Related Experiment Video
Updated: Jul 11, 2026
05:40
The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans
Published on: April 28, 2022
The sensitivity of variance component estimates to underreporting: method and application to substance abuse data
Paul W Andrews1, Kenneth S Kendler, Nathan Gillespie
1Virginia Institute for Psychiatric and Behavioral Genetics,Virginia Commonwealth University, Richmond,Virginia 23298-0126, USA. pandrews@vcu.edu
Summary
Underreporting sensitive behaviors like drug use can skew research. This study introduces a method to assess how underreporting affects genetic and environmental variance estimates in behavioral genetics research.
Area of Science:
- Behavioral Genetics
- Psychological Research Methods
Background:
- Studies on human behavior often depend on self-disclosure, which is unreliable for sensitive topics like illicit drug use.
- Underreporting of sensitive behaviors can lead to biased estimates in behavioral genetic analyses.
Purpose of the Study:
- To develop and demonstrate a method for evaluating the sensitivity of variance component estimates to underreporting.
- To assess the impact of underreporting on estimates of genetic and environmental influences on illicit drug abuse and dependence (DAD).
Main Methods:
- Estimating original population response class proportions using maximum likelihood.
- Adjusting these proportions based on hypothesized underreporting parameters.
- Applying the method to a twin study of drug abuse and dependence (DAD) to analyze variance components (A, C, E).
Main Results:
- The proposed method allows researchers to quantify the potential impact of underreporting on variance component estimates.
- Demonstrated how underreporting influences estimates of additive genetic (A), common environment (C), and specific environment (E) variance for DAD.
Conclusions:
- The developed method provides a robust way to assess the reliability of genetic and environmental estimates when dealing with underreported sensitive behaviors.
- Researchers can use this approach to understand the potential biases in their findings and interpret results more cautiously.
Related Concept Videos
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:15Systematic Error: Methodological and Sampling Errors
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
01:15Variance
The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.The standard deviation measures the spread in the same units as the data.
01:11Variability: Analysis
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
01:29Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is: