Digression: Never Use Bar Charts

Surprise! There was a third random thing about this plot: the choice to use a bar chart! Bamboozled again! Won’t you ever learn?!

It’s not a popular idea, but bar charts are terrible. They misleadingly show plot graphics (bars) where there is no data — in our case, the data is the department and the admission rate, the latter of which is a single value for each department for men and women.

But the bar covers all rates from 0 to the actual rate. This violates one of the few hard and fast rules of visualization: never visualize data that don’t exist!

Here’s one possible alternative you could use that more accurately draws the eye to the differences in rates by genders:

ggplot(plot_data, 
       aes(
         x = reorder(Dept, 100 - abs(Gender_Difference)), # change the order to the absolute value of gender difference (subtract from 100 so that largest differences are first)
         y = `Percent Admitted`, 
         color = Gender,
         shape = Gender,
         group = Gender_Difference)
       ) +
  geom_line(color = "black", linetype = "dotted") +
  geom_point(size = 3) +
  scale_y_continuous(labels = percent_format(scale = 1), limits = c(0, 100)) +
  scale_color_manual(values = colors[1:2]) +
  labs(x = "Dept") + 
  coord_flip() +
  theme_minimal() +
  theme(
    panel.background = element_rect(fill = NA, color = NA),
    plot.background = element_rect(fill = NA, color = NA),
    legend.background = element_rect(fill = NA, color = NA),
    legend.box.background = element_rect(fill = NA, color = NA)
  )

See the dotted black lines? The longer they are, the larger the difference in admission rates for that department. If viewing the above plot elicits a pleasant massaging feeling in your occipital lobe, that’s normal!

Here’s another alternative:

set.seed(350)
plot_data %>% 
  mutate(
    label = round(`Percent Admitted`, 1),
    label = format(label, nsmall = 1),
    label = paste(label, "%", sep = "")
         ) %>% 
  ggplot(aes(x = Gender, y = `Percent Admitted`, color = Dept)) +
  geom_point(size = 3, shape = "circle", show.legend = FALSE) +
  geom_line(aes(group = Gender_Difference)) +
  geom_label_repel(aes(label = label), label.size = 0, show.legend = FALSE) +
    scale_y_continuous(labels = percent_format(scale = 1), limits = c(0, 100)) +
  scale_color_manual(values = colors) +
  labs(color = "Dept") +
  theme_minimal() +
  theme(
    panel.background = element_rect(fill = NA, color = NA),
    plot.background = element_rect(fill = NA, color = NA),
    legend.background = element_rect(fill = NA, color = NA),
    legend.box.background = element_rect(fill = NA, color = NA)
  )

This one is nice because you don’t have to order the departments to see where the biggest differences are — they are biggest when the slopes are steepest. Credit to this post by Dr. Rebecca Barter for the idea!

Focus Only On What You Can Control

The goal of this guide is to help you focus on what you can control. As I’ve said at the outset, you are probably qualified. The most unfair outcome for you, at least in my opinion, is that your application is disqualified for bullshit that has nothing to do with your actual qualifications. Let’s minimize the bullshit.

Consistent with this philosophy, any therapist will tell you that the key to maintaining well-being during stressful times is to focus only what you can control, and to let go of what you can’t. One common suggestion is to draw a circle, think of some factors that could influence your application outcome, and ask yourself “what could I do about that?” If you can think of a reasonable answer, write that factor inside the circle. If your answer is “I can’t imagine being able to do a damn thing about that”, then write that factor outside the circle.

To help you let go of what you can’t control - even the unjust and ugly factors - I’ve gone ahead and drawn the circles for you. Feel free to explore the graph below by clicking on the circles to preview some of the factors:

CV Factors Relevant to Qualifications

• Research experience: As early as humanly possible, get yourself involved in a research lab. Target the subfield that you want to eventually research as a graduate student. The earlier you start, the more opportunity you will have to rule out subfields you don’t like, as is often the case. Although there are other (and perhaps better) guides out there, here are the most common ways to get involved in research:

  • Become a research assistant. Find a professor who runs a lab at your school, and inquire about becoming their research assistant. Preferably, this professor will do research in an area you are interested in pursuing, though beggars cannot always be choosers.

    • Sometimes, you can get paid as a research assistant. Try for those first!

    • Usually, you can count your research assistantship for course credit. Try that next!

    • Always, you can volunteer as a research assistant. Only do this if you have ruled out getting paid or taking it for course credit. You are not made of money, you poor undergraduate!

