# [Interview]: Interview with a cookie fanatic: Get to know Jake

#### bugman

##### Super Moderator
Jake has kindly offered to be interviewed for the greater good.

Jake has been around for over a year now, he is a PhD candidate in social psychology.
Jake has an uncanny ability with mixed models so I personally value his contributions to this forum. He also wears a cookie monster suit that is probably disguising his true underlying raptor morphology.

This aside, ask some questions and get to know Jake!:wave:

#### bugman

##### Super Moderator
Jake

1) are you a linux user?

2) which book would you recommend to a novice on mixed modelling?

3) what is you favorate film in the last year?

#### Jake

1) are you a linux user?
I'm afraid not. I tried it out for a little while in high school because it was supposed to be the cool thing to do (this might also give you a clue about the kind of people I was hanging out with in high school), but ended up deleting it after a while because I never really saw any benefits during that time. Maybe now that I am (a little) older and (a little) wiser I will try it again sometime.

2) which book would you recommend to a novice on mixed modelling?
The two books that I got the most out of when I was first learning it all were Pinheiro & Bates, 2000, Mixed-effects models in S and S-plus, and Gelman & Hill, 2006, Data analysis using regression and multilevel/hierarchical models. That plus attempting to read a lot of papers most of which were way over my head.

3) what is you favorate film in the last year?
That's a tough one. I don't think there have been any movies released in the past year that have really blown me away. But I guess if I had to pick a favorite I'd either go with Chronicle or A Dangerous Method.

#### trinker

##### ggplot2orBust
When you were a 15 year old cookie muncher did you know I want to be a statistician? Would you you call yourself a statistician yet? Why?

#### Jake

When you were a 15 year old cookie muncher did you know I want to be a statistician?
Certainly not! Around this time I had a vague but nevertheless pretty determined idea that I was going to be some kind of I.T. person. I was just beginning to learn that I was pretty good at programming -- better than most of my peers anyway -- and I was completely fascinated by how the Internet worked and by computer networks in general. It wasn't until early in college that I finally abandoned this vision of my future self.

Would you you call yourself a statistician yet? Why?
I wish that I could! No, I would say that I am a competent data analyst, but I lack the mathematical expertise necessary for one to be considered a good statistician.

#### trinker

##### ggplot2orBust
Are you a health nut (aside from the cookies)?
Favorite equation and why?

#### Lazar

##### Phineas Packard
Top 5 songs and top5 books of all time?

#### spunky

##### Can't make spagetti
wondering if there was any person (prof usually but could be, i dunno... TA, colleague, etc.) that had a particular influence on you which made you start focusing on stats, methods and data analysis more? i dunno, someone who once told you that you're talented at it or a particular course that blew your mind and you just had to say "woha! this is *so* me!"

#### Dason

What statistical methods would you like to know more?

What is your favorite probability distribution?

I already know the answer to this but I think your response should be on the record:

Are there any cookies you don't like? If so explain why they are horrible (and go into as much detail as possible)

#### bryangoodrich

##### Probably A Mammal
I've been impressed with your knowledge of multilevel modeling. You're definitely a resource to turn to for help in that area!

With that said, what area of statistics would you say you're most weak at? What would you like to learn more about (if not merely methodological, so as not to repeat Dason's Q here)?

#### Jake

Are you a health nut (aside from the cookies)?
No, no really. For one thing my exercising tends to come and go in phases that last a few months at a time. I guess when I am in a phase where I am running a lot I am pretty healthy overall, but as it stands now I haven't gone for a run in about 3 months. As for my diet, I have a few habits that are generally considered to be healthy -- e.g., I don't eat meat, try to limit my consumption of animal products, and I gave up drinking alcohol a little while ago -- but the relevant health issues were not a major factor in most of these decisions. Overall I would say that I am somewhat health-conscious, but quite far from being nutty about it.

Favorite equation and why?
Well this is more of a "conceptual" equation than a proper equation, but:

$$DATA = MODEL + ERROR$$

This equation summarily represents the enterprise of data analysis.

$$DATA$$: A set of observations representing the thing we are trying to predict
$$MODEL$$: A set of rules or formulas that make a specific prediction for each observation in DATA
$$ERROR$$: The amount by which MODEL mispredicts DATA

Top 5 songs and top5 books of all time?
This is obviously a tough one but I'll try to adhere to the top5 format.

SONGS
1. Beethoven - Piano Sonata No. 14 (Moonlight Sonata)
2. The Beatles - Strawberry Fields Forever
3. Dave Brubeck/Paul Desmond - Take Five
4. Paul McCartney - Band On The Run
5. The Flaming Lips - Do You Realize??

