Interpreting Output for Multiple Regression in SPSS

hello this is dr. Grande welcome to my video on interpreting the output from a multiple regression using SPSS as always if you find this video useful please like it and subscribe to my channel I certainly appreciate it I have in the SPSS data editor four variables these are fictitious data I have an ID variable I have a hundred participants in this design and I have two predictor variables and one outcome variable so for the first predictor variable this one is named career limitations so let's assume that we have participants that are in a career training program and we develop this series of questions to determine how many limitations they're facing in terms of getting back into the workforce and these questions go through a scoring process and end up in this variable and this is an index so certain characteristics or occurrences may be weighted more heavily than others potential limitations could include a criminal history low educational level an active substance use disorder and other factors so a higher value in this variable would represent more limitations or more severe limitations then we have experienced an experience we've measured in years this be the number of years a participant had either a full-time or part-time job then we have days until employed so after the completion of the Career Training Program the number of days until the participant finds employment is measured and for this example the maximum would be 365 days so we could have a couple hypotheses here before we conduct a multiple regression career limitations we believe the higher the number on the career limitations variable the longer it would take to become employed and for the experience variable the more experience associated with fewer days with a smaller number of days until the participant becomes employed now there are assumptions from multiple regression but here I'm gonna be focused on the output so I'm not going to check those assumptions but just know there are assumptions before running a multiple regression that would need to be checked to make sure these data would be appropriate for that statistic so here under analyze regression and linear I have the dialog for linear regression and you can see there's one space for a dependent variable or an outcome variable and that's going to be days until employed and you can have multiple independent variables or predictor variables in this case I have two career limitations and experience under statistics by default we have estimates and model fit I'm just going to add r-squared change and descriptives here as well as the confidence intervals at the 95% level continue and I'm not going to make any other changes here under the buttons on the right so ready to conduct the multiple regression click OK and let's take a look at the tables we have the descriptive statistics here up top then correlations variables entered and removed both of the variables I put into the model used here model summary we have R square and adjusted r-square we're going to be interpreting adjusted r-square so with this model we have the two predictor variables the one dependent variable and we have an adjusted r-square of 0.23 seven this tells us that 23.7% of the variance in the dependent variable is explained by the independent variables moving down to ANOVA we have a statistically significant finding here less than point zero five for the p-value then we have the coefficients table so we have your career limitations experience and the unstandardized coefficients for career limitations it's two point six five eight and for experience it's negative four point zero four four we also have the standardized coefficients and p-values so let's start with the p-values here for career limitations we have point zero zero nine that's statistically significant so this variable has a statistically significant impact on the outcome variable on the days until employed taking a look here at the p-value associated with experience you can see this is also less than point zero five so we have statistically significant contribution from the experience predictor variable looking at the unstandardized coefficients for career limitations we have a value here of two point six five eight and what this tells us is as the career limitations index increases by a value of one for every one unit of change for career limitations we're going to see a two points six five eight change in the days until employed variable so one point on the Kermit Asians one additional point is associated with two point six five eight days increase on the dependent variable so the more career limitations we have as measured by that scale by that index the longer it takes the participant to find employment experience however works differently with the experienced independent variable we have a negative value for the unstandardized coefficient negative four point zero four four so if this tells us is as experience increases by one year because experience is measured in years that's the unit of analysis for that variable the number of days into employed decreases by about four so more experience associated with a smaller number of days of unemployment now when thinking about this in terms of standard deviations we would look at the standardized coefficients so for every full standard deviation of movement we see in career limitations very one standard deviation of movement we see what this variable the dependent variable days until employed increases by 0.23 three standard deviations for every one standard deviation of movement we see an experience as an experience increases by one standard deviation we have a decrease on the dependent variable base until employed of negative 0.4 three six standard deviations and then moving over to the confidence interval and this is for the unstandardized coefficient we interpret before we can see there's a 95% chance that the actual value of the unstandardized coefficient is between 0.67 one and 4.6 or four and the actual value for experience we can be 95% confident that is between negative five point six six three and negative two point four two six I hope you found this video on interpreting the output from multiple regression in SPSS to be helpful as always if you have any questions or concerns feel free to contact me I'll be happy to assist you

Channel: Dr. Todd Grande

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Keywords: SPSS, regression, multiple, multiple regression, output, unstandardized, standardized, coefficients, unstandardized coefficients, standardized coefficients, coefficients table, predictor, outcome, predictor variable, outcome variable, standard deviation, ANOVA, adjusted r square, r square, confidence interval, p value, F test, t test, independent variables, dependent variable, variable, statistical significance, data, analysis, counseling, Grande

Id: WQeAsZxsXdQ

Length: 8min 40sec (520 seconds)

Published: Sun Nov 27 2016