What should we want from publishers?

Last week I attended a conference organized by the university libraries in the Netherlands with the straightforward title: “What do we want from publishers?” The conference served as a consultation with librarians and other academics, before the universities in the Netherlands start negotiations about deals with commercial publishers such as Elsevier. I had missed the morning session, in which a panel of representatives from publishers and academia shared their views on the current state and future directions of academic publishing. In the sessions I attended I noticed signs of rebellion and resistance, with one speaker saying “we should be prepared to walk away” when commenting about the way libraries should approach negotiations with publishers.

Elsevier makes outrageously high profits, as do other academic publishers such as Springer/Nature, Taylor & Francis, and Wiley. As others have noted before (see here, here, and here), these profits often exceed those made by tech giants such as Apple and Google. The reason that profits of 30-40% per year are so outrageous is that publishers create these profits with unpaid work by academics. Scientists publishing in academic journals do not get paid for their contributions. In contrast: scientists often have to pay for publishing their articles. Publishers charge ‘Article Processing Charges’ (APCs) or open access fees to authors, to make the research available to the general public. Work that usually has been paid already by the general public (more about the business model of academic publishing here).

At the conference I learned that publishers seriously think that they add value to the work of academics by enticing us to spend our time as volunteer editors and peer reviewers. I’ll return to the merits of that argument below. First: why are academics – myself included – willing to voluntarily spend their time for commercial companies?

One reason is that academics see it as their duty and responsibility to review work by their peers, so that they can improve it. The peer review system is an economy of favors that relies on generalized reciprocity. As a responsible academic and decent human being, you should not free ride and expect your peers to review your work, while providing no service in return. If you receive three reviews for every article submitted, you should be prepared to review three other articles for the journal. Also there are individual benefits in review work. You learn about new research before it gets published, and you get a chance to influence it.

A collective trap

The systemic problem is that the academic community is trapped. Professional organizations of researchers such as the American Psychological Association benefit financially from the publishing industry. The associations are not prepared to give up the revenues from journals – they would not be able to finance their conferences, scholarships and other association activities without the journal revenues. In my former role as secretary of a professional association of researchers in the field of nonprofit and philanthropy research I completely understand this.

Universities and research funding agencies rely on the academic publishing industry to make decisions on grants for research, tenure and promotion to higher ranks, allocate time for research, and even bonuses to researchers. Universities and funding agencies do not directly evaluate the quality of the research that their employees produce. Instead they reward researchers who publish more proficiently, and especially in journals that are cited more frequently. Universities and funding agencies assume that a publication in a peer reviewed journal, especially in a journal that in the past published work that is cited more often, is a signal of quality. But here’s the catch: it’s not.

The average citation count of articles published in the past is not a good indicator of the quality of a newly published article. Citations follow a power law: a few articles are cited very often, while the majority of articles receive only a low number of citations. Because publishing in journals with a higher average number of citations is more attractive, these journals also attract more fraud and bad science. Journals are struggling to maintain the quality of published research, and the number of retractions is increasing strongly.

Counter to public belief, the fact that a study was peer reviewed does not indicate that it is of high quality. A skyrocketing number of ‘peer reviewed’ articles appear in predatory journals, that accept papers in exchange for payment, without any substantial or critical review. So-called ‘paper mills’ sell publications. Even citations are for sale. Also at respectable journals, peer reviewers are not good at detecting errors. The peer review system is under pressure. As the number of submissions and publications continues to increase, the willingness of researchers to review articles declines. It takes more time and more invitations to get researchers to say yes to requests to review.

Journals seem to do everything to make it difficult to evaluate the quality of the peer review. An overwhelming majority of journals does not publish the peer reviews of accepted articles. Still we know that peer review does not add quality in most cases. Published versions of articles that were posted earlier as a preprint are very similar (see here and here). If the version published in a journal is the same as the preprint, the journal have literally added nothing.

What we want from publishers

On my way to the conference I imagined four answers to the question what we want from publishers.

1) We want our money back, because publishers have extorted profits from academia.

2) Nothing at all, because publishers have not added value.

3) Radical transformation: commercial publishers exploiting public funding have no right to existence and should be transformed into non-profit organizations or social enterprises that put their profits back into science. Funding research on peer review, for instance.

Admittedly these three demands are quite revolutionary and rebellious. At the conference they worked as advocate of the devil statements forcing a clear statement by conversation partners on the value that publishers could add.

4) If commercial publishers are a necessary evil, and continue to exist, we want value for our money. Let publishers propose changes that improve the transparency and quality of the publications. Here are some ideas.

These are just some of the things that journals could have done, but have neglected to enact in the past decades. It would be good if new deals contain agreements on these innovations.

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How often do we replicate previous research?

Surprisingly little – at least to my taste. Science is supposed to be cumulative: we should build knowledge on solid foundations. But how do we know what knowledge is solid? Through independent verification. If someone claims to have found a regularity, it is only through repeated testing of the same claim that we know that the result holds in other populations and contexts. It turns out that scientists rarely conduct such replications. Here are all the estimates of the prevalence of replications in various scientific disciplines that I could find in an extensive search on Google Scholar.

Some observations on the prevalence rates above.

The replication prevalence in psychology increased strongly after 2013. The detection of fraud by Diederik Stapel and the publication of a paper on ‘precognition’ by Daryl Bem may have contributed to this increase.

In marketing, in contrast, fraud cases such as those of Dirk Smeesters and Brian Wansink did not increase the prevalence of replications. In the period 2000-2011 the proportion of articles replicating previous research was lower than in the preceding period 1990-2004.

So called ‘top’ journals – publishing papers with a higher citation count on average – are more likely to publish replications in criminology. This also holds generally speaking in the social sciences – see the graph below.

There is no hierarchy of the sciences in the prevalence of replications. The “hard sciences” do not attempt replications more often than the social sciences – it’s the other way around. Publications in economics are far less likely to attempt replications than publications in marketing or in communication science. The prevalence of replications in ecology is devastatingly low at 0.03% (11 out of 38730 published papers).

If you know of a study I have missed that allows for an estimate of the prevalence of replications, please let me know. I could not find studies in public administration, political science, or sociology. The lack of studies documenting the prevalence of replication attempts in public administration and political science is surprising because in these disciplines several journals considered to be leading in the field have introduced data availability policies.

The table below and the ppt with the graphs above contains the data and references to the sources represented.

