Hidden Time Effects
- Factors that change over time can distort observed relationships between variables.
- Such effects are often hard to detect and can produce spurious associations when the underlying population or measurement methods change.
- Researchers should anticipate these effects and use statistical controls to avoid incorrect conclusions.
Definition
Section titled “Definition”Hidden time effects in data are factors that can affect the relationship between variables in a study over time. These factors can be difficult to identify and account for, and as a result, they can potentially lead to incorrect conclusions being drawn from the data.
Explanation
Section titled “Explanation”Hidden time effects arise when something that influences the relationship between variables changes over the period of a study. Two common sources are shifts in the underlying population being studied and changes in measurement methods or instruments. Because these changes occur over time and may not be apparent, they can create apparent associations (spurious relationships) that do not reflect actual causal links. To address hidden time effects, researchers need to consider the possibility of temporal changes and apply statistical techniques that control for such confounders.
Examples
Section titled “Examples”Population change example
Section titled “Population change example”Consider a study on the relationship between income and education level in a particular city. If the population of the city is becoming increasingly educated over time, this could potentially lead to a spurious relationship between income and education level in the data, even if there is no actual causal relationship between the two variables.
Measurement change example
Section titled “Measurement change example”Consider a study on the relationship between blood pressure and age. If the blood pressure measurement methods or instruments being used in the study are improved over time, this could potentially lead to a spurious relationship between blood pressure and age in the data, even if there is no actual causal relationship between the two variables.
Use cases
Section titled “Use cases”- Researchers assessing relationships between variables over time should evaluate the potential for hidden time effects.
- Apply statistical techniques to control for potential confounders arising from changes in population composition or measurement methods.
Notes or pitfalls
Section titled “Notes or pitfalls”- Hidden time effects can be difficult to identify and account for.
- If unaddressed, they can lead to incorrect conclusions from a study.
- Careful consideration of population changes and measurement methods, and the use of appropriate controls, helps ensure more accurate and reliable conclusions.
Related terms
Section titled “Related terms”- Confounders
- Spurious relationship
- Causal relationship
- Measurement methods or instruments
- Underlying population