YouTube Transcript:
Review of SPSS macros to carry out multiple regression with robust standard errors (new_ 2018)

Skip watching entire videos - get the full transcript, search for keywords, and copy with one click.

Video Transcript

View:

okay in this video I'm going to review a

couple of macros that you can download

from the web for use with SPSS in order

to carry-out

regression analysis using robust

standard errors basically when you run

regression analysis through the standard

approach just a standard linear

regression it doesn't take into account

the possibility that you might have

heteroscedasticity in terms of your

residuals and if that is the case and

what that can do it may not it won't

bias your regression coefficients but it

can lead to biases in terms of the

standard errors and ultimately your

significance tests and confidence

intervals so this is basically a major

reason why we care about this particular

issue so what I'm going to do is I'm

going to review two macros in particular

the first one is Andrew Hayes macro he

has it on his website basically on his

book website it looks like there's a new

book out regression analysis and linear

models and at this particular downloaded

spot you can download a zip file

containing the R ALM macro and you can

install it to SPSS and then be able to

run it straight from there so in order

to do that if you just download it from

there you'll just go to extensions go to

utilities install custom dialog and I

basically have my I have it saved under

a certain folder here if you click on

this folder here go down to our LM macro

and click on it there's there's the

macro click on open it'll install it to

your program and so you can see it's

it's installed right here as as our LM

macro by Andrew Hayes so that's the

first one I'm going to demonstrate the

second one actually comes from this

website right here and I just happened

upon it by when I

ran across this YouTube video explaining

it and the nice thing about this

particular macro is it also allows you

to generate robust standard errors for

your regression analysis but it also

incorporates the brush of pagan test and

the cannot connect or test which are

basically utilized in the evaluation of

the homoscedasticity of variance

assumption so our constant variance

assumption so the basic idea is that if

you find statistical significance for in

terms of these tests right here that

would be an indicator that you have

heteroscedasticity which would call into

question perhaps the use of the standard

least squares regression that does not

make any kind of adjustments to the

standard errors and so that's so with

this program though you can also ask for

heteroscedasticity adjusted standard

errors so making them essentially robust

standard errors so I'm gonna walk

through both of these programs right

here are macros so and I've it installed

that pretty much doing the same thing

that did with Andrew Hayes's and you'll

see the under regression I've got it

right here heteroscedasticity test and

so at this point we'll run those

analyses I also have on here Andrew

Hayes is older macro the H basically

it's an older approach to giving robust

standard errors and that is actually

coming from this macro down here so you

might find it HC reg this is just a much

more drilled drilled down version of

what you would get with respect to this

particular macro so as he says on his

website it's becoming obsolete with the

release of the R LM so but I will just

kind of show that as a point of contrast

so you can see what it looks like so for

starters let's start with Andrew Hayes's

macro so we're going to go to animal

as good a regression rlm macro by Andrew

Hayes now first of all really quickly

the the basic model that we're going to

be looking at we have a dependent

variable which will be achieved and we

have a set of independent variables

right here so gender something matter

interest mastery goals and anxiety and

so I'm gonna move achieve which is going

to be my dependent variable to this box

and move the remaining variables over to

the regressors box I can ask for

standardized coefficients I can ask for

you know a number of different things

one of the kind of cool things about

this particular macro is you can even

get all subsets regression that's really

pretty slick and if you want regression

Diagnostics you know basically cut you

know in the same way that you might get

if you're running the analysis through

regression just standard regression

module and click on safe you know you

can ask for various things like

Mahalanobis and cooks D and and things

like that you can get some of that

through this macro as well so basically

will do so create a new file that would

incorporate some of those those

diagnostic indicators so so you don't

actually have to do it I'm just saying

that you you know it is available to you

so I'm gonna I'm just gonna stick with

this right here notice the confidence

interval is already defaulted at 95%

there are other options if you want it

and down here where it says covariance

estimator

HC so if I click on this there are

various adjustments to the standard

errors and I'm going to pivot off of a

haze in Kies 2007 article where they

actually recommended using either the HC

3 or HC 4 estimators so I'm going to use

HC 3 so I'm going to click on this go

down to HC 3 and at this point I'll

click on OK and so you can see we've got

several you know a number of pieces of

information we have the multiple are

right here the r-square value the f-test

and you know there's our p-value for

testing the significance related to our

square down here where it says

regression model you can see we have the

efficients and then a column containing

SC and so these are the robust standard

errors using that particular adjustment

that means that them that the t-value

p-values and confidence intervals are

all going to be reflecting the

adjustment to the standard errors so if

you scroll down a little bit further you

can see we've got we also asked for

those standardized assessments including

the correlation simple correlation

between each of the end of the

independent variables and the dependent

variable semi partial correlations and

then partial correlations right here and

then these would be your standard as

regression weights or you know basically

we call them beta weights so at any rate

that's you know that's what we have

right there if we want to compare this

just against what you would get in terms

of the standard errors we just run our

analysis I'm going to take these off run

our analysis without the adjustment you

can see the difference the for the

standard error is the standard error for

model one well for our model that for

the intercept is thirty three point nine

seven eight you can see it's forty seven

point nine one nine for gender it's one

point eight forty nine versus two point

one then 0.195 for the for the next one