FieldRateReferenceRemarks
Social Sciences1.3% (2/156)Hardwicke T.E., Wallach, J.D., Kidwell, M.C., Bendixen,  T., Crüwell, S. & Ioannidis, J.P.A. (2020). An empirical assessment of transparency and reproducibility-related research practices in the social sciences (2014–2017). Royal Society Open Science, 7(2), 7190806190806. https://doi.org/10.1098/rsos.190806Random sample of 250 articles, 2014-2017
Social sciences2.8% (44/1559)McNeeley, S., & Warner, J. J. (2015). Replication in criminology: A necessary practice. European Journal of Criminology12(5), 581-597 https://doi.org/10.1177/14773708155781975 ‘top’ journals, 2006-2010
Natural sciences1.4% (94/6637)McNeeley, S., & Warner, J. J. (2015). Replication in criminology: A necessary practice. European Journal of Criminology12(5), 581-597 https://doi.org/10.1177/14773708155781975 ‘top’ journals, 2006-2010
DisciplineRateReferenceRemarks
Psychology5.32% (10/188)Hardwicke, T. E., Thibault, R. T., Kosie, J. E., Wallach, J. D., Kidwell, M. C., & Ioannidis, J. P. (2022). Estimating the prevalence of transparency and reproducibility-related research practices in psychology (2014–2017). Perspectives on Psychological Science17(1), 239-251. https://doi.org/10.1177/1745691620979806Random sample of 250 articles, 2014-2017
Communication science3.7% (21/562)Keating, D. M., & Totzkay, D. (2019). We do publish (conceptual) replications (sometimes): Publication trends in communication science, 2007–2016. Annals of the International Communication Association, 43(3), 225-239. https://doi.org/10.1080/23808985.2019.163221810 journals, 2007-2016
Advertising2.87% (82/2856) interstudy replicationsPark, J. H., Venger, O., Park, D. Y., & Reid, L. N. (2015). Replication in advertising research, 1980–2012: a longitudinal analysis of leading advertising journals. Journal of Current Issues & Research in Advertising, 36(2), 115-135. https://doi.org/10.1080/10641734.2015.10238744 ‘top’ journals, 1980-2012
Criminology2.32% (16/691)McNeeley, S., & Warner, J. J. (2015). Replication in criminology: A necessary practice. European Journal of Criminology12(5), 581-597 https://doi.org/10.1177/14773708155781975 ‘top’ journals, 2006-2010
Experimental linguistics1.81% (153/8437)Kobrock, K., & Roettger, T. B. (2023). Assessing the replication landscape in experimental linguistics. Glossa Psycholinguistics, 2(1). http://dx.doi.org/10.5070/G6011135100 journals, 1945–2020
Marketing1.01% (12/1185)Kwon, E. S., Shan, Y., Lee, J. S., & Reid, L. N. (2017). Inter-study and intra-study replications in leading marketing journals: a longitudinal analysis. European Journal of Marketing, 51(1), 257-278. https://doi.org/10.1108/EJM-07-2015-04505 ‘top’ journals, 2000-2011
Marketing1.70% (41/2409)Evanschitzky, H., Baumgarth, C., Hubbard, R., Armstrong, J.S. (2007). Replication research’s disturbing trend. Journal of Business Research, 60, (4), 411-415. https://doi.org/10.1016/j.jbusres.2006.12.0035 ‘top’ journals, 1990-2004
Business and management1.48% (1235/83682)Ryan, J. C., & Tipu, S. A. (2022). Business and management research: Low instances of replication studies and a lack of author independence in replications. Research Policy51(1), 104408. https://doi.org/10.1016/j.respol.2021.104408All 121 journals, 2008-2017  
Psychology1.08% (347/321411)Makel, M. C., Plucker, J. A., & Hegarty, B. (2012). Replications in psychology research: How often do they really occur?. Perspectives on Psychological Science7(6), 537-542. https://doi.org/10.1177/1745691612460688Random sample of 500 articles, 1900-2012
Criminology0.45% (178/39275)Pridemore, W. A., Makel, M. C., & Plucker, J. A. (2018). Replication in criminology and the social sciences. Annual Review of Criminology1, 19-38. https://doi.org/10.1146/annurev-criminol-032317-091849All journals, until 2014
Addiction Medicine0.41% (1/244)Adewumi, M. T., Vo, N., Tritz, D., Beaman, J., & Vassar, M. (2021). An evaluation of the practice of transparency and reproducibility in addiction medicine literature. Addictive Behaviors112, 106560. https://doi.org/10.1016/j.addbeh.2020.106560All journals in PubMed, 2014-2018
Management0.15% (240/159242)Block et al. 2022 https://doi.org/10.1007/s11301-022-00269-656 top journals until 2020
Economics0.10% (130/126505)Müller-Langer et al. 2019 https://doi.org/10.1016/j.respol.2018.07.019Top 50 journals, 1974-2014
Educational science0.13% (221/164589)Makel et al. 2014 https://doi.org/10.3102/0013189X14545513Top 100 journals, until 2013
Ecology0.02% (11/38730)Kelly, C. D. (2019). Rate and success of study replication in ecology and evolution. PeerJ7, e7654. https://doi.org/10.7717/peerj.7654All 160 journals, until 2017; Python code at https://doi.org/10.17605/OSF.IO/WR286

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Data? What Data?

Scientific research is based on data. How should researchers treat and document the data they analyze? Research Data Management policies recommend that data should be “as open as possible, and as closed as necessary”. What does that mean in practice?

Only publish data you are allowed to publish. Certainly “as open as possible” does not mean that researchers should make public all data they have. Researchers cannot share sensitive data of living individuals, because doing so would violate the GDPR. To arrive at data researchers can share responsibly, they have to process the data in such a manner that individuals cannot be identified.

Only publish data others can understand. In addition, researchers should process the data in such a manner that other researchers can work with the data. Fellow researchers should be able to understand how the conclusions drawn from a study were based on the data. This requires researchers to document each of the steps they took to collect the data, and to prepare, process, and analyze them.

Varieties of data: seven sources of data at five stages of processing. In practice, the things researchers do to prepare and process raw data vary enormously from one study to the the other. Also the things that researchers end up with to draw conclusions from can be very different. In the matrix below, you find examples and suggestions for data management for seven common sources of data, at five stages of processing.

1. Raw Data2. Preparation3. Processed Data4. Data Analysis5. Analyzed Data
SurveysAnswers to survey questionsAnonymization, aggregating, recoding, cleaningAnonymized survey data in .sav, .dta, .xls(x) or .csv formatAnalysis script in .sps, .do or .rmd formatNumbers, graphs, and tables
Interviews and focus groupsInterview recordings and notesAnonymization, pseudonymization, aggregating, cleaningAnonymized interview transcripts in .doc(x) or .txt formatCoding tree, open coding, axial codingInterpretative deductions, quotes, tables
ObservationsPhysical measures, digital traces, log entries, photos, videosAnonymization, pseudonymization, aggregating, recoding, cleaningAnonymized files and descriptions in .doc(x), .txt, xls(x) or .csv formatAnalysis script in .sps, .do or .rmd format; coding tree, open coding, axial codingNumbers, graphs, tables, Interpretative deductions, vignettes
Participant observationField notes, photos, videos, collected objects, memoriesCodingNotes and descriptions in .doc or .txt formatAnonymization and reflection on positionalityInterpretative deductions, vignettes
Administrative dataEntries in official registersAnonymization, aggregating, recoding, cleaningAnonymized register data in .sav, .dta, .xls(x) or .csv formatAnalysis script in .sps, .do or .rmd formatNumbers, graphs, and tables
News and social media dataNews items, posts on social media platformsWeb scraping or API script compiling, aggregating, recoding, cleaningAnonymized textual and visual media dataAnalysis script in .sps, .do or .rmd formatNumbers, graphs, and tables
MaterialsOriginal materials of human or natural origin, or fragments thereofCopying, reproducing, treating, sampling, described in lab or field notebookCopies, facsimile reproductions, images and database entries in .xls(x), .csv, jsonAnalysis script in .py or .rmd formatNumbers, graphs, and tables
Archiving, documentation and sharing requirementsShould be preserved and archivedShould be documented and sharedShould be documented and sharedShould be documented and sharedShould be documented and shared
Research Data Management suggestions and examples for seven sources of data at five stages of processing

Raw, Processed, and Analyzed Data. A common distinction in research data management is between Raw Data (the first column), Processed Data (the third column) and Analyzed Data (the fifth and final column). Raw Data are the original sources that researchers ultimately rely upon to do their work. Once they’ve worked with the Raw Data in one way or another, by filtering them, categorizing, copying, treating, preserving, or in any way modifying them, they become Processed Data. Often researchers engage in multiple rounds of processing before they analyze the data. It is also possible that researchers redo some of the data processing after they have conducted an initial analysis. The data analysis produces the Analyzed Data that end up in the research report in the form of an exemplary picture, a table of results, an infographic, a figure, or a literal quote from an interview.

