Multilevel analysis (MLA) is used for the analysis of hierarchical data. Data at the lowest level are clustered in higher levels. An important application for MLA is in ESM/EMA data. This type of data are called intensive longitudinal data. Subjects have measurements on multiple occasions. The occasions are clustered within the subjects. In these examples, the dataset/dataframe is called
dat, the predictor is called
predictorVariable(more than one are possible), the clustervariables are called
idVariable1, the dependent variable is called
dependentVariable. For longitudinal data there is an
indexVariable, which indicates the repeated measures. Furthermore, you may need a lagged variable to control for autocorrelation: for example, a lagged one variable:
In SPSS the MIXED procedure can be used to do multilevel analysis. A simple model with one predictor and two random efffects, for respectively the intercept and the predictor, can be run with the following syntax.
MIXED dependentVariable WITH predictorVariable /PRINT= SOLUTION TESTCOV /METHOD= ML /FIXED= INTERCEPT predictorVariable /RANDOM= INTERCEPT predictorVariable| SUBJECT(idVariable) COVTYPE(UN).
Example 2 shows a longitudinal example.
MIXED dependentVariable WITH predictorVariable indexVariable /PRINT=SOLUTION TESTCOV /FIXED=INTERCEPT predictorVariable | SSTYPE(3) /RANDOM=INTERCEPT predictorVariable | SUBJECT(idVariable2) COVTYPE(VC) /REPEATED=indexvariable | SUBJECT(idVariable) COVTYPE(AR1).
In R the function is called
lmer, which is in the package
lme4. The data are in dataset
dat. Example 1 is called as follows.
model <- lmer(dependentVariable ~ predictorVariable + (1 + predictorVariable| idVariable, data = dat)
Example 2 is called with the following code.
model <- lmer(dependentVariable ~ predictorVariable + predictorVariableLag1 + (1 + predictorVariable| idVariable, data = dat)
The output can be put in an object and with, for example, the
r summary() function this output can be inspected.