Intercoder-Reliabilität bei der Berufscodierung nach der ISCO-88 und Validität des sozioökonomischen Status

Maaz, Kai; Trautwein, Ulrich; Gresch, Cornelia; Lüdtke, Oliver; Watermann, Rainer

doi:10.1007/s11618-009-0068-0

Cited by 23 publications

(12 citation statements)

References 7 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Additionally, we argue that in the cases where the choice between different codes is hard to make, the occupations in question are more similar with respect to prestige and SES than a randomly chosen pair of occupations would be. In a study by Maaz et al (2009), occupational information was coded by professional coders and lay coders (trained student assistants). The level of consistency between these codings was generally not as high as desirable.…”

Section: Challenges Of Occupation Coding and Previous Researchmentioning

confidence: 99%

How Standardized is Occupational Coding? A Comparison of Results from Different Coding Agencies in Germany

Massing

Wasmer

Wolf

et al. 2019

Journal of Official Statistics

View full text Add to dashboard Cite

As occupational data play a crucial part in many social and economic analyses, information on the reliability of these data and, in particular on the role of coding agencies, is important. Based on our review of previous research, we develop four hypotheses, which we test using occupation-coded data from the German General Social Survey and the field test data from the German Programme for the International Assessment of Adult Competencies. Because the same data were coded by several agencies, their coding results could be directly compared. As the surveys used different instruments, and interviewer training differed, the effects of these factors could also be evaluated. Our main findings are: the percentage of uncodeable responses is low (1.8–4.9%) but what is classified as “uncodeable” varies between coding agencies. Inter-agency coding reliability is relatively low κ ca. 0.5 at four-digit level, and codings sometimes differ systematically between agencies. The reliability of derived status scores is satisfactory (0.82–0.90). The previously reported negative relationship between answer length and coding reliability could be replicated and effects of interviewer training demonstrated. Finally, we discuss the importance of establishing common coding rules and present recommendations to overcome some of the problems in occupation coding.

show abstract

Section: Challenges Of Occupation Coding and Previous Researchmentioning

confidence: 99%

How Standardized is Occupational Coding? A Comparison of Results from Different Coding Agencies in Germany

Massing

Wasmer

Wolf

et al. 2019

Journal of Official Statistics

View full text Add to dashboard Cite

show abstract

“…To ensure high data quality, researchers from various countries have discussed and compared various procedures for occupation coding (Biemer and Caspar, 1994;Campanelli et al, 1997;Bushnell, 1998;Biemer and Lyberg, 2003;Maaz et al, 2009;Svensson, 2012;Belloni et al, 2016). Although they focused on errors that arise from coding after the interview, all these researchers also observed that insufficient, low quality verbal answers from the interview are another possible source of errors.…”

Section: Motives For Interview Codingmentioning

confidence: 99%

Occupation Coding During the Interview

Schierholz

Gensicke

Tschersich

et al. 2017

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

Summary. Currently, most surveys ask for occupation with open-ended questions. The verbal responses are coded afterwards, which is error prone and expensive. We present an alternative approach that allows occupation coding during the interview. Our new technique uses a supervised learning algorithm to predict candidate job categories. These suggestions are presented to the respondent, who in turn can choose the most appropriate occupation. 72.4% of the respondents selected an occupation when the new instrument was tested in a telephone survey, entailing potential cost savings. To aid further improvements, we identify some factors for how to increase quality and to reduce interview duration.

show abstract

“…Though some attempts have been made to improve automated coding for ISCO (see Hoffmeyer-Zlotnik et al 2004, Hoffmeyer-Zlotnik and Warner (2012)), the rule-based method currently employed by Geis (2011) has a production rate lower than 50% and manual checking is intended. The quality for coding according to the ISCO-88 classification (390 categories) has been investigated by Maaz et al (2009). In their study, two professional institutes and two research assistants without prior coding experiences have coded occupations from the parents of 300 high school graduates.…”

Section: Quality Of Occupation Codingmentioning

confidence: 99%

Occupation Coding of Job Titles: Iterative Development of an Automated Coding Algorithm for the Canadian National Occupation Classification (ACA-NOC)

Bao¹,

Baker²,

Adisesh³

2020

JMIR Form Res

View full text Add to dashboard Cite

Background In many research studies, the identification of social determinants is an important activity, in particular, information about occupations is frequently added to existing patient data. Such information is usually solicited during interviews with open-ended questions such as “What is your job?” and “What industry sector do you work in?” Before being able to use this information for further analysis, the responses need to be categorized using a coding system, such as the Canadian National Occupational Classification (NOC). Manual coding is the usual method, which is a time-consuming and error-prone activity, suitable for automation. Objective This study aims to facilitate automated coding by introducing a rigorous algorithm that will be able to identify the NOC (2016) codes using only job title and industry information as input. Using manually coded data sets, we sought to benchmark and iteratively improve the performance of the algorithm. Methods We developed the ACA-NOC algorithm based on the NOC (2016), which allowed users to match NOC codes with job and industry titles. We employed several different search strategies in the ACA-NOC algorithm to find the best match, including exact search, minor exact search, like search, near (same order) search, near (different order) search, any search, and weak match search. In addition, a filtering step based on the hierarchical structure of the NOC data was applied to the algorithm to select the best matching codes. Results The ACA-NOC was applied to over 500 manually coded job and industry titles. The accuracy rate at the four-digit NOC code level was 58.7% (332/566) and improved when broader job categories were considered (65.0% at the three-digit NOC code level, 72.3% at the two-digit NOC code level, and 81.6% at the one-digit NOC code level). Conclusions The ACA-NOC is a rigorous algorithm for automatically coding the Canadian NOC system and has been evaluated using real-world data. It allows researchers to code moderate-sized data sets with occupation in a timely and cost-efficient manner such that further analytics are possible. Initial assessments indicate that it has state-of-the-art performance and is readily extensible upon further benchmarking on larger data sets.

show abstract

Intercoder-Reliabilität bei der Berufscodierung nach der ISCO-88 und Validität des sozioökonomischen Status

Cited by 23 publications

References 7 publications

How Standardized is Occupational Coding? A Comparison of Results from Different Coding Agencies in Germany

How Standardized is Occupational Coding? A Comparison of Results from Different Coding Agencies in Germany

Occupation Coding During the Interview

Occupation Coding of Job Titles: Iterative Development of an Automated Coding Algorithm for the Canadian National Occupation Classification (ACA-NOC)

Contact Info

Product

Resources

About