Access to data can be restricted. At all stages, access to the data can be restricted to specific groups (e.g., scientists), for specific purposes (e.g., a review, audit) or specific conditions (e.g., physical sites or secure online environments). In most cases, raw data include personal information that researchers should never publicly share.

Transparency is crucial. Especially when the raw data cannot be shared, providing documentation of the data processing steps (the second column) allows fellow researchers to retrace the production of the analysis file from the raw data. Also researchers can produce synthetic datasets that contain the same properties as the original raw data, but do not contain data on actual individuals. To provide transparency in research decisions and enable others to trace and redo the steps taken between raw, processed and analyzed data, researchers should identify and document processed data (the third column) along with executable code files (the fourth column) that produce the results they report as analyzed data in their publications.

Example: interview data

Raw interview data. Suppose for example that a researcher conducts interviews with persons about stressful life events for a study on coping strategies. If all went well, researchers ask those to be interviewed for permission to record the conversation, after providing them with information about the purposes and topics of the interview, that participation is voluntary, and what rights the interviewed have with respect to the information they provide. In such a study, the Raw Data are the recording of the interview, on a device such as a tape, dictaphone, smartphone, laptop, or on a cloud server.

Processed interview data. After the interview, the researcher transcribes the conversation in an electronic document, which indicates who said what in chronological order. The transcription is Processed Data. Documenting how the transcription was made is essential: did the researcher type up the conversation, did someone else do it, or was it done by an automatic transcription service? Typically, as a first step, the recording of the interview is transcribed verbatim, including all “eh”s, “ah”s and “….” (silences) in the conversation. Next, the transcription is edited to hide the identity of persons interviewed and other persons and organizations mentioned in the interview. Researchers can hide the identities of persons and organizations by using other names (pseudonymization) or using numbers and letters (“interview 1 at organization A”) to represent those interviewed and mentioned. The pseudonymized data is also Processed Data (version 2). Usually such editing is not enough to effectively hide the identities of those interviewed or mentioned, because details in the conversations will be unique enough for others to still be able to identify them. In this case, a third round of data processing will be needed, omitting unique details altogether or presenting specific information at a higher level of aggregation: “a colleague at organization A” or “my aunt” can be represented as “a person I know”. For transparency it is important that researchers document the rules they applied to edit the transcription.

Analyzed interview data. Researchers use a wide variety of techniques to analyze interview data. A common technique is to assign codes or tags to (parts of) sentences, organize them into a hierarchical tree, and note the co-occurrence of certain codes. Typically, quotes from the interviews that illustrate patterns in the data are presented in the research report. These quotes are the Analyzed Data. Another common category of Analyzed Data in interviews is an overview of codes and their co-occurrences in the form of a table.

Data transparency

Regardless of the exact methods of analysis researchers use, it is important to uniquely identify which data they have started from to draw a certain conclusion. A persistent digital object identifier (DOI) is the best way to do so. The DOI does not change, and is unique, so that it is clear upon which data the conclusions of research are based. In the example of the interview data above, the raw data of the audio recordings are not publicly accessible, but have their own DOI that is different from the DOI of the processed data of the edited and de-identified interview transcripts that can be accessed for review purposes. The analyzed data of the coding tree also has its own DOI.

Varieties of data

The seven sources of data cover most types of data researchers use. The groups are very broad categories. There are many varieties of observations for example, including in-person observation by researchers (such as leadership behavior by managers in firms, parent-child interactions in a home setting, interruptions in parliamentary debates), and observation through measurement equipment (such as images of traffic violations on cameras, heart rate measurements by wristbands, DNA samples from swabs). The same holds for the other sources. Materials in behavioral genetics are very different from materials in cultural heritage studies. Furthermore, for each of these sources, researchers use a wide variety of techniques to collect, process, and analyze data. Though the table above does not include an exhaustive list of all varieties within each category, the seven categories are intended to be comprehensive. If you work with a category of data that is not included in the table, I’d love to hear from you!

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5 ways students do not use ChatGPT and AI (you won’t believe #2!)

Gotcha. Or did the clickbait title not get you here? Regardless, I will try not to disappoint you. But before we get to the promised list, here’s why it is important.

The nightmare of every business: suddenly a new competitor emerges, wiping out their entire firm’s market share. When ChatGPT was released a little over a year ago, it was clear that it posed a threat to the business model of schools and universities. If freely accessible large language models can produce texts within seconds that students would otherwise have taken hours to write, and teachers find these texts acceptable, how can educational institutions continue to rely on writing assignments to evaluate student learning?

Some educational institutions, like mine, quickly forbid AI writers for students. That was shortsighted because it was clear that the technology was going to be omnipresent in the future. Why not teach students how to use it in a responsible way? The simple answer was: because schools and universities were not ready. My university did not only forbid AI writers, but also tools that could detect their use. It took a long time before the center for teaching and learning gave guidelines for generative AI and meanwhile the university has changed its policy to allow students to use AI tools, but only if their teachers would explicitly do so. So I decided to develop a policy for the master programme I am directing and test it.

A course I am teaching myself (course manual here) served as the test case. In the course, students read a lot of articles, most of them from disciplines other than their own. Each week we discussed a set of articles from another perspective. Throughout the course students develop an essay, in which they analyze a case from at least two of the perspectives discussed in the course. To help students prepare for the essay I gave them suggestions on their draft research question, and an example of a good essay from a previous year’s student, along with the rubric I had completed to evaluate it. They knew exactly what elements should go into the essay. Also students peer reviewed each other’s draft essays.

This is the text we included in the course manual:

“It is fine to use generative artificial intelligence tools such as ChatGPT, Bing, Bard, Claude, Perplexity, Elicit or Research Rabbit, as long as you identify that you have used them, and how you have used them. Do so in sufficient detail for others to be able to reproduce your findings. This means that you specify the software version, settings, date of usage, the prompts and commands, and output with a URL or a screen dump. Whenever you use AI-generated content, independently verify the claims made and insert references to sources supporting the claims including DOIs (for scholarly publications) or URLs (to non-scholarly sources such as Wikipedia).”

