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How to stop being busy and become productive

With the rise of social media, potential distractions have risen to unseen levels; they dominate our daily lives. Do you check Facebook, Twitter, Snapchat, Instagram, or Email on a constant basis? Do you have an embarrassing relationship with your alarm clock’s snooze button? Do you pass on social invites, telling other people that you are too busy? As a generation, we have lost the ability to focus sharply on the task at hand; instead, we work on a multitude of things simultaneously, lamenting that we do not achieve what we seek to achieve.

Picture of a busy person on a computer (Lego).

In this post, we share useful tips, tricks, and tools for you to stay on top of your day and move quickly from task to task, accomplishing the things that matter. In addition to linking to further resources, we suggest a three stage actionable program for you to go through in order to stop being busy and start being productive. As we (Fabian and Lea, the authors of this post) have experienced first hand, making the jump from being busy to being productive — from workaholism to strictly separating work and play, from social exclusion to social inclusion — has the promising potential of increasing quality time spent with friends and family, accelerating the pace of skill development, avoiding burnouts, and leading to increased subjective well-being.

Challenges in the 21st Century

Why would one want to become more productive? In additional to personal reasons — leading a more happier, more accomplished, more balanced life — there are societal reasons. The 21st century presents us with unique challenges, and the way we tackle them will define the future of our species. The three most important challenges are the exploitation of the Earth (including climate change), income inequality (including world poverty), and the “rise of robots” which includes digitalisation and its impact on work. In this post, we want to focus on the latter and make the argument that, in order to stay lean, one needs to cultivate what Cal Newport calls Deep work habits, enabling one to quickly adapt to changing work environments. Additionally, these habits also increase the effectiveness with which we can tackle the three challenges.

Take data science as an example. Few fields move as fast as data science. In its current form, it didn’t even exist fifteen years ago (for a very short history of the field, see this). Now “data scientist” has become the “sexiest job of the 21st century”.

The job market will change dramatically in the coming years. It is predicted that many jobs will fall out of existence, being taken over by machines, and that new jobs will be created (see this study and these books). Humanity is moving at an incredibly fast pace, and each individual’s challenge is to stay sharp amidst all those developments. To do so requires the ability to quickly learn new things, and to spend time productively — the two skills which make you most employable.

Being busy vs being productive

Every day, week, and month we have a number of tasks and obligations we need to address; the way we organize the time spent on getting these done differs strongly among individuals. It is here that the distinction between being busy and being productive becomes apparent.

When thinking of someone who is busy, usually we picture someone who tries to complete a task while in the same time thinking about some other task, checking social media, email, or conversing with other people. The splitting of attention on multiple things at once, while claiming to be working on a really important task, is a dead giveaway. This causes the task at hand to take forever to be completed. Oddly enough, the extensive time this task takes to be completed need not bother a busy person. On the contrary, it provides an opportunity to talk a lot about being busy, having so much to do, having so many exams, etc. This leads to cancellations of social plans and less time for leisure activities. Too many things to do, not enough time. One gets more and more frustrated.

On the other hand, a productive person is a responsible person with a focus on setting clear, few priorities and thinking of measurable steps how to achieve her goal. While working, an intense focus and undivided attention is directed on a single activity. Keeping track of progress gives a clear idea of what has been achieved during the day and what is left for tomorrow.

The distinction between being busy and being productive is at the core of this blog post. Table 1 below gives an overview of what distinguishes these two states.

Table describing the difference between being busy and being productive

Table 1. Describes the difference between being busy and being productive.

Learning how to learn

In addition to personal productivity, which will be the focus of the remaining sections, being able to monitor one’s learning progress and learning new things quickly is another very important skill. Barbara Oakley and Terrence Seijnowski have designed an online course over at Coursera called “Learning How To Learn” in which they discuss, among other things, the illusion of competence, memory techniques, and how to beat procrastination. It is the most popular, free course on Coursera and we highly recommend it.

Tips, tricks, and tools

Note that these are personal recommendations. Most of them are backed by science or common sense, but they need not work for you. This is a disclaimer: your mileage may vary.

