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edited Feb 26, 2014 at 21:09

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Significant first-step coefficient becomes non-significantinsignificant in second step of hierarchical multiple regression

I've tried to find the answer to this on this website but haven't been able to, so apologies if this has already been resolved. I am carrying out a hierarchical multiple regression. In the first step, the model is significant, and the predictors X1$X_1$, X2$X_2$ and X3$X_3$ have significant coefficients. When I add X4$X_4$ in the second step, the model is significant and the R square$R^2$ change is significant. The coefficient for X4$X_4$ is also significant. However, the previously significant coefficient of X1$X_1$ becomes non-significantinsignificant. Why would this be happening?

There do not appear to be any problems with multicollinearity. I don't know if the following is relevant, but X1$X_1$ and X4$X_4$ are moderately positively correlated and are equally correlated with the dependent variable. X2$X_2$ and X3$X_3$ are dummy variables of a categorical variable with 3 levels. All other variables are continuous. Thanks!

Significant coefficient becomes non-significant in hierarchical multiple regression

I've tried to find the answer to this on this website but haven't been able to so apologies if this has already been resolved. I am carrying out a hierarchical multiple regression. In the first step, the model is significant and the predictors X1, X2 and X3 have significant coefficients. When I add X4 in the second step the model is significant and the R square change is significant. The coefficient for X4 is also significant. However, the previously significant coefficient of X1 becomes non-significant. Why would this be happening? There do not appear to be any problems with multicollinearity. I don't know if the following is relevant but X1 and X4 are moderately positively correlated and are equally correlated with the dependent variable. X2 and X3 are dummy variables of a categorical variable with 3 levels. All other variables are continuous. Thanks!

Significant first-step coefficient becomes insignificant in second step of hierarchical multiple regression

I've tried to find the answer to this on this website but haven't been able to, so apologies if this has already been resolved. I am carrying out a hierarchical multiple regression. In the first step, the model is significant, and the predictors $X_1$, $X_2$ and $X_3$ have significant coefficients. When I add $X_4$ in the second step, the model is significant and the $R^2$ change is significant. The coefficient for $X_4$ is also significant. However, the previously significant coefficient of $X_1$ becomes insignificant. Why would this be happening?

There do not appear to be any problems with multicollinearity. I don't know if the following is relevant, but $X_1$ and $X_4$ are moderately positively correlated and are equally correlated with the dependent variable. $X_2$ and $X_3$ are dummy variables of a categorical variable with 3 levels. All other variables are continuous. Thanks!