Course manual, Foundations of Resilience 2023-2024, page 7

To evaluate the policy, I held short meetings with all students in the course who had submitted their final essay. I asked questions about the process. I was curious how students went about writing their essays. Did they start with an outline? How did they search for relevant literature? How did they come up with the case? Had they considered a comparison with other cases? How did they decide to select certain perspectives and not others? I figured that students who had not written their essays but instead had generative AI produce them would not be able to justify the decisions the language model had made. I asked them what software, databases and tools they had used. Did they use generative AI, and what were their experiences?

Short answer: students did not use AI tools much, and the tools they did use didn’t help them much. Students had not yet learned how to use the tools available in a sophisticated manner.

Five ways students did use AI

Before we get to the list of ways in which students didn’t use ChatGPT and other AI tools, let’s go over the tasks they did use them for.

1. Clarify terms

Several students said they had used ChatGPT throughout the course to clarify terms they did not know. The terms appeared in the weekly readings and other articles. They found the explanations useful. For this task they did not need references to sources, which ChatGPT does not provide.

2. Find synonyms

One surprising tool based on language models that students used was a thesaurus to find synonyms. One student said she disliked using the same words over and over again, and used a tool to suggest synonyms to use instead. I totally recognize the love of variety, but using synonyms is not a good idea for a scientific text. Scientific language should be boringly precise. It’s better to use the same term for the same thing throughout your essay instead of using different terms.

3. Correct typos and improve writing style

Many students reported using writing assistants such as Grammarly, the auto complete suggestions of MS Word and Google Docs and the spell checkers integrated in them. Using these is fine of course. Students also used ChatGPT to improve their writing. I could tell this from their use (not: ‘utlization’) of words that hardly anybody uses in their own writing style.

Remarkably, very few students submitted a perfect list of references. Many DOIs and URLs were missing. One feature of references copied directly from Google Scholar that gives away they were not checked is the inconsistency in using capitals for the First Letters of Words in Names of Journals. Another identifying feature is the lack of final pages of articles. Using a reference manager like Zotero is the easiest way to obtain such a list (more about how to provide references here).

4. Draft a conclusion section

One student had fed his draft essay to ChatGPT and asked it to write a conclusion section. It created a text including some of the statements from the main text, but it was not integrating them in a synthesis. He deleted it and wrote his own conclusion.

5. Search for relevant research

Students primarily used Google Scholar, the university library search tool, or Scopus to find relevant publications. A problem that all students encountered was that the numbers of search results exceeded their screening capacity. Identifying the most relevant results was a challenge for them (suggestions on how to do that here). Once they’d identified a small set of relevant publications, some students used a backward search strategy to find more relevant publications, starting from the references cited. One student used a forward search using the ‘cited by’ option in Google Scholar.

Tools such as Research Rabbit and Elicit are very handy, but students were not yet familiar with them before the course. Those who tried them did not find them particularly helpful, but also did not use them in the most productive ways. One student had entered keywords in Elicit as if it was an ordinary database, instead of asking substantive questions. The results were not better than those produced by the university library or Google Scholar.

5 ways students did not use AI

Now, as promised, let’s get to the list of ways in which students did not use ChatGPT and other AI tools. By the way, I did not use AI tools to write this text, other than the spell checker embedded in the blog editor software.

1. Creating an outline

Several students said they had started working on their essay with an outline. For some this was their default way of working which they had developed over the years, while others tried it for the first time. It worked well for those who worked with an outline (suggestions on how to do so here). None of the students said they had used some form of AI to produce the outline. I’ve not tried it myself, but I guess that large language models can easily produce an outline based on the assignment text.

2. Criticizing previous research

One aspect that students did not do well on in the essays was criticism of previous research. They had just taken the claims in the sources they cited as given, as if the rule is that once something is published, it must be true. This is unfortunate. The number of retractions is skyrocketing and this is only the tip of the iceberg of bad science. Though the course was not designed to teach the identification of weaknesses in research design and analysis, I had given some examples of the grave shortcomings of peer review as a quality control instrument throughout the course.

I had hoped that students would demonstrate some awareness of the idea that every piece of research is an inherently provisional and partial answer to a question about a complex reality. That hope did not materialize in the essays, despite the fact that the rubric explicitly rewarded a critical approach.

3. Answering their research question

None of the students used AI writers in the way I had feared. I received no submissions in the typical confident mansplaining language that characterizes ChatGPT. In the essays, I could recognize the students’ own language from the weekly assignments they had submitted throughout the course. Also students had benefited from their peers’ reviews in ways that improved their research questions, cases and approaches. These suggestions were much more useful than the answers large language models could have provided.

4. Visualization

None of the students had added an AI generated image to illustrate the essay. In fact, only one of the essays included an image on the front page, and none of the others had graphics or other visuals. I missed them: a photo, graph or image often helps to capture the reader’s attention and spark the imagination, and summarize results.

5. Generate ideas for similar cases or future research

Many students had considered comparing the cases discussed in their essays with similar cases, in other times and locations. Though I imagine that large language models can easily produce comparable cases, none of the students had used them for this purpose. They had no trouble coming up with ideas for other cases. Also the word limit for the essay had made it difficult for them to create a comparison.

Another aspect that was missing in many essays was a paragraph with suggestions for future research. Again, large language models will easily produce suggestions for future research, but none of the students used them for this purpose.

Finally, none of the students reported how they had used generative AI in the essay. Perhaps this was the result of the rather poor results they obtained. Three students who did use form of AI assistance apologized for not having disclosed it. Perhaps this was also a result of the fact that the rubric did not include a section explicitly mentioning and rewarding disclosure of AI tools. Next time I will be sure to include the tools transparency section in the rubric.

The takeaway

In retrospect, the policy was a solution for a problem that didn’t exist. But it was worth the investment. I learned that students didn’t use AI tools in sophisticated ways that could help them. Perhaps this blog will change that.

Also it is clear that we should educate students in critical thinking and analysis. Large language models will provide arguments for pretty much any hunch you feed them, and AI powered research assistants are great tools to find sources that support these claims. While this is one of the major weaknesses of large language models, it can also be used in a productive way. Once you’ve got your argument ready, provide the opposite claim to a language model, and take the output it provides as a starting point for critical thinking and analysis. In a conceptual form of critical analysis, students could find the flaws in the arguments, and criticize the quality of published research (e.g., using rules of thumb such as these) to weigh the evidence in favor of either claim. We should educate our students to become Bad Science Detectives. In two further forms of analysis, students could learn to be Good Science Practitioners: students should be able to design research that tests the empirical validity of alternative claims (suggestions here), and conduct empirical data analysis to adjudicate the claims.

Thanks to the students in the Research Master Social Sciences for a Digital Society at VU Amsterdam

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Filed under academic misconduct, AI, ChatGPT, fraud, incentives, law, plagiarism, publications, research, research integrity, teaching, VU University

Haal perverse prikkels uit de financiering van universiteiten

Wat kunnen we na de verkiezingen van november 2023 verwachten voor de universiteiten? In de partijprogramma’s voor de komende verkiezingen komen twee punten bij veel partijen terug: studiemigratie, en de financiering van het onderwijs. Bij sommige partijen zien we ook ideeën over de financiering van wetenschappelijk onderzoek.