Manage your time. Time is your most important commodity. You can’t get it back, so consider spending it wisely. To facilitate that, we highly recommend the Bullet Journal. It is an “analog tool designed for the digital world”. All you need is a notebook — we use a Leuchtturm1917, but any other would do, too — and a pen. Here is a video explaining the basics. It combines the idea of keeping track of your time and obligations while providing a space for creativity.

Schedule tasks & eat your frog first. Write down what needs to get done the next day on the evening before. Pick out your most despised task — your frog — and tackle it first thing in the morning. If you eat your frog first, there is nothing more disgusting that can happen during the day. Doing this mitigates procrastination and provides a sense of accomplishment that keeps you energy levels up.

Avoid social media. Social media and email have operantly conditioned us; we get a kick out every notification. Thousands of engineers are working on features that grab our attention and maximize the time we spent on the platforms they build (see also this fascinating interview). However, checking these platforms disrupts our workflow and thought process. They train us to despise boredom and instill in us the unfortunate need of having something occupy our attention at all times. Therefore, we recommend having fixed time points when you check email, and not spend too much time on social media before late in the afternoon or evening, when energy is low. More important tasks require attention during the day when your mind is still sharp.

We feel that quitting social media altogether is too extreme and would most likely be detrimental to our social life and productivity. However, we did remove social media apps from our phones and we limit the number of times we log onto these platforms per day. We recommend you do the same. You will very soon realize that they aren’t that important. Time is not well spent there.

Stop working. There is a time for work, and there is a time for play. We recommend setting yourself a fixed time when you stop working. This includes writing and responding to emails. Enjoy the rest of the day, read a book, learn a new skill, meet friends, rest your mind. This helps your mind wander from a focused into a diffuse mode of thinking which helps with insight problems such as “Thiss sentence contains threee errors.” If you do this, you will soon realize a boost in your overall creativity and subjective well-being. Cal Newport has structured his schedule according to this principle, calling it fixed-schedule productivity.

Build the right habits. Being productive is all about building the right habits. And building habits is hard; on average, it takes 66 days to build one, although there is great variability (see Lally et al., 2009, and here). In order to facilitate this process, we recommend Habitica, an app that gamifies destroying bad habits and building good habits; see Figure 1 below.

Figure 1. From left to right, shows the apps Habitica, Calm, and 7 Minute. The important thing is to not break the chain. This creates a psychological need for continuation. Note the selection bias here. It took me over a month to get to level 3 in Habitica. Don’t expect miracles; take small, consistent steps every day.

Workout. In order to create high quality work, you need to take care of your body; you can’t really be productive when you are not physically fit. Staying fit by finding an exercise routine that one enjoys and can manage is one of the best things we do, and we can only recommend it. Being able to climb stairs without getting out of breath is just one of the many rewards.

Meditate or go for a run. In order to increase your ability to focus and avoid distractions, we recommend meditation. For this purpose, we are using Calm, but any other meditation app, for example Headspace, yields similar results. (Of course, nothing beats meditating in a Buddhist centre.) This also helps during the day when some stressful event happens. It provides you with a few minutes to recharge, and then start into the day afresh. Going for a run, for example, does the same trick.

Someone asked a Zen Master, “How do you practice Zen?”
The master said, “When you are hungry, eat; when you are tired, sleep.”
“Isn’t that what everybody does anyway?”
The master replied, “No, no. Most people entertain a thousand desires when they eat and scheme over a thousand thoughts when they sleep.”

Powernap. This is one of the more unconventional recommendations, but it has worked wonders for our productivity. In the middle of the day, take a short power nap. It provides a boost of energy that lasts until bedtime (for more, see this).

Process versus Product. For starting to work, focusing on process rather than product is crucial. Set yourself a timer for, say, 25 minutes and then fully concentrate on the task at hand. Take a short break, and start the process again. In this way, you will focus on bursts of concentrated, deep work that bring you step by step towards your final outcome, say a finished blog post.

This approach is reminiscent of the way Beppo, the road sweeper, works in Michael Ende’s book Momo. About his work, he says

“…it’s like this. Sometimes, when you’ve a very long street ahead of you, you think how terribly long it is and feel sure you’ll never get it swept. And then you start to hurry. You work faster and faster and every time you look up there seems to be just as much left to sweep as before, and you try even harder, and you panic, and in the end you’re out of breath and have to stop — and still the street stretches away in front of you. That’s not the way to do it.

You must never think of the whole street at once, understand? You must only concentrate on the next step, the next breath, the next stroke of the broom, and the next, and the next. Nothing else.

That way you enjoy your work, which is important, because then you make a good job of it. And that’s how it ought to be.

And all at once, before you know it, you find you’ve swept the whole street clean, bit by bit. What’s more, you aren’t out of breath. That’s important, too.”

This technique is sometimes called the “Pomodoro”, and apps help achieving that abound. Although you need no app for this, apps are nice because they keep track of how many Pomodoros you have finished on a given day, providing you with a direct measure of your productivity. We can recommend the Productivity Challenge Timer.

Write down ten ideas. This recommendation comes from James Altucher, who wrote Reinvent Yourself which is an entertaining book with chapters such as “Seven things Star Wars taught me about productivity” and “The twenty things I’ve learned from Larry Page”. The habit is simple: write down ten ideas every day, on any topic. The basic rationale behind this is that creativity is a muscle, and like every other muscle, training it increases its strength. Most of the ideas will be rather useless, but that doesn’t matter. Now and then there will be a really good one. This habit probably has strong transfer effects, too, because creativity is required in many areas of life.

Read, Read, Read. There’s a saying that most people die by age 25 but aren’t put into a coffin until age 75. Reading allows your mind to continuously engage with novel ideas. We recommend Goodreads to organize and structure your reading.

Reflect on your day. Take a few minutes in the evening to reflect on your day. Keep a gratefulness journal in which you write down five things you are grateful for each day (this might also increases your overall happiness, see, e.g., here). Summarize your day in a few lines, pointing out the new things you have learned.

Does it work? Quantifying oneself

It is important to once in while take a cold, hard look into the mirror and ask: What am I doing? Am I working on things that matter, am I helping other people? Am I progressing, or am I stagnating in the comfort zone? Am I enjoying my life?

A useful habit to build is to, every evening, reflect on one’s behaviour and the things that have happened during the day. To achieve this, I (Fabian) have created a Google Form that I fill out daily. It includes, among others, questions on what I have eaten during the day, on the quality of my social interactions, on what the most important thing I have learned today; see Figure 2 below. It also asks me to summarize my day in a few lines.

Figure 2. Quantified Self questions. Every evening I reflect on the day by answering these questions. You can create your own, adapting the questions to your needs.

I have not done much with the data yet, but I know that just the process of answering the questions is very reflective and soothing. It is also valuable in the sense that, should there be too many days in which I feel bad, this will be directly reflected in the data and I can adjust my behaviour or my environment. I can wholeheartedly recommend this tiny bit of quantified self at the end of the day.

Incidentally, there is a whole community behind this idea of quantifying oneself. They go much further. As with most things, it is all about finding the right balance. It is easy to become overwhelmed when engaging with too many tools that measure your behaviour; you might end up being busy and chasing ghosts.

A 3 Stage program

In order to succeed in whatever area of life, commitment is key. Reading a blog post on productivity is the first step in a long journey towards actual behaviour change. In order to help you take this journey, we suggest three “stages”. Note that they are not necessarily sequential; you can take ideas from Stage 3 and implement them before things listed in Stage 1. The main reason behind these stages is that you should avoid being overwhelmed. Take small steps and stick to them. The first two stages will probably take one or two months, while the latter will take a bit longer.

Stage 1

Stage 1 is about getting started. It is about you becoming clear of your motivation; why do you want to be productive? What are the issues that plague or annoy you in the way you currently work? We recommend that you

  • Figure out and write down your motivation for why you want to be productive
  • Become aware of your social media use
  • Enroll in and complete Learning How to Learn
  • Start using the Pomodoro technique
  • Create an account on Habitica, adding habits you want to build or destroy
  • Uninstall social media apps from your phone
  • Set yourself a time point after which you will not check email nor social media

Stage 2

Stage 2 is about staying committed and developing a healthier and more consistent lifestyle.

