1. Taking the average of the 10 standard deviations is not correct. Following Rubin's rules for multiple imputation, you can use the following formula to get the pooled standard deviation

$$Var(\bar{\beta}) = m^{-1} \sum_{k=1}^m Var(\hat{\beta_k}) + (1+m^{-1})(m-1)^{-1} \sum_{k=1}^m (\hat{\beta_k} - \bar{\beta})$$ where $\hat{\beta}_k$ is the estimated point estimate for imputation indexed by $k$, $Var(\hat{\beta})$ is the variance of the point estimates for a single imputation, and $$\bar{\beta} = m^{-1} \sum_{k=1}^m \hat{\beta_k}$$ for $m$ imputations (10 in your case). For the standard deviation, you would $\sqrt{Var(\bar{\beta})}$. The above calculation is important since it accounts for the variation between imputations and within imputations, which the average of the standard deviations from imputations does not.

2. Correct. You would use $\bar{\beta}$ and $Var(\bar{\beta})$ to calculate the p-value.

3. I believe you should include the outcome is the first model. It should be possible with SPSS to indicate the order in which the variables are imputed. You would want the outcome to be imputed last. My knowledge of SPSS is limited, so I can't help on this point as much. Hopefully someone else can comment on this part.

1. Taking the average of the 10 standard deviations is not correct. Following Rubin's rules for multiple imputation, you can use the following formula to get the pooled standard deviation

$$Var(\bar{\beta}) = m^{-1} \sum_{k=1}^m Var(\hat{\beta_k}) + (1+m^{-1})(m-1)^{-1} \sum_{k=1}^m (\hat{\beta_k} - \bar{\beta})^2$$ where $\hat{\beta}_k$ is the estimated point estimate for imputation indexed by $k$, $Var(\hat{\beta})$ is the variance of the point estimates for a single imputation, and $$\bar{\beta} = m^{-1} \sum_{k=1}^m \hat{\beta_k}$$ for $m$ imputations (10 in your case). For the standard deviation, you would $\sqrt{Var(\bar{\beta})}$. The above calculation is important since it accounts for the variation between imputations and within imputations, which the average of the standard deviations from imputations does not.

2. Correct. You would use $\bar{\beta}$ and $Var(\bar{\beta})$ to calculate the p-value.

3. I believe you should include the outcome is the first model. It should be possible with SPSS to indicate the order in which the variables are imputed. You would want the outcome to be imputed last. My knowledge of SPSS is limited, so I can't help on this point as much. Hopefully someone else can comment on this part.

1. Taking the average of the 10 standard deviations is not correct. Following Rubin's rules for multiple imputation, you can use the following formula to get the pooled standard deviation

$$Var(\bar{\beta}) = m^{-1} \sum_{k=1}^m Var(\hat{\beta}) + (1+m^{-1})(m-1)^{-1} \sum_{k=1}^m (\hat{\beta_k} - \bar{\beta})$$ where $\hat{\beta}_k$ is the estimated point estimate for imputation indexed by $k$, $Var(\hat{\beta})$ is the variance of the point estimates for a single imputation, and $$\bar{\beta} = m^{-1} \sum_{k=1}^m \hat{\beta_k}$$ for $m$ imputations (10 in your case). For the standard deviation, you would $\sqrt{Var(\bar{\beta})}$. The above calculation is important since it accounts for the variation between imputations and within imputations, which the average of the standard deviations from imputations does not.

2. Correct. You would use $\bar{\beta}$ and $Var(\bar{\beta})$ to calculate the p-value.

3. I believe you should include the outcome is the first model. It should be possible with SPSS to indicate the order in which the variables are imputed. You would want the outcome to be imputed last. My knowledge of SPSS is limited, so I can't help on this point as much. Hopefully someone else can comment on this part.

1. Taking the average of the 10 standard deviations is not correct. Following Rubin's rules for multiple imputation, you can use the following formula to get the pooled standard deviation

$$Var(\bar{\beta}) = m^{-1} \sum_{k=1}^m Var(\hat{\beta_k}) + (1+m^{-1})(m-1)^{-1} \sum_{k=1}^m (\hat{\beta_k} - \bar{\beta})$$ where $\hat{\beta}_k$ is the estimated point estimate for imputation indexed by $k$, $Var(\hat{\beta})$ is the variance of the point estimates for a single imputation, and $$\bar{\beta} = m^{-1} \sum_{k=1}^m \hat{\beta_k}$$ for $m$ imputations (10 in your case). For the standard deviation, you would $\sqrt{Var(\bar{\beta})}$. The above calculation is important since it accounts for the variation between imputations and within imputations, which the average of the standard deviations from imputations does not.

2. Correct. You would use $\bar{\beta}$ and $Var(\bar{\beta})$ to calculate the p-value.

3. I believe you should include the outcome is the first model. It should be possible with SPSS to indicate the order in which the variables are imputed. You would want the outcome to be imputed last. My knowledge of SPSS is limited, so I can't help on this point as much. Hopefully someone else can comment on this part.