Imo State University, Owerri
All statistical procedures have underlying assumptions, some more stringent than others. In some cases, violation of these assumptions will not change substantive research conclusions. In other cases, violation of assumptions will undermine meaningful research. Establishing that one's data meet the assumptions of the procedure one is using in an expected component of all quantitatively based journal articles, theses, and dissertations.
This write-up provides a general overview of the most common data assumptions which the researcher will encounter in statistical research.
All forms of statistical analysis assume sound measurement, relatively free of coding errors. It is good practice to run descriptive statistics on one's data so that one is confident that data are generally as expected in terms of means and standard deviations, and there are no out-of-bounds entries beyond the expected range.
When the range of the data is reduced artificially, as by classifying or dichotomizing a continuous variable, correlation is attenuated, often leading to underestimation of the effect size of that variable.
Avoiding Tautological Correlation
When the indicators for latent variable A conceptually overlap with or even include one or more of the indicators for latent variable B, definitional overlap confounds the correlation of A and B. This is particularly problemetic when indicators on the independent side of the equation conceptually overlap with indicators on the dependent side of the equation. Avoiding tautological correlation is the issue of establishing discriminant validity, discussed in the separate "blue book" volume on validity.
PROPER MODEL SPECIFICATION
Specification of a Model