We used the program MLwiN for
conducting multilevel analysis and used an adapted set up, described by Hox (2002) to make our models suitable for meta - analysis.
Not exact matches
Multilevel analyses were
conducted to estimate infant weight gain by type of milk and feeding mode.
After that, using a methodology known as
multilevel analysis, they
conducted an
analysis of group and individual variables, simultaneously studying peer attachment and group emotional intelligence.
All statistical
analyses were
conducted using SAS software V. 9.4, estimating the logistic
multilevel models with the GLIMMIX procedure.
Two
multilevel models were estimated, one without baseline functioning and one including baseline outcome variables when they were collected with the first
multilevel model similar to the
analysis conducted in Sure Start.
A series of
multilevel negative binomial regression
analyses were
conducted.
Multilevel analyses with child perceptions of PDT and child emotional and
conduct problems were
conducted (parenting differences and favoritism) in a multi-informant design.
The study involved
conducting multilevel factor
analyses on large multistate data sets and using the findings from those
analyses to assess the predictive validity of the SAI in regard to student academic achievement.
First we
conducted an additional
analysis in the
multilevel models that included a four - category couple drinking variable and gender as well as the interaction between gender and couple drinking categories as the predictors.
Prior to testing our hypotheses, we
conducted a
multilevel confirmatory factor
analysis (CFA) to examine the discriminant validity of our research variables.