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Introducing jamovi: Free and Open Statistical Software Combining Ease of Use with the Power of R

For too long, Psychology has had to put up with costly, bulky, and inflexible statistics software. Today, we’d like to introduce you to a breath of fresh air: jamovi, free statistics software available for all platforms that is intuitive and user-friendly, and developed with so much pace that its capabilities will potentially soon outrun SPSS.

Screenshot of jamovi


As can be seen above, jamovi has a beautiful user interface with some very handy features: It does real-time computation and presents and updates results immediately with beautiful figures and neat APA tables. These results can then be copy-pasted into your editing software such as Word. Basic analyses (e.g., t-tests, ANOVAs, correlations, contingency tables, proportion tests) are already available and more will be arriving shortly. What’s more, packages from the powerful R software can be easily adapted so that they can be used within jamovi’s beautiful user interface. In this way, jamovi can give you access to the power of the R language, but without having to learn the R syntax. For those wanting to learn R, jamovi can help there too: with just one mouse click jamovi delivers the R syntax underlying each analysis.

Another gadget of jamovi is live data management: You can edit your data directly in the software, and if you change something, results that depend on these changes are immediately updated in the output window. Imagine how this would work in SPSS: Change a data point, click through all the menus again or re-activate the relevant syntax, manually delete the old output, all in order to get ugly figures and tables that need additional time investment to become beautiful or in accordance with APA-format; with jamovi, these strenuous days are over!

One particular and useful type of analysis is also already available in jamovi: The TOSTER module. This analysis allows testing whether data support a null hypothesis (e.g., the absence of a meaningful effect), which is often what we want to know but not possible to test with most statistics packages.

Thus, there are many reasons to install and use jamovi right away, and if you want to help your peers, you can develop your own R-based jamovi modules and make them freely available for everyone in the jamovi store.

Interview with Jonathon Love, jamovi co-founder and developer

jamovi might remind you of another recently established free stastistics software: JASP. Indeed, Jonathon Love, Damian Dropmann, and Ravi Selker were all developers of JASP who now develop jamovi. The two software packages may at first seem similar, but they emphasize different functionality. This means both packages will continue to be developed, and users can enjoy the benefits of both. Let’s see what Jonathon, former lead developer and designer of JASP, and now one of the jamovi core developers, has to say about this and more:

During our last interview, you were lead developer of JASP. What was your motivation to start jamovi and what happened since then?

So developing JASP was really fabulous, and something we all really enjoyed doing. But we did find that our ambitions, hopes and dreams went beyond JASP’s original goals. JASP has always been heavily focused on Bayes, and we wanted the freedom to explore other statistical philosophies.

At the same time, a number of technologies had matured to the point where we could build a more advanced software architecture in a much shorter amount of time. When I began JASP, I had to choose between older, “tried and true” technologies (C++ and QWidgets), and the newer, up and coming HTML5+js technologies. At the time, I concluded the newer technologies just weren’t mature enough for a large project like JASP.

Fast-forward a few years, and everything has changed. HTML5+js have overcome leaps and bounds and have become a capable, mature framework. Similarly, other developments have made things that before were very difficult, much more straight forward. For example, the R6 R package has enabled us to create a much more elegant analysis framework, allowing rich graphical analyses to be developed in much less time, and to support data-editing. Similarly, it has made it feasible to provide one of the most requested features: R syntax for each analysis.

So the decision to begin jamovi was a combination of ambitions beyond the JASP project’s core goals, and seeing the opportunities that newer technologies provided.

You launched jamovi a couple of weeks ago and so far only few analyses are available. When will jamovi offer a scope of analyses comparable to SPSS?

So we actually think SPSS is overwhelming, making the user navigate a huge labyrinth of menus filled with analyses most people will never use. We do want to provide a lot of analyses, but we’ll do it in a different way. Our intention is to provide all the basic analyses used in undergraduate social science courses in the next few months, and we have the ambitious roadmap of being a viable (and compelling!) alternative to SPSS for the majority of social science researchers by August, providing all these analyses, and providing complete data-editing, cleaning, filtering and restructuring.

For additional, or more specialised analyses, we hope to build a community of developers providing analyses as “jamovi modules”. jamovi modules are R packages which have been augmented to run inside jamovi and provide analyses with a user-interface. Importantly, these modules still function  as R packages making the analyses usable from both platforms. People are then able to publish jamovi modules they create through the “jamovi store” (and CRAN), making them available to anyone. We recently worked with Daniel Lakens to produce a jamovi module of his TOSTER package, and that’s come together very nicely. There’s a few more modules in development that we know about, and you can expect further announcements in the coming weeks!

