OhSoSolipsistic

This is an old thread, but it comes up on google searches for combinations of "human factors programs" so I thought I'd contribute.
For specifically human factors, the largest domain is tech. At least in silicon valley, human factors tech tends to be broken into software and hardware, or user experience/design and devices. Tech tends to be more focused with quicker turn around, so there's less emphasis on conducting formal experiments and heavy analysis, and a bit more on design. If you're into this, look up UX/UI - there's certificate programs if you want to explore education options. A career in this field is often lucrative and fast-paced. A lot work on projects they need to sign NDAs for - virtual reality stuff, Apple devices, etc.
Another branch is human systems integration (which is still under human factors in most academic programs, but it's shifting professionally), and the largest domains are transportation, medical, and security. Obviously this still can be focused on one component, but human systems tends to be different in both application and type of work, and is a bit more government/institution-oriented. In the majority of cases, there's a heavy focus on simulation work within a larger system (e.g., pilot/crew--flight deck components--communication components--air traffic controller--ground system components), so more emphasis is placed on advanced experimental design and analysis, as well as systems engineering and, to a lesser but increasing degree, programming. This field is already fairly sizable, but still relatively obscure to those outside the profession.
I've worked with a lot of recent human factors terminal MA/MS grads, and the best well-rounded program by far is CSU Long Beach - at least on the west coast. If looking into human factors at San Jose State University (which is in close connection to CSULB, and Northridge, and Purdue), also look into the Experimental Psych MA program if you think human systems is more what you're interested in.

The workload is on the heavy side, but the hours per week sound reasonable and fairly standard for many grad students. But I second TakeruK's hesitations about taking on added commitments during your first semester. On top of getting used to a new environment and developing a good first year record for future PhD applications, the initial transition to grad programs is often noticeably stressful in and of itself. Graduate-level work isn't necessarily “harder” than undergrad, it's just very different and takes time to develop (e.g., backstroke swimmer now learning butterfly for the first time).

That's not to say that you shouldn't take on any extra volunteer commitments while you're making that transition. You know the situation and yourself best, and some programs do a good job at mitigating that stress. Is there someone in your program you can ask about typical first-semester stress to get better awareness of your situation?

You likely already know this, but:
Many times, professors look for prospective students with specific features that their lab needs at a particular time, which change yearly with the lab's focus and composition. If their lab has a student with an unusual strength/background/whatever who happens to be graduating and leaving this year, the professor may look for a new student with similar features. It doesn't reflect poorly on the other candidates who are vying for that same spot - no one can possibly have every strength or background or sets of experiences - it just is what it is, unfortunately.
So often it just really is all about being an unusually-shaped puzzle piece who just happens to fit the lab's or program's jigsaw needs at the right time, and the final selection genuinely is out of an outstanding pool of candidates of which include you.
Coping tool: know this, and then binge on a great tv series.

I worked as a researcher in an human factors/human systems lab at a gov facility for about 6 years after my terminal masters. I'd say my work experience was more valuable but not by much, and I wouldn't have got that position without my masters. The research lab (and entire division) recruited grad interns and fresh grads from three California State University campuses in human factors/experimental psych MA/MS programs, and all of those programs took at least two (usually three) years to complete with a thesis requirement.
The fact that my research lab was particularly publication-heavy was very beneficial, and I'm almost certain made my application more attractive to adcomms than if I spent the 6 years in industry. Industry experience is better than no experience, but experience in a public or academic institution where you're writing and attending conferences strengthens your application much more.
I don't know of any positions where you can do both basic and applied research outside of an academic institution, but that's not saying those positions don't exist. Even in universities, I think most HCI work is applied - but I'm looking at it from an applied psych background, so that may not be true for all HFE/HCI disciplines. In my case, I want to stay in the same gov/academic sector, but most of the positions I want require a PhD due to the complexity of the research. I also prefer more autonomy at a level that's difficult to achieve without a doctorate (more of a PI position). I think HFE/HCI is moving toward systems and we'll see a lot more professional opportunities in the next few decades, so I'm not that concerned about employability.