Het eerste punt is het aantal studenten uit het buitenland dat in Nederland studeert. NSC van Pieter Omtzigt, de PVV, JA21 en 50 plus willen de studiemigratie sterk beperken. Ook de VVD, BBB, het CDA, de Partij voor de Dieren, de SP en de FvD willen de instroom van studenten uit het buitenland verminderen. GroenLinks/PvdA heeft een kopje ‘grip op internationalisering’, maar zegt niet dat het aantal buitenlandse studenten omlaag moet. BIJ1 heeft er geen standpunt over. Volt en de ChristenUnie zijn voor meer lokaal maatwerk. Alleen D66 zegt expliciet dat internationalisering een goede zaak is.

Het tweede punt is de financiering van het hoger onderwijs. NSC, GroenLinks/PvdA, BBB, de Partij voor de Dieren, D66, het CDA, JA21, en de FvD leggen een verband met de studiemigratie, en willen allemaal de manier waarop we het hoger onderwijs in Nederland financieren veranderen. Omdat de inkomsten van hoger onderwijsinstellingen afhankelijk zijn van het aantal diploma’s dat zij uitreiken en omdat het aantal studenten uit Nederland de laatste jaren niet meer toeneemt, is het gunstig om meer studenten uit het buitenland te trekken. Nederland is tenslotte een gaaf land. Dat maakt ons aantrekkelijk voor studiekiezers.

Hoe moet het nieuwe financieringsstelsel er uit komen te zien? Het CDA, Groenlinks/PvdA, de Partij voor de Dieren, ChristenUnie, D66 en BIJ1 willen de financiering van het hoger onderwijs minder afhankelijk maken van het aantal uitgereikte diploma’s. Dat is een goed idee. De huidige outputfinanciering (bij invoering ‘prestatiebekostiging’ genoemd) heeft het voor onderwijsinstellingen in de afgelopen 23 jaar aantrekkelijk gemaakt de kwaliteit van het onderwijs te verlagen; hier mijn eigen reflectie daarop. De VVD en NSC zeggen in hun programma’s uitdrukkelijk capaciteitsbekostiging in te willen voeren, een systeem dat Jo Ritzen in plaats van de prestatiebekostiging voorstelde in 1996 en nu ook de wens is van de Universiteiten van Nederland. In dit systeem bepalen universiteiten eerst hun capaciteit voor het aantal studenten dat zij kunnen opleiden en leggen die vervolgens vast in afspraken met het Ministerie van Onderwijs, Cultuur en Wetenschappen. Dit stelsel biedt kansen voor meer rust in de financiering van het hoger onderwijs.

Maar alles hangt af van de exacte uitwerking en de omvang van de begroting. Als de vergoeding voor universiteiten ongeveer hetzelfde blijft, dan kunnen zij de kwaliteit van het onderwijs verhogen door het aantal studenten dat zij opleiden te verlagen. De universiteiten moeten dan wel maximum aantallen kunnen opleggen en scherper gaan selecteren. Studeren aan de universiteit wordt daarmee weer exclusiever. Een deel van de studenten die nu onderwijs krijgen aan de universiteit zal dan op het HBO terecht komen.

Het derde punt is de financiering van onderzoek. De VVD wil onderzoeksgeld meer verdelen aan de hand van economische criteria en herverdelen naar technisch, medisch en bèta-onderzoek. BIJ1 en de Partij voor de Dieren willen juist de invloed van bedrijven op wetenschappelijk onderzoek verminderen. De VVD, ChristenUnie, D66 en GroenLinks/PvdA willen dat onderzoek meer aansluit bij ‘maatschappelijke opgaven’. Het CDA noemt specifiek groene technologie, D66 waterstof en batterijen, en Volt noemt een groot aantal innovaties en thema’s (waaronder kernenergie) waaraan wetenschappelijk onderzoek kan bijdragen. De Partij voor de Dieren wil onderzoek zo veel mogelijk proefdiervrij maken. NSC noemt Nederlandse beleidsvraagstukken, specifiek sociologische vraagstukken, openbaar bestuur, fiscaliteit en welzijnswerk. GroenLinks/PvdA wil een Nationale Investeringsbank opzetten om commerciële en technologische toepassingen voor maatschappelijke uitdagingen te financieren. De partij zegt ook een fonds te willen oprichten voor onafhankelijk wetenschappelijk onderzoek waaraan bedrijven kunnen bijdragen. De FvD wil NWO afschaffen, en alle onderzoeksmiddelen rechtstreeks aan de universiteiten uitkeren. Volt pleit voor ‘vaste financiering voor fundamenteel onderzoek, toegepast onderzoek en praktijkgericht onderzoek’. Ook NSC is voor de versterking van de eerste geldstroom – dat wil zeggen: meer geld rechtstreeks naar universiteiten.

Demissionair minister Dijkgraaf van OCW heeft goed geluisterd naar WO in Actie en een begin gemaakt met de verlaging van de werkdruk op universiteiten door vrije onderzoeksmiddelen beschikbaar te stellen voor recent aangestelde universitair docenten met vaste aanstellingen. Door de val van het kabinet kon hij de financieringsstructuur van universiteiten niet meer veranderen.

Hoewel de VVD aan kop gaat in de peilingen, is nu nog niet te zeggen welke partij na de verkiezingen het initiatief gaat nemen in de kabinetsformatie. NSC en GroenLinks/PvdA staan op korte afstand en in de komende weken kan er nog veel gebeuren. Toch kunnen we er al wel iets over zeggen. Omdat een kabinet zonder NSC onwaarschijnlijk is, zijn de standpunten van die partij cruciaal voor de universiteiten in de komende jaren. De partij kan straks linksom formeren met GroenLinks/PvdA of rechtsom met de VVD. Het inhoudelijke zwaartepunt van het onderzoeksbeleid hangt af van wie er in de coalitie komt. De VVD is voor economisch eigen belang van Nederland, dat op verschillende punten in het NSC programma terugkomt. GroenLinks/PvdA heeft meer aandacht voor maatschappelijke kwesties zoals participatie en welzijn.

Voor de financiering van toegepast technologisch onderzoek maakt het niet uit in wat voor formatie NSC in de regering komt: daar zijn zowel GroenLinks/PvdA als de VVD voor. Ook voor de manier waarop het hoger onderwijs gefinancierd wordt ligt een consensus voor de hand. Samen met de VVD is NSC voor capaciteitsbekostiging, en ook GroenLinks/PvdA wil de financiering van universiteiten minder afhankelijk maken van het aantal diploma’s. De Universiteiten van Nederland kunnen op deze punten dus alvast nota’s voorbereiden om de perverse prikkels uit de financiering van onderwijs en onderzoek te halen.


Naschrift, 1 december 2023: Intussen is de verkiezingsuitslag bekend; de PVV is de grootste partij geworden en mag als eerste proberen een kabinet te formeren. Of dit nu lukt of niet, de analyse hierboven blijft geldig: NSC is nodig voor een meerderheid in de tweede kamer.

Tweede naschrift, 22 januari 2024: Intussen heeft Rosanne Hertzberger van NSC haar maidenspeech gehouden in de Tweede Kamer. Ze bepleitte het belang van vrij wetenschappelijk onderzoek en deed het verzoek aan de minister om in kaart te brengen welk deel van de financiering niet naar vrij onderzoek gaat. Ook vroeg ze om steun in Europa voor vrije toegang tot wetenschappelijke publicaties. Ze gaf geen analyse van het probleem van de bekostiging van het hoger onderwijs, en geen duidelijkheid over de ideeën van NSC voor capaciteitsbekostiging.

