A Commented Review of Applications

Yamasaki, Sakura; Rihoux, Benoît

doi:10.4135/9781452226569.n6

Cited by 38 publications

(27 citation statements)

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“…The calibration process is crucial for the later analyses (Schneider & Wagemann, ). Therefore, the calibration of all conditions is ideally based on in‐depth case knowledge that allows clearly defined thresholds between the different degrees of set‐membership (Yamasaki & Rihoux, ). Case knowledge has been obtained through an analysis of the regulatory frameworks and policy mixes in all 26 cantons as well as with semistructured expert interviews.…”

Section: Methodsmentioning

confidence: 99%

Factors Driving the Promotion of Hydroelectricity: A Qualitative Comparative Analysis

Kammermann

2017

Review of Policy Research

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In the wake of the COP21 conference in Paris, the transition to a low-carbon energy supply remains a central issue on the political agenda. The deployment of renewable energies is often challenged by multiple issues (e.g., public acceptance, landscape protection, and so forth). Political actors try to overcome such challenges with various measures; however, the policy instruments used vary greatly in their strength. This article questions what factors lead to the adoption of strong policy instruments promoting hydroelectricity. Explanatory factors are derived from Kingdon's multiple streams framework and are analyzed with fuzzy-set qualitative comparative analysis within the Swiss cantons. The findings show that the strength of policy promoting hydroelectricity depends on the conjunction of mainly two factors: ambitious climate targets and an already well-established hydroelectricity sector that generates large tax revenues for the cantons. Depending on the context, the strength of left-wing and green parties as well as the current level of exploitation play an important role with the aforementioned factors.

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Section: Methodsmentioning

confidence: 99%

Factors Driving the Promotion of Hydroelectricity: A Qualitative Comparative Analysis

Kammermann

2017

Review of Policy Research

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“…Contradictory simplifying assumptions occur when the software uses the same assumptions to reduce both the [1] and [0] outcomes (positing that the same combinations of conditions lead to both the presence and absence of an outcome) (Yamasaki and Rihoux 2009). An analysis of the simplifying assumptions used for both the [1] outcome and [0] outcome reveal no contradictory assumptions, increasing confidence in the parsimonious solution paths.…”

Section: Boolean Minimization On [0] Outcome With Logical Remaindersmentioning

confidence: 99%

Gender Expertise in Public Policy

Hoard

2015

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This timely new series publishes leading monographs and edited collections from scholars working in the disciplinary areas of politics, international relations, and public policy with specific reference to questions of gender. The series showcases cutting-edge research in Gender and Politics, publishing topical and innovative approaches to gender politics. It will include exciting work from new authors and well-known academics and will also publish high-impact writings by practitioners working in issues relating to gender and politics.The series covers politics, international relations, and public policy, including gendered engagement with mainstream political science issues, such as political systems and policymaking, representation and participation, citizenship and identity, equality, and women's movements; gender and international relations, including feminist approaches to international institutions, political economy, and global politics; and interdisciplinary and emergent areas of study, such as masculinities studies, gender and multiculturalism, and intersectionality.

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“…We apply an inductive approach to identify data fabrication characteristics (Yamasaki and Rihoux 2009), where the transcripts are read and discussed (CHJH and student-assistant) to identify data fabrication characteristics. Subsequently, transcripts are coded for these characteristics by two independent coders.…”

Section: Procedures Projectmentioning

confidence: 99%

The value of statistical tools to detect data fabrication

Hartgerink¹,

Wicherts²,

Assen³

2016

RIO

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We aim to investigate how statistical tools can help detect potential data fabrication in the social-and medical sciences. In this proposal we outline three projects to assess the value of such statistical tools to detect potential data fabrication and make the first steps in order to apply them automatically to detect data anomalies, potentially due to data fabrication. In Project 1, we examine the performance of statistical methods to detect data fabrication in a mixture of genuine and fabricated data sets, where the fabricated data sets are generated by actual researchers who participate in our study. We also interview these researchers in order to investigate, in Project 2, different data fabrication characteristics and whether data generated with certain characteristics are better detected with current statistical tools than others. In Project 3 we use software to semi-automatically screen research articles to detect data anomalies that are potentially due to fabrication, and develop and test new software forming the basis for automated screening of research articles for data anomalies, potentially due to data fabrication, in the future. Problem statementThere is a clear need to develop and validate statistical tools to detect (potential) data fabrication in the social-and medical sciences. 2% of researchers admit to have either falsified or fabricated research data once in their professional career (Fanelli 2009), but only a dozen cases are discovered in the U.S. and the Netherlands per year, while combined they cover ~300,000 researchers. Arguably, then, the cases that are discovered are only the tip of the iceberg.Considering that data fabrication undermines the epistemological pursuit of science and negatively affects the validity of published findings, fairness in the scientific reward system, and trust in science, it is important to improve its detection. In the last decade, journals have started using automated tools as a screening device to detect image manipulation and plagiarism in submitted or accepted articles. Numerous case studies (e.g., in the Journal of Cell Biology) suggest that these screening tools are effective and useful to detect various forms of research misconduct. In the social-and medical sciences, image detection tools are practically useless because data are primarily quantitative and based on observations of behavior, questionnaires, (cognitive) tests, etc. Despite their potential use as a screening device, there are currently no well-established tools to detect data anomalies (potentially) due to fabrication of quantitative data.Statistical tools to detect data fabrication have been successful in several ad hoc investigations in the social-and medical sciences, of which the Diederik Stapel case is perhaps the most well known. As in the Fuji case in anesthesiology (Carlisle 2012), statistical results reported in the articles of Stapel allowed for statistical tests that indicated his results were too good to be true (Levelt Committee et al. 2012). Similarly, the raw data...

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A Commented Review of Applications

Cited by 38 publications

References 0 publications

Factors Driving the Promotion of Hydroelectricity: A Qualitative Comparative Analysis

Factors Driving the Promotion of Hydroelectricity: A Qualitative Comparative Analysis

Gender Expertise in Public Policy

The value of statistical tools to detect data fabrication

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