(Part 2) Top products from r/IOPsychology

This is a great mentality, and you are learning a lot already.

As you spend more time in this role, the work that takes you 70-80% of your time should take less. You should be able to finish some work faster, automate things (cleaning data in R may be faster if you can run scripts on consistent data that shows up in the same places every time, etc), and prevent issues so you don't have to deal with things breaking as much.

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The next stage you can move to, broadly speaking, is helping your company make evidence-based decisions that increase the effectiveness of their organization. I've put some links here that can help you explore this approach; it's not a huge leap from what we study, but it does show you a systematic process for making evidence-based decisions that you can use to add value for your organization.

https://www.cebma.org/

Evidence Based Management book

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One example of helping your company make evidence-based decisions is to understand why people leave. For this, you can look to the scientific evidence (I added a link to an evidence summary below), use your organization's data (so building a model like you mentioned), speak with practitioners in your company (reach out to people in talent management and learn what they've experienced regarding turnover in their time with the company), and speak to stakeholders who are affected by retention interventions (like employees, HR, line managers, etc).

https://scienceforwork.com/blog/evidence-based-employee-turnover/

All these sources of evidence will help you make a full report on 1) existing predictors of turnover in the literature, 2) which predictors matter of the ones you have (but recognize that your company may not collect high-quality variables to use in the model, like commitment, reliable and predictive measures of engagement, motivation, etc), 3) what practitioners in the company know and recommend, and 4) what the people affected think about the state of turnover in the organization.

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Then you can turn your focus to solving retention issues, if it's a priority in your company. Then I'd recommend looking to other articles from scienceforwork.com, other research papers, and reading books on behavioural design and nudging (making small changes in the organization to change behaviour and increase desired outcomes. After developing solutions that have a strong foundation and have worked before, you want to measure their impact (ideally with a controlled test). Happy to provide articles on this too.

After addressing turnover from a challenge and a solution perspective, you could move on to another key "people challenge" in your company - maybe it's teamwork, leadership development, recruitment, or anything else we can cover. You can use the same approach to tackle that, and evaluate the impact of those solutions. Before you know it, you'll have a whole diagnosis, solution development, and impact evaluation engine running that can help your company make better people decisions for a lot cheaper and a lot faster (because you won't be chasing useless fads) than they otherwise would. Good luck!

In another life I went the route of scouting analyst/sabermetrician in baseball. I've explored this option a bit so have some input to offer.

I've seen the NBA post IO positions in their front office. Not exactly a sports team per say, but not far off. Additionally, all teams have HR groups as any other organization so more HR oriented routes are available. There is some disparity of analytical savvy among teams, but that gap is rapidly closing.

I agree with /u/nckmiz, scouting departments seems the most interesting and likely route to me. But they don't pay particularly well (which I was surprised to see). They also expect you to travel with the team and be available all hours of the day during parts of the season (draft time and trade deadline probably). Additionally, they really want people who know the sport, can do the math, and speak in layman's terms. Often this operationalizes as somebody who has published extensively in blogs, reddit, Fangraphs, or other. They expect a portfolio that demonstrates you understand the critical aspects of a sport, can piece together new trends and pitch it in a way that the business folks can understand.

If any of this interests you, you might like the book The MVP Machine. Ben is awesome and has a podcast I would suggest as well if you like baseball as much as I do. Recently, his former cohost took a job with the Tampa Bay Rays analytics department. Maybe not true IO, but this was the route that interested me.

Also this might be of interest to people : http://www.sloansportsconference.com/

And if you're really jazzed and want to take the plunge: https://blogs.fangraphs.com/category/job-postings/ (but if anybody here gets one of these jobs you have to talk to me all the time about it or I will ban you)

Cascio & Boudreau is a really good starter book if you want to start developing an analytical perspective. It's not an especially detailed/technical approach, but it's a good "mindset" read.

For programming, I'd look at Data Camp online courses. You can build up introductory skill in R and Python pretty easily with self-guided learning. Richard Landers also has some really great course materials publicly available that build on Data Camp modules:
http://neoacademic.com/2018/01/03/complete-course-social-scientists-data-science-using-r/

Carver and Scheier for life, baby!

