diff --git a/R/regressionlogistic.R b/R/regressionlogistic.R
index f1aae796..aaed4f13 100644
--- a/R/regressionlogistic.R
+++ b/R/regressionlogistic.R
@@ -354,8 +354,10 @@ RegressionLogisticInternal <- function(jaspResults, dataset = NULL, options, ...
       rows <- vector("list", length(glmObj))
 
       for (midx in seq_along(glmObj)) {
-        .linregAddPredictorsInModelFootnote(jaspResults[["modelSummary"]], 
-                                            options[["modelTerms"]][[midx]][["components"]], midx)
+  
+        if (options$method == "enter")
+          .linregAddPredictorsInModelFootnote(jaspResults[["modelSummary"]], 
+                                              options[["modelTerms"]][[midx]][["components"]], midx)
         mObj <- glmObj[[midx]]
         if (midx > 1) {
           if (options$method == "forward" ||
diff --git a/inst/help/Correlation.md b/inst/help/Correlation.md
index c0e5d65d..c5938144 100755
--- a/inst/help/Correlation.md
+++ b/inst/help/Correlation.md
@@ -54,7 +54,7 @@ The Correlation analysis allows estimation of the population correlation, as wel
 #### Assumption checks
 
 - Multivariate normality
-  - Shapiro: Computes the Shapiro-Wilk statistic to test the null hypothesis that the selected variables have multivariate normal distribution.
+  - Shapiro:  Generalized Shapiro-Wilk test for multivariate normality by Villasenor-Alva and Gonzalez-Estrada (2009), using the mvShapiroTest package.
 
 - Pairwise normality
   - Shapiro: For each possible combination of the selected variables, computes the Shapiro-Wilk statistic to test the null hypothesis that the variable pair has a bivariate normal distribution.
@@ -108,6 +108,7 @@ The Correlation analysis allows estimation of the population correlation, as wel
 - Whitlock, M. C., & Schluter, D. (2015). *The analysis of biological data (2nd ed.)*. Greenwood Village, Colorado: Roberts and Company Publishers.
 - Sellke, T., Bayarri, M. J., & Berger, J. O. (2001). Calibration of *p* values for testing precise null hypotheses. *The American Statistician, 55*(1), 62-71.
 - Caruso, J.C., & Cliff, N. (1997). Empirical Size, Coverage, and Power of Confidence Intervals for Spearman's Rho. *Educational and Psychological Measurement, 57*(4), 637-654.
+-	Villasenor-Alva, J.A. and Gonzalez-Estrada, E. (2009). A generalization of Shapiro-Wilk's test for multivariate normality. *Communications in Statistics: Theory and Methods*, 38, 1870-1883.
 - Xu, W., Hou, Y., Hung, Y.S., & Zou, Y. (2013). A comparative analysis of Spearman’s rho and Kendall’s tau in normal and contaminated normal models. *Signal Processing, 93*, 261-276.
 
 ### R Packages
diff --git a/tests/testthat/test-correlation.R b/tests/testthat/test-correlation.R
index 57d5bf30..660e8ab9 100644
--- a/tests/testthat/test-correlation.R
+++ b/tests/testthat/test-correlation.R
@@ -24,19 +24,19 @@ results <- jaspTools::runAnalysis("Correlation", "debug.csv", options)
 test_that("Shapiro-Wilk Test for Multivariate Normality table results match", {
   table <- results[["results"]][["assumptionsContainer"]][["collection"]][["assumptionsContainer_multivariateShapiro"]][["data"]]
   jaspTools::expect_equal_tables(table,
-                      list(0.878989128563944, 6.30818027438384e-06))
+                                 list(0.951937236443559, 1.26292374637229e-05))
 })
 
 test_that("Shapiro-Wilk Test for Bivariate Normality table results match", {
   table <- results[["results"]][["assumptionsContainer"]][["collection"]][["assumptionsContainer_pairwiseShapiro"]][["data"]]
   jaspTools::expect_equal_tables(table,
-                      list(0.901474720121716, 1.65965808553626e-06, "-", "contNormal", "contGamma",
-                           0.966220519133756, 0.0114432512312019, "-", "contNormal", "contcor1",
-                           0.966282842281815, 0.0561697740909883, "-", "contNormal", "debMiss30",
-                           0.873628887299087, 9.84839266547295e-08, "-", "contGamma", "contcor1",
-                           0.862918050703853, 1.7604538483754e-06, "-", "contGamma", "debMiss30",
-                           0.990143883746405, 0.861444767503725, "-", "contcor1", "debMiss30"
-                      ))
+                                 list(0.920133622718739, 9.26266088028011e-09, "-", "contNormal", "contGamma",
+                                      0.975095766862836, 0.0197633225756002, "-", "contNormal", "contcor1",
+                                      0.97421223116433, 0.108462090826961, "-", "contNormal", "debMiss30",
+                                      0.930015026431291, 8.89751236523928e-08, "-", "contGamma", "contcor1",
+                                      0.933648725575745, 2.37704417812631e-05, "-", "contGamma", "debMiss30",
+                                      0.98166946114741, 0.416182497522526, "-", "contcor1", "debMiss30"
+                                 ))
 })
 
 test_that("Correlation plot matches", {