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The level of data is part of what determines which statistical procedure is appropriate to one's research. That is, particular data levels constitute assumptions for particular procedures. If a procedure is used which assumes a higher level of data than one has (ex., using ordinal data with interval procedures like regression), standard errors will be underestimated and Type I errors (false positive findings) will be higher than the significance level (ex., .05) would suggest. If a procedure assumes a lower level of data than one has, power will be lower than it should be due to loss of information and Type II errors (false negative findings) will be increased. Ideally, the researcher selects a procedure which matches his or her data level. As a general rule, when variables are of different levels, one must select the procedure matching the lower level of data. However, violation of data level assumptions may or may not be critical. Treating nominal-level data as interval leads to nonsensical results, but treating ordinal data as interval is commonly done in social science and usually does not alter substantive research conclusions.

Overview	4
Key Concepts and Terms	4
Levels of Measurement	4
Nominal data	4
Ordinal data	5
Interval data	5
Data Levels and Measures of Central Tendency	6
Frequently Asked Questions	7
Is it acceptable to use ordinal variables such as Likert items in interval-level procedures like regression or path analysis which assume interval data?	7
Is it acceptable to use Likert scales in interval-level procedures?	8
Is it acceptable to use dichotomous variables in procedures like regression or path analysis which assume interval data?	8
What is attenuation of correlation?	9
I have categorical data but I want to do interval procedures like factor analysis and regression. What do I do?	11
When I have a mixture of categorical and interval independents, what procedure should I use?	12
Bibliography	12