Fraud = scientifc misconduct.

• Falsifying or fabricating data.
• This is intentional, not accidental.
• Puts all science under a bad light.
• Markedly different from QRPs (next).

• Diederik Stapel, social psychologist. Suspended in 2011. Fabricating and manipulating data.
• Marc Hauser, psychologist at Harvard. Resigned in 2011. Scientific misconduct.
• Jens Förster, social psychologist. Resigned in 2017. Data tampering.
  
  
  

Today we don’t talk about fraud explicitly.

We talk about something much harder to identify and erradicate:

Questionable research practices (QRPs).

Term coined by John, Loewenstein, & Prelec (2012).
See also Simmons, Nelson, & Simonsohn (2011).

• Not necessarily fraud.
• Includes the (ab)use of actually acceptable research practices.
• Problem with QRPs:
  
  • Introduce bias (typically, in favor of the researcher’s intentions…).
  • Inflated power at the cost of inflated Type I error probability (\(\gg 5\%\)).
  • Results not replicable.

(John et al., 2012; Schimmack, 2015).

• Omit some DVs.
• Omit some conditions.
• Peeking: Sequential testing — Look and decide:
  • \(p > .05\): Collect more.
  • \(p < .05\): Stop.
• Only report \(p<.05\) results.
• \(p\)-hacking: E.g.,
  • Exclusion of outliers depending on whether \(p<.05\).
  • \(p = .054 \longrightarrow p = .05\).
• HARKing (Kerr, 1998): Convert exploratory results into research questions.
• …

• Researchers have a multitude of decisions to make (experiment design, data collection, analyses performed); Wicherts et al. (2016), Steegen, Tuerlinckx, Gelman, & Vanpaemel (2016).
• It is very possible to manipulate results in favor of one’s interests.
• This is now known as researcher’s degrees of freedom (Simmons et al., 2011).
• Consequence: Inflated false positive findings (Ioannidis, 2005).

From Bem (2004):

“(…) [L]et us (…) become intimately familiar with (…) the data. Examine them from every angle. Analyze the sexes separately. Make up new composite indices. If a datum suggests a new hypothesis, try to find further evidence for it elsewhere in the data. If you see dim traces of interesting patterns, try to reorganize the data to bring them into bolder relief. If there are participants you don’t like, or trials, observers, or interviewers who gave you anomalous results, drop them (temporarily). Go on a fishing expedition for something– anything– interesting.”

This is not OK unless the exploration is explicity stated.

Daryl Bem is the author of the famous 2011 precognition paper
(data used in Part 2 of today’s workshop).

Prof. Brian Wansink at Cornell University.

His description of the efforts of a visiting Ph.D student:

I gave her a data set of a self-funded, failed study which had null results (…). I said, “This cost us a lot of time and our own money to collect. There’s got to be something here we can salvage because it’s a cool (rich & unique) data set.” I had three ideas for potential Plan B, C, & D directions (since Plan A had failed). I told her what the analyses should be and what the tables should look like. I then asked her if she wanted to do them.

Every day she came back with puzzling new results, and every day we would scratch our heads, ask “Why,” and come up with another way to reanalyze the data with yet another set of plausible hypotheses. Eventually we started discovering solutions that held up regardless of how we pressure-tested them. I outlined the first paper, and she wrote it up (…). This happened with a second paper, and then a third paper (which was one that was based on her own discovery while digging through the data).

This isn’t being creative or thinking outside the box.

This is QRPing.

• He was severely criticized, his work was scrutinized (e.g., van der Zee, Anaya, & Brown, 2017).
• Over 100 (!!) errors in a set of four papers…
• Has now 40 (!!) publications retracted (as of July 2019).
• After a year-long internal investigation, he was forced to resign.

Yes.

• Martinson, Anderson, & Vries (2005): “Scientists behaving badly”.
• Fanelli (2009): Meta-analysis shows evidence of science misconduct.
• John et al. (2012): Evidence for QRPs in psychology.
• Mobley, Linder, Braeuer, Ellis, & Zwelling (2013): Reported evidence of pressure to find significant results.
• Agnoli, Wicherts, Veldkamp, Albiero, & Cubelli (2017): Evidence of QRPs, now in Italy.
• Fraser, Parker, Nakagawa, Barnett, & Fidler (2018): In other fields of science.
  
  

Interestingly, science misconduct has been a longtime concern (see Babbage, 1830).

And for the sake of balance:
There are also some voices against this description of the current state of affairs (e.g., Fiedler & Schwarz, 2016).

Well, maybe not (yet).

Here’s an interesting preprint (from July 2019!) from a Japanese research group (Kyushu University):

Ikeda, A., Xu, H., Fuji, N., Zhu, S., & Yamada, Y. (2019). Questionable research practices following pre-registration [Preprint]. doi: 10.31234/osf.io/b8pw9

It is strongly related to incentives (Nosek, Spies, & Motyl, 2012; Schönbrodt, 2015).

• “Publish or perish”:
  Publish a lot, at highly prestigious journals.
  • Journals only publish a fraction of all manuscripts.
  • Journals don’t like publishing null findings…
• Get tenured.
• Get research grant.
• Fame (prizes, press coverage, …).
• …
  
  
  

But, very importantly, it also happens in spite of the researcher’s best intentions.

• Deficient statistics education (yes, statisticians need to acknowledge this!…).
• Perpetuating traditions in the field.

Until very recently (Makel, Plucker, & Hegarty, 2012).

• Very low rate of replications in Psychology (estimated ~1%).
• Until 2012, majority of replications were actually successful!!
• However, in most cases both the original and replication studies shared authorship…
• Conflict of interest?…

• The Marshmallow Test (Watts, Duncan, & Quan, 2018)
• Ego depletion (Friese, Loschelder, Gieseler, Frankenbach, & Inzlicht, 2019; Hagger et al., 2016; Vadillo, Gold, & Osman, 2018)
• Power posing (Ranehill et al., 2015)
• The Stanford Prison Experiment (Griggs, 2014; Reicher & Haslam, 2006)
• The facial feedback hypothesis (Wagenmakers et al., 2016)
• Newborn babies’ imitation (Oostenbroek et al., 2016)
• The blocking effect (Maes et al., 2016)
• The stereotype threat (Flore, Mulder, & Wicherts, 2019)
• The facial expression (Gendron, Crivelli, & Barrett, 2018)
• ESP, of course! (Galak, LeBoeuf, Nelson, & Simmons, 2012; Ritchie, Wiseman, & French, 2012)
• The Mozart Effect (McKelvie & Low, 2002; Steele, Bass, & Crook, 1999)
• …

http://www.tylervigen.com/spurious-correlations

See here.