  • Stay committed to your habits and review your motivation
  • Review what you have accomplished during the last months
  • Develop a consistent sleep-wake cycle
  • Develop a morning ritual
  • Eat healthy food, not too much, mostly plants
  • Start to exercise regularly (at least 3x a week)
  • Start a Bullet Journal

Stage 3

Stage 3 is about exceeding what you have accomplished so far. It is about figuring out your goals and the skills you want to develop. It is about not staying in your comfort zone, about building a habit of reading a variety of books, and becoming more engaged with others. It is from other people that we can learn the most.

  • Stay committed to your habits and review your motivation
  • Review what you have accomplished during the last months
  • Figure out what skills you want to develop
  • Read Deep Work and figure out a Deep Work routine that suits you
  • Engage with others and exchange ideas and practices
  • Find mentors for the skills you want to develop (e.g., writing, programming)
  • Create an account on Goodreads and organize your reading
  • Read at least two books per month

Conclusion

We have started this blog post discussing the future of work. But it’s not really about work. Sure, applying the ideas we have sketched will make you more productive professionally; but it’s not about running in a hamster wheel, meeting every objective at work or churning out one paper after another. Instead, it’s about finding the right balance of work and play, engaging in meaningful activities, and enjoying life.

If you take anything from this blog post, it should be the following three points.

If you work, work hard. If you’re done, be done. This means sharply separating work from play. It is important for avoiding burning out, for creating an atmosphere in which creativity and novel ideas flourish, for enhancing your life through spending time with friends and family, and, overall, for increasing the amount of play in your life. After all, play is what makes life joyful.

Never be the smartest person in the room. This is about learning from others. Identify the skills you want to develop, and seek out mentors for those skills; mentors will rapidly speed up your learning. Additionally, hang out with people with different backgrounds. This exposes you to ideas that you would not otherwise be exposed to. It is the people who we barely know that have the capacity to change our lives the most.

Be relevant. This is the culmination of the whole post. It is about helping others and having a lasting impact. This might entail donating to the world’s poorest; being there for a friend in dire times; pushing people to expand their horizons; helping them develop in the direction they want to develop in; working on projects that have a lasting positive impact. It is about doing the things that matter.

Recommended Resources

80.000 hours
Learning How To Learn
Deep Work (or How to Become a Straight-A Student)
– Cal Newport’s fixed-schedule productivity

This post was written together with Lea Jakob and is based on a workshop we have presented at the 31st EFPSA Congress in Qakh, Azerbaijan in April — twice. The feedback we got from participants was extremely positive, and so we decided to write up the main points. This post will also act as a reminder to ourselves should we ever be lead astray and fall back into old habits.

Fabian Dablander

Fabian Dablander is currently finishing his thesis in Cognitive Science at the University of Tübingen and Daimler Research & Development on validating driving simulations. He is interested in innovative ways of data collection, Bayesian statistics, open science, and effective altruism. You can find him on Twitter @fdabl.

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Are You Registering That? An Interview with Prof. Chris Chambers

There is no panacea for bad science, but if there were, it would certainly resemble Registered Reports. Registered Reports are a novel publishing format in which authors submit only the introduction, methods, and planned analyses without actually having collected the data. Thus, peer-review only focuses on the soundness of the research proposal and is not contingent on the “significance” of the results (Chambers, 2013). In one strike, this simple idea combats publication bias, researchers’ degrees of freedom, makes apparent the distinction between exploratory and confirmatory research, and calms the researcher’s mind. There are a number of journals offering Registered Reports, and this is arguable the most important step journals can take to push psychological science forward (see also King et al., 2016). For a detailed treatment of Registered Reports, see here, here, here, and Chambers (2015).

