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ADP Research Institute (ADPRI) and the Stanford Digital Economy Lab (the “Lab”) announced they will retool the ADP National Employment Report (NER) methodology to provide a more robust, high-frequency view of the labor market and trajectory of economic growth. In preparation for the changeover to the new report and methodology, ADPRI will pause issuing the current report and has targeted August 31, 2022, to reintroduce the ADP National Employment Report in collaboration with the Stanford Digital Economy Lab (the “Lab”). We look forward to providing an even more comprehensive labor market analysis and will be in touch with additional details closer to the re-launch, later this summer.  For more information on this announcement, please visit here.

The Definitive Series: Response Scales Across Countries

Executive Summary

The Definitive Series: Response Scales Across Countries

April 27, 2021 | read time icon 5 min

Dr. Frances Chumney, Dr. Mary Hayes
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A cross-national study is an exciting opportunity to study phenomena across countries, cultures, and languages. In the world of corporate research on employee experiences (e.g., employee engagement, satisfaction, wellbeing, etc.), cross-national research can provide compelling insights into the similarities and differences between groups of people and help leaders better understand how to tailor approaches to meet the needs of diverse members of an organization.

At first glance, research studies that result in data collected from multiple populations appear relatively straight-forward: identify the construct(s) of interest, develop and test items, translate them, deploy the items to each population, analyze the data, ad draw conclusions. However, one area where the reality of a sound project is more complex than it appears is the preparation of data for analysis.

This Executive Summary provides an overview of response style biases and how they vary by country — as well as the 7-Step Approach developed by the ADP Research Institute to standardize responses. Failure to study and correct for response style differences across multiple populations is likely to yield invalid comparisons and inferences.

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