What Is the Adjusted Mean In Statistics?
The adjusted mean arises when statistical averages must be corrected to compensate for data imbalances and large variances. Outliers present in data sets will often be removed, as they have a large impact on the calculated means of small populations. An adjusted mean can be determined by removing these outlier figures.
- Adjusted mean is used to correct statistical averages with obvious imbalances. It is calculated by removing outliers from the data set.
- Adjusted means are calculated using multiple regression equations.
- This is a preferred method for most professionals who rely heavily on statistics and their accuracy.
- Covariates are variables that the researcher can’t control but they still affect the results.
How Adjusted Mean Works
Adjusted means are also called "least-squares means" and are calculated using a multiple regression equation. Multiple regression equations are the preferred method of many researchers and most personnel professionals in achieving accurate results and information in their studies. This method will provide a more accurate result and more reliable data at the conclusion of the study, and it has been heavily relied on by scientific, financial, and various other research groups for many years.
For example, in studying both men and women who participate in a particular behavior or activity, it may be necessary to adjust the data to account for the impact of gender on the results. Without using adjusted means, results that might at first seem attributable to participating in a certain activity or behavior could be skewed by the impact of participants' gender.
In this example, men and women would be considered covariates, a type of variable that the researcher cannot control but that affects an experiment's results. Using adjusted means compensates for the covariates to see what the effect of the activity or behavior would be if there were no differences between the genders.
Comparing the original and the adjusted means of any study can give you a better idea of just how much the individual factors come into play in the study overall.
Example of an Adjusted Mean
Consider the financial markets, which might adjust a mean average for a regime change, which is the term for the replacement of one government regime with another one. In theory, a new government is likely to introduce new policies and other changes, rendering comparisons between two different government styles meaningless. In order to obtain accurate results, data will need to be updated or adjusted accordingly.
Another example where an adjusted mean would be necessary for accuracy comes from the time of the Great Recession. In 2009, to ease banks' capital controls, FASB suspended the mark-to-market rule. Thereby, instantly improving the large banks’ balance sheets. If an analyst were reviewing trends in balance sheet change in 2010 for the trailing ten years, the mean average would be problematic and inaccurate.
After the suspense of mark-to-market accounting methods, the banks’ balance sheets were materially better (on paper) than they were before the change in the accounting rule. Thus, to someone simply looking at a ten year average in 2010, the results would be rather skewed without adjusting the mean for the change in mark-to-market accounting.
Using adjusted means in similarly imbalanced examples and other situations can vastly change the outcome and results, without requiring the researcher to begin the study all over again. There are a variety of other alternative methods that can be used in a research study to achieve similar results, but most of them will be significantly more challenging and time-consuming.