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How is stratification used in a data analysis?
It is a technique used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together, the meaning of the data can be difficult to see. This data collection and analysis technique separates the data so that patterns can be seen and is considered one of the seven basic quality tools.
Which is an example of a stratified scatter diagram?
For example, on a scatter diagram where data are stratified into data from source 1 and data from source 2, draw quadrants, count points, and determine the critical value only for the data from source 1, then only for the data from source 2.
When do you use different marks for stratified data?
When plotting or graphing the collected data on a scatter diagram, control chart, histogram, or other analysis tool, use different marks or colors to distinguish data from various sources. Data that are distinguished in this way are said to be “stratified.”
How does correlation structure affect standard error estimates?
Intracluster correlation is a good measurement of the extent of correlation within clusters. Correlation structure in GEE, unlike mixed models, does not affect the marginal parameter estimates (which you are estimating with GEE). It does affect the standard error estimates though. This is independent of any link function.
When to use stratified random sampling in research?
There are many situations in which researchers would choose stratified random sampling over other types of sampling. First, it is used when the researcher wants to examine subgroups within a population.
How are sampling fractions different in different stratified samples?
In disproportionate stratified random sampling, the different strata do not have the same sampling fractions as each other. For instance, if your four strata contain 200, 400, 600, and 800 people, you may choose to have different sampling fractions for each stratum.