# SPSS – Hierarchical Multiple Linear Regression

– [Instructor] Okay, up for right now is a complete example of hierarchical multiple linear regression so we’re gonna cover how to from start to finish, run a multiple regression that has steps including data screening, power, and what you might write in the write up, and example of a possible representation of the data So this is data set two from blackboard and what’s in the data is that we have gender, where zero is female, one is male Age of the participant, and extroversion, so high scores are extroverted, low scores are introverted We’re really looking at how well they take care of their cars and so the dependent variable is car Are they washing it, or cleaning it, or they gave it oil change, they’re getting checkups, that sort of thing And so what we’re gonna do is we’re gonna control for demographic variable of sex and age, and then test if extroversion adds something to that equation in predicting how well people take care of their cars Okay? And so you’ll wanna start with power, and power for the (mumbles) not limited here in G power, is just, there’s only really a couple of options, so click on F tests, and then pull down that window, and you’ll get two options, linear multiple regression, R squared from zero, that tested the overall model is significant, or R squared increase, which you could use for this type of model, and that would test if extroversion is an addition to the model I wanna go deviation from zero, ’cause I kind know overall it’s significant, but both options are viable If you don’t know, this is F squared, so not your normal aida or R squared So if you hover over it, it’ll give you the convention sizes or you can hit Determine out here, and kind of calculate from a different, a couple of different things but this square multiplication that’s row, you can do R squared there, and that will calculate it for you So I’m gonna close this bad boy and leave it at .15, alpha is always .05, power is 80%, and this case we have three predictors total, so we use three That says we need 77 people to detect a significant effect I only have 40, so let’s see what happens It’s gonna tell you my calculate power The next thing I wanna do is the really intense process of data screening for regression But this isn’t a fake regression, it’s a real regression, so it’s a little easier ’cause I don’t have to create some random variables to test this The first thing is always missing data and accuracy of your data, so go Analyze, Descriptives, and then Frequencies I’m gonna select everything and move it over And under Statistics, really you need the min and the max, but it doesn’t hurt if you kind of look in the means and the standard deviations, if it is this your own research field and it’s not sort of a silly example You can notice things like, wait why is that score so low? Oh no, maybe I forgot to reverse code it, that sort of thing And then okay, let’s look at the output here It indicates that my data is zero to one, which is good ’cause gender should be evenly split My ages don’t seem abnormal, like you wouldn’t expect somebody to be four and have a car My extroversion score, is it find me what that scale was, I think it’s zero to 100, so we’re doing pretty good And the car scale is also zero to 100 So how well they’re taking care of their car So far everything looks good And I don’t have any missing data here, so see, no missing So that first assumption check works out Now, to do outliers, what we’re gonna do is we’re actually gonna set up the regression to run as if we were ready to test and then check for outliers in three different ways The reason I picked these three, they do seem to be the most popular To me they really get at the point of what regression is testing, and they sort of will cover you There are lots and lots of options as you’ll see here in a second to test for outliers in regression, and these seem to be, to me these were the best three Okay, so let’s set up the analysis as if we’re gonna run it So Analyze, Regression, Linear Our DV is car Now, this is a hierarchical regression, so we’re gonna get to use these different blocks here, and they’re not actually called blocks in the output, it’s called models, so block just means what do you want to do next So first, we’re gonna control for demographics