Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
A GLMM analysis of data from the Sinovuyo Caring Families Program (SCFP)
We present an analysis of the data from a longitudinal randomized control trial that assesses the impact of an intervention program aimed at improving the quality of childcare within families. The SCFP was a group-based program implemented over two separate waves conducted in Khayelitsha and Nyanga....
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Statistical Sciences
2019
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | We present an analysis of the data from a longitudinal randomized control trial that assesses the impact of an intervention program aimed at improving the quality of childcare within families. The SCFP was a group-based program implemented over two separate waves conducted in Khayelitsha and Nyanga. The data were collected at baseline, post-test and at one-year follow-up via questionnaires (self-assessment) and observational video coding. Multiple imputation (using chained equations) procedures were used to impute missing information. Generalized linear Mixed Effect Models (GLMMs) were used to assess the impact of the intervention program on the responses, adjusted for possible confounding variables. These summed scores were often right skewed with zero-inflation. All the effects (fixed and random) were estimated through the method of maximum likelihood. Primarily, an intention-to-treat analysis was done after which a per-protocol analysis was also implemented with participants who attended a specified number of the group sessions. All these GLMMs were implemented in the imputation framework. |
|---|