    • Regardless, expect to dedicate between 5 to 15 hours per week to your research assistantship.

  • Apply for student research grants. Usually your school will offer grant competitions for undergraduates to fund their own research opportunities. I applied to this one and got it (yes, I used to shave my head, it feels cool and saved me poor undergraduate self $20 a month, I regret nothing!). It was a great experience!

    • The psychwiki has a good resource for finding undergraduate research grants.

  • Carry out a senior research thesis. Sometimes undergraduate programs require this anyway. Good, do it and make sure to get an A. Sometimes senior research theses are optional, or part of an honors course. Opt in if possible.

  • Take an independent research course. Propose your own study and carry it out. Do this in addition to a senior thesis or honors thesis. Usually, this is a bit different from getting research credit as a research assistantship, as you are leading your own research rather than assisting on someone else’s project. More work, but more in-depth experience.

  • Work at research outside of your school. Summer research internships or camps hosted at other schools or through associations, research at hospitals or government agencies, research institutes or think tanks, non-profit or community organizations (I conducted demography research at a homelessness agency for a time), and research internships in the private sector, to name a few examples.

• Research Products: Experience is one thing; outcomes are another. Wherever you can, try to make your hard work count for more than cool stories. Here are some ways to do that:

  • Publish. Before you embark on any of your research, be it a research grant, senior project, honors thesis, or independent research course, ask your professor if they’d be willing to supervise the project through to publication in a peer-reviewed journal. If you are a research assistant, ask if your lab supervisor would let you take enough responsibility to take on a coauthorship. Be explicit on the front end that you expect coauthorship for doing any above-and-beyond work. Also it be known that undergraduate students can get publications in top-tier peer-reviewed journals; I did, and so did the professor I published with, when he was an undergraduate. There are also undergraduate journals, and believe it or not, those count too!

  • Present at conferences. Same as the above; but research conferences don’t always have as high of a bar for acceptance. There are undergraduate research conferences as well, some of which are well-known and respectable in their own right. If you are a research assistant, ask to help with the poster in exchange coauthorship. Also be on the lookout for research showcases at home. I presented at my school’s research showcase twice.

  • Present your research at lab meetings. No matter what capacity you are doing research in, there are usually opportunities to present at lab meetings, be they the meetings held by your professors, lab supervisors, other labs at the school who do similar research, and so on. Sometimes you can present on your contributions to others’ work, such as data analysis you did as a research assistant.

• Awards and Honors: It’s time to become an award hound. I’ll leave you to decide whether actively seeking awards for yourself is virtuous or vicious, but awards can have a big impact, especially if they are related to research.

  • University or departmental awards. If your school or department has research award competitions, apply. These are very often self-nominated; for those awards that aren’t, ask your mentor if they’d be willing to nominate you, or what you might need to do to earn a nomination (if necessary).

  • Association awards. These are offered through professional organizations, like the Association for Psychological Science or Psi Chi undergraduate honors soceity. Actively seek out awards and apply for them. Same advice applies for these as school or departmental awards.

    • The psychwiki has a good resource for finding undergraduate research awards.

  • Dean’s list, “top student” awards, and the like. These don’t hurt either, but don’t break your back over them. Getting into Psi Chi can be good, but it’s not very impressive on its own. But, it is very useful for getting undergraduate research grants and awards.

• Teaching or Tutoring: You will mostly likely be teaching undergraduate students as a PhD student. Although you should prioritize your research, having other experience that demonstrates your competence can go a long way, and showing that you’re an expert among your peers is a great way to do that. Consider, for at least a semester, doing one or more of the following:

  • Teaching assistantship. Are you a stats wiz? Has your professor complimented you on your Research Methods paper? Consider becoming a teaching assistant or apprentice. Often, you can get course credit for teaching assistantships, which usually consist of you working with a professor to carry out class activities, grade (sometimes), implement changes to the curriculum, provide tutoring hours to students outside of class, or lead labs, depending on the needs of you professor.

  • Tutoring. Schools usually have tutoring centers, which you can volunteer at or, occasionally, acquire a part-time job at (i.e., money!). Sometimes work-study programs can include tutoring too. Try to tutor for statistics or research methods rather than content areas like personality or clinical psychology. Furthermore, Psi Chi will often offer tutoring programs in departments where they hold chapters. My undergraduate program did, and still does.

• Professional Experience: This one is a mixed bag, so make sure to take this into consideration before you start spending time adding this to your CV.

