A covariate is a continuous variable that affects a process that is not the direct target of the study. Excluding known covariates from an investigation can lead to skewed or biased results, which can reduce the value of the analysis or even render it worthless for its intended purpose.
Accounting for known covariates is crucial in lean six sigma management, which strives to reduce manufacturing error to extremely low levels. However, some covariates can be expensive or cumbersome to include and make a study too difficult to conduct or provide little value compared to the additional cost.
Covariates are also known as concomitant variables.
Pros and Cons
Reducing management errors to the lowest possible levels requires regular measurement of significant processes. But, carrying out these studies involves judgment. As mentioned above, every covariate you include adds complexity and cost. In addition, sometimes your organization may lack to capacity to track some known covariates, and adding them will add no value to the study.
1. Accounting for all known concomitant variables increases the accuracy of research.
However, precision only increases if your ability to measure the covariates is at least as precise as your ability to measure the variables that are the focus of your study.
2. Listing all covariates can help identify the source of inaccuracy if the results of a study are unsatisfactory.
3. Each covariate you add to a study increases its cost and difficulty to conduct.
4. Sometimes, adding covariates to a study creates the illusion of improved accuracy when a better experimental design that eliminates these variables is the better choice.
Why Are Covariates Important to Understand?
Understanding covariates is critical in obtaining meaningful results from any measurement you make of your management processes. Also, understanding what covariates apply to a study impacts the statistical methods you use to analyze the results.
For example, if you attempt to understand the average error rate of three different manufacturing methods, you could apply an ANOVA statistical analysis (analysis of variance). However, if the age of the machines in question can vary significantly, ANOVA might not provide accurate results. Therefore, you might need to include a covariant, which would then change the statistical method to ANCOVA (analysis of covariance).
Understanding concomitant variables can be important even if you do not measure them in a study. Knowing all of the possible factors that can affect the outcome of a system helps managers conceptually understand how the process works. For example, if managers determine that one manufacturing method includes more covariants than another, that fact alone could create a rational basis for choosing the less-variable option.
Industry Examples of a Covariate
Suppose managers want to understand the correlation between the population density of a city and the volume of ice cream sales. But, a concomitant variable that could affect ice cream sales is the weather. Experience suggests that locations with a higher average temperature enjoy higher sales of cold treats. So, in this situation, the weather would be a covariate of a study about population density and ice cream sales.
Another example might be the benefits of two different weight-loss products. However, the BMI (body mass index) of study participants could be a concomitant variable affecting the participants’ weight loss in the experiment.
Five Best Practices When Thinking About Covariates
The following five tips will help you effectively use covariates in conducting lean six sigma studies.
1. Often, covariates are variables you cannot eliminate from your measurements.
For example, in the above ice cream sales example, you might not have sales outlets in enough cities with varying population densities with similar weather to remove the effect of average temperature. In this situation, you must use satistical methods to normalize the impact of weather rather than remove it through experimental design.
2. Remember that adding a covariate will have little benefit if you cannot accurately measure that variable.
For example, if the best weather data you have is only within a 10-degree range, including the average daily temperature in your study is unlikely to increase its precision.
3. Experience in the process under study can be critical in identifying possible covariates.
For example, it is hard to design an experiment when a manager doesn’t understand how the process works. Likewise, researchers with limited practical experience struggle to identify all the potential concomitant variables.
4. Large numbers of variables can create more risk of measurement error.
Common sense tells you that measuring a large number of things will increase the number of potential mistakes.
5. You can use random samples to get rid of covariates.
In many cases, a random sample of a large population will eliminate the need to account for a concomitant variable because you presume that the variable affects each population equally.
Frequently Asked Questions About Covariates
Managers frequently ask the following questions about covariates:
What is the difference between a control variable and a covariate?
A control variable varies within a defined range (usually limited) in every trial. Conversely, a covariate can have wildly different values in different tests. These contiguous variables are generally handled by applying regression analysis to normalize them.
2. How do statistical methods separate the impact of covariates from the variables that are the focus of the study?
Typically, regression analysis creates a curve that estimates the impact of the covariate on the variable under study. These methods then assume a typical result rather than the actual measured outcome and apply it to “smooth out” the differences of the covariates in various trials.
3. When is it impossible to use a random sample to eliminate a covariate?
The typical reason you cannot get rid of a covariate is that the sample size isn’t large enough or a random sample is unavailable. For example, in the above ice cream sales study, the company might not have sales outlets in every possible weather environment.
Proper Use of Covariates Is Critical to Analyzing Processes
Lean Six Sigma management depends on reducing inefficiency by eliminating manufacturing errors. However, without accurate measurement, managers cannot assess their processes’ precision.
Understanding covariates and their effect on management processes are critical to accurately discovering the amount of waste and inefficiency in your management system.