1. Model Adequacy Running a Real Regression Analysis
Testing Assumptions, Checking for Outliers, and More

2. Hold up
Before the data was collected, did you bother to do a power analysis to estimate the sample size needed?
Same ingredients are necessary for determining N as before: alpha, effect size, desired power/beta
See an example in the commentary
Example in R: p is the number of predictors
library(MBESS)
ss.power.R2(Population.R2=.5, alpha.level=.05, desired.power=.85, p=5)

3. Linearity
One should obviously check to see if the relation is a linear one
Check the regression of the DV on the composite
Tests can be done which examine curvilinear possibilities and whether those would be viable

4. Normal distribution of residuals
Our normality assumption applies to the residuals
One can simply save them and plot a density curve/histogram
Often a quantile-quantile plot is readily available, and here we hope to find most of our data along a 45 degree line
*After fitting the model, models/graphs/basic diagnostic plots in R-commander. 1 click provides all of them

5. Homoscedasticity
We can check a plot of the residuals vs our predicted values to get a sense of the spread along the regression line
We prefer to see kind of a blob about the zero line (our mean), with no readily discernable pattern
This would mean that the residuals don’t get overly large for certain areas of the regression line relative to others

6. Collinearity
Multiple regression is capable of analyzing data with correlated predictor variables
However, problems can arise from situations in which two or more variables are highly intercorrelated
Perfect collinearity
Occurs if predictors are linear functions of each other (ex., age and year of birth), when the researcher creates dummy variables for all values of a categorical variable rather than leaving one out, and when there are fewer observations than variables
No unique regression solution
Less than perfect (the usual problem)
Inflates standard errors and makes assessment of the relative importance of the predictors unreliable
Also means that a small number of cases potentially can affect results strongly

7. Collinearity Simple and Multi- Collinearity
When two or more variables are highly correlated
Can be detected by looking at the zero order correlations
Better is to regress each predictor on all other variables and look for large R2s*
Although our estimates of our coefficients are not necessarily biased, they become inefficient
Jump around a lot from sample to sample
*You don’t have to actually do that. The tolerance statistic is just 1- that R2, and if the Variance Inflation Factor is given

8. Collinearity diagnostics
Tolerance*
Proportion of a predictors’ variance not accounted for by other variables
Looking for tolerance values that are small, close to zero as problematic
Means they are not contributing anything new to the model
tolerance = 1/VIF
VIF
Variance inflation factor
Looking for VIF values that are large
E.g. individual VIF greater than 10 should be inspected
VIF=1/tolerance
Other Indicators of Collinearity
Eigenvalues
Small values, close to zero
Condition index
Large values (15+)
*Essentially, it is 1 - the R2 for the model in which the other predictors are predicting that predictor. While I prefer it on an intuitive level, the VIF is often reported.

9. Dealing with collinearity
Collinearity not necessarily a problem if the goal is to predict, not explain
Inefficiency of coefficients may not pose a real problem
Larger N might help reduce standard error of our coefficients
Combine variables to create a composite, Remove variable
Must be theoretically feasible
Centering the data (subtracting the mean)
Interpretation of coefficients will change as variables are now centered on zero
Recognize its presence and live with the consequences

10. Independence of residuals
We need to have our data points be independent of one another, as we have in other statistical analyses
As an example, subsets of various demographic categories could in theory result in a lack of independence
Include the variable in the model
The Durbin-Watson statistic is usually examined
0 to 4, 2 good, deviations from it are to be examined
< 1 (indicates positive serial correlation), >3 (negative)
Better, use a statistical test for it
Like many tests regarding assumptions, we would prefer not to have a significant result

11. Regression Diagnostics
Of course all of the previous information would be relatively useless if we are not meeting our assumptions and/or have overly influential data points
In fact, you shouldn’t be really looking at the results unless you test assumptions and look for outliers, even though this requires running the analysis to begin with
Various tools are available for the detection of outliers
Classical methods
Standardized Residuals (ZRESID)
Studentized Residuals (SRESID)
Studentized Deleted Residuals (SDRESID)
Ways to think about outliers
Leverage
Discrepancy
Influence
Thinking ‘robustly’

