7.12 Missing data in continuous variables.

7.10 Multilevel Multiple Imputation models.

7.9 Sporadically and systematically missing data.

7.7 Restructuring from wide to long in R.

7.6 Restructuring from wide to long in SPSS.

7.5 Longitudinal Multilevel data - from wide to long.

7.4 Multilevel data - Clusters and Levels.

7.1 Advanced Multiple Imputation models for Multilevel data.

7 Multiple Imputation models for Multilevel data.V Part V: Advanced Multiple Imputation methods.

6.4.2 Variable Selection with Cox Regression models in R.6.4.1 Variable Selection with Logistic Regression models in R.6.3 Cox Regression with a categorical variable in R.6.2 Logistic regression with a categorical variable in R.6.1 Regression modeling with categorical covariates.6 More topics on Multiple Imputation and Regression Modelling.5.2.6 Analysis of Variance (ANOVA) pooling.5.2.2 Pooling Means and Standard Deviations in R.5.2.1 Pooling Means and Standard deviations in SPSS.5 Data analysis after Multiple Imputation.IV Part IV: Data Analysis After Multiple Imputation.4.14 Number of Imputed datasets and iterations.4.13 Imputation of categorical variables.4.12.1 Predictive Mean Matching, how does it work?.4.12 Predictive Mean Matching or Regression imputation.4.10 Guidelines for the Imputation model.4.4 The output of Multiple imputation in SPSS.4.1 Multivariate imputation by chained equations (MICE).3.4.2 Bayesian Stochastic regression imputation in R.3.4.1 Bayesian Stochastic regression imputation in SPSS.3.4 Bayesian Stochastic regression imputation.3.3.4 Stochastic regression imputation in R.2.8.2 Compare and test group comparisons.2.7.2 Compare and test group comparisons.II Part II: Basic Missing Data Handling.1.15 Useful Missing data Packages and links.1.6.4 Indexing Vectors, Matrices, Lists and Data frames.1.6.3 Vectors, matrices, lists and data frames.

Merge datasets in spss code