Picture of Chris Chambers

Chris Chambers is the initiator of the “Registration Revolution”, the man behind the movement. He has introduced Registered Reports into psychology, has written publicly about the issues we currently face in psychology, and has recently published a book called the “7 Deadly Sins of Psychology” in which he masterfully exposes the shortcomings of current academic customs and inspires change. He is somebody who cares deeply about the future of our field, and he is actively changing it for the better.

We are very excited to present you with an interview with Chris Chambers. How did he become a researcher? Where did he get the idea of Registered Reports from? What is his new book about, and what can we learn from hard sciences such as physics? Find out below!


Tell us a bit about your background. How did you get into Psychology and Cognitive Neuroscience? What is the focus of your research?

Since my teenage years I had been interested in psychology (the Star Trek Next Generation episode “Measure of a Man” left me pondering the mind and consciousness for ages!) but I never really imagined myself as a psychologist or a scientist – those seemed like remote and obscure professions, well out of reach. It wasn’t until the final year of my undergraduate degree that I developed a deep interest in the science of psychology and decided to make a run for it as a career. Applying to do a PhD felt like a very long shot. I have this distinct memory, back in 1999, scrolling down the web page of accepted PhD entrants. I searched in vain for my name among the list of those who had been awarded various prestigious scholarships, and as I neared the bottom I began pondering alternative careers. But then, as if by miracle, there was my name at the end. I was last on the list, the entrant with the lowest successful mark out of the entire cohort. For the next two and half years I tried in vain to replicate a famous US psychologist’s results, and then had to face having this famous psychologist as a negative reviewer of every paper we submitted. One day – about two years into my PhD – my supervisor told me about this grant he’d just been awarded to stimulate people’s brains with electromagnetic fields. He asked if I wanted a job and I jumped at the chance. Finally I could escape Famous Negative Reviewer Who Hated Me! Since then, a large part of my research has been in cognitive neuroscience, with specific interests in attention, consciousness and cognitive control.

You have published an intriguing piece on “physics envy” (here). What can psychology learn from physics, and what can psychologists learn from physicists?

Psychology can learn many lessons from physics and other physical sciences. The physics community hinges reputation on transparency and reproducibility – if your results can’t be repeated then they (and you) won’t be believed. They routinely publish their work in the form of pre-prints and have successfully shaped their journals to fit with their working culture. Replication studies are normal practice, and when conducted are seen as a compliment to the importance of the original work rather than (as in psychology) a threat or insult to the original researcher. Physicists I talk to are bemused by our obsession with impact factors, h-indices, and authorship order – they see these as shallow indicators for bureaucrats and the small minded. There are career pressures in physics, no doubt, but at the risk of over-simplifying, it seems to me that the incentives for individual scientists are in broad alignment with the scientific objectives of the community. In psychology, these incentives stand in opposition.

One of your areas of interest is in the public understanding of science. Can you provide a brief primer of the psychological ideas within this field of research?

The way scientists communicate with the public is crucial in so many ways and a large part of my work. In terms of outreach, one of my goals on the Guardian science blog network is to help bridge this gap. We’ve also been exploring science communication in our research. Through the Insciout project we’ve been investigating the extent to which press releases about science and health contribute to hype in news reporting, and the evidence suggests that most exaggeration we see in the news begins life in press releases issued by universities and academic journals. We’ve also been looking at how readers interpret common phrases used in science and health reporting, such as “X can cause Y” or “X increases risk of Y”, to determine whether the wording used in news headlines leads readers to conclude that results are more deterministic (i.e. causal) than the study methods allow. Our hope is that this work can lead to evidence-based guidelines for preparation of science and health PR material by universities and journals.

I’m also very interested in mechanisms for promoting evidence-based policy more generally. Here in the UK I’m working with several colleagues to establish a new Evidence Information Service for connecting research academics and policy makers, with the aim to provide parliamentarians with a rapid source of advice and consultation. We’re currently undertaking a large-scale survey of how the academic community feels about this concept – the survey can be completed here.

You have recently published a book titled “The 7 Deadly Sins of Psychology”. What are the sins and how can psychologists redeem themselves?