You likely already know this, but:
Many times, professors look for prospective students with specific features that their lab needs at a particular time, which change yearly with the lab's focus and composition. If their lab has a student with an unusual strength/background/whatever who happens to be graduating and leaving this year, the professor may look for a new student with similar features. It doesn't reflect poorly on the other candidates who are vying for that same spot - no one can possibly have every strength or background or sets of experiences - it just is what it is, unfortunately.
So often it just really is all about being an unusually-shaped puzzle piece who just happens to fit the lab's or program's jigsaw needs at the right time, and the final selection genuinely is out of an outstanding pool of candidates of which include you.
Coping tool: know this, and then binge on a great tv series.

Being hooded at graduation is pretty much equivalent to a neck hug.

I started my master's program not in a lab and switched advisors three times in the first year, and overall it was a positive experience. In my case, there were unexpected availability conflicts in addition to wanting to explore other research areas - both reasons were valid and handled well. Professors are professionals, they understand some movement is going to happen with students and generally don't take it personally.
It's usually more difficult to switch if a professor/lab is providing funding for you, has a RA/TA contractual obligation, or heavily invested in you in some other way. Even then, movement between labs is often more of a logistical issue than anything else (e.g. waiting until the end of an academic year). I know there are programs in which labs are more structured due to politics or other reasons, but you should be able to get info on this ahead of time.
Look in your program handbook to see if there's any guidelines or policies on this. If not, email the program coordinator and ask for details to be better informed of how your program operates.

I have mixed feelings about showing the amount. When I applied to US PhD programs, I did list the amounts because the names of the Canadian fellowships are not well known to US professors and I wanted to show that I basically received the equivalent of a NSF grad fellowship in Canada during my Masters. I also see amounts on CVs of "soft money" positions (faculty-like positions) to show things like how much grant money they can bring in. I only included awards that are worth a significant chunk of my stipend, i.e. $10,000 or more (because the point is to show that you have been externally funded in the past), or awards that are prestigious but carry no monetary value.
For this past year, when applying to postdoc positions, I opted to go for a much more simple CV (like the ones professors and postdocs in my field have) and basically removed all descriptors on almost everything. So the dollar values got removed this time. In the future, I think grad school awards would be obsolete in my CV so I probably will not include those dollar values, however, if I am a PI or co-PI on a future grant, I would probably include it.
In your case, I think you should leave out the dollar values since you don't know the amount of the second award. If you only included one, it would seem like the second one was not worth money.
As for declined funding, I would only list them if you could have actually accepted the money but had to decline since you already received other funding etc. For example, if you applied to 3 PhD programs and got a University-specific fellowship from all three so you could only accept one, I would not list the other two fellowships since there's no way you could have accepted them since you declined their admission offer. However, if you win a national-level fellowship but your school funding is better and precludes you from accepting the national fellowship, then yes, definitely show that it was declined. Or, if you win multiple national-level fellowships and can only accept one, etc.

(http://profiles.asee.org/profiles/7281/screen/33?school_name=University+of+Pennsylvania)
The "national average" is a meaningless. Comparison between competitor programs is what matters. I assume you've been offered a fellowship and are trying to get them to throw in a little more money?

What applications are you interested in? From a broad perspective the east coast has more overall activity (academia, internships, careers, etc.) in transportation, gov, military, financial, and other human system operations besides those that are heavily focused on the tech/AI/internet industry. There's some healthcare HCI/HSI presence out here too. I've worked as a transportation HSI researcher in the bay area for several years, so there is non-tech industry stuff out here but not nearly as much. I personally would choose Colombia, but I'm only superficially familiar with its Industrial Engineering/Operations Research program.

Oh, and if you're not already familiar with his work, check out Haidt and The Righteous Mind if you're interested in conflict resolution and the political spectrum. There's some debate on moral foundations theory, but Righteous Mind is outstanding regardless - highly recommended.