  • Counseling or Clinical Experience. Do not do this, for two reasons.

    • One, if you are not going for a clinical PhD, no one will care. Spend the time on research instead.

    • If you are going for a PhD in clinical, no will care - but worse, they may care but not in a good way. While it’s tempting to think that clinical programs or your potential PA would like to see clinical experience. After all, wouldn’t this minimize how much time and effort needed to train you? The truth is, your previous experience will be ignored at best. At worst, the program will have to untrain you in order to instill their own training philosophy. A blank slate is actually preferable to previous experience, because it is less work to train to puppy to do new tricks than an old dog who was taught tricks the old way. Focus on your research instead - you can always be on being expected to hit the ground running on the research front.

• Other Qualifications: Be creative and re-frame non-academic or non-psychology experiences to be relevant to PhD work. Include anything and everything that you could reasonable construe as highlighting a skill or experience that makes you prepared for the work you’ll do as a PhD student. You’ll be surprised at how much of your experience could be presented in this way, without lying. It’s just a matter of thinking in terms of skills, rather than rigid superstitions about which positions “count” and which don’t. Here are some examples:

  • Project management. Have you been in charge of recruiting team members, leading the team, setting agendas, etc? Highlight it. Why? Because you will be in charge leading teams as a PhD student, like your army research assistants, or your coauthors who have designated you the lead author.

  • Software skills. Have you had a job that required database management? Did you recently take a summer crash course on C++? Highlight it, because PhD life will require you to learn new statistical software, and possibly others. I learned python, and fellow PhD students of mine have learned javascript during their studies.

  • Non-academic teaching or training. Have experience teaching or training people? Highlight it. Why? Because you will be teaching, and training, many students and mentees as a PhD student.

  • Other stuff - be creative. I talked to a prospective PhD student who had an M.A. in Film, where he taught acting classes and graded screenplays. He talked about the strategies he learned for managing actors’ egos, and for finessing his feedback on their screenplays. After all, actors are vulnerable when they are on stage, and creative writing is often near and dear to writers’ hearts, and it was important to maximize their receptivity to constructive criticism. He was surprised when I recommended he put this experience on his CV. As a PhD student, you may have to train research assistants to “act”, such as training them as a confederate, or “pretend co-participant”. You will almost certainly be grading papers, or providing feedback on the writing of your research assistants, or editing drafts written by coauthors. For academics and students, writing is near and dear to their hearts too, and it takes finesse to offer constructive criticism in an actually-helpful way. This student’s skills working with actors would make him an above-average teacher and mentor, as he will not have to waste time learning how to provide constructive feedback.

    • Too long/didn’t read: think about your previous experience in terms of how it might translate to you being prepared for life as a PhD student.

CV Factors Irrelevant to Qualifications

Feel free to use my CV as an example format. There are no rules for CV formatting really, but there are definite principles to follow. If I had to do it over again, I would have written a bit less turgidly, gotten rid of the passive voice, moved to a bullet-point format over the paragraph format, and perhaps overall written a bit less or trimmed things down a bit. Most academic CVs will not include descriptions of what was done; for student CVs, I recommend that describe the projects and experiences as long as the content is easy to access (i.e., formatted well). Make sure that your CV is not redundant with your Personal Statement! For example, if you discuss a project at length in your statement, don’t recap it on your CV.

• Formatting. Shouldn’t PAs only consider the content on your CV? Yes, but that is not what is going to happen in real life. In real life, your PA and everyone else reviewing your CV will likely not have time to read every detail. You will therefore want to make your CV as easy to read as possible, and arrange the information in such a way that minimizes the amount of work it takes to see your qualifications clearly. This is the order I recommend:

  • Education section: Include the usual stuff, with the name of your senior or honors research thesis if you have one, and your major GPA followed by overall GPA.

  • Awards/Grants: Don’t be humble, put everything ya got here. If you have more than, say, 4 or 5 grants and 4 or 5 honors, break those up into separate sections (Grants and Scholarships, and Honors and Awards, for example). For any grants or scholarships, list the dollar amount.

  • Publications and Presentations: This is where you’ll list your “research products”. Did you present the same poster at two conferences? List both of them. Have a manuscript under review at a journal? List it, along with the journal name it’s being reviewed at. Have a manuscript in preparation? If you have a copy that you could, hypothetically, provide to someone who might ask for it, then list it, otherwise leave it off. List everything, use APA style. Include links to your work where applicable. Break up sections - “publications and manuscripts”, and “presentations” is a good start.

