Related Experiment Video
Updated: Jul 3, 2026
06:48
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
Published on: June 25, 2019
Centering predictor variables in cross-sectional multilevel models: a new look at an old issue
Craig K Enders1, Davood Tofighi
1Department of Psychology, Arizona State University, Tempe, AZ 85287-1104, USA. cenders@asu.edu
Psychological Methods
|June 15, 2007
Summary
Centering Level 1 predictors is crucial for interpreting multilevel models (MLMs). This guide clarifies grand mean centering and group mean centering, offering practical recommendations for MLM applications.
Area of Science:
- Multilevel modeling
- Statistical analysis
- Quantitative psychology
Background:
- Centering Level 1 predictors is essential for accurate interpretation of multilevel models (MLMs).
- Misconceptions regarding centering techniques persist in the statistical literature.
- Understanding centering is vital for researchers utilizing multilevel modeling.
Purpose of the Study:
- To provide a comprehensive overview of grand mean centering and group mean centering.
- To elucidate the distinctions between these two centering methods in 2-level MLMs.
- To offer practical guidance for making informed centering decisions in MLM analyses.
Main Methods:
- Detailed explanation of grand mean centering and group mean centering.
- Exploration of centering differences using prototypical research questions.
- Empirical analyses with artificial datasets to illustrate key concepts.
Main Results:
- Grand mean centering and group mean centering yield different interpretations of intercept and slope parameters.
- The choice of centering impacts the estimation and interpretation of fixed effects in MLMs.
- Empirical examples demonstrate the practical implications of centering choices.
Conclusions:
- Proper centering is fundamental for valid interpretation in multilevel modeling.
- Researchers should carefully consider the research question and model context when choosing a centering strategy.
- This article provides actionable recommendations to improve the application of centering techniques in MLMs.
Related Concept Videos
02:20Longitudinal Research
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
01:50Cross-Sectional Research
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
01:14Multicompartment Models: Overview
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
01:27Confounding in Epidemiological Studies
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
01:25Strategies for Assessing and Addressing Confounding
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:26Longitudinal Studies
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...