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Multivariate regression analysis is a statistical technique used to analyze the relationship between multiple independent variables and a dependent variable. In other words, it helps to understand how changes in one or more variables affect the outcome of another variable. This method is commonly used in data analysis to identify and measure the strength of relationships between variables and to make predictions about the dependent variable based on the independent variables. In SPSS data analysis, multivariate regression can be used to model and analyze complex data sets with multiple variables, allowing researchers to gain insights into the underlying relationships and patterns within the data. It is a powerful tool for understanding and interpreting data in various fields such as social sciences, healthcare, and business.
Multivariate Regression Analysis | SPSS Data Analysis Examples
As the name implies, multivariate regression is a technique that estimates a single regression model with more than one outcome variable. When there is more than one predictor variable in a multivariate regression model, the model is a multivariate multiple regression.
Please Note: The purpose of this page is to show how to use various data analysis commands. It does not cover all aspects of the research process which researchers are expected to do. In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics and potential follow-up analyses.
Examples of multivariate regression
Example 1. A researcher has collected data on three psychological variables, four academic variables (standardized test scores), and the type of educational program the student is in for 600 high school students. She is interested in how the set of psychological variables is related to the academic variables and the type of program the student is in.
Example 2. A doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects (e.g., how many ounces of red meat, fish, dairy products, and chocolate consumed per week). She wants to investigate the relationship between the three measures of health and eating habits.
Example 3. A researcher is interested in determining what factors influence the health African Violet plants. She collects data on the average leaf diameter, the mass of the root ball, and the average diameter of the blooms, as well as how long the plant has been in its current container. For predictor variables, she measures several elements in the soil, as well as the amount of light and water each plant receives.
Description of the data
Let’s pursue Example 1 from above. We have a hypothetical dataset with 600 observations on seven variables. he psychological variables are locus of control (locus_of_control), self-concept (self_concept), and motivation (motivation). The academic variables are standardized tests scores in reading (read), writing (write), and science (science), as well as a categorical variable (prog) giving the type of program the student is in (general, academic, or vocational).
Let’s look at the data mvreg. Note that there are no missing values in this data set.
descriptives variables = locus_of_control self_concept motivation read write science. Descriptive Statistics N Minimum Maximum Mean Std. Deviation locus_of_control 600 -2.00 2.21 0.0965 0.67028 self_concept 600 -2.53 2.09 0.0049 0.70551 motivation 600 -2.75 2.58 0.0039 0.82240 read 600 24.62 80.59 51.9018 10.10298 write 600 20.07 83.93 52.3848 9.72645 science 600 21.99 80.37 51.7633 9.70618 Valid N (listwise) 600 correlations variables = locus_of_control self_concept motivation. Correlations locus_of_control self_concept motivation locus_of_control Pearson Correlation 1 0.171 0.245 Sig. (2-tailed) <0.001 <0.001 N 600 600 600 self_concept Pearson Correlation 0.171 1 0.289 Sig. (2-tailed) <0.001 <0.001 N 600 600 600 motivation Pearson Correlation 0.245 0.289 1 Sig. (2-tailed) <0.001 <0.001 N 600 600 600 correlations variables = read write science. Correlations read write science read Pearson Correlation 1 0.629 0.691 Sig. (2-tailed) <0.001 <0.001 N 600 600 600 write Pearson Correlation 0.629 1 0.569 Sig. (2-tailed) <0.001 <0.001 N 600 600 600 science Pearson Correlation 0.691 0.569 1 Sig. (2-tailed) <0.001 <0.001 N 600 600 600
Analysis methods you might consider
Below is a list of some analysis methods you may have encountered. Some of the methods listed are quite reasonable while others have either fallen out of favor or have limitations.
Multivariate multiple regression, the focus of this page.
Separate OLS Regressions – You could analyze these data using separate OLS regression analyses for each outcome variable. The individual coefficients, as well as their standard errors will be the same as those produced by the multivariate regression. However, the OLS regressions will not produce multivariate results, nor will they allow for testing of coefficients across equations.
Canonical correlation analysis might be feasible if you don’t want to consider one set of variables as outcome variables and the other set as predictor variables.
Multivariate regression
To conduct a multivariate regression in SPSS, we can use either of two commands, glm or manova. Using the lmatrix subcommand in the glm command, you can test if all of the equations, taken together, are statistically significant. The F-ratios and p-values for three multivariate criterion are given, including Wilks’ lambda, Lawley-Hotelling trace, Pillai’s trace, and Roy’s largest root. We can get the regression coefficients from either the glm or the manova command by including the print subcommand with the keyword parameter.
Below we run the glm command. Notice that we have multiple dependent variables listed before the SPSS keyword with. (The SPSS keyword with indicates that continuous predictor variables will follow.) We use the lmatrix subcommand to request the test of overall model. Semi-colons are required between each predictor.
glm locus_of_control self_concept motivation with read write science
/print = parameters
/lmatrix 'multivariate test of entire model' write 1; read 1; science 1.Multivariate Testsa
Effect Value F Hypothesis df Error df Sig.
Intercept Pillai's Trace 0.165 39.239b 3.000 594.000 <0.001
Wilks' Lambda 0.835 39.239b 3.000 594.000 <0.001
Hotelling's Trace 0.198 39.239b 3.000 594.000 <0.001
Roy's Largest Root 0.198 39.239b 3.000 594.000 <0.001
read Pillai's Trace 0.027 5.529b 3.000 594.000 <0.001
Wilks' Lambda 0.973 5.529b 3.000 594.000 <0.001
Hotelling's Trace 0.028 5.529b 3.000 594.000 <0.001
Roy's Largest Root 0.028 5.529b 3.000 594.000 <0.001
write Pillai's Trace 0.056 11.807b 3.000 594.000 <0.001
Wilks' Lambda 0.944 11.807b 3.000 594.000 <0.001
Hotelling's Trace 0.060 11.807b 3.000 594.000 <0.001
Roy's Largest Root 0.060 11.807b 3.000 594.000 <0.001
science Pillai's Trace 0.017 3.397b 3.000 594.000 0.018
Wilks' Lambda 0.983 3.397b 3.000 594.000 0.018
Hotelling's Trace 0.017 3.397b 3.000 594.000 0.018
Roy's Largest Root 0.017 3.397b 3.000 594.000 0.018
a Design: Intercept + read + write + science
b Exact statisticTests of Between-Subjects Effects
Source Dependent Variable Type III
Sum of Squares df Mean Square F Sig.
Corrected Model locus_of_control 45.230a 3 15.077 40.135 <0.001
self_concept 1.892b 3 0.631 1.269 0.284
motivation 30.586c 3 10.195 16.224 <0.001
Intercept locus_of_control 37.608 1 37.608 100.116 <0.001
self_concept 0.807 1 0.807 1.624 0.203
motivation 19.477 1 19.477 30.993 <0.001
read locus_of_control 4.802 1 4.802 12.784 <0.001
self_concept 0.257 1 0.257 0.517 0.472
motivation 3.893 1 3.893 6.195 0.013
write locus_of_control 5.391 1 5.391 14.352 <0.001
self_concept 0.351 1 0.351 0.705 0.401
motivation 11.928 1 11.928 18.982 <0.001
science locus_of_control 0.827 1 0.827 2.202 0.138
self_concept 0.623 1 0.623 1.254 0.263
motivation 2.670 1 2.670 4.249 0.040
Error locus_of_control 223.886 596 0.376
self_concept 296.259 596 0.497
motivation 374.542 596 0.628
Total locus_of_control 274.707 600
self_concept 298.165 600
motivation 405.138 600
Corrected Total locus_of_control 269.116 599
self_concept 298.151 599
motivation 405.129 599
a R Squared = 0.168 (Adjusted R Squared = 0.164)
b R Squared = 0.006 (Adjusted R Squared = 0.001)
c R Squared = 0.075 (Adjusted R Squared = 0.071)
Parameter Estimates Dependent Variable Parameter B Std. Error t Sig. 95% Confidence Interval Lower Bound Upper Bound locus_of_control Intercept -1.555 0.155 -10.006 <0.001 -1.861 -1.250 read 0.013 0.004 3.575 <0.001 0.006 0.021 write 0.013 0.003 3.788 <0.001 0.006 0.020 science 0.005 0.004 1.484 0.138 -0.002 0.013 self_concept Intercept -0.228 0.179 -1.274 0.203 -0.579 0.123 read 0.003 0.004 0.719 0.472 -0.005 0.012 write -0.003 0.004 -0.840 0.401 -0.011 0.004 science 0.005 0.004 1.120 0.263 -0.004 0.013 motivation Intercept -1.119 0.201 -5.567 <0.001 -1.514 -0.724 read 0.012 0.005 2.489 0.013 0.003 0.021 write 0.019 0.004 4.357 <0.001 0.011 0.028 science -0.010 0.005 -2.061 0.040 -0.019 0.000