The sins, in order, are bias, hidden flexibility, unreliability, data hoarding, corruptibility, internment and bean counting. At the broadest level, the path to redemption will require wide adoption of open research practices such as a study preregistration, open data and open materials, and wholesale revision of the systems we use to determine career progression, such as authorship rank, journal rank, and grant capture. We also need to establish robust provisions for detecting and deterring academic fraud while at the same time instituting genuine protections for whistleblowers.

How did you arrive at the idea of Registered Reports for Psychology? What was the initial response from journals that you have approached? How has the perception of Registered Reports changed over the years?

After many years of being trained in the current system, I basically just had enough of publication bias and the “academic game” in psychology – a game where publishing neat stories in prestigious journals and attracting large amounts of grant funding is more rewarded than being accurate and honest. I reached a breaking point (which I write about in the book) and decided that I was either going to do something else with my life or try to change my environment. I opted for the latter and journal-based preregistration – what later became known as Registered Reports – seemed like the best way to do it. The general concept behind Registered Reports had been suggested, on and off, for about 50 years but nobody had yet managed to implement it. I got extremely lucky in being able to push it into the mainstream at the journal Cortex, thanks in no small part to the support of chief editor Sergio Della Sala.

The initial response from journals was quite cautious. Many were – and still are – concerned about whether Registered Reports will somehow produce lower quality science or reduce their impact factors. In reality, they produce what in my view are among the highest quality empirical papers you will see in their respective fields – they are rigorously reviewed with transparent, high-powered methods, and the evidence also suggests that they are cited well above average. Over the last four years we’ve seen more than 50 journals adopt the format (including in some prominent journals such as Nature Human Behaviour and BMC Biology) and the community has warmed up to them as published examples have begun appearing. Many journals are now seeing them as a strength and a sign that they value reproducible open science. They are realising that adding Registered Reports to their arsenal is a small and simple step for attracting high-quality research, and that having them widely available is potentially a giant leap for science as a whole.

Max Planck, the famous German Physicist, once said that science advances a funeral at a time. Let’s hope that is not true —  we simply don’t have the time for that. What skills, ideas, and practices should the next generation of psychological researchers be familiar and competent with? What further resources can you recommend?

I agree – there is no time to wait for funerals, especially in our unstable political climate. The world is changing quickly and science needs to adapt. I believe young scientists can protect themselves in two ways: first, by learning open science and robust methods now. Journals and funders are becoming increasingly cognisant of the need to ensure greater reproducibility and many of the measures that are currently optional will inevitably become mandatory. So make sure you learn how to archive your data, or preregister your protocol. Learn R and become familiar with the underlying philosophy of frequentist and Bayesian hypothesis testing. Do you understand what a p value is? What power is and isn’t? What a Bayes factor tells you? My second recommendation is to recognise these tumultuous times in science for what they are: a political revolution. It’s easy for more vulnerable members of a community to be crushed during a revolution, especially if isolated, so young scientists need to unionise behind open science to ensure that their voices are heard. Form teams to help shape the reforms that you want to see in the years ahead, whether that’s Registered Reports or open data and materials in peer review, or becoming a COS Ambassador. One day, not long from now, all this will be yours so make sure the system works for you and your community.

Fabian Dablander

Fabian Dablander is currently finishing his thesis in Cognitive Science at the University of Tübingen and Daimler Research & Development on validating driving simulations. He is interested in innovative ways of data collection, Bayesian statistics, open science, and effective altruism. You can find him on Twitter @fdabl.

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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: dev.jamovi.org, 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

http://www.theguardian.com/science/blog/2013/jun/05/trust-in-science-study-pre-registration

Gelman & Loken (2013): Garden of forking paths

http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf

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

bayes_hot_scaled

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 is currently finishing his thesis in Cognitive Science at the University of Tübingen and Daimler Research & Development on validating driving simulations. He is interested in innovative ways of data collection, Bayesian statistics, open science, and effective altruism. 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 http://jmbh.github.io/.

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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

OLYMPUS DIGITAL CAMERA

 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

creative-brain

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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