Lurking variable is a variable which "is camouflaged by another variable that is usually deceptively appealing" (Borradaile G. J. 2003, s. 136). Lurking variable may have an effect on other variables that are taken into consideration. It is however, "hiding in the background and it affects the data". So before we are able to make any assumptions, we need to take into consideration the effect of lurking variable on other data. This is especially difficult as this variable is often not even recognized to be relevant for the association (Bolstad W. M. 2004, s. 2).
Features of lurking variables
Lurking variables have the following features (Brase C. H., Brase C. P. 2010, s.129; Mathews P. G. 2005, s.173):
- may be known or unknown;
- may affect other variables in an unpredictable way;
- "may cause erroneous biases to appear" in the calculation.
Randomization helps protect the calculation from lurking variables although the effect of lurking variables is still not completely remunerated because we are not able to foresee all of the lurking variables and their effect. This conclusion is widely accepted therefore without randomizing a calculation involving lurking variables, your calculation will not be positively perceived by the people that have knowledge in the area (Mathews P. G. 2005, s.173-175).
The effect of lurking variables
Lurking variables affect two types of variables: the predictor variable and the response variable (Tamhane A. C. 2012).
An example of this may be a situation when a teacher does not take into account the possibility that introverted students may react in a different way to a positive reinforcement than extraverted students. Extraverted students tend to react more positively when positively reinforced whereas introverted students tend to react more positively when negatively reinforced. If the teacher misses this lurking variable and comes up with a training program based mainly on positive reinforcement, the effect will be visible only on some of the students.
Therefore, here a reaction to positive reinforcement (predicted variable) as well as the actual end effect (response variable) get affected by lurking variable, which is student's introverted or extroverted personality (Lerner R. M. 2015, s. 791).
Examples of Lurking variable
- Income: Income is a variable which is often overlooked but can have a direct relation to other variables such as education, healthcare and even happiness. The true effect of income on other variables is often masked by other variables.
- Gender: Gender is another variable which can be a lurking variable. Many studies look at the effects of education or health on different groups of people but overlook the effect of gender. This can lead to inaccurate results as gender can have a great influence on the outcomes.
- Age: Age is another factor that can be a lurking variable. Many studies overlook the effects of age on outcomes, leading to inaccurate results. Age can have a significant effect on variables such as health, education, and even income.
- Social Class: Social class is a lurking variable which can affect many other variables. It is often overlooked in studies as it is difficult to measure. However, social class can have a great effect on variables such as health, education, and even happiness.
Advantages of Lurking variable
Lurking variables can be beneficial in understanding the relationships between different variables and the data. Here are some advantages of lurking variables:
- Firstly, they can help to identify potential causal relationships between variables that may not be immediately apparent. This can help researchers make more accurate predictions and conclusions about the data.
- Secondly, they can also provide insight into potential confounding factors that can influence the results. Identifying and accounting for these factors can help to increase the accuracy of the results.
- Thirdly, they can also provide insight into potential relationships between variables that may not have been considered previously. This can be used to develop new theories and hypotheses.
- Finally, lurking variables can also be helpful in detecting errors or biases in data collection or analysis. Being able to identify and correct these errors can help to improve the accuracy and validity of the results.
Limitations of Lurking variable
- One of the limitations of the lurking variable is that it is often difficult to identify and measure. This is because the variable is hidden or disguised, so it is difficult to recognize and measure its impact on the data.
- Another limitation of the lurking variable is that it can introduce bias into the results of the study. Since the variable is not taken into account, its effect on the data can be overlooked, leading to unreliable or inaccurate results.
- Additionally, the lurking variable can lead to false conclusions if its effects are not taken into consideration. For example, a study may mistakenly conclude that two variables are not associated due to the presence of a lurking variable that is influencing the results.
- Lastly, lurking variables can cause problems with replication of results. If a lurking variable is present, it may be difficult to reproduce the results of the study in subsequent studies, as the variable is not taken into account.
One approach to dealing with lurking variables is by identifying and controlling them. Here are some of the strategies used to do this:
- Randomization: Randomization is a method used to control for the effects of lurking variables by randomly assigning participants to different groups, so that any effects of the lurking variable are evenly spread across all groups.
- Matching: Matching is a method used to control for the effects of lurking variables by ensuring that each group contains the same number of participants with similar characteristics, such as age or gender.
- Blocking: Blocking is a method used to control for the effects of lurking variables by dividing participants into subsets that share similar characteristics, such as age or gender.
- Stratification: Stratification is a method used to control for the effects of lurking variables by separating participants into groups based on their characteristics, such as age or gender.
By using these strategies, researchers are able to identify and control for lurking variables, which can help ensure that their results are more reliable and accurate. In summary, lurking variables can have an effect on the results of an experiment, and it is important to identify and control for them in order to get more reliable results.
- Bolstad W. M.(2004), Introduction to Bayesian Statistics Wiley Interscience, s. 25-27.
- Borradaile G. J. (2003), Statistics of earth science data: their distribution in time, space and orientation Springer Science & Business Media, s. 136.
- Brase C. H., Brase C. P. (2010), Understanding basic statistics Brooks/Cole, Cengage learning, s. 129-131.
- Lerner R. M. (2015), Handbook of child psychology and developmental science, theory and method John Wiley & Sons, s. 791,807.
- Mathews P. G. (2005), Design of experiments with MINITAB ASQ Quality Press, s. 172-174.
- Tamhane A. C. (2012), Statistical analysis of designed experiments: theory and applications John Wiley & Sons, chapter 1.1.
Author: Justyna Szczepaniec