If you are not familiar with
Bivariate Regression or standard
Multiple Regression, then I strongly recommend returning to those previous
tutorials and reviewing them prior to reviewing this tutorial.
Multiple Linear Regression
while evaluating the influence of a covariate.
Multiple regression simply refers to a regression model with multiple
predictor variables. Multiple regression, like any regression analysis, can have a couple of different purposes.
Regression can be used for prediction or determining variable importance, meaning how are two or
more variables related in the context of a model. There are a vast number of
types and ways to conduct regression. This tutorial will focus exclusively on
ordinary least squares (OLS) linear regression. As with many of the tutorials on
this web site, this page should not be considered a replacement for a good
textbook, such as:
Pedhazur, E. J. (1997). Multiple regression in behavioral research:
Explanation and prediction (3rd ed.). New York: Harcourt Brace.
For the duration of this tutorial, we
will be using
RegData001.sav
Standard Multiple Regression. Standard multiple regression is
perhaps one of the most popular statistical analysis. It is extremely
flexible and allows the researcher to investigate multiple variable
relationships in a single analysis context. The general interpretation of
multiple regression involves: (1) whether or not the regression model is
meaningful, (2) which variables contribute meaningfully to the model. The first
part is concerned with model summary statistics (given the assumptions are met), and
the second part is concerned with evaluating the predictor variables (e.g. their
coefficients).
Assumptions: Please notice the mention of assumptions above.
Regression also likely has the distinction of being the most frequently abused
statistical analysis, meaning it is often used incorrectly. There are many
assumptions of multiple regression analysis. It is strongly urged that one
consult a good textbook to review all the assumptions of regression, such as
Pedhazur (1997). However, some of the more frequently violated assumptions will
be reviewed here briefly. First, multiple regression works best under the
condition of proper model specification; essentially, you should have all the
important variables in the model and no unimportant variables in the model.
Literature reviews on the theory and variables of interest pay big dividends
when conducting regression. Second, regression works best when there is a lack
of multicollinearity. Multicollinearity is a big fancy word for: your predictor
variables are too strongly related, which degrades regression's ability to
discern which variables are important to the model. Third, regression is
designed to work best with linear relationships. There are types of regression
specifically designed to deal with nonlinear relationships (e.g. exponential,
cubic, quadratic, etc.); but standard multiple regression using ordinary least
squares works best with linear relationships. Fourth, regression is designed to
work with continuous or nearly continuous data. This one causes a great deal of
confusion, because 'nearly continuous' is a subjective judgment. A 9point
Likert response scale item is NOT a continuous, or even nearly continuous,
variable. Again, there are special types of regression to deal with different
types of data, for example, ordinal regression for dealing with an ordinal
outcome variable, logistic regression for dealing with a binary dichotomous
outcome, multinomial logistic regression for dealing with a polytomous outcome
variable, etc. Furthermore, if you have one or more categorical predictor
variables, you cannot simply enter them into the model. Categorical predictors
need to be coded using special strategies in order to be included into a
regression model and produce meaningful interpretive output. The use of dummy
coding, effects coding, orthogonal coding, or criterion coding is appropriate
for entering a categorical predictor variable into a standard regression model.
Again, a good textbook will review each of these strategiesas each one lends
itself to particular purposes. Fifth, regression works best when outliers are
not present. Outliers can be very influential to correlation and therefore,
regression. Thorough initial data analysis should be used to review the data,
identify outliers (both univariate and multivariate), and take appropriate
action. A single, severe outlier can wreak havoc in a multiple regression
analysis; as an esteemed colleague is fond of saying...know thy data!
Covariates in Regression. Introducing a covariate to a multiple
regression model is very similar to conducting sequential multiple regression
(sometimes called hierarchical multiple regression). In each of these
situations, blocks are used to enter specific variables (be they predictors or
covariates) into the model in chunks. The use of blocks allows us to isolate the
effects of these specific variables in terms of both the predictive model and
the relative contribution of variables in each block. Multiple variables (be
they covariates or predictors) can be entered in each block. The order of entry
of each block is left to the discretion of the research; some prefer to enter
the covariate(s) block first, then the predictor(s) block; while others enter
the predictor(s) block then the covariate(s) block. The results would be the
same in terms of R² change.
However, the use of blocks in sequential/hierarchical regression and the
use of blocks in evaluating a covariate or covariates is NOT the same as
stepwise regression. Stepwise regression will not be discussed in this tutorial.
To conduct a standard multiple regression with the evaluation of a covariate,
start by clicking on Analyze, Regression, Linear...
First, highlight the y variable and use the top
arrow button to move it to the Dependent: box. Next, highlight the covariate
(c1) and use the second arrow button to move it to the Independent(s): box.
Then, click the Next button (marked with a red
ellipse here). That was our first block. Next, highlight all three predictor
variables (x1, x2, x3) and use the second arrow button to move them to the
Independent(s): box. Notice, we now have two blocks specified. Now click on the
Statistics... button.
Next, select Estimates (default), Confidence intervals, Model fit (default),
R square change, Descriptives, and Part and partial correlations. Then,
click the Continue button.
Next, click on the Plots... button. Then, highlight *ZRESID and use the top
arrow button to move it to the Y: box. Then, highlight *ZPRED and use the bottom
arrow button to move it to the X: box. Then click the Next
button (marked here with a red ellipse). Then select Histogram and Normal
probability plot. Then click the Continue button.
We could then click on the Save button and select one of the distance metrics
to allow us to evaluate outliers as was done in the previous tutorial. However,
we will skip that step here to save space. Next, click the OK button to conduct
the regression analysis. The output should be similar to that displayed below.
