Contents
How do multilevel models handle missing data?
Multilevel models have the ability to handle models with varying time points, which is an advance over traditional repeated-measures ANOVA, where the usual treatment is to remove the entire case if one of the outcomes is missing.
What is single level data?
‘Single-level’ means that the analysis is carried out at one analytical level – typically the individual level, although sometimes the single level is an aggregate construct, such as the ‘country’.
What is a single level model?
‘Single-level’ means that the analysis is carried out at one analytical level – typically the individual level, although sometimes the single level is an aggregate construct, such as the ‘country’. In this example there would be one pair of values of each country: the unemployment rate and the crime rate.
Which is advanced multiple imputation methods for multilevel data?
V Part V: Advanced Multiple Imputation methods 7Multiple Imputation models for Multilevel data 7.1Advanced Multiple Imputation models for Multilevel data 7.2Characteristics of Multilevel data 7.3Multilevel data – Example datasets 7.4Multilevel data – Clusters and Levels
How to use multiple imputation in regression modelling?
6More topics on Multiple Imputation and Regression Modelling 6.1Regression modeling with categorical covariates 6.2Logistic regression with a categorical variable in R 6.3Cox Regression with a categorical variable in R
How to create multiple imputation models in your VII?
8.2.2Passive multiple imputation 8.2.3Passive multiple imputation in R VII Part VII: Background information to Multiple Imputation Methods 9Rubin’s Rules 9.1Pooling Effect estimates 9.2Pooling Standard errors 9.3Significance testing 9.4Degrees of Freedom and P-values 9.5Confidence Intervals
How to create multiple imputation models in SPSS?
4Multiple Imputation 4.1Multivariate imputation by chained equations (MICE) 4.2Multiple imputation in SPSS 4.2.1The Variables tab 4.2.2The Method tab 4.2.3The Constraints tab 4.2.4The Output tab 4.2.5Customizing the Imputation Model 4.3Random number generator 4.4The output of Multiple imputation in SPSS 4.4.1The Imputed datasets