Contrast Results (K Matrix)a Contrast Dependent Variable locus_of_control self_concept motivation L1 Contrast Estimate 0.013 -0.003 0.019 Hypothesized Value 0 0 0 Difference (Estimate - Hypothesized) 0.013 -0.003 0.019 Std. Error 0.003 0.004 0.004 Sig. <0.001 0.401 <0.001 95% Confidence Interval for Difference Lower Bound 0.006 -0.011 0.011 Upper Bound 0.020 0.004 0.028 L2 Contrast Estimate 0.013 0.003 0.012 Hypothesized Value 0 0 0 Difference (Estimate - Hypothesized) 0.013 0.003 0.012 Std. Error 0.004 0.004 0.005 Sig. <0.001 0.472 0.013 95% Confidence Interval for Difference Lower Bound 0.006 -0.005 0.003 Upper Bound 0.021 0.012 0.021 L3 Contrast Estimate 0.005 0.005 -0.010 Hypothesized Value 0 0 0 Difference (Estimate - Hypothesized) 0.005 0.005 -0.010 Std. Error 0.004 0.004 0.005 Sig. 0.138 0.263 0.040 95% Confidence Interval for Difference Lower Bound -0.002 -0.004 -0.019 Upper Bound 0.013 0.013 0.000 a Based on the user-specified contrast coefficients (L') matrix: multivariate test of entire model
Multivariate Test Results Value F Hypothesis df Error df Sig. Pillai's trace 0.220 15.745 9.000 1788.000 <0.001 Wilks' lambda 0.784 16.856 9.000 1445.791 <0.001 Hotelling's trace 0.269 17.707 9.000 1778.000 <0.001 Roy's largest root 0.244 48.565a 3.000 596.000 <0.001 a The statistic is an upper bound on F that yields a lower bound on the significance level.
Univariate Test Results Source Dependent Variable Sum of Squares df Mean Square F Sig. Contrast locus_of_control 45.230 3 15.077 40.135 <0.001 self_concept 1.892 3 0.631 1.269 0.284 motivation 30.586 3 10.195 16.224 <0.001 Error locus_of_control 223.886 596 0.376 self_concept 296.259 596 0.497 motivation 374.542 596 0.628
The tests for the overall model, shown in the first table above, indicate that the model is statistically significant, regardless of the type of multivariate criteria that is used (i.e., all of the p-values are less than 0.0001). Below the overall model tests are the between-subjects tests. The variables locus_of_control and motivation are statistically significant; the variable self_concept is not. In the third table, we see the parameter estimates for each of the predictor variables for each of the dependent variables. The fifth table gives the multivariate tests, which are all statistically significant, regardless of which test is used. The last table gives the univariate tests, two of which are statistically significant.
The output from the manova command contains most of the same information given by the glm command, but it is organized a little differently.
The first table gives the number of observations, number of parameters, RMSE, R-squared, F-ratio, and p-value for each of the three models.
manova locus_of_control self_concept motivation with read write science
/print parameters. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
The default error term in MANOVA has been changed from WITHIN CELLS to
WITHIN+RESIDUAL. Note that these are the same for all full factorial designs.
* * * * * * * * * * * * * * * * * A n a l y s i s o f V a r i a n c e * * * * * * * * * * * * * * * * *
600 cases accepted.
0 cases rejected because of out-of-range factor values.
0 cases rejected because of missing data.
1 non-empty cell.
1 design will be processed.
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* * * * * * * * * * * * * * * * * A n a l y s i s o f V a r i a n c e -- Design 1 * * * * * * * * * * * * * * * * *
EFFECT .. WITHIN CELLS Regression
Multivariate Tests of Significance (S = 3, M = -1/2, N = 296 )
Test Name Value Approx. F Hypoth. DF Error DF Sig. of F
Pillais 0.22030 15.74505 9.00 1788.00 0.000
Hotellings 0.26889 17.70671 9.00 1778.00 0.000
Wilks 0.78439 16.85640 9.00 1445.79 0.000
Roys 0.19644
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EFFECT .. WITHIN CELLS Regression (Cont.)
Univariate F-tests with (3,596) D. F.