When attending panels on career advice and reading books and blogs on the job search process, I came across one key piece of advice that would apply here too. The search process is not about the applicant. It's all about the employer finding the right fit for their position. Or, in this case, the school finding the right candidates for their programs. Their focus will be on the candidates they want to attract and when we are on their shortlist then we'll get information. Otherwise, no information is literally that: no information. It could mean that we are the next person to get information. Or it could mean that we are already rejected but they haven't told us yet.
Note that being left in limbo doesn't mean you are at the bottom of the pile....most schools will accept 5% to 10% of applicants and maybe only the top 20% will receive interview requests or notification about shortlists etc. I'd estimate that in most cases, over 80% of applicants will hear nothing until they finish making decisions. Here are also some more practical reasons why most applicants are left in limbo:
1. The school wants to keep you in reserve in case they need to make more offers later. It's almost impossible to reject someone and then "un-reject" them and have them still seriously consider you for their PhD schooling. 
2. The school has limited resources to communicate with applicants so they need to prioritize the accepted candidates. From the other side, I know that planning a prospective student Open House is a ton of work. My department accepts about 40 people and we invite them all to come on the same 2-3 days for a visit. It takes weeks to plan everything and to ensure we are able to meet everyone's food requests, accommodation needs, travel plans etc. And they need to arrange a personalized itinerary for every single visiting student. Some students need to visit on separate days. And there's all the reimbursements to process. Most people that get a rejection take it graciously but some people will write back to ask for feedback or other details. The staff doesn't have time to do everything.
3. The school has limited resources and they also need to prioritize the current students. In my program, we have one single staff member in charge of all things grad students related. This staff member does all of the prospective visit stuff but also helps the current student do our graduate milestone paperwork. They approve and prepare our candidacy paperwork. They arrange qualifying exam committees. They arrange thesis committees. We talk to them when there is an issue in our stipends not going through. Or if we have an issue with a professor mistreating students etc. Remember that most programs will not have dedicated staff simply for admissions/applications.
My school does try to be very fast though. We make all decisions in January but the people who are rejected don't find out until mid-February, after all of the visit planning is done. I know that this information isn't very comforting and to be honest, it really is a matter of prioritizing limited resources to maximize the best outcomes for the school and the current students. But I hope that knowing some of the logic/reasoning behind the decisions makes the limbo less bad. I know it's easier to say that than to actually feel it, because I also felt high levels of anxiety while in limbo for post-PhD job applications too! But it will pass

Congrats! When my bf got info on stipends last year it was always in the official letter so I'm sure it'll come along with that!

Just got accepted to ASU! Does anyone know if funding info is usually stated in the official acceptance letter? My letter is still being processed and I can't find any details about stipend, fellowships, etc. anywhere else.

Good news, and it's not uncommon. Some schools haven't wrapped up their adcomm meetings for final review yet, so no one has heard anything positive yet from those programs (e.g., both of the adcomms for the programs I applied to are meeting this Wed). It's still fairly early.  

Exactly how I feel! Couldn't sleep for shit last night. I'm not sure how to deal with myself for the next month. I applied to Iowa, Oregon State and Portland State. All three seem to send out notices in March. I need a holiday or something! I need to climb a mountain with no phone or internet...okay, so it's Sunday, we can get through the weekend okay? We can. We can. 
 

Good news, and it's not uncommon. Some schools haven't wrapped up their adcomm meetings for final review yet, so no one has heard anything positive yet from those programs (e.g., both of the adcomms for the programs I applied to are meeting this Wed). It's still fairly early.  

