Improving the measurement of group-level constructs by optimizing between-group differentiation

Area of Science:

• Multilevel modeling
• Psychometrics
• Quantitative psychology

Background:

• Group-level constructs are often measured by aggregating individual responses to multi-item scales.
• Current validation practices for group-level constructs may not effectively differentiate between groups.
• Existing validation methods primarily use procedures designed for individual-level constructs, potentially missing item-level group differentiation information.

Purpose of the Study:

• To evaluate current measurement validation practices for group-level constructs.
• To propose an enhanced validation approach incorporating item-level group differentiation.
• To demonstrate the utility of item-level intraclass correlation coefficients (ICC(1)) in improving group-level scale validation.

Main Methods:

• A 10-year review of multilevel articles in top journals was conducted.
• Previously published datasets were re-analyzed.
• Intraclass correlation coefficient (ICC(1)) was calculated for each scale item.

Main Results:

• Current validation practices for group-level constructs may be suboptimal for differentiating groups.
• Item-level ICC(1) estimates provide unique information not captured by traditional methods.
• Utilizing item-level ICC(1) can lead to group-level scales with higher ICC(1) values.
• Enhanced scales demonstrate improved predictive validity.

Conclusions:

• Group-level measurement validation should be augmented with item-level group differentiation information.
• Researchers should supplement conventional validation with item-level ICC(1) values.
• This approach enhances the development and modification of scales for assessing group-level constructs.