Variable Sq. Mul. R Adj. R-sq. Hypoth. MS Error MS F Sig. of F
locus_of 0.16807 0.16388 15.07664 0.37565 40.13509 0.000
self_con 0.00635 0.00135 0.63077 0.49708 1.26896 0.284
motivati 0.07550 0.07084 10.19548 0.62843 16.22382 0.000
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Regression analysis for WITHIN CELLS error term
--- Individual Univariate0.9500 confidence intervals
Dependent variable .. locus_of_control
COVARIATE B Beta Std. Err. t-Value Sig. of t Lower -95% CL- Upper
read 0.0133466809 0.2011716274 0.00373 3.57542 0.000 0.00602 0.02068
write 0.0129191741 0.1874705938 0.00341 3.78845 0.000 0.00622 0.01962
science 0.0054541421 0.0789802632 0.00368 1.48403 0.138 -0.00176 0.01267
Dependent variable .. self_concept
COVARIATE B Beta Std. Err. t-Value Sig. of t Lower -95% CL- Upper
read 0.0030876697 0.0442156218 0.00429 0.71906 0.472 -0.00535 0.01152
write -0.0032943533 -0.0454171665 0.00392 -0.83980 0.401 -0.01100 0.00441
science 0.0047345432 0.0651360875 0.00423 1.11988 0.263 -0.00357 0.01304
Dependent variable .. motivation
COVARIATE B Beta Std. Err. t-Value Sig. of t Lower -95% CL- Upper
read 0.0120168331 0.1476238599 0.00483 2.48889 0.013 0.00253 0.02150
write 0.0192165750 0.2272728015 0.00441 4.35678 0.000 0.01055 0.02788
science -0.0097985928 -0.1156455467 0.00475 -2.06130 0.040 -0.01913 -0.00046
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* * * * * * * * * * * * * * * * * A n a l y s i s o f V a r i a n c e -- Design 1 * * * * * * * * * * * * * * * * *
EFFECT .. CONSTANT
Multivariate Tests of Significance (S = 1, M = 1/2, N = 296 )
Test Name Value Exact F Hypoth. DF Error DF Sig. of F
Pillais 0.16540 39.23945 3.00 594.00 0.000
Hotellings 0.19818 39.23945 3.00 594.00 0.000
Wilks 0.83460 39.23945 3.00 594.00 0.000
Roys 0.16540
Note.. F statistics are exact.
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EFFECT .. CONSTANT (Cont.)
Univariate F-tests with (1,596) D. F.
Variable Hypoth. SS Error SS Hypoth. MS Error MS F Sig. of F
locus_of 37.60832 223.88586 37.60832 0.37565 100.11601 0.000
self_con 0.80711 296.25868 0.80711 0.49708 1.62370 0.203
motivati 19.47692 374.54227 19.47692 0.62843 30.99314 0.000
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Estimates for locus_of_control adjusted for 3 covariates
--- Individual univariate 0.9500 confidence intervals
CONSTANT
Parameter Coeff. Std. Err. t-Value Sig. t Lower -95% CL- Upper
1 -1.5552772264 0.15544 -10.00580 0.00000 -1.86055 -1.25001
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Estimates for self_concept adjusted for 3 covariates
--- Individual univariate 0.9500 confidence intervals
CONSTANT
Parameter Coeff. Std. Err. t-Value Sig. t Lower -95% CL- Upper
1 -0.2278406241 0.17880 -1.27424 0.20307 -0.57900 0.12332
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Estimates for motivation adjusted for 3 covariates
--- Individual univariate 0.9500 confidence intervals
CONSTANT
Parameter Coeff. Std. Err. t-Value Sig. t Lower -95% CL- Upper
1 -1.1192470175 0.20104 -5.56715 0.00000 -1.51409 -0.72440
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Abbreviated Extended
Name Name
locus_of locus_of_control
motivati motivation
self_con self_concept
If you ran a separate OLS regression for each outcome variable, you would get exactly the same coefficients, standard errors, t- and p-values, and confidence intervals as shown above. So why conduct a multivariate regression? As we mentioned earlier, one of the advantages of using multivariate regression is that you can conduct tests of the coefficients across the different outcome variables.
Things to consider
The residuals from multivariate regression models are assumed to be multivariate normal. This is analogous to the assumption of normally distributed errors in univariate linear regression (i.e., OLS regression).
Multivariate regression analysis is not recommended for small samples.
The outcome variables should be at least moderately correlated for the multivariate regression analysis to make sense.
Cite this article
stats writer (2024). What is multivariate regression analysis and how is it used in SPSS data analysis?. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/stats/what-is-multivariate-regression-analysis-and-how-is-it-used-in-spss-data-analysis/
stats writer. "What is multivariate regression analysis and how is it used in SPSS data analysis?." PSYCHOLOGICAL SCALES, 29 Jun. 2024, https://scales.arabpsychology.com/stats/what-is-multivariate-regression-analysis-and-how-is-it-used-in-spss-data-analysis/.
stats writer. "What is multivariate regression analysis and how is it used in SPSS data analysis?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/stats/what-is-multivariate-regression-analysis-and-how-is-it-used-in-spss-data-analysis/.
stats writer (2024) 'What is multivariate regression analysis and how is it used in SPSS data analysis?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/stats/what-is-multivariate-regression-analysis-and-how-is-it-used-in-spss-data-analysis/.
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