Unlike most other SAPA datasets available on Dataverse, these data are specifically tied to the reproducible manuscript entitled "The SAPA Personality Inventory: An empirically-derived, hierarchically-organized self-report personality assessment model." Most of these files are images that should be downloaded and organized in the same location as the source .Rnw file. A few files contain data that have already been processed (and could be independently re-created using code in the .Rnw file) - these are included to shorten the processing time needed to reproduce the original document. The raw data files for most of the analyses are stored in 3 separate locations, 1 for each of the 3 samples. These are: Exploratory sample - doi:10.7910/DVN/SD7SVE Replication sample - doi:10.7910/DVN/3LFNJZ Confirmatory sample - doi:10.7910/DVN/I8I3D3 . If you have any questions about reproducing the file, please first consult the instructions in the Preface of the PDF version. Note that the .Rnw version of the file includes many annotations that are not visible in the PDF version (https://sapa-project.org/research/SPI/SPIdevelopment.pdf) and which may also be useful. If you still have questions, feel free to email me directly. Note that it is unlikely that I will be able to help with technical issues that do not relate of R, Knitr, Sweave, and LaTeX.
Many journal editors are a failing to implement their own authors’ instructions, resulting in the publication of many articles that do not meet basic standards of transparency, employ unsuitable data analysis methods and report overly optimistic conclusions. This problem is particularly acute where quantitative measurements are made and results in the publication of papers that lack scientific rigor and contributes to the concerns with regard to the reproducibility of biomedical research. This hampers research areas such as biomarker identification, as reproducing all but the most striking changes is challenging and translation to patient care rare.
Science is facing a "reproducibility crisis" where more than two-thirds of researchers have tried and failed to reproduce another scientist's experiments, research suggests. This is frustrating clinicians and drug developers who want solid foundations of pre-clinical research to build upon. From his lab at the University of Virginia's Centre for Open Science, immunologist Dr Tim Errington runs The Reproducibility Project, which attempted to repeat the findings reported in five landmark cancer studies.
According to Vinay Prasad, an oncologist and one of the authors of the Mayo Clinic Proceedings paper, medicine is quick to adopt practices based on shaky evidence but slow to drop them once they’ve been blown up by solid proof. As a young doctor, Prasad had an experience that left him determined to banish ineffective procedures. He was the medical resident on a team caring for a middle-aged woman with stable chest pain. She underwent a stent procedure and suffered a stroke, resulting in brain damage. Prasad, now at the Oregon Health and Sciences University, still winces slightly when he talks about it. University of Chicago professor and physician Adam Cifu had a similar experience. Cifu had spent several years convincing newly postmenopausal patients to go on hormone therapy for heart health—a treatment that at the millennium accounted for 90 million annual prescriptions—only to then see a well-designed trial show no heart benefit and perhaps even a risk of harm. "I had to basically run back all those decisions with women," he says. "And, boy, that really sticks with you, when you have patients saying, 'But I thought you said this was the right thing.'" So he and Prasad coauthored a 2015 book, Ending Medical Reversal, a call to raise the evidence bar for adopting new medical standards. "We have a culture where we reward discovery; we don’t reward replication," Prasad says, referring to the process of retesting initial scientific findings to make sure they’re valid.
At AAAS 2017, a pair of panel discussions addressed the reproducibility crisis in science, particularly biomedical science, and suggested that it is manageable, provided stakeholders continue to demonstrate a commitment to quality. One panel, led by Leonard P. Freedman, Ph.D., president of Global Biological Standards Institute (GBSI), was comprehensive. It prescribed a range of initiatives.
As a principal investigator, how do you run your lab for reproducibility? I submit the following action areas: commitment, transparency and open science, onboarding, collaboration, community and leadership. Make a public commitment to reproducible research—what this means for you could differ from others, but an essential core is common to all. Transparency is an essential value, and embracing open science is the best route to realize it. Onboarding every lab member with a deliberate group “syllabus” for reproducibility sets the expectations high. What is your list of must-read literature on reproducible research? I can share mine with you: my lab members helped to make it. For collaborating efficiently and building community, we take inspiration from the open-source world. We adopt its technology platforms to work on software and to communicate, openly and collaboratively. Key to the open-source culture is to give credit—give lots of credit for every contribution: code, documentation, tests, issue reports! The tools and methods require training, but running a lab for reproducibility is your decision. Start here–>commitment.