One of the neat things about the jamovi store is that it allows us to keep jamovi itself simple, and allows people to only install the analyses that are important to them. For those familiar with R, this is exactly how it works with CRAN, and we hope to duplicate its success, but for analyses with rich, accessible user interfaces.

jamovi is built on the idea that developers create jamovi modules for their R packages. Why should they do that?

There are two answers here: for science, and for themselves.

For science, because not everyone is, can be, or needs to be an R programmer. People have strengths in different areas. As long as new analyses are only available to people who can work with R, there are a lot of scientists who will be left behind. So I think it is imperative that we make new and advanced analyses available and accessible to everyone – that’s one of the core motivations for jamovi.

But creating jamovi modules can also be significant for the authors of analyses. One of the most significant metrics in science is how widely someone’s work is used, and a jamovi module ensures an analysis is accessible to the greatest number of people possible. So there are good career incentives for people to develop jamovi modules too.

Therefore, we encourage R developers to look into developing jamovi modules. The jamovi developer hub provides tutorials walking you through the process of writing a jamovi module:, and if people would like help or advice, we can pair them up with a “dev mentor”. There are also forums where people can post questions. We’re keen to support the developer community in whatever way we can.

Readers of this interview will inevitably compare jamovi to JASP. What do you see as jamovi’s most distinctive features? Where can you borrow from JASP’s approach?

So our distinctive features are: data-editing, our R syntax mode, and the jmv R package.

Data-editing is one of my favourite features, because it takes something crazy complex, and makes  it seem really easy! You’ll notice that if you run an analysis, say descriptives, and then start changing some values in the data view, the descriptives analysis updates in real-time. This in  itself is cool, but you’ll also notice that only the columns in the descriptives analysis affected by the data changes are updated. Under the hood, jamovi is dynamically figuring out which values in the results need to change in response to the data changing – and only recalculating those. This is pretty neat.

R syntax mode is another favourite. jamovi can be placed in “syntax mode”, where the R code for producing each analysis in R is provided. This is super-cool, because it makes it easy for people to see and learn R code, and it also allows them to copy and paste the R code into an interactive R session. This allows people to make the jump to R, if that’s an area they are wanting to develop skills in.

Our jmv R package is the other half of “syntax mode”; an R package which provides all the  analyses included in jamovi. This is awesome, because it means that a single R package will cover entire undergraduate social sciences programs. In the past, doing something like an ANOVA with all the contrasts, assumption checks, post-hoc corrections, etc. required in the order of 7 packages. So it’s been exciting to bring all of those elements together, and make them simpler for R users as well.

With respect to JASP’s approach, Eric-Jan Wagenmakers and the JASP guys have put a lot of effort, and continue to put a lot of effort into making new Bayesian analyses accessible to a broader audience. Their analyses represent a truly fabulous contribution and we’ll definitely be keeping a keen eye on what they get up to. You should too!

What are the biggest challenges ahead in developing and disseminating jamovi?

The chicken and egg problem. Always the chicken and egg problem!

People are reluctant to adopt a new platform when not all the supporting materials, videos, textbooks, etc. have been created yet. At the same time, the content creators are reluctant to provide supporting materials, because people seem reluctant to adopt it. In this way, markets tend to resist change, and overturning the status-quo often poses a frustrating challenge.

This phenomena isn’t just limited to software; you’ll find that it applies to many areas in science. Of course, change can, does, and must take place, and so the challenge is putting all the pieces in place so that new ideas, new paradigms, and new pieces of software can be adopted. In my view, this is almost always the biggest challenge, but it must be overcome — progress depends upon it!

So it’s been pretty exciting seeing the level of support coming from the community. We’ve had a surprising number of very promising talks with authors and publishers. I think we’ll have some pretty exciting announcements in the coming months, and it looks like we’re well on the way to hatching that chicken … or egg … or whatever.

How is jamovi being funded? How can users be sure of its continuing existence?

So at the moment jamovi is still in the early stages, and our emphasis has been on demonstrating  that we have the sort of trajectory that people can get behind, and so we currently don’t have a lot of funding. I work for the university of Newcastle, and volunteer my time on jamovi, and the same applies to the other core developers. However, people can still feel confident in the future of jamovi.