    • Note: If you have research products in the works, but have not been realized by the time of application, you can still list them if you fully and confidently expect to have them ready by the time you interview (late January is a safe deadline). Have a manuscript that is not ready to be read on the deadline of November 30th, but you are sure that you will have a readable draft by late January? List it.

  • Research Experience, or Research and Related Experience. List your positions in reverse chronological order, and draw attention to your tangible accomplishments and skills. This is where you can do some describing.

  • Teaching Experience, or Teaching and Related Experience. List any positions, along with what you did. Straightforward.

• Prose.

  • When writing, be exhaustive, but brief. A contradiction, you say. There is an art to writing efficiently; I revised my CV about ten times, and trimmed it down from eight pages to three-and-a-half pages. With CVs, the traditional advice is “longer is better”, but this is only true if every bit of writing is content and very little, if any, is inefficient or empty fluff.

  • Adopt a classic style. Now is not the time to overwhelm with jargon, or show off your poetry skills. Some professors will insist that you write in an abstruse, pseudo-sophisticated, jargon-laden manner, as if you were writing an academic manuscript — or at least you might be tempted to think they expect that from you. Regardless of the real or imagined directive to write like an academic, just say “no”: instad, make your content as readable and comprehensible as you can. In fact, make it a pleasure to read. Those reviewing your application sometimes have to read hundreds of applications, meaning you will make make yourself memorable by being one of the few that actually avoided being a drain on their mental (and emotional) energy.

• Maybe leave off signs political or religious affiliation?

  • This one’s controversial. Research shows that, in general, it can hurt your job application if it reveals that you would be in the political minority at that job, regardless of industry or what the political majority happens to be. Academia is no exception; one study found that 38% of social psychologists were at least somewhat inclined to choose a liberal over conservative job candidate, even if the candidates were equally qualified. Another study found a similar percentage, included numerous academic disciplines in their sample, and found that even though 32% of conservatives gave the same rating, all disciplines except one (agriculture, if you would like to know) were overwhelmingly liberal. We also know that there is a sizable body of research showing that academics have prejudices against conservative Christians.

  • Does this mean you should leave off experience working at a church or conservative think tank? I do not have the answer. Would hiding them increase your odds of getting an offer letter? Probably. Is it right that people will judge you based on where you worked rather than on your qualifications alone? No, it’s not right. Would I list my experience working at a church again on my CV if I had to do things over again? No, I would not. Personally, I would prefer to ruthlessly maximize my chances of getting the offer letter. But for some, personal integrity is more important, and that is understandable too.

LOR: Factors Relevant to Qualifications

• Identify writers who can speak to your potential. Early on, you’ll want to identify a few professors that you could work long-term with, in some capacity. If that is not possible, identify professors whom you had good relationships during class with. Cultivate relationships early on so that you can be given opportunities to demonstrate your talents, as well as opportunities to demonstrate your “mentorability”, or extent to which you’re willing to take good advice and apply what you learn. Remember, I had zero social capital, and had to seek it out from scratch. Generally, professors enjoy mentoring highly motivated students - I’m speaking from experience on both ends of that!

• Ask for LORs. Don’t be shy - you have the qualifications, you the potential, you just need to someone to vouch for you!

• Request that letters to touch on important points. If your PhD programs give descriptions of what they like to see in their candidates, make sure your writers have everything they need to speak directly to those qualities. Or if there’s an accomplishment that you are particularly proud of, but your writer doesn’t know about it, ask the writer to mention it in the letter if it seems relevant. Always provide everything your authors need to write a good letter for you, including your CV, additional information about previous research experience or accomplishments, etc.

  • Occasionally, writers will ask you to review the letter for grammar or content. It’s awkward, I know. But do not be afraid to edit their letter! Advocate for yourself!

  • More rarely, writers will ask you to write the letter for them, which they will then sign. More common than you think. Write yourself a damn good letter - they will read it, and make it their job to tone down the bragging! The worst that could happen is they’ll walk it back to a more appropriate level. They’re the expert judge for that, not you, so write the best letter you can and let the experts place the gutter rails where they need to be.

LOR: Factors Irrelevant to Qualifications

There is a lot of leg work the needs to be done before you even ask your professors to write them. If you are not on top of these things, your professors could be irritated, you could get behind, your professors could get behind, and many other unnecessary obstacles, all of which will leave you with an avoidable headache.

• Ask for one more letter than the highest number required among your programs. Meaning that if the highest number of letters any school on your short list asks for is 3, then ask 4 professors to author your letter. The 4th is a backup; see below.