I started out with a Magoosh study plan too, but I eventually centered my quant prep on the Magoosh math videos with Mike McGarry as the narrator. His explanations are very concise and direct, and he focuses on the underlying mathematical themes that many concepts share in a way that most other test preps lack. This combo make the lessons remarkably clear and thorough. Additionally, the Magoosh layout, question history, cross references, and responsiveness to student questions are all superb, so I'd say the $149 for the full set of Magoosh lesson videos for 6 months is well worth it. Alternatively, I've heard the GreenLightTestPrep videos and ETS's Khan videos are similar in their approach - both free. Once you've decided on a set of lessons, make a flexible schedule to stay on track with room for 1-3 videos per study session (e.g., Arithmetic & Fractions module first 3 wks of March).
As you watch each lesson, make flash cards/main idea cards for every concept that you don't know or forgot, and review them often. I might have gone overboard with my cards (pic below), but just the process of making them is very effective. For in-lesson questions, pause the video and solve each one - even if you're sure you already know it. At the end of each lesson, find and solve related practice questions from your other prep materials. For every incorrect answer, find exactly why you made that error(s). If you tend to make similar errors in a particular type of problem or calculation, make an index card with a sample question and solution that points out your repeated error. At the end of each module, spend at least a day on review: flip through index cards, work on more realistic and difficult practice questions, watch any lessons a second time for extra review, etc. The Magoosh blog is also helpful for further math review.
You might want to structure a similar session plan for verbal if you prefer a more formal or in-depth study, but I decided to devote less energy on my verbal score so my approach was different. Whenever I was waiting on things, exercising, or couldn't sleep, I would spend 5-20 minutes answering practice questions or reviewing pre-made flashcards on my phone - basically replacing any social media time with verbal prep. I found both the Magoosh Vocab Builder and Vocab Flashcard app to be the most helpful for memorization, and the Manhattan GRE Prep app has the 5-lb book practice questions in a great mobile format.
What I wish I would have done/known when I started:
Even if you only have 30 min some days to spend on GRE stuff, try to devote at least 5 days a week to it. Cut out distractions and treat it like a job.
I found most major test prep's practice questions helpful for different purposes. In general, Manhattan, Princeton, and Kaplan quant questions tend to be easier or equivalent to easy GRE questions, so they're better suited for learning and reviewing specific concepts. Magoosh GRE & GMAT quant questions are generally more challenging and can cause frustration when mastering concepts, but they're amazing prep for actual GRE questions once you have a firm grasp of a module.
Manhattan verbal is similar to Magoosh quant: many challenging, realistic problems with high-quality explanations to solutions (particularly in reading comprehension), especially with respect to how ETS frames and structures verbal questions. Both Magoosh and Manhattan were good with sentence equivalency/completion questions, but Magoosh can be a bit too murky with sentence structure and possible solutions, and Manhattan's vocab can be too esoteric. Both critiques are minor though. Kaplan verbal seems to rely too heavily on definitions and not enough on sentence structure/context, so don't use it for anything other than vocab recall. I haven't had much experience with Princeton verbal.
Good luck!
ETA: for what it's worth, my verbal and quant score were 80th percentile... well, one was 79th but I can never remember which.

Hold on to that feelayayaying!!! Street light! Peahhpaaaaaaaaaalllll!!!!