We expect to provide a complete and practical alternative to SPSS by August – with full data-editing, filtering, restructuring, the works. At that time, jamovi could be considered “complete”. We don’t intend on stopping developing then, but if we did, jamovi would still be (in our view) one of the best tools available for social scientists, probably for years to come. It won’t require a lot of effort to continue to maintain jamovi into the future, and people can feel confident that jamovi will be here for years to come. (There’s a persistent myth, that the maintenance of software once written requires substantial resources to maintain. Indeed, in proprietary software it’s often a problem that old software “just keeps working”, and it’s hard to persuade customers to pay for newer versions!)

Having said all that, we are keen to develop funding and business models to support additional development of jamovi – and we have big plans going into the future. In the short-term, our efforts are concentrated on creating a viable alternative to SPSS, but longer term we want to provide a range of additional paid services that make the lives of researchers easier. jamovi itself will always (and must!) be free and open-source, but there’s a range of areas where we think we can provide services to make researchers more productive, and where it would be reasonable to charge a fee.

We’re also keen for benefactors, so if you or your institution benefit or stand to benefit from the work of the jamovi team, you could consider making a financial contribution to our work. Such a contribution would allow us to ramp up development, and provide a greater range of features. If there are particular features and analyses which are important to you or your institution, you could sponsor their development (e.g.,  reproducibility in a spreadsheet? We’d love to do that!). Do drop us a line.

How does the curious reader get started with jamovi?

jamovi is pretty straight forward to use, and it contains several example data-sets that make it easy  to get up and running. I’d recommend downloading and installing jamovi, and just playing around with it. We also have a user-guide, complete with neat little videos demonstrating the basic features. If you’ve used SPSS before, you should find the user interface concepts quite familiar; like the dragging and dropping of variables for an analysis. It’s designed to be easy and straight-forward to use, and if you find this not to be the case, do drop us a line in the forums. We’re very keen for feedback, and to make jamovi the best it can be!


Peter Edelsbrunner

Peter Edelsbrunner

Peter is currently doctoral student at the section for learning and instruction research of ETH Zurich in Switzerland. He graduated from Psychology at the University of Graz in Austria. Peter is interested in conceptual knowledge development and the application of flexible mixture models to developmental research. Since 2011 he has been active in the EFPSA European Summer School and related activities.

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JEPS introduces Registered Reports: Here is how it works

For  more than six years, JEPS has been publishing student research, both in the form of classic Research Articles as well as Literature Reviews. As of April 2016, JEPS offers another publishing format: Registered Reports. In this blog post we explain what Registered Reports are, why they could be interesting for you as a student, and how the review process works.

What are Registered Reports?

Registered Reports are a new form of research article, in which the editorial decision is based on peer review that takes place before data collection.  The review process is thereby divided into two stages: first, your research question and methodology is evaluated, while the data is yet to be collected. In case your Registered Report gets in-principle accepted, you are guaranteed to get your final manuscript published once the data is collected – irrespective of your findings. The second step of the review process then only consists of checking whether you sticked to the methodology you proposed in the Registered Report.

The format of Registered Reports alleviates many problems associated with the current publishing culture, such as the publication bias (see also our previous post): For instance, the decision whether the manuscript gets published is independent of the outcome of statistical tests and therefore publication bias is ruled out. Also, you have to stick to the hypothesis and methodology in your Registered Report and therefore a clear line between exploratory and confirmatory research is maintained.

How does the review process work exactly?

You submit a manuscript consisting of the motivation (introduction) of your research and a detailed description of your hypotheses and the methodology and analysis you intend to use to investigate your hypotheses. Your research plan will then be reviewed by at least two researchers who are experts in your field of psychology. Note that in case Registered Reports Pipeline

Reviewers might ask for revisions of your proposed methodology or analysis. Once all reviewer concerns have been sufficiently addressed, the Registered Report is accepted. This means that you can now collect your data and if you don’t make important changes to your hypotheses and methodology, you are guaranteed publication of  your final manuscript, in format very similar to our Research Articles. Any changes have to be clearly indicated as such. In the second stage of the review process, they will be examined. 


Why are Registered Reports interesting for you as a student?

First, you get feedback about your project from experts in your field of psychology. It is very likely that this feedback will make your research stronger and improves your design design. This avoids the situation that you collected your data but then realize during the review process that your methodology is not watertight. Therefore, Registered Reports offer you the chance to rule out methodological problems before collecting the data, possibly saving a lot of headache after. And then having your publication assured.