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Research Integrity Policies in Social Science Research at Vrije Universiteit Amsterdam

René Bekkers, October 10, 2023. An MS Word version of this post is here.

At the Faculty of Social Sciences (FSS) of the Vrije Universiteit Amsterdam, research integrity is governed by seven policies:

  1. The overarching policy is the Netherlands Code of Conduct for Research Integrity adopted by the Royal Academy of Arts & Sciences (KNAW), the Netherlands Association of Universities (Universiteiten van Nederland, formerly VSNU), the Netherlands Organization for Scientific Research (NWO) and other organizations; https://www.universiteitenvannederland.nl/files/documents/Netherlands%20Code%20of%20Conduct%20for%20Research%20Integrity%202018.pdf
  2. The code of ethics for research in the social and behavioural sciences, adopted by the Deans of the Social Sciences (DSW); https://www.nethics.nl/onewebmedia/CODE%20OF%20ETHICS%20FOR%20RESEARCH%20IN%20THE%20SOCIAL%20AND%20BEHAVIOURAL%20SCIENCES%20v2%20230518-2018.pdf
  3. The procedures for ethics review at the Faculty of Social Sciences (FSS); https://assets.vu.nl/d8b6f1f5-816c-005b-1dc1-e363dd7ce9a5/c7e3795f-62b7-4b3f-9282-48859461e87e/RERC-Regulations-Feb18_tcm249-880617.pdf
  4. The national guidelines for archiving research data in the behavioural and social sciences; https://www.utwente.nl/en/bms/datalab/datasharing/guideline-faculties-of-behavioural-sciences-def.pdf
  5. The FSS data management policy, available here: https://vu-fss.github.io/RDM/fss-guidelines-rdm.html
  6. For PhD candidates: the doctorate regulations (‘promotiereglement’) of Vrije Universiteit Amsterdam: https://assets.vu.nl/d8b6f1f5-816c-005b-1dc1-e363dd7ce9a5/08b4502d-de82-47ca-9c4c-fd5ad70d47f0/20220901%20VU%20doctorate%20regulations.pdf
  7. For PhD candidates: the Graduate School for Social Sciences policies: see https://vu.nl/en/about-vu/more-about/the-graduate-school-of-social-sciences under ‘Assessments during your PhD trajectory’: the ‘go/no-go product’, https://assets.vu.nl/d8b6f1f5-816c-005b-1dc1-e363dd7ce9a5/7a3d5a64-b995-47c0-ad93-63fc80ad1a60/VU-GSSS%20Go%20No%20Go%20assessment%20-%20introduction%20and%20explanations.pdf the plagiarism check, https://assets.vu.nl/d8b6f1f5-816c-005b-1dc1-e363dd7ce9a5/e4be9bed-388e-45a9-8847-3bc085d0dec0/VU-GSSS%20Plagiarism%20check%20-%20background%20and%20procedure%20(1).pdf and particularly the final PhD portfolio, https://assets.vu.nl/d8b6f1f5-816c-005b-1dc1-e363dd7ce9a5/88862747-95eb-4f9e-9780-bdac1868f4a1/VU-GSSS%20Final%20PhD%20portfolio%20%28fill-in%20document%29.docx

In your particular discipline, additional policies or codes of conduct may apply:

Throughout the cycle of empirical research, researchers and students at the Faculty of Social Sciences should act in line with the principles and guidelines expressed in the above codes of conduct and policies. The policies employ four instruments to encourage research integrity:

  1. Personal responsibility – your own conscience and internalized norms of good research and ethical standards.
  2. Transparency – the openness you give about the procedures you have followed in your research.
  3. Peer review – the scrutiny of your work by others: supervisors, colleagues, critics.
  4. Complaint procedures – violations of norms of good research and ethical standards may be punished by the Board of the Faculty of Social Sciences, the academic integrity committee at Vrije Universiteit Amsterdam, and ultimately by the Netherlands office of research integrity (LOWI).

Note: The Faculty of Social Sciences does not have audits of research projects.

Step by step guide

At https://vu.nl/en/employee/social-sciences-getting-started/data-management-fss you’ll find step by step guidelines for the organization of research projects.

A. Planning your research

When you are planning research, check whether your study requires ethics review by the FSS Research Ethics Review Board (RERC). Make sure you complete the checklist well ahead of the start of the data collection. In most cases ethics review takes less than a month, but in case the research plans raise ethics issues, you may need three months to complete the entire ethics review process.

  1. Do the FSS ethics review self-check at https://vuletteren.eu.qualtrics.com/jfe/form/SV_6hCj2czIWzboW6V. Save the pdf you get. If the result is that your research does not need further review, you can start with your research. If the result is that your research needs further review, go to step 2.
  2. Discuss the risks with your supervisor and your department’s representative on the FSS Research Ethics Review Committee (RERC), https://vu.nl/en/employee/social-sciences-getting-started/research-ethics-review-fss. Revise your research plan to reduce and tackle risks. Go back to step 1: complete the self-check again based on the revised plan. If the result is still that full ethics review is necessary, proceed to step 3.
  3. Prepare a full ethics review. With your research team, create 1. A short description of the research questions, the societal and scientific relevance of the research, and the research design (max. 1 A4);  2. the information for participants; 3. the consent form; 4. the research materials (manipulations, questionnaire, topic list); 5. the anonymization procedure; and 6. the data management plan. You can find examples of these materials at https://vu.nl/en/employee/social-sciences-getting-started/fss-templates-and-example-documents. If you have everything (i.e., six documents), go to step 4.
  4. Complete the online Ethics Review Application Form at https://vuass.eu.qualtrics.com/jfe/form/SV_9tBjPqFq6bxv2Sx and upload the required documents. Note that only research project leaders can submit an application for ethics review. If you are a PhD candidate, ask your supervisor to submit the materials.

B. Data collection

General information about research data management at the Faculty of Social Sciences is available at https://vu.nl/en/employee/social-sciences-getting-started/data-management-fss. If your project involves collection or analysis of data, write a Data Management Plan (DMP) before you start the data collection. Go to https://dmponline.vu.nl and create a new plan. DMPonline will guide you through the elements that comprise a good DMP. You can share your DMP with the faculty’s data steward Koen Leuveld (k.leuveld@vu.nl) to get feedback. Share the DMP with everyone involved in the research project. Update the data management plan when things change during the research project. Make sure to properly version the document, so changes can be tracked.

Store the data in a secure location. The Faculty of Social Sciences recommends using Yoda, https://portal.yoda.vu.nl/.

Pseudonymize raw data before analysis to prevent data leaks. Avoid working with the raw data to prevent data loss. Store raw data and the pseudonimyzation key file in a secure location where it cannot be lost, corrupted, or accidentally edited. This could possibly be the same place where your raw data will be archived after the project. Make sure that wherever they are stored, the raw data are accompanied by all information needed to understand the data. This includes metadata on when, where, why and by who the data was collected, and all documentation needed to understand variables, such as interviewer manuals. The faculty data steward can help in identifying what documentation or metadata to include.