BOOKS
1. Richard Dawkins - The Selfish Gene
3. Jack London - The Call of the Wild
4. JRR Tolkien - The Hobbit
5. Judd, McClelland & Ryan - Data Analysis: A Model Comparison Approach (my introduction to $$DATA = MODEL + ERROR$$)

wondering if there was any person (prof usually but could be, i dunno... TA, colleague, etc.) that had a particular influence on you which made you start focusing on stats, methods and data analysis more? i dunno, someone who once told you that you're talented at it or a particular course that blew your mind and you just had to say "woha! this is *so* me!"
Chick Judd, who, among other things (see book #5 above!), first planted in my head the idea of writing a stats/methods paper, ultimately expanding my view of what kinds of research I can do and leading me to finally see myself as, at least in part, a "methods person."

What statistical methods would you like to know more?
1. Time series analysis
2. Bayesian statistics
3. Not sure if this counts as a statistical method or methods per se, but I would like to delve deeper into the areas of measurement theory. I have some basic facility with item response theory but would one day like to be an expert in this area. I also am fascinated by what little I know (and it is just a little) about conjoint measurement theory.

What is your favorite probability distribution?
Uniform.

Just kidding... how boring would that be?

Probably the binomial distribution, simply because it is one of the only distributions that I actually feel like I really understand (e.g., how it is derived).

Are there any cookies you don't like? If so explain why they are horrible (and go into as much detail as possible)
Snickerdoodle... I won't go into the detail that you requested as I am afraid it will upset me too much.

I am on board with Bayes, although I have primarily only a high-level, conceptual understanding of Bayesian statistics. There are some who hold up Bayesian methods as the only sensible way of analyzing data, the solution to all of science's problems, etc., and while I am rather skeptical of the ideal portrait that I think some would like to paint of Bayes, I do want to learn more about the Bayesian statistical approach and am open to the prospect that this might really be a more sensible way to do things in a lot of cases.

With that said, what area of statistics would you say you're most weak at? What would you like to learn more about (if not merely methodological, so as not to repeat Dason's Q here)?
I like to think that I have at least a basic, passing familiarity with most of the major statistical methods that someone in my position is likely to come across, but there are a few areas that I am frighteningly ignorant about. For example, I have no idea what a "random forest" is, nor a "graphical model." However, I think the biggest and most conspicuous gap in my statistical knowledge is in the area of multivariate linear models, that is, linear models with multiple response variables (e.g., MANOVA, multivariate multiple regression). I simply have never spent any appreciable time learning about these methods and I don't have any good excuses for why not.

#### vinux

##### Dark Knight
Jake is like an invincible person around here, I don't remember when he joined, in short span of time you become an important MVC here.
Now questions.

1) How did you get into social psychology? What was your graduation?
2) What motivate you to a regular visitor in TS

3) Do you think human can beat bots? Does raptors are protector of human kind?
4) Favourite food/drink?

This song is for you (probably a punishment). VIDEO

#### victorxstc

##### Pirate
Jake, please tell us the top six to 10 characteristics about yourself you enjoy them or are proud of.

Then please name the worst six to 10 characteristics you hate about, or you are not proud of, or you are planning to change them in future (this one is 100% optional).

Don't you regret that you left programming as the main profession of your life? Don't you miss it?

Which aspect of psychology do you like the most? (minimum three items!)
Why?

Which aspect of it you don't like or you hate? (minimum 3!)
Why?

#### bugman

##### Super Moderator
Jake, I am currently reading: Mixed Effects Models and Extensions in Ecology with R (2009), by Zuur, Ieno, Walker, Saveliev and Smith.

In it they introduce ML and REML by saying that in most text books, the explanation of the two methods is either too complex for a layman, or REML is simplified too much by saying it is a "mystical way to correct for degrees of freedom".

Do you agree with this? and how would you explan these methods to a layman without over simplifying it?

#### Jake

1) How did you get into social psychology? What was your graduation?
I felt pretty certain that I wanted to get a PhD and end up as a university professor. As an undergraduate I believed, and still believe, that there were any number of fields I could have gone into and been pretty much perfectly happy. I ultimately decided to pursue social psychology because I decided it was the most interesting out of the options I was considering (which included cognitive psychology, evolutionary biology, and ecology).

2) What motivate you to a regular visitor in TS
1. Procrastination
2. The hope that I will learn about new statistical methods from others
3. The desire to stay sharp on statistical methods that I don't use often in my own research

3) Do you think human can beat bots? Does raptors are protector of human kind?
I think, given the chance, bots and raptors both would brutally subjugate humankind, and humans would not stand a chance. I for one welcome our new overlords whether they be robotic or reptillian.