Second, it takes away the pressure to get “good results” as your results are published regardless of the outcome of your analysis. Further, the fact that your methodology was reviewed before data collection allows to give null-results more weight. Normally, registered reports also include control conditions that help interpreting any (null-) results.

Lastly, Registered Reports enable you to be open and transparent about your scientific practices. When your work is published as a Registered Report, there is a clear separation between confirmatory and exploratory data analysis. While you can change your analysis after your data collection is completed, you have to declare and explain the changes.This adds credibility to the conclusions of your paper and increases the likelihood that future research can build on your work.

And lastly, some practical points

Before you submit, you therefore need to think about, in detail, the research question you want to investigate, and how you plan to analyse your data. This includes a description of your procedures in sufficient detail that others can replicate it and of your proposed sample, a definition of exclusion criteria, a plan of your analysis (incl. Pre-processing steps), and, if you want to do Null Hypothesis significance testing, a power analysis.

Further, you can withdraw your study at any point – however, when this happens after the in-principle acceptance, many journals will publish your work in a special section of the journal called “Withdrawn Reports”. The great thing is that null-result need not to dishearten you – if you received an IPA, your study will still be published – and given that it was pre-registered and pre-peer reviewed, chances are high that others can built on your null-result.

Lastly, you should note that you need not register your work with a journal – you can also register it on the Open Science Framework, for example. In this case, however, your work won’t be reviewed.

Are you as excited about Registered Reports as we are? Are you considering submitting your next project as a Registered Report? Check out our Submission guidelines for further info. Also, please do not hesitate to contact us in case you have any questions!

Suggested Reading

Chambers et al., (2013): Open letter to the Guardian

Gelman & Loken (2013): Garden of forking paths

Katharina Brecht

Katharina Brecht

Aside from her role as Editor-in-Chief of the Journal of European Psychology Students, Katharina is currently pursuing her PhD at the University of Cambridge. Her research interests revolve around the evolution and development of social cognition.

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Bayesian Statistics: Why and How


Bayesian statistics is what all the cool kids are talking about these days. Upon closer inspection, this does not come as a surprise. In contrast to classical statistics, Bayesian inference is principled, coherent, unbiased, and addresses an important question in science: in which of my hypothesis should I believe in, and how strongly, given the collected data?  Continue reading

Fabian Dablander

Fabian Dablander

Fabian Dablander is currently doing his masters in cognitive science at the University of Tübingen. You can find him on Twitter @fdabl.

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Interview with Prof. Ralph Hertwig

Ralph Hertwig is director of the Center for Adaptive Rationality at the Max Planck Institute for Human Development in Berlin. He is well known for his interdisciplinary research on cognitive search, judgment, and decision making under risk and uncertainty. To this end, his lab uses a wide array of methods, ranging from experiments, surveys, and computer simulations to neuroscientific tools. 

Ralph Hertwig

What I enjoy most about my job as a researcher … What I most enjoy is the opportunity to team up with people from other fields or schools of thought and produce something I could never have come up with on my own. Continue reading

Jonas Haslbeck

Jonas Haslbeck

Jonas is a Senior Editor at the Journal of European Psychology Students. He is currently a PhD student in psychological methods at the University of Amsterdam, The Netherlands. For further info see

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What Do Whigs Have To Do With History of Psychology?

The 2013 December issue of the journal Theory & Psychology saw a forceful exchange between Kurt Danziger and Daniel N. Robinson on the nature of psychology’s disciplinary history. For those unfamiliar with the names, both are eminent scholars in (among other things) history of psychology. The exchange boils down to Danziger accusing Robinson of creating a romanticized history of psychology, tying the discipline down to Ancient Greek philosophies.  What Danziger cannot forgive in such a way of writing history of science is the idea of a concept that stays the same throughout history, and then finds its way into psychology. For example (Danziger, 2013, p. 835): “This understanding of psychology’s history has always relied on the belief that the concept of ‘human nature’ represents some historically unchanging essence guaranteeing continuity, no matter how great the gulf that appears to separate the present from the remote past.” Robinson, in turn, answers with two articles in the same issue defending his position with insinuations that Danziger and his supporters are not familiar enough with Aristotle’s body of work to mount such a criticism. His repartees, sans the scholastic posturing, can be summed up well with the sentence (Robinson, 2013a, p. 820): “It is worth noting in order to make clear that the past can be highly instructive without being causally efficacious.” Continue reading

Ivan Flis

Ivan Flis is a PhD student in History and Philosophy of Science at the Descartes Centre, Utrecht University; and has a degree in psychology from the University of Zagreb, Croatia. His research focuses on quantitative methodology in psychology, its history and application, and its relation to theory construction in psychological research. He had been an editor of JEPS for three years in the previous mandates.