C. Analysis & write-up

During the preparation of your research report, it is a good idea to discuss the analysis strategy and the findings with your supervisors and other colleagues. Document the code that produces the results reported. For suggestions see https://renebekkers.wordpress.com/2021/04/02/how-to-organize-your-data-and-code/.

To receive feedback on your work and improve it, you can prepare a working paper that you share with discussants and present at an internal research seminar. After internal discussion, it is a good idea to post a working paper in a public preprint repository such as SocArxiv or Zenodo and invite the academic community to review it and suggest improvements. Next, you can present your working paper at conferences. Based on the comments you received from peers, revise the working paper before submitting it to a journal, book editor, or to the funders of your research.

D. Publication

When you submit research reports based on the data you have collected for peer review to a journal or to book editors, also create a publication package containing pseudonymized data, analysis scripts, documentation, and metadata. Archive the publication package in a public repository. You can use Yoda for this purpose, https://portal.yoda.vu.nl/. Alternatively, you can use Dataverse https://dataverse.nl/dataverse/vuamsterdam, or Zenodo, https://zenodo.org/. You can also store data on the Open Science Framework, https://osf.io/ if you select an EU storage location.

Have a DOI assigned to your data so others can cite the data you have collected. You can choose to upload data, documentation and metadata separately and have multiple publication packages refer to the same data set if this works better for your project.

Never share privacy-sensitive raw data with the public. Such data should be stored securely. The Faculty of Social Sciences recommends using Yoda, https://portal.yoda.vu.nl/.

E. Review

When you are invited to review the work of others, it is a good principle to check whether the authors have made the data and the code available that they have used to produce the results they report. If not, you can request them or the editors of the journal that invites you to review to done so. With the data and code, you can verify whether the data and code produce the results and you can conduct robustness analyses.

When you review research reports by others, do so in a constructive way. Here are some suggestions on how to review empirical research: https://osf.io/7ug4w/

The guidelines for peer review of the Committee of Publication Ethics apply to all types of research: https://publicationethics.org/files/Ethical_Guidelines_For_Peer_Reviewers_2.pdf

Getting advice on ethics and integrity issues

When you are planning your research and have questions on ethical dilemmas, ask the FSS Research Ethics Review Committee (RERC) for advice. When you have questions on dilemmas during your research, ask colleagues and supervisors for advice. When you find errors in your own research after you published it, write to the journal or book editors to notify them of the error. In case of a minor problem, prepare a correction. When you no longer support the publication as a whole, ask for a retraction.

When you have questions about the integrity of research of others, consult https://vu.nl/en/about-vu-amsterdam/academic-integrity. Step 1 is to talk to one of the confidential counsellors for integrity (vertrouwenspersoon integriteit), https://vu.nl/en/about-vu-amsterdam/academic-integrity/confidential-counsellor. When you have good reasons to believe that others have violated norms of good science or ethical standards, you can submit a complaint to the executive board of the university, which can forward it to the Academic Integrity Committee (CWI). See the complaints procedure at https://assets.vu.nl/d8b6f1f5-816c-005b-1dc1-e363dd7ce9a5/facfccb1-2b51-4f42-b32c-8bebfb29b89f/Academic%20Integrity%20Complaints%20Procedure%20Vrije%20Universiteit%20Amsterdam%20April%202022.pdf

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Preregistration: why and how?

In the social sciences it is increasingly common to preregister hypotheses, research designs and analysis plans before a study is conducted (Ferguson et al., 2023). Because the time-stamp on the preregistration documents when the ideas for the study were deposited, a preregistration is an intellectual honesty device. Preregistration of study hypotheses prevents alteration of hypotheses to match empirical results – also known as hypothesizing after results are known (HARKing, Kerr, 1998) because such alterations will be transparent from a comparison of the preregistration and the eventual write-up in manuscripts. Preregistration of an analysis plan reduces researcher degrees of freedom (Simons, Nelson & Simonsohn, 2011) in the garden of forking paths (Gelman & Loken, 2014) because p-hacking and other questionable research practices will be evident from a comparison of the study plans and their execution. Without a preregistration, confirmations of hypotheses and stark findings are less convincing. Results from preregistered experiments are less often confirming the study hypotheses than experiments that were not preregistered (Scheel, Schijen & Lakens, 2021).

Preregistration is less common in research that does not rely on data from experiments (figure 7, Ferguson et al., 2023), and hence it is less likely in sociology than in economics, political science and psychology (in that order). The benefits of preregistration are similar though. If you haven’t created a preregistration before, it will be helpful to see some guidance and examples. Also it is good to know that a preregistration is not a prison cell – you can update it and diverge from it in practice, as long as you motivate and document the discrepancies.

General information about preregistrations is here: https://www.cos.io/initiatives/prereg. The Open Science Framework guidance to create preregistrations is here: https://help.osf.io/article/158-create-a-preregistration

For an online field experiment testing effects of social information on charitable giving to culture through a crowdfunding platform (Van Teunenbroek & Bekkers, 2020) we used AsPredicted. The design of the experiment and the key hypotheses were preregistered in 2016 at https://aspredicted.org/u5w9u.pdf. The publication appeared in 2020. All materials are at the OSF: https://osf.io/epuj6/. A key learning from this project was that it is important to think about procedures to handle outliers, which in the analysis of the data turned out to be influential.

For an analysis of data from an experiment embedded in a survey conducted among a population sample and a sample of high net worth individuals (Bekkers, Whillans, Smeets & Norton, 2019) we created a pre-registration in 2018, posted here: https://osf.io/x69ds. A paper has not yet been published. Materials are here: https://osf.io/bvs6t/

For an analysis of data on prosocial values and behavior a pre-analysis plan is here: https://osf.io/3pnj5. The current version of the paper is here: https://osf.io/d9wte.

For a study analyzing multiple longitudinal panel survey datasets including information on health and volunteer work (De Wit, Qu & Bekkers, 2022) we created an analysis plan before the analyses were conducted. The pre-analysis plan is publicly available at the Open Science Framework page. The code for the analyses, the paper and supplementary materials are also available on the page.

References

Bekkers, R. (2023). Prosocial Values for the Philanthropy Commons. WZB conference Pro-sociality: Corporate Philanthropy and Individual Donations, Berlin, June 20, 2023. https://osf.io/mwkca

Bekkers, R., Whillans, A., Smeets, P.M., & Norton, M. (2019). The Joy of Giving: Evidence from a Matching Experiment with Millionaires and the General Population. Paper presented at the ASSA meeting, January 4, 2019. https://osf.io/r6p32

De Wit, A., Qu, H. & Bekkers, R. (2022). The health advantage of volunteering is larger for older and less healthy volunteers in Europe: a mega-analysis. European Journal of Aging. https://doi.org/10.1007/s10433-022-00691-5

Ferguson, J., Littman, R., Christensen, G., Levy Paluck, E., Swanson, N., Wang, Z., Miguel, E., Birke, D. & Pezzuto, J-H. (2023). Survey of open science practices and attitudes in the social sciences. Nature Communications, 14, 5401. https://doi.org/10.1038/s41467-023-41111-1

Gelman, A., & Loken, E. (2014). The statistical crisis in science data-dependent analysis—a “garden of forking paths”—explains why many statistically significant comparisons don’t hold up. American Scientist, 102(6), 460-465. https://www.jstor.org/stable/43707868

Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196-217. https://doi.org/10.1207/s15327957pspr0203_4

Scheel, A. M., Schijen, M. R., & Lakens, D. (2021). An excess of positive results: Comparing the standard Psychology literature with Registered Reports. Advances in Methods and Practices in Psychological Science, 4(2). https://doi.org/10.1177/25152459211007467  

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 1359-1366. https://doi.org/10.1177/0956797611417632

Van Teunenbroek, P.S.C. & Bekkers, R. (2020). Follow the Crowd: Social Information and Crowdfunding Donations in a Large Field Experiment. Journal of Behavioral Public Administration, 3(1): 1-17. https://doi.org/10.30636/jbpa.31.87

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New Research Plan: Wealth and Generosity

To what extent does the accumulation of wealth reduce feelings of responsibility for society and behavioral manifestations of generosity, such as charitable giving and bequeathing? Since 2003, the level of generosity of the Dutch population, defined as the proportion of resources donated to charitable causes, has declined by about 40% (Bekkers, Gouwenberg, Koolen-Maas & Schuyt, 2022). At the same time, levels of wealth have increased (Rijksoverheid, 2022) and those with more wealth give considerably less as a proportion of their wealth (Wiepking, 2007; Bekkers, De Wit, & Wiepking, 2017). Why is that? Are more generous persons less likely to accumulate wealth? Or does wealth make people care less about those in need? How can generosity for public welfare among the wealthy in the Netherlands be enhanced?

To answer these understudied questions, I’ve designed a research project with innovative longitudinal analyses of unique administrative and survey data and with field experiments. It will be the first study of wealth and generosity in the Netherlands over the life course as well as after death through bequests. Also we will conduct field experiments in order to increase the amount donated to charities. Today is the deadline for pre-proposals; I’ve submitted one for the Open Competition-L in the Social Sciences and Humanities scheme at NWO. Following through on the commitment to the principle of transparency, I’ve made the plan publicly available at https://osf.io/e3mux/. I will also post evaluations, reviews and responses there.

Update, 18 February 2024: the full proposal is submitted. You can read it here: https://osf.io/59jvk. Also I’ve created a list of Frequently Asked Questions and answers – see here: https://osf.io/45dky.

References

Bekkers, R., Gouwenberg, B., Koolen-Maas, S. & Schuyt, T. (2022, Eds.). Giving in the Netherlands 2022: Summary. Amsterdam: Amsterdam University Press. https://osf.io/c6pju

Bekkers, R., De Wit, A. & Wiepking, P. (2017). Jubileumspecial: Twintig jaar Geven in Nederland. Pp. 61-94 in: Bekkers, R. Schuyt, T.N.M., & Gouwenberg, B.M. (Eds.). Geven in Nederland 2017: Giften, Sponsoring, Legaten en Vrijwilligerswerk. Amsterdam: Lenthe. https://renebekkers.files.wordpress.com/2018/06/bekkers_dewit_wiepking_17.pdf

Rijksoverheid (2022). Licht uit, spot aan: de vermogensverdeling. IBO Vermogensverdeling. Den Haag: Rijksoverheid. https://www.rijksfinancien.nl/sites/default/files/extrainfo/ibos/IBO%20Vermogensverdeling%20rapport%20-v2.pdf

Wiepking, P. (2007). The Philanthropic Poor: In Search of Explanations for the Relative Generosity of Lower Income Households. Voluntas, 18, 339–358 (2007). https://doi.org/10.1007/s11266-007-9049-1

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Where did all the time go?

It’s one of those days – at 13.25 I have not yet worked on any of the main tasks I envisioned for myself. Where did all the time go this morning? I do know that I handled a lot of messages I received through e-mail. I can even feel a little satisfaction about having cleared away the clutter in my inbox. At the same time, I see that I am getting behind schedule on three main tasks that I was looking forward to working on. It’s the relatively quiet summer weeks before the new cohort of students comes in. As a professor of philanthropy my first task is research on nonprofit organizations, charitable giving and volunteering. I was looking forward to planning new research on Wealth and Generosity. In another role, as chair of the Research Ethics Review Committee at the Faculty of Social Sciences, my second task is to handle requests for ethics review by researchers. I was looking forward to writing up suggestions that students gave in June to create a culture of ongoing discussion of research integrity. My third main task is directing the two-year research master program at the Faculty. I was looking forward to designing a future course on Digital Society Research.

Yet I have not been working on any of these things this morning. Instead, I’ve been busy with a bunch of small tasks. They are all tangentially related to the tasks I formally agreed to. I know I can leave some of them unanswered, but I also know there’s a limit to that. They all came from emails I received, by people who are waiting for an answer. If I don’t answer them that list of people will only grow further.

  • Who will we nominate for the best thesis prize this year? I revisited the theses defended by students in our Research Master program in the past year and agreed to invite a nomination letter from a supervisor.
  • Is an Associate Professor in my department worthy of a promotion? I considered the invitation by my Head of Department to act as an internal reviewer substituting a colleague who is temporarily unavailable, read the instructions from the Faculty Board, sent an email agreeing with the request and created an agenda item and reminder to assess the portfolio.
  • Should I support a postdoctoral researcher from China who wants me to sign an acceptance letter for a year long stay at my research group funded by the China Scholarship Council? I ignored the request for the moment because I don’t know the researcher, the institution where the person is from, or anyone there. Also the publications indicate the research does not use open science practices.
  • Why do we not provide an estimate of the economic value of volunteering? I explained the methodology in Giving in the Netherlands to an interested practitioner who asked this question in an email.
  • Am I quoted correctly? I checked quotes in an interview with a journalist, writing an email that all is fine.
  • Is the latest paper accepted for publication already on my CV somewhere? No. I added it to my resume.
  • Can I do an interview with students for their undergraduate thesis? I considered the request, declined it implicitly by answering their questions with links to some of my published research.
  • Can I review an article for a scientific journal? I considered the request, and wrote an email asking for access to the data and code.
  • Why can’t I login to the university system with my credentials? I called the IT helpdesk, did not get through, and wrote an email instead.
  • Am I being cited correctly? I checked a reference to my work in a new article by a well-known researcher in my field, finding that it is OK. It’s an interesting study, by the way, so I filed it in a folder to read later.

So why not write up this list to tell you that you’re not the only ones who cannot get their work done in the hours that you have?

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Filed under Center for Philanthropic Studies, contract research, open science, publications, research, research integrity, teaching, volunteering, writing

How not to collect data on Qualtrics

If you’re using the Qualtrics platform to collect data, in most cases you will not need the exact IP address or geolocation data such as longitude and latitude coordinates. As a rule, you don’t want to collect personal information that you do not need. But did you know that the default setting on the platform is to collect this type of information nonetheless? So here’s how not to collect personal data in your Qualtrics survey.

Go to your survey, and click on the survey options icon on the left.

Next, click on Security, and scroll to the bottom to switch the slider for ‘Anonymize responses’ from ‘Off’ (the default) to ‘On’.

If you’ve already collected responses to your survey, the data file will contain IP addresses and longitude/latitude data. You should delete these columns before you share the data with anyone else, to avoid the spread of personal information.

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Filed under data, privacy, research integrity, survey research