I started out with a Magoosh study plan too, but I eventually centered my quant prep on the Magoosh math videos with Mike McGarry as the narrator. His explanations are very concise and direct, and he focuses on the underlying mathematical themes that many concepts share in a way that most other test preps lack. This combo make the lessons remarkably clear and thorough. Additionally, the Magoosh layout, question history, cross references, and responsiveness to student questions are all superb, so I'd say the $149 for the full set of Magoosh lesson videos for 6 months is well worth it. Alternatively, I've heard the GreenLightTestPrep videos and ETS's Khan videos are similar in their approach - both free. Once you've decided on a set of lessons, make a flexible schedule to stay on track with room for 1-3 videos per study session (e.g., Arithmetic & Fractions module first 3 wks of March).
As you watch each lesson, make flash cards/main idea cards for every concept that you don't know or forgot, and review them often. I might have gone overboard with my cards (pic below), but just the process of making them is very effective. For in-lesson questions, pause the video and solve each one - even if you're sure you already know it. At the end of each lesson, find and solve related practice questions from your other prep materials. For every incorrect answer, find exactly why you made that error(s). If you tend to make similar errors in a particular type of problem or calculation, make an index card with a sample question and solution that points out your repeated error. At the end of each module, spend at least a day on review: flip through index cards, work on more realistic and difficult practice questions, watch any lessons a second time for extra review, etc. The Magoosh blog is also helpful for further math review.
You might want to structure a similar session plan for verbal if you prefer a more formal or in-depth study, but I decided to devote less energy on my verbal score so my approach was different. Whenever I was waiting on things, exercising, or couldn't sleep, I would spend 5-20 minutes answering practice questions or reviewing pre-made flashcards on my phone - basically replacing any social media time with verbal prep. I found both the Magoosh Vocab Builder and Vocab Flashcard app to be the most helpful for memorization, and the Manhattan GRE Prep app has the 5-lb book practice questions in a great mobile format.
What I wish I would have done/known when I started:
Even if you only have 30 min some days to spend on GRE stuff, try to devote at least 5 days a week to it. Cut out distractions and treat it like a job.
I found most major test prep's practice questions helpful for different purposes. In general, Manhattan, Princeton, and Kaplan quant questions tend to be easier or equivalent to easy GRE questions, so they're better suited for learning and reviewing specific concepts. Magoosh GRE & GMAT quant questions are generally more challenging and can cause frustration when mastering concepts, but they're amazing prep for actual GRE questions once you have a firm grasp of a module.
Manhattan verbal is similar to Magoosh quant: many challenging, realistic problems with high-quality explanations to solutions (particularly in reading comprehension), especially with respect to how ETS frames and structures verbal questions. Both Magoosh and Manhattan were good with sentence equivalency/completion questions, but Magoosh can be a bit too murky with sentence structure and possible solutions, and Manhattan's vocab can be too esoteric. Both critiques are minor though. Kaplan verbal seems to rely too heavily on definitions and not enough on sentence structure/context, so don't use it for anything other than vocab recall. I haven't had much experience with Princeton verbal.
Good luck!
ETA: for what it's worth, my verbal and quant score were 80th percentile... well, one was 79th but I can never remember which.

Should hear back sometime next week for one of my schools.
Bleurg... I can handle constant anxiety, but I've had this weird, heavy mix of dread, apathy, and bleak insomnia for the past 24 hrs.
Death to weekends.
 

Should hear back sometime next week for one of my schools.
Bleurg... I can handle constant anxiety, but I've had this weird, heavy mix of dread, apathy, and bleak insomnia for the past 24 hrs.
Death to weekends.
 