• Turn your LORs in on time. To do so, you’ll need start way earlier than you probably think. Start these steps at the beginning of the Fall semester of your applications, and have them completed before you ask you professors to write and/or submit your letters:

  • Ask your current department what paperwork you need for LORs. When I was an undergraduate, my institution required me to request and complete FERPA forms (Family Educational Rights and Privacy Act) from the Psychology Department in order to get a letter of recommendation. These were needed in order for my writers to legally disclose information about me as a student to other institutions. I don’t know where this applies, but definitely find out and provide these materials with your requests if they turn out to be required.

  • Ask your writers how they would like institutional info: steady stream, or all at once.

  • Provide these to your writers in an organized, bullet-pointed Word doc (or several, depending on writers’ answers to the last bullet point). Alternatively, you can share a Google Sheets spreadsheet:

    • A full list of the names of the programs you will be applying to, with links to the labs or program pages.

    • The addresses, and contact info, of the relevant departments.

    • The names of the individuals to whom to address the letter, where applicable.

    • The names of the PAs on your short list (which I’ll review soon).

    • Deadlines for when letters are due for each institution.

    • Instructions for submitting the letters (e.g., if they should look for an invite link from application).

    • The specialization of the program you are applying to (e.g., “social psychology”)

• Waive your right to view the letters. Application portals will ask if you want to waive your right to view the letters. Do it, or else your letters risk losing credibility.

• Send your formal requests for letters to your authors.

  • Usually, you will invite professors through the school application portals, so make sure to have your professors’ preferred email addresses.

  • Send these by the end of September, at the latest, and give your authors a heads-up that you are about to do so.

  • Note that you can usually skip through the application portal to the part that asks you to submit letters of recommendation. In other words, you can send the invites whenever you want, and they can submit the letters whenever they want, regardless of the status of the rest of your application.

• Periodically confirm that letters have been submitted. Check you application portals to see if they’ve been processed. DO NOT submit your application unless the letters have been processed, if the program has a different indicator. To use my own experience as an example: I submitted an application where the status of one of my letters said “submitted”. But two days later after the deadline had passed, the status changed to “processed”. The fine print of the application read “all materials must be processed by the deadline”. Did my materials get thrown out because they were late? I will never know. Don’t put yourself in that position.
  
  One thing you’ll need to plan for is that it takes authors time to write and submit the letters and to confirm that the letter has been received and processed. If for some reason you find yourself in a situation where one author cannot write your letter, and this puts you short a letter, and you find out say, 3 weeks before the deadline, you now have 3 weeks to ask some other extremely-busy professor to set aside time to write and submit your letters for the n number of schools you are applying to.
  
  I for some reason you do not already know whether your authors have successfully written and submitted your letters by about one month before the deadline, get a “yes or no” confirmation from them that your letters will be submitted no later than one week before the deadlines. If a writer has not submitted the letter by four weeks prior to the application deadline, simple ask:

  • “Thank you again for agreeing to write my letter of recommendation. I don’t know if you realized, but the deadline to submit the letter is one month from now. Based on your schedule, can you confirm that you would be able to submit my letter by [date of one week before the deadline]?”

  If you do not get a response, or fail to get a clear confirmation, then provide a gentle, but firm, ultimatum:

  • “Although I would prefer a letter from you, I would fully understand if this timeline is not compatible with your schedule; if that is the case, will you let me know now so that I may plan accordingly?”

• Granted, this is a rare situation and in most cases and things probably won’t come to this, especially because you already asked for a backup letter. But it’s common enough to talk about here. If you don’t hear back, or the author is being wishy-washy, ask your backup professor if they would be able to write a letter. Apologize for the late request, you can mention that another professor left you in the lurch if you want if you want, and that this is why you are asking at the last minute. But do not mention the other professor by name.

Schedule Your Exam

I highly recommend taking the GRE one year in advance, preferably in say, August of the summer before the summer of your fall application. This way, you spread out your workload. Studying for the GRE is no joke, as you are about to see, and you can’t reasonably do that and write and revise several statements of purpose and write and revise your CV and fill our your applications and…etc. all at the same time!

Before You Start: Some Encouragement

Take a look at the plot above. At the time that I took the GRE, the population average for test-takers for the quantitative section was 152.3, with a standard deviation of 9.12. You’ll see that my first score on the GRE as almost an entire standard deviation less than the average test-taker. On my actual application, I turned in the results from my second test: a 153, just above the average.

