now we have the rank of a penis and the rank of price let's do a Pearson correlation for the rank to see if it's going to be the same result as the Spearman correlation done on the original happiness level and price of gift we go again to analyze correlate and bivariate we remove these original variables and put in the rank variable now we're going to do a Pearson correlation on the rank variable click on OK now as you can see the first one we did which is the Spearman correlation of a penis and the cost of gift is point nine nine one and the Pearson correlation we did on the rank of those two variables also point nine nine this just shows you that Spearman is done on the rank of the original variable so it's basically a Pearson correlation done on rank which explains the named Spearman rank order correlation now let's look at the interpretation of the results let's just reduce this screen here and we're going to focus on our Spearman result from the SPSS output now if we move this to the side we see that there so here we look at the strength of relationship and the direction the direction is in a positive or negative as you can see here this is positive the relationship between apenas and cost of gift is positive and strong so we can report that there is a positively strong relationship between happiness and cost of gifts

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Keywords: Spearman Correlation SPSS, correlation spss, spss, spearman spss, spss spearman, bivariate correlation, spearman ranked order correlation, spearman, correlation

Id: XGxMCp8cg-c

Length: 7min 28sec (448 seconds)

Published: Wed Jan 06 2016