This is going to be a quick reply (sorry!) but I can elaborate more if you'd like.
Reading:
1) It's not necessarily about skimming but about reading effectively. There's a chapter in Hacking the Academy about how to read an academic book in an hour. The chapter itself is two pages long so, even if you don't like their method, you won't lose much time reading about it. Another option would be to check out this PDF by Paul Edwards at the University of Michigan. I'm actually assigning it to my students this semester so they can learn more about how to effectively read a book (most of my undergrads have never had to read a book cover to cover before). 
2) For a lot of books, you probably really only need to read the intro, conclusion, and then the intro and conclusion to each specific chapter IF there are parts particularly relevant to your research/interests. Only read the entire book if you really have to. 
3) There's lots of info out there about how to increase your reading speed (e.g., by not reading each word aloud in your head). YMMV but you may want to look into some of these techniques.
Dissertation:
1) The best way to get a sense of what a dissertation should look like is to read some. I recommend reading (okay, well skimming) at least five. You want to read 1-2 written by students of your PhD advisor (regardless of their topic) and another 1-2 recently completed ones from your dept (again, topic is unimportant). This is to get a sense of what your advisor and department are expecting in terms of length and depth. It'll also give you a sense of how people in your program structure their dissertations.
Then, read another 2-3 written on your topic in the last five years. There's two reasons for this. First, a lot of the most recent scholarship can be found in dissertations that haven't yet been turned into books or articles, so you may get a better sense of what is cutting-edge or unknown in your area (and, as a bonus, avoid rehashing something someone else just did in their dissertation*). Second, it'll give you a sense of whether the way people are writing dissertations in your department is what's common for your topic/area. Plus, you know, you'll have a great bibliography to mine.
*The logic here is that you'll end up competing with that person for jobs and to get your book published and they've already got a head start on you.
2) There are a bunch of books on writing your dissertation in 15 minutes a day and that sort of thing. I never read any of them. Instead, I gave myself specific word count targets for each day and made sure that I met them.
3) It's difficult for any of us to tell you how to approach the actual writing because we don't know your typical writing process. Did you write a MA thesis? If so, use the same techniques if those worked for you. Personally, I made a detailed outline of the entire project (really, the Table of Contents), then decided about how long each part needed to be, then decided what to work on. I ended up not writing my chapters in order (that's never been my style so I wrote Ch. 3-5, then ch. 2, then the conclusion, then ch 1) because it made the most sense to me when I was writing it. I think I also ended up shuffling the order of the chapters around at some point.
4) If you want to finish in a year, set up a timeline and work backwards to ensure that you give yourself sufficient time to complete everything. That is, if you have to defend by X date to file, then you want to make sure your final draft is to your committee about a month before that. From there you can figure out what targets you need to hit and by when. 
5) Whether or not you can keep working while various committee members are reviewing chapters is entirely dependent on your committee. No one on either my MA or PhD committees wanted to read anything until I had a full rough draft. That meant that I didn't have to wait for feedback all the time but it also made it nervewracking because there were fewer checks to make sure I was on the right track. So, after your orals, I'd ask your entire committee how they want to proceed in terms of chapters and drafts, as well as what you can expect in terms of turnaround. You'll also want to ask how frequently they want progress reports/updates, if they want the entire committee to reconvene every few months, etc. All of these things vary widely based on who is on one's committee.
Good luck! You can do this!
 

I'm actually mixed about this, at least on the funding side of things in general. I know there's quite a bit of freezing and slashing of fed funds, but I have a suspicion that we're going to see a uptick in contract work overall. Which isn't necessarily bad - for 7.5 yrs, I was contracted through a state school for research at a fed agency and it was amazing - but the downside is more overhead, less employee protections, and greater ability for management abuse. There's a ton of that going on already in a bunch of different sectors, and it'll likely just get worse. But if you're not actually working for an agency, this administration might actually push more funding to non-fed organizations depending on your specific field, sector, and domain. How that'll get spent is another story.
Anyway, Grants.gov is a good source to poke around for fed stuff to monitor the status of grants over the next few months and to compare it with previous years. Mental health is divided into several different agencies depending on your specific field, and those agencies have employees who dole out the contracts and grants (some you wouldn't expect - a lot of I/O psych in FAA, and I think military). I can't remember now if other types of funding are also on that site, but contracts, cooperative agreements, etc. should be at least linked somewhere.

Approach in-class lectures differently than student presentations, especially if you usually over-prepare to ease any public speaking anxiety. A good instructor and a high-achieving glossophobic student have different pragmatic goals, even if they cover the same content with identical slides/materials. Limit your focus on delivering a polished presentation which will likely magnify any minor mistakes and increase anxiety about how the audience is judging you. Otherwise, you'll just exhaust yourself with weekly lecture prep to try to improve a set of evaluations that isn’t really the point anymore. Instead, focus more on developing a lecturing style that fits you, your students, and course context. Allow yourself to mess up and learn to recover, explore different angles, etc.  
Be appropriately relational with your students - brief and informational heads-up and short discussions help avoid disengagement. Learn names when you can. Engage with students who aren’t doing well in class early on while staying firm with policies and expectations. Be aware of attribution biases - try to view a student's poor/mediocre performance as something situational/environmental instead of something lacking in either them or you. Similarly, let students know about your situational/environmental limitations within reason. If your workload is overwhelming during a particular semester, let them know that feedback/grades may be returned a little later than you would like at times. If you’re trying a new activity/assignment, let them know and get feedback.