12. Regression Diagnostics
Standardized Residuals (ZRESID)
Standardized errors in prediction
Mean 0, Sd = std. error of estimate
To standardize, divide each residual by its s.e.e.
At best an initial indicator (e.g. the +2 rule of thumb), but because the case itself determines what the variance would be, almost useless
Studentized Residuals (SRESID)
Same thing but studentized residual recognizes that the error associated with predicting values far from the mean of X is larger than the error associated with predicting values closer to the mean of X
standard error is multiplied by a value that will allow the result to take this into account
Studentized Deleted Residuals (SDRESID)
Studentized in which the standard error is calculated with the case in question removed from the others

13. Regression Diagnostics
Mahalanobis’ Distance
Mahalanobis distance is the distance of a case from the centroid of the remaining points (point where the means meet in n-dimensional space)
Cook’s Distance
Identifies an influential data point whether in terms of predictor or DV
A measure of how much the residuals of all cases would change if a particular case were excluded from the calculation of the regression coefficients.
With larger (relative) values, excluding a case would change the coefficients substantially.
DfBeta
Change in the regression coefficient that results from the exclusion of a particular case
Note that you get DfBetas for each coefficient associated with the predictors

14. Regression Diagnostics
Leverage assesses outliers among the predictors
Mahalanobis distance
Relatively high Mahalanobis suggests an outlier on one or more variables
Discrepancy
Measures the extent to which a case is in line with others
Influence
A product of leverage and discrepancy
How much would the coefficients change if the case were deleted?
Cook’s distance, dfBetas

15. Outliers Influence plots
With a couple measures of ‘outlierness’ we can construct a scatterplot to note especially problematic cases
After fitting a regression model in R-commander, i.e. running the analysis, this graph is available via point and click
Here we have what is actually a 3-d plot, with 2 outlier measures on the x and y axes (studentized residuals and ‘hat’ values, a measure of leverage) and a third in terms of the size of the circle (Cook’s distance)
For this example, case 35 appears to be a problem

16. Summary: Outliers
No matter the analysis, some cases will be the ‘most extreme’. However, none may really qualify as being overly influential.
Whatever you do, always run some diagnostic analysis and do not ignore influential cases
It should be clear to interested readers whatever has been done to deal with outliers
As noted before, the best approach to dealing with outliers when they do occur is to run a robust regression with capable software

17. Suppressor variables
There are a couple of ways in which suppression can occur or be talked of, but the gist is that this masks the impact the predictor would have on the dependent if the third variable did not exist
In general suppression occurs when i falls outside the range of 0  ryi
Suppression in MR can entail some different relationships among predictors
For example one suppressor relationship would be where two variables, X1 and X2, are positively related to Y, but when the equation comes out we get
Y-hat = b1X1 – b2X2 + a
Three kinds to be discussed
Classical
Net
Cooperative

18. Suppression: Technical side
When dealing with standardized regression coefficients, note that

19. Suppression Consider the following relationships
a.  Complete independence: R2Y.12 = 0
                            
b.  Partial independence: R2Y.12 = 0 but  r12   0,
                              
d.  Partial independence again, both rY1 and rY2 ≠ 0, but r12 = 0
                                   

20. Suppression e. Normal situation, redundancy: no simple correlation = 0
Each semi-partial correlation, and the corresponding beta, will be less than the simple correlation between Xi and Y. This is because the variables share variance and influence
f.  Classical suppression:  rY2 = 0

21. Suppression Recall from previously  If ry2 = 0, then 
With increasingly shared variance between X1 and X2 we will have an inflated beta coefficient for X1
X2 is suppressing the error variance in X1
In other words, even though X2 is not correlated with Y, having it in the equation raises the R2 from what it would have been with just X1. 

