The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Collecting and analyzing qualitative data for system dynamics: methods and models
Abstract
System dynamics depends heavily upon quantitative data to generate feedback models. Qualitative data and their analysis also have a central role to play at all levels of the modeling process. Although the classic literature on system dynamics strongly supports this argument, the protocols to incorporate this information during the modeling process are not detailed by the most influential authors. Data‐gathering techniques such as interviews and focus groups, and qualitative data analysis techniques such as grounded theory methodology and ethnographic decision models could have a strong, critical role in rigorous system dynamics efforts. This article describes some of the main qualitative, social science techniques and explores their suitability in the different stages of the modeling process. Additionally, the authors argue that the techniques described in the paper could contribute to the understanding of the modeling process, facilitate communication among modelers and clients, and set up a methodological framework to promote constructive discussion around the merits of qualitative versus quantitative modeling. Copyright © 2003 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Weber, R. (1990) · doi:10.4135/9781412983488 · Cited by 7,750
Cited by
Cybersecurity in Hospitals: A Systematic, Organizational Perspective
Jalali, M., & Kaiser, J. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3100364 · Cited by 265
Assessing the Long-term Effects of Autonomous Vehicles: A Speculative Approach
Gruel, W., & Stanford, J. M. (2016) · Transportation Research Procedia · doi:10.1016/j.trpro.2016.05.003 · Cited by 166
Metadata
- Authors
- Luna‐Reyes, L. F., & Andersen, D. L.
- Venue
- System Dynamics Review
- Publisher
- Wiley
- Published
- 2003-12
- Volume
- 19
- Issue
- 4
- Pages
- 271-296
- ISSN
- 0883-7066 (ISSN-L)
- 1099-1727
- OpenAlex
- W2170793689
- Cited by
- 505
- References
- 1
- FWCI
- 7.72
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.