Related Experiment Video
Updated: Dec 30, 2025

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
Published on: July 1, 2015
A Comparison of Frequency- and Agreement-Based Response Formats in the Measurement of Burnout and Engagement
Jiajin Tong1, Robert M Bickmeier2, Steven G Rogelberg2
1School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China.
Abstract:
The present research compares and contrasts frequency versus agreement response formats, two approaches to measuring job burnout and work engagement. Construct-based and measurement-based arguments for the superiority of the frequency response format in measuring burnout/engagement are provided, demonstrating that frequency-based measurements will explain relatively more variance in outcome variables. Fair comparison, time order counterbalance, and multiple measuring waves justify the comparison and reduce common method errors of self-report measures. Sample 1 (N = 242) was composed of employees from multiple organizations, while the participants in Sample 2 (N = 281) were employees from one company. Relative importance analysis showed that frequency outperforms the agreement response format in measuring burnout and engagement in both samples. These findings suggest that the frequency response format provides a more valuable method of detecting the dynamic nature of burnout/engagement, which offers methodological guidance for future research involving dynamic constructs. These findings can lead to improvements in the measurement of the dynamic experiences of burnout and engagement. This is one of the first studies to provide evidence whether the dynamic nature of the constructs would have any bearing on the response formats.
Related Concept Videos
Expected Frequencies in Goodness-of-Fit Tests
Response Surface Methodology
The process of RSM involves several key steps:
Ordinal Level of Measurement
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
Goodness-of-Fit Test
Interval Level of Measurement
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
What is a Frequency Distribution