I also consider myself blessed for having been exposed to Bill McGuire's perspectivist approach to theory. "The opposite of a great truth is also true."

Great question, OP!

https://www.amazon.com/gp/aw/d/0470129182/ref=ya_aw_od_pi?ie=UTF8&psc=1

That's a good overview book to get introduced into the topics. It's very high level, easy to read, and makes the information understandable to people outside the field.

You don't need to know any business related stuff really. You'll learn what's relevant to the field as you go, but you don't need to have a degree in business to get in.

Good luck!

MCDA is an active field of research. Volume 23 of the International Series in Operations Research & Management Science Multiple Criteria Decision
Analysis State of the Art Surveys can give you a feel for the lay of the land.

Personally, I am reading French, Maule and Papamichail and Stewart and Belton before I jump into a state of the art survey. The former is a broad introduction to decision support the latter a an introduction to MCDA. My plan is to move on to state of the art level stuff by next year.

I don't think it is necessary, but it highly recommend you at least skim through an intro to operations research. Here is a youtube playlist that briefly covers most of the topics you would find in an intro to OR book. You can also look at any of the texts the man in the videos recommends. At university we used Winston's. The benefit would be that you can get an overview of the various methods used in prescriptive analytics. In Winston's book you can also get single chapter introductions to mathematics of decision making under uncertainty, game theory and goal programming (one approach to MCDA).

Flawless Consulting is pretty well known and respected.

Standard 1st year PhD I book: http://www.amazon.com/Applied-Psychology-Human-Resource-Management/dp/0136090958/ref=sr_1_1?s=books&ie=UTF8&qid=1368770356&sr=1-1&keywords=cascio+%26+aguinis

O book: http://www.amazon.com/Organizational-Psychology-Scientist-Practitioner-Steve-Jex/dp/0470109769/ref=sr_1_1?s=books&ie=UTF8&qid=1368770387&sr=1-1&keywords=jex

The O book is a much easier / more enjoyable read, the I book is much more.... textbooky....

Both should be approachable by someone with a BA

Undergrade level books: http://www.amazon.com/Psychology-Applied-Work-Paul-Muchinsky/dp/0578076926/ref=sr_1_sc_2?ie=UTF8&qid=1368770501&sr=8-2-spell&keywords=Muchensky

Older edition: http://www.amazon.com/Psychology-Applied-Work-Introduction-Organizational/dp/0980147808/ref=sr_1_sc_1?ie=UTF8&qid=1368770501&sr=8-1-spell&keywords=Muchensky

The new edition apparently touches on some of the things that have been effected by the recession. IDK if it is worth the extra $$$ though.

I would get the Jex book 1st, and if you like that check out the Cascio & Aguinis book, or vice versa.

Not finished with it yet, but so far Judea Pearl’s the Book of Why is really good too. His research and philosophy is extremely unique IMO bec
ause he is a computer scientist by training educated in Machine and deep learning, but a lot of his work has focused on understanding causality. The book discusses why causality is so important and the need for us to solve that problem before we can get computers to pass the Turing Test. IMO extremely relevant to I/Os attempting to blend theory with AI.

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

I haven't actually read it, but Adam Grant's book "Give and Take" might fit the bill of translating some work of I/O to the public.

Understanding Psychology as a Science by Zoltan Dienes

https://www.amazon.com/Understanding-Psychology-Science-Introduction-Statistical/dp/023054231X/ref=sr_1_1?ie=UTF8&qid=1484625691&sr=8-1&keywords=understanding+psychology+as+a+science

Second Tabachnik & Fiddle along with Hunter & Schmidt

Rogelberg's IO Handbook

Brannick & Levine's JA text

HLM - Raudennbush & Bryk

Ployhart et al's Staffing Organizations

I have lots of other O-oriented things, but those will depend on your specific area.

Edit: This!

Superforecasting: The Art and Science of Prediction by Philip Tetlock.