is versus point one nine oh so you can

see that these standard errors in this

in this column right here tend to be a

little bit larger than those that we

have when we don't adjust for the

possibility of heteroscedasticity so at

any rate there you go

so that's kind of the difference that

you that you get right there let me just

show you one more time let's just I'll

show you what to get if you click on

regression Diagnostics will click on

okay and what you'll see it's just going

to be the same output but now it

generates a new file that contains all

of our variables as well as Diagnostics

and so you can see you know here we've

got the fitted values residuals hella

novus distance Kooks D and so forth

that's that's essentially Andrew Hayes's

macro

the new one the old one by the way it

looks like this if we want to use that

one the H see we're Greg regression when

basically you can see right here we

would just be putting our T variable

into the dependent box gender and

through anxiety into the predictors box

you can click on HC method and I've

already got set for HC three and so

that's the difference right there so

it's quite a difference in terms of

what's printed out but you see that we

have our F test right here the standard

errors the robust standard errors the

the T tests and the p-values all those

reflecting the adjustments for any kind

of heteroscedasticity they may be

present okay so that that takes care of

Andrew Hayes's version now we're going

to go to this one right here what we're

going to look at that and and again the

nice thing about this particular macro

is it does incorporate tests for

heteroscedasticity the brush pagan and

conic er test and and basically to carry

out that analysis once you've installed

it we're gonna go to analyze go to

regression and here it is so

heteroscedasticity test will click on it

and I'm just gonna reset it and just

kind of you know sometimes it's just

nice nice to have a visual walkthrough

so I'm just gonna put my independence in

the explanatory box outcome and the

achieve and the outcome box you can see

you've got the robust standard error

options hc3 I'm gonna click on OK and so

now you can see that here we've got the

standard least squares regression output

there's our R square value you can see

as before all of our regression

coefficients are exactly the same

whether we're using the default OLS

versus the heteroscedasticity robust

standard errors but you see the

differences lie in the column four

standard errors right here versus here

so we basically were able to generate

both of those using

and replaces macros and and then also

juxtaposing those against the standard

errors from the standard least squares

regression so then we have our T values

down here significance levels 95 percent

confidence intervals all down here in

this area adjusted for or after we've

adjusted the standard errors well scroll

down and you'll see that we have the

ANOVA summary table down here this looks

like this maybe from the standard least

squares regression priority any kind of

adjustments so that's something to kind

of note there when you look at Andrew

Hayes's F value that's printed out up

here you can see it's nine point zero

307 the FA from this macro is printed

out as far as it out there is nine point

five to four if we compare that with our

previous regression earlier on you can

see it's a nine point five to four so

that's where I'm getting that

supposition from okay so with the brush

pagan and Conacher tests I hope I'm

pronouncing that right I probably am not

but you can see down here that with

respect to those tests in this little

area we've got the the test results and

you've got the boys pagan test and the

concur tests these are the tests values

using a Lagrange multiplier test we have

significance levels right here and the

Pete you know basically if your p-values

are are greater than say 0.05

conventional alpha then we would assume

that the assumption of homoscedasticity

is met in other words the constant error

assumption is met if it's less than 0.05

then we would maybe infer that that we

violated that particular assumption just

keep in mind that these kinds of tests

are impacted by sample size and

you know the drill that the larger the

sample size the greater likelihood of

rejecting the null so just keep that in

mind as you're evaluating whether or not

you've violated or whether we violated

the assumption and can't use the

standard least squares regression if

it's so in this particular case it looks

like based on these tests or there's

evidence that we would not have violated

that assumption if we were using the

standard least squares regression so

that would call into question the need

to utilize adjusted standard errors or

robust standard errors in the way that

we have but you know also keep in mind

that this is like I said this is a

significance test it's probably not a

bad idea to also you know look at

residuals plots and so forth to further

evaluate whether the assumption is met

so at any rate that that pretty well

light lays it out for you and I hope you

find this useful that like I said these

are two really neat macros that are

available to you you know the nice thing

about Andrew Hayes macro the first one

is it does contain a lot more options in

terms of you know different types of

output and it does incorporate that nice

little aspect of the all subsets

regression if you choose to go with the

latter approach the nice thing is is

that you do get significance tests

related to you know testing the

assumption of constant variances but you

don't get quite as much in the way of

the individual output in other areas so

at any rate just uh I hope this I hope

you find this useful and good luck with

your research

Click on any text or timestamp to jump to that moment in the video

Most transcripts ready in under 5 seconds

One-Click Copy125+ LanguagesSearch ContentJump to Timestamps

Paste YouTube URL

Enter any YouTube video link to get the full transcript

Most transcripts ready in under 5 seconds

Get Our Chrome Extension

Get transcripts instantly without leaving YouTube. Install our Chrome extension for one-click access to any video's transcript directly on the watch page.

Add to Chrome — Free

Works with YouTube, Coursera, Udemy and more educational platforms

Get Instant Transcripts: Just Edit the Domain in Your Address Bar!

YouTube

←

→

↻

https://www.youtube.com/watch?v=UF8uR6Z6KLc

YoutubeToText

←

→

↻

https://youtubetotext.net/watch?v=UF8uR6Z6KLc

YouTube Transcript:Review of SPSS macros to carry out multiple regression with robust standard errors (new_ 2018)

Video Transcript

Paste YouTube URL

Transcript Extraction Form

Get Our Chrome Extension

Get Instant Transcripts: Just Edit the Domain in Your Address Bar!

YouTube Transcript:
Review of SPSS macros to carry out multiple regression with robust standard errors (new_ 2018)