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Looking for New Contributors

bulletin contributorsThe Journal of European Psychology Students’ Bulletin blogs about academic writing, scientific publishing, and essential research skills in the field of psychology. The JEPS Bulletin aims to connect psychology students from all over Europe by providing a unique platform for learning and sharing of knowledge, and subsequently, serving as an indispensable companion for students in the process of conducting and reporting psychological research. The JEPS Bulletin is proud to have a great number of active Contributors who are psychology students throughout Europe. Currently, the JEPS Bulletin is recruiting new Contributors so in case you want to be part of the list on your left keep reading.

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Pedro Almeida

Pedro Almeida

Pedro Almeida is a graduate student and research assistant at the University of Groningen, Netherlands. His main research interests are evolutionary psychology and the intersection between marketing and psychology. Previously, he worked as an Editor for the Journal of European Psychology Students (JEPS).

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Why meta-analysis? A guide through basic steps and common biases


 Meta: meta- combining form. From Greek meta ‘with, across, or after.’  Pertaining to a level above or beyond.

 Analysis: analysis |əˈnaləsis| noun. From Greek analuō ‘I unravel,  investigate’. Detailed examination of the elements or structure of  something,
Often times, researchers and students find themselves going through a  dense amount of papers on a certain topic only to find results that don’t  really seem to point towards a coherent or homogenous conclusion. Does this treatment work?
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Luís Miguel Tojo

Luís Miguel Tojo

Luís Miguel Tojo is a MSc student in Cognitive Neuroscience (Neuropsychology) at Maastricht University, Netherlands, since 2012, having finished his BSc in Psychological Sciences at the University of Coimbra, Portugal. He is currently Vice President of EFPSA (2013-2014) and has been the Research Officer for the Junior Researcher Programme since early 2012. His research interests cover neuroprotective factors against neurodegenerative diseases and brain insults, and neuropsychopharmacological approaches to mental illness.

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A Change of View: Using Visual Methods to Explore Experience in Qualitative Research


The topic of this bulletin arose from a talk given by Dr. Anna Bagnoli, who had used a variety of visual methods in addition to verbal interviews in order to holistically study young people’s identities.  Intrigued by the question of how such data could be collected and analysed to contribute to understandings of psychological topics, the author of this post recently carried out an interview with Dr. Bagnoli on behalf of the Open University Psychological Society (Rouse, 2013).  In this bulletin post the author will share what she has learnt from this interview and by researching the use of visual methods to explore experience and meaning.

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Lorna Rouse

Lorna Rouse

Lorna graduated from the Open University in 2009 with a BSc (honours) in psychology and is currently studying for an MSc in Psychological Research Methods at Anglia Ruskin University. Lorna has worked as a Research Assistant at the University of Cambridge, providing support for studies investigating recovery from traumatic brain injury. In her spare time she organises events for the Cambridge branch of the Open University Psychological Society. She is particularly interested in qualitative research methods and intellectual disabilities.

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Unpaid Psychology Positions – A Graduate’s Perspective

For a present-day psychology graduate it can sometimes seem like one has entered the profession at the wrong time. Last year shone a glaring spotlight on fraud and misconduct in scientific research, the Reproducibility Project and Psych File Drawer began to prise open a lack of replication in psychological literature, and there was criticism and concern over an increasing number of unpaid research assistant/assistant psychologist jobs in the U.K. However this self-reflection and criticism should be seen as an opportunity for correction, and ultimately, a gradual change within the profession.

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Life is a box of chocolates

Sitting in a classroom and being lectured, I often felt a sense that I should not question what I am being taught. This was not due to any fault of the lecturers who mostly were very welcoming of students’ opinions. However, simply knowing that this was an area that they had spent years researching and seeing them sharing at their computers screen, or head in a book every time you look through their office window gave the sense that they must have all the answers and have a justified reason for their opinions whereas mine always felt too subjective to be taken seriously. During my undergraduate degree, my essays became more and more focused on the areas which we had been taught in class and less inclusive of the breath of what were my own opinions. This was simply because having a controversial argument seemed to lead to more frustration in conceiving the lecturer’s than arguing what was the ‘popular’ approach.


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