4) Favourite food/drink?
Food: It's hard to beat a really good portabello mushroom burger...
Drink: Chocolate almond milk!

I've been playing guitar for about 10 years and accompanying this with singing for about 5. I play at least a little bit almost every day and hate having to go more than a couple of days without access to my guitar.

Sadly I spend a lot of time reading dry academic papers/books, and after doing it for so long, I even kind of like it... =\

I love to play 8-ball and it is probably the only sport I've ever been any good at (if you do consider it a sport).

This song is for you (probably a punishment). VIDEO
Thanks

Jake, please tell us the top six to 10 characteristics about yourself you enjoy them or are proud of.

Then please name the worst six to 10 characteristics you hate about, or you are not proud of, or you are planning to change them in future (this one is 100% optional).
Oh god, the "strengths and weaknesses" question... I'm definitely going to pass on this one, sorry...

Don't you regret that you left programming as the main profession of your life? Don't you miss it?
I have a small amount of regret in leaving computer science. When I decided to leave that field, it was because I figured that my possible careers in that field basically included being a programmer, network engineer, etc. At the time the idea of going to graduate school for computer science was a completely foreign idea that just never occurred to me. Now I can see that if I had ended up going that route (the computer science PhD route) I probably would have been pretty happy with it. But I am also pretty happy where I did end up, so I don't really worry about it.

Which aspect of psychology do you like the most? (minimum three items!)
Why?
1. Well, first and most obviously, I just find psychological questions extremely interesting. A lot of the questions that psychologists deal with are questions that I have always spent a lot of time thinking about.
2. I like that there is still a lot of room for strong theorizing in psychology. I think the chances of one making an important theoretical contribution to a field like e.g. physics are pretty close to nil. But psychology is still young and undeveloped enough that there is a nontrivial chance one can actually have an influence on the field if you are good and/or lucky.
3. I also like that a lot of areas of psychology routinely deal with methodological issues that can be exceedingly complex if you let them be, so there are a lot of opportunities to apply advances in e.g. statistics to problems in psychological research.

Which aspect of it you don't like or you hate? (minimum 3!)
Why?
1. It is sometimes said that we are the Rodney Dangerfield of the sciences (we "get no respect").
2. "So are you, like, analyzing me right now?"
3. I think there are a lot of problems with the way we educate young psychology students. In a way there are 3 faces of psychology: (a) psychology as seen by the public, (b) psychology as seen from the perspective of an undergraduate psychology major, and (c) psychology as seen by psychologists. Unfortunately there are some rather huge gulfs between these 3 views of psychology. In particular a lot of undergrad psych majors find themselves unprepared and misled when it comes time to start doing actual psychology research. I will just leave it at that.

Jake, I am currently reading: Mixed Effects Models and Extensions in Ecology with R (2009), by Zuur, Ieno, Walker, Saveliev and Smith.

In it they introduce ML and REML by saying that in most text books, the explanation of the two methods is either too complex for a layman, or REML is simplified too much by saying it is a "mystical way to correct for degrees of freedom".

Do you agree with this? and how would you explan these methods to a layman without over simplifying it?
I actually have Zuur et al. and have read most (but not all) of it. I find their characterization to be pretty much right on and I find their level of explanation of the issue to be excellent.

As for how I would explain it to a layperson without oversimplifying. To be honest, I think I wouldn't. Let's face it, the difference is really quite a mathematical subtlety and it's not at all obvious why we should expect a layperson to be able to easily and accurately understand it after only a brief explanation by someone like me. When I try to explain these general issues (not necessarily ML/REML specifically) to students with at least a little statistical grounding, I usually go about it from the $$DATA = MODEL + ERROR$$ perspective mentioned above, appealing to the intuitions that (a) as MODEL gets biggers and bigger, ERROR is necessarily going to get correspondingly smaller and smaller, (b) but it is desirable for our estimate of ERROR not to depend on how many things we happened to have put in the MODEL, (c) therefore we want to apply a correction to ERROR to account for the size of the MODEL. I am of course oversimplifying, so like I said, in answer to your second question: I wouldn't.

#### trinker

##### ggplot2orBust
Visualization is important to statistics. What type(s) of visualization(s) are your favorite(s)? If it's some more obscure type maybe you could throw an example up.

#### Jake

Trellis plots are indispensable when working with mixed models. Since I work with mixed models a lot these days, I would probably have to pick those as my top choice right now. I also quite like violin plots.

#### vinux

##### Dark Knight
Thanks Jake.

1) Favourite programming language other than R?
2) favourite case study in social psychology(SP). what would you suggest some reading in SP for somebody good at statistics and a beginner in SP.
3) Have you gone outside US? Favourite place for your ideal vacation.