#Regression Analysis using SPSS: How to Run, Interpret, and Report the Regression Results in SPSS

concept of regression regression technique is used to assess the strength of relationship between one dependent and one independent variable it helps in predicting the dependent variable from one or more independent variables regression analysis helps in predicting how much variance how much change is being accounted in a single response that is your dependent variable by one or more than one variables this session will look into two kinds of regression bivariate regression and multiple regression so what is bivariate regression bivariate regression is similar to bivariate correlation because both are designed for situation in which there are just two variables whereas multiple regression is on the other hand is for cases where there are three or more variables one of them is dependent variable while the others are independent variable now let's do an example of regression now let's say we've got these four variables here and i've got life satisfaction as my dependent variable and these could be my independent variables now here is the problem that i want to investigate i want to investigate the impact of servant leadership on life satisfaction now based on this problem i proposed this hypothesis there is a significant impact of servant leadership on life satisfaction now here are uh here is how you could report the results but first let's run a bivariate regression and we'll run a multivariate regression as well so in order to do this you go to analyze regression and linear analyze regression and linear so what is my dependent variable in this case my dependent dependent variable is life satisfaction my independent variable is servant leadership there are numbers of options but we do not need those options for now let's press ok and here are your results variables entered or removed so you have entered servant leadership and your dependent variable is life satisfaction so this gives you a overall summary of your regression analysis just have a review of this just to make sure that you have entered all the variables in the right place now model summary so what does this tell us here is your r value which is the correlation between servant leadership and life satisfaction r square the square of r and this is your adjusted r square so you will use this when you've got a higher number of cases and iv ibs in this case let's use r square which is 0.276 and if you change this into percentage this would mean that 27.6 percent change in life satisfaction can be accounted by servant leadership but is this r square significant is this impact or change significant for this you will have to go to anova table and look at this regression row here and look at this significance value this is point zero zero zero which is less than 0.001 so you will say that there is a significant impact of servant leadership on life satisfaction now in this case we have got only one independent variable so your beta standardized beta is similar to r here and your t is 9.143 which is greater than 1.96 this means obviously there is a significant impact of summit leadership on life satisfaction now how do we report these results you just need to copy these from here and put it in a table like this where your hypothesis your regression weight servant leadership is influencing life satisfaction your beta coefficient which is 0.579 you just copy it from here and put it in here your r square 0.276 your f statistics here it is here it is 83.599 your p value this is your p value but what if you've got multiple independent variables in this case you can add another row here and just report the same values here but obviously the f value would be the same because you've got one regression or one regression equation can have one r oh sorry f value but when you've got multiple independent variables you will have to add another column here which is t value and you report the values from this table let's say we've got multiple independent variables so we go to analyze regression linear and let's add all these three variables here here are your three variables press ok and have a look at this so your f value is has increased your r square has increased so it's 0.581 so 58.1 percent change in life satisfaction can be accounted to these three variables which of these three variables are significant overall the model is significant but if you want to assess whether all of them oh sorry each one of them is significant you have to get two coefficients table and look at this standardized coefficient and unstandardized coefficient we normally report the unstandardized coefficient in multiple regression here is your t value just copy this table and you can put these values in the table above to report your multiple regression let's see how it is reported for let's say bivariate uh regression and what you can do is later just copy these results and do for the other relationships as well for h2 h3 h4 h5 any number of hypotheses the hypothesis tests if servant leadership carries a significant impact on life satisfaction the dependent variable life satisfaction was regressed on y on x predicting variable survey leadership to test the hypothesis h1 similarly you can just copy and paste it and do it for the other variables as well servant leadership significantly predicted life satisfaction f 1 2 1 9 and this comes from here in this case obviously it's 3 because you have got 3 predictors and if in bivariate regression it was 1 and here it would have been 2 1 9 and here is your bivariate the f value from bivariate regression the p which is which was 0.000 which was less than or which is rather less than point zero zero one this indicates that servant leadership can play a significant role in shaping life satisfaction and then you report your beta value with the p value from the coefficients table the results clearly direct the positive effect of servant leadership moreover the r square which was 0.276 depicts that the model explains 27.6 percent change or variance in life satisfaction and similarly you can just copy and paste and do it for the other variables as well i hope now you understand how to run interpret and report regression analysis thank you you

Channel: Research With Fawad

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Keywords: What is Regression, Regression Analysis, Regression Model SPSS, SPSS, Bivariate Regression, Multiple Regression, Difference between Bivariate and Multivariate Regression, Interpreting Regression Analysis, Reporting Regression Analysis, Report SPSS Results, SPSS Tutorial, SPSS Data Analysis

Id: kt4Br_vI-n4

Length: 8min 2sec (482 seconds)

Published: Thu Nov 04 2021