Why standard error is high in cluster sampling?
In fact if secondary units within a cluster tend to be more similar to each other than to units in other clusters, then the true standard error of your estimates will be much higher than those obtained from simple random sampling.
What is difference between stratified and cluster sampling?
In Cluster Sampling, the sampling is done on a population of clusters therefore, cluster/group is considered a sampling unit. In Stratified Sampling, elements within each stratum are sampled. In Cluster Sampling, only selected clusters are sampled. In Stratified Sampling, from each stratum, a random sample is selected.
When do you not need to cluster standard errors?
You want to say something about the association between schooling and wages in a particular population, and are using a random sample of workers from this population. Then there is no need to adjust the standard errors for clustering at all, even if clustering would change the standard errors.
How are clustered standard errors consistent in the presence of heteroscedasticity?
Analogous to how Huber-White standard errors are consistent in the presence of heteroscedasticity and Newey–West standard errors are consistent in the presence of accurately-modeled autocorrelation, clustered (or “Liang-Zeger”) standard errors are consistent in the presence of cluster-based sampling or treatment assignment.
When to cluster EHW and LZ standard errors?
Special case: even when the sampling is clustered, the EHW and LZ standard errors will be the same if there is no heterogeneity in the treatment effects.
When do you need to make a cluster adjustment?
The general rule is that you still need to cluster if either the sampling or assignment to treatment was clustered. However, the authors show that cluster adjustments will only make an adjustment with fixed effects if there is heterogeneity in treatment effects.