The first two tables are selfexplanatory provide straightforward
descriptive statistics for each variable in our models. The second table,
Variables Entered/Removed, displays which variables were in which model;
here the use of the word model is synonymous with the word block. In the
first block/model, the only independent variable entered was c1 (the
covariate). In the second block/model, x1, x2, and x3 were entered (and c1
was not removed).
The next table, Model Summary, provides the usual multiple correlation
coefficient (R), R²,
adj.R², and standard error for each model. The table also displays a few
new statistics which were not used in previous tutorials. The R²
change shows how much R²
changed (first from zero to model 1, then from model 1 to model 2). Then, F
statistics with degrees of freedom and associated pvalues are given for each
change in R² to
determine if the change was significantly different from zero. The table shows
that for this example, the majority of influence is held by the predictors, not
the covariatealthough the covariate by itself does contribute what may be a
meaningful amount (prior literature should inform interpretation). It is
important to realize that because we entered the covariate first in its own
model and did not remove it, the second model and subsequent R²
are cumulative. In other words, it would be incorrect to suggest that model 2
includes just the 3 predictors and accounts for 95.5 % of the variance in the
outcome variable (using adj.R²).
It would be appropriate to suggest that model 2, which includes all 3 predictors
and the covariate, accounts for 95.5 % of the variance in the outcome
variable (using adj.R²).
It would also be appropriate to suggest there was a significant increase in R²
from block 1 to block 2 such that the combination of the three predictors and
the covariate seem to account for a meaningful share of the variance in the
outcome variable.
The ANOVA table displays the test of each
model's R² to determine
if it is significantly different from zero. Essentially, if a model is
significant, then we are accounting for significantly more than 0% of the
variance in the outcome with that model's independent variables (be they
predictors or covariates).
Next, we have the Coefficients
table which shows the unstandardized and standardized coefficients necessary for
constructing a predictive regression equation in unstandardized or standardized
form. We can also use the information in this table to get some idea of variable
importance. So, for instance, in the first model where the covariate (c1) is the
only independent; we know the Beta (β) coefficient is simply the correlation
between the covariate and the outcome (because model 1 is simply a bivariate
regression). Furthermore, if we square that standardized coefficient, then we
get the squared multiple correlation from the model summary table above (.394² =
R² = .155) which means the covariate explains 15.5% of the variance in
the outcome. So, we know each Beta is simply a correlation coefficient between a
predictor (or covariate) and the outcome. However, Beta coefficients in model 2
are interpreted slightly differently. For instance, we could say that the x1
variable accounts for 35.5% of the variance in the outcome variable after
controlling for the covariate (c1). The x1 Beta (β = .596) can be squared to
give us the percentage (.596² = .355). These standardized coefficients (Beta or
β) represent slopes, or rise over run, in a standardized linear regression
equation. So, the larger the Beta, the more influential the variable it is
associated with, if multicollinearity is not present. The greater the
multicollinearity, less reliable the Beta coefficients will be at indicating
variable importance. Essentially, if your predictors and/or covariates are
strongly related, then you can not rely on the Beta coefficients as indicators
of variable importance.
Next, we have the Excluded Variables table which shows which variables were
excluded from each model. Next, we have the Residuals Statistics table which
reports descriptive statistics for the predicted and residual values.
Finally, we have our
histogram of standardized residuals, which we expect to be centered on zero; and
our Normal PP Plot where we hope to see the expected standardized residuals and
the observed standardized residuals closely following the reference line.
Keep in mind the distinction
between a covariate and a predictor is often simply a matter of semantics. It
may be the case that sociodemographic variables (i.e. age, income, etc.) are
influential predictors in one study, where in another they are considered
covariates or confounds in comparison to predictors of interest (i.e.
standardized measures of intelligence, depression inventories, body mass index,
etc.). In either case, the phrase sequential or hierarchical regression may be
used to describe the procedure of using blocks to distinguish between one group
of predictors (i.e. sociodemographic variables) and another group of predictors
(i.e. measures of intelligence).
REFERENCES & RESOURCES
Achen, C. H. (1982). Interpreting and using regression. Series: Quantitative
Applications in the Social Sciences, No. 29. Thousand Oaks, CA: Sage Publications. (1)
Akaike, H. (1974). A new look at the statistical model identification.
I.E.E.E. Transactions on automatic control, AC 19, 716 – 723. (1)
(2)
(3)
Allison, P. D. (1999). Multiple regression. Thousand Oaks, CA: Pine Forge
Press.
Cohen, J. (1968). Multiple regression as a general dataanalytic system.
Psychological Bulletin, 70(6), 426  443. (1)
Hardy, M. A. (1993). Regression with dummy variables. Series:
Quantitative Applications in the Social Sciences, No. 93. Thousand Oaks, CA: Sage
Publications. (1)
Harrell, F. E., Lee, K. L., & Mark, D. B. (1996). Multivariate prognostic
models: Issues in developing models, evaluating assumptions and adequacy, and
measuring and reducing errors. Statistics in Medicine, 15, 361 – 387. (1)
(2)
(3)
Kass, R. E., & Raftery, A., E. (1995). Bayes factors. Journal of the
American Statistical Association, 90, 773 – 795. (1)
(2)
(3)
Pedhazur, E. J. (1997). Multiple regression in behavioral research:
Explanation and prediction (3rd ed.). New York: Harcourt Brace.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of
Statistics, 6, 461 – 464. (1)
(2)
(3)
Tabachnick, B. G., & Fidell, L. S. (2001). Using Multivariate
Statistics. Fourth Edition. Boston: Allyn and Bacon.
