Statistical Methods in Safety Metrics

Proper safety metrics requires the proper analysis of the collected data. The purpose of the data analysis is to make comparisons between the results and the goals or benchmarks. Significant trends or differences between the obtained results and the benchmarks should be identified. There are a number of statistical procedures that can be used to determine if there are any trends in the data or if the results meet what is anticipated. 

Data can be classified into four major categories based upon the characteristics of the numbers. It is important for the safety manager to understand these categories because some kinds of data are more useful than others are. From least useful to most useful, the categories are categorical, ordinal, interval, and ratio (Horvath 1974,16). 

Ratio data is the most useful because it can be used with all statistical procedures. Categorical data, on the other hand, is the least useful because it can only be used with statistical procedures designed specifically for categorical variables. 

Ordinal data is rank order data. Ordinal means that the data is ordered in some fashion. For example, rankings from best to worst, highest to lowest, or poor, fair, good, etc., would be considered ordinal data. A safety manager could use this type of data to evaluate safety practices. For example, a survey that asked employees, How often do you use protective gloves when handling chemicals and offered almost always, often, seldom, and almost never as choices would be an example of ordinal data. 

Common examples of ordinal data are Likert scales.When a person is asked to select a number on a Likert scale (for example, evaluate the safety program from 1 to 5), it is 

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