22. Suppression Other suppression situations Net Cooperative
All rs positive
2 ends up with a sign opposite that of its simple correlation with Y
It is always the X which has the smaller ryi which ends up with a  of opposite sign
 falls outside of the range 0  ryi, which is always true with any sort of suppression
Cooperative
Predictors negatively correlated with one another, both positive with DV
Or positively with one another and negatively with Y
Example of Cooperative:
Correlation between social aggressiveness (X1) and sales success (Y) = .29
Correlation between record keeping (X2) and sales success (Y) = .24
r12 = -.30
Regression coefficients for predictors = .398 and .359 respectively

23. Suppression
Gist: weird stuff can happen in MR, so take note of the relationship of the predictors and how it may affect your overall interpretation
Compare the simple correlations of each predictor with the DV and compare to their respective beta coefficients*
If coefficient noticeably larger than simple correlation (absolute value) or of opposite sign one should suspect possible suppression
*For predictors contributing notably to the model.

24. Model Validation
Overfitting
Validation
Bootstrapping

25. Overfitting External validity
In some cases, some of the variation the parameters chosen are explaining is variation that is idiosyncratic to the sample
We would not see this variability in the population
So the fit of the model is good, but it doesn’t generalize as well as one would think
Capitalization on chance

26. Overfitting Example from Lattin, Carroll, Green
Randomly generated 30 variables to predict an outcome variable
Using a best subsets approach, 3 variables were found that produce an R2 of .33 or 33% variance accounted for
As one can see, even random data has the capability of appearing to be a decent fit

27. Validation
One way to deal with such a problem is with a simple random split
With large datasets one can randomly split the sample into two sets
Calibration sample: used to estimate the coefficients
Holdout sample: used to validate the model
Some suggest a 2:1 or 4:1 split
This would require typically large samples for the holdout sample to be viable
Using the coefficients from the calibration set one can create predicted values for the holdout set
The squared correlation between the predicted values and observed values can then be compared to the R2 of the calibration set
In previous example of randomly generated data the R2 for the holdout set was 0

28. Other approaches Subsets approach Jackknife Validation
Create estimates with a particular case removed
Use the coefficients obtained from analysis of the n-1 remaining cases to create a predicted value for the case removed
Do for all cases, and then compare the jackknifed R2 to the original
Subsets approach
Create several samples of the data of roughly equal size
Use the holdout approach with one sample, and obtain estimates from the others
Do this for each sample, obtain average estimates

29. Bootstrap
With relatively smaller samples*, cross-validation may not be as feasible
One may instead resample (with replacement) from the original data to obtain estimates for the coefficients
Use what is available to create a sampling distribution of for the values of interest
*But still large enough such that the bootstrap estimates would be viable. There are numerous ways to do so in R, but easiest are the specific functions to do so such as the validate function from the ‘Design’ library.

30. Summary
There is a lot to consider when performing multiple regression analysis
Actually running the analysis is just the first step, and if that’s all we are doing, we haven’t done much
Inferences are likely incomplete at best, innaccuarate at worst
A lot of work will be necessary to make sure that the conclusions drawn will be worthwhile
And that’s ok, you can do it!

31. Summary of how one could do regression
Idea pops into your head
Have some loose hypotheses about correlations among some variables
Collect some data
Run the regression analysis
Use R2 and standard-fare metrics of variable importance
Rely on statistical significance when you don’t have any real effects to talk about
This would be a bad way to do regression.

32. Summary of how to do a real regression analysis
1. Have an idea
2. Propose a theoretical (possibly causal) model in which you have thought about other viable models (including how predictors might predict one another, moderating and mediating possibilities* etc.)
3. Collect appropriate and enough data (based on a power analysis)
Must have reliable measures
4. Spend time with initial examination of data including obtaining a healthy understanding of the variables descriptively, missing values analysis if necessary, inspection of correlations etc.
5. Run the analysis. Might as well ignore for now.
6. With the model in place, test assumptions, look for collinearity, identify outliers. Take appropriate steps necessary to deal with any issues including bootstrapped regression or robust regression
7. Rerun the analysis.
8. Validate the model. Note any bias.
9. Interpret results. Focus on bias corrected estimates of R2, interval estimates of coefficients, interpretable measures of variable importance (test for differences on them)
*Note that moderating and mediating situations must be theoretically plausible, it’s not something one ‘explores’ unless you really are doing an exploratory regression (e.g. of a stepwise nature). Several have come into RSS wanting to ‘see if there might be moderators or mediators’. They are entirely different theoretical models. We’ll talk more on the distinction later.