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Related Experiment Video

Updated: Dec 26, 2025

Experimental Paradigm for Measuring the Effects of Self-distancing in Young Children

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Published on: March 1, 2019

Methods of Detecting Insufficient Effort Responding: Comparisons and Practical Recommendations.

Maxwell Hong¹, Jeffrey T Steedle², Ying Cheng¹

¹University of Notre Dame, Notre Dame, IN, USA.

Educational and Psychological Measurement

|March 12, 2020

Summary

This study explores insufficient effort responding (IER) in assessments. Different detection methods impact test validity and reliability, offering practical guidance for researchers analyzing data for careless or random responses.

Keywords:

data quality insufficient effort responding outlier detection validity evidence

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Last Updated: Dec 26, 2025

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Area of Science:

Psychometrics
Educational Measurement
Psychological Assessment

Background:

Insufficient effort responding (IER) is a significant issue in educational and psychological assessments.
Existing research covers IER types, psychometric impacts, and detection methods, but lacks practical guidance for applied researchers.
A gap exists in strategies for evaluating multiple IER indicators and optimal data preprocessing.

Purpose of the Study:

To demonstrate how various IER detection methods influence psychometric properties like predictive validity and reliability.
To evaluate the effectiveness of different data cleansing procedures in detecting diverse IER types.
To provide practical recommendations for researchers dealing with potential careless, random, or biased responding in assessment data.

Main Methods:

Simulation studies to model the effects of IER detection strategies.
Applied analysis using the ACT's Engage assessment data.
Comparison of psychometric properties (reliability, validity) under different IER preprocessing conditions.

Main Results:

The choice of IER detection methods significantly affects psychometric properties of test scores.
Different data cleansing procedures identify distinct patterns of insufficient effort responding.
Evidence-based recommendations are provided for applied researchers and psychometricians.

Conclusions:

Applied researchers must carefully consider IER detection and data preprocessing strategies.
The selection of methods impacts the integrity and interpretability of assessment results.
Further research is needed to refine best practices for managing IER in various assessment contexts.