ESRA 2025 Preliminary Program
All time references are in CEST
Measurement and coding of job-related information: Occupation, industry, and skill 2 |
| Session Organisers |
Dr Malte Schierholz (LMU Munich)
Ms Olga Kononykhina (LMU Munich)
Dr Calvin Ge (TNO)
|
| Time | Wednesday 16 July, 16:00 - 17:30 |
| Room |
Ruppert 111 |
Occupation coding refers to coding a respondent’s text answer (or the interviewer’s transcription of the text answer) about the respondent’s job into one of many hundreds of occupation codes. Relatedly, many surveys gather data about the person’s industry or her various skills in similar ways. We welcome any papers on how to best measure jobs and job-related information, including, but not limited to:
- measurement of occupations, industries, and skill (e.g., mode, question design, …)
- handling of different occupational and industry classifications (e.g., ISIC, NACE, NAICS, ISCO, ESCO and national classifications)
- problems of coding (e.g., costs, data quality, …)
- techniques for coding (e.g., automatic coding, computer-assisted coding, manual coding, interview coding)
- computer algorithms for coding (e.g., machine learning, LLMs, rule-based, …)
- cross-national and longitudinal issues
- Measurement of derived variables (e.g., ISEI, ESeC, SIOPS, job-exposure matrices, …)
- other methodological aspects related to the measurement and coding of job-related information
Keywords: measurement, coding, occupation, industry, skill, long-list questions
Papers
The Subjectivisation of Labour Scale: Validation and Multi-Group Analysis by Industry Classifications (NACE), Occupational Position and Survey Mode
Ms Britta Maskow (Chemnitz University of Technology) - Presenting Author
Professor Jochen Mayerl (Chemnitz University of Technology)
A quantitative and qualitative change in the relationship between companies and their employees in new forms of work has been discussed for many years (Pongratz and Voß 2003). The resulting shift of the transformation problem to employees has consequences for employment and may also lead to a change in the general constitution of labour capacity in our society. Subjectivisation of Labour is a social process in which the 'whole person' is or should be included in companies' rationalisation strategies in order to gain extended access to individual competences (working definition, cf. Minssen, 2012).
Most measurement instruments for this construct have been developed qualitatively. Fritz et al. developed the first quantitative Workforce Entrepreneur Scale (16 items) based on Pongratz & Voß (2003), Nievergelt (2004), and Schmitz & Schwarzer (1999). Our research design includes a mixed mode sample (online and postal survey, N = 788) and an online access panel survey (N = 1500) in Germany. The presentation discusses the results of a confirmatory factor analysis with randomly split data, which cross-validates a new Subjectivisation of Labour Scale. To test the scale in different sub-populations, we conducted several multi-group analyses based on industry classifications (NACE), occupational position, survey mode, and demographics using the online access panel survey.
Using mental models research to review how we collect SIC and SOC information
Miss Danielle Watson (Office for National Statistics) - Presenting Author
Standard Industrial Classification (SIC) of economic activities is a five-digit classification introduced in the UK for use in classifying business establishments and other statistical units by the type of economic activity in which they are engaged. Standard Occupational Classification (SOC) is the standard UK classification of jobs in terms of their skill content and skill level, used for the production of occupationally classified information and the processing of occupational data.
The assignment of accurate SIC and SOC codes require survey respondents to categorise themselves accurately. This is challenging and unpopular with them, especially in self-completion surveys where there’s no interviewer to help them. They are asked to describe their industry and occupation, but their descriptions can’t always be matched to the arbitrary categories. The Qualitative and Data Collection Methodology (QDCM) team at the Office for National Statistics (ONS) were tasked with conducting exploratory work to understand respondents’ mental models, specifically how they conceptualise their SIC and SOC information and the language they use, to establish whether current frameworks and language used align to respondents own mental models of their SIC and SIC information. By conducting this exploratory work, we were able to focus on forming a base for a comparison of current language used in SIC/SOC index and frameworks with how respondents naturally phrase their industry and occupation.
Many options for improving SIC and SOC questions had been suggested but, without knowing how our respondents conceptualised and communicated their information in depth, it was difficult to estimate the potential success or limitations of each option. This presentation will outline the research conducted, what was found and how these findings allowed us to review the options available for improving SIC and SOC survey questions; evaluating each whilst keeping our respondents at the heart of our designs.
Optimising Occupation and Industry Data Collection in Cross-National Surveys
Mr Russell Castañeda (Verian Belgium) - Presenting Author
Mr Nicolas Becuwe (Verian Belgium)
Collecting detailed and accurate information on respondents' occupations and industries is a significant challenge in social research, particularly in cross-national surveys. This data is typically gathered through open-ended questions and later recoded into standard classifications like ISCO-08 and NACE. Coding these responses is costly and time-consuming, requiring well-trained coders, standardised training, detailed guidelines, and verification processes to ensure accuracy. The variability in respondent information and coder interpretation further complicates this task, especially in online surveys without direct interviewer oversight.
To address these challenges, the Synergies for Europe's Research Infrastructures in the Social Sciences (SERISS) developed occupation and industry databases to harmonise data collection. At Verian, we initially adopted these databases and then customised them to suit the unique requirements of our high-quality cross-national online surveys targeting the EU working population. Our presentation will share our experience with this practice, detailing its implementation across all survey stages: questionnaire design, scripting, translation, piloting, fieldwork, and data processing. Like any tool, it has potential improvements such as optimising search functionality, adding broader categories, and ranking results by relevance. This approach sets a new standard for cross-national surveys by improving data accuracy and consistency, while saving time and resources.