Let this serve as empirical evidence that the GRE is not an IQ test, but is a preparation test. In fact, the revised GRE, which has been used since 2011, was purposely designed to have a lower correlation with IQ than the old version. In fact, by my experience, as well as the advice I received from my study preparation materials, the biggest impact on your score is time management during the test. Who knew?

My Strategy for an Improved Score

It is a common misconception that you need a lot of money to do well on the GRE. Kaplan prep courses are about $1,000, which as far as I understand, actually do not make that much of a difference. This is not to say that people who use private prep courses like Kaplan do not score higher on the GRE; they do. But they also have an instructor hounding them with assignments, which makes sure that they study consistently. They also might be so committed to getting a good score that they would spend $1,000 of their hard earned money on a GRE course. These people would probably have gotten high scores anyway, given their apparent obsession. You could pay for a course like that, but…

…I’m calling bullshit on those courses. Your study habits will affect your score, not the course or some teacher. You can do that for very little money, or for free if you’re crafty with the interwebs. Your school library probably has GRE practice materials, or may even offer courses for free or at a reduced price.

You can also buy loads of lessons and practice problems pretty cheaply. At the time of this writing, Manhattan Prep section-based strategy guides are going for $5 a piece on ebay, as is the “5 pound book of GRE practice questions”. Regardless of when you apply, be it the year before or the year of, I recommend dedicating a summer to the GRE. Not an entire summer; think of it as a small part-time job you have that you show up for during a scheduled time five days out of the week.

Statement Factors Relevant to Qualifications

I have broken up the Statements of Purpose section into factors that are relevant to your qualifications and factors irrelevant to your qualifications, both of which are within your control.

You need one two-to-three-page statement of purpose per PA. This statement should have a cohesive narrative, or story, to tell about the development of your research interests and how they might be realized in the lab of the PA to whom it is addressed. In terms of the goals that your statement should accomplish, it should illustrate (through example, not through telling!):

1. Your work ethic.

2. Your curiosity.

3. Your ability to generate, and follow through on, your research ideas.

4. Your interests, and how they tie in with the PA’s.

5. Your ability to write good prose.

Write a rough draft during summer, but leave out point 4 above. The moment you have your short list finalized, make as many copies of this draft as there are PAs on your short list. Adapt each copy to demonstrate how your research interests tie in specifically with each PA’s. Read their recent papers (within last 3 years) and cite them. In my statement, I dedicated the last few lines, or last paragraph in some cases, to tying in my interests to my PAs’. I would recommend making it about a 40/60 split, where 60% of the statement is just you, and 40% is a deliberate effort to sell yourself as a good fit for your PA.

Statement Factors Irrelevant to Qualifications

• Prose. Some will recommend more formal approach; others will recommend more of a “catchy” or story-telling approach. Some people call this “the detective approach”. Ultimately, the approach is less important than the content, and it’s not even close. Success is frequently found both ways. If you do go with the catchy approach, which is admittedly more difficult to pull off (though will go further when pulled off well), only commit to it if you can back it up with a compelling narrative that effectively illustrates the content in the last section. Based on the positive feedback I received from multiple PAs, you might use my statement as an example of the catchy approach. Either way, adopt a classic style, and for the love of god, follow the advice of avoiding bad academic writing.

1. Perhaps include some personal background, but only if it helps to tell your story. Do not overshare. DO NOT OVERSHARE.

   1. Now is NOT the time for “my time spent counseling on the suicide hotline was inspired was inspired by own experience struggling to find meaning in life”. You would not believe how common this is. Do not write like that. If you share anything personal, it should only be done with the explicit purpose of demonstrating points 1 through 5 above, and should be incorporated very carefully and with much professionalism.

2. Perhaps some diversity/advocacy/altruistic type stuff/etc., but only if it helps to illustrate points 1 through 5 above. Even in today’s day and age where everyone wants to hear about diversity, this will still be true. Never be weepy, sentimental, attempt to inspire, or attempt to display moral virtue. You are auditioning to be a competent scientist, even if you would like to help people with your research or see patients eventually. Focus on illustrating points 1 through 5 above and nothing else, and remember that you have the opportunity to address those other aspects in the diversity statement.

Importantly, you will want to write, and revise, and revise, and revise some more until your prose is tight, flowing, logical, and moving. Have your mentor go back and forth on some drafts with you. If they won’t, find another professor who will. Run your draft by the your school’s writing center, but keep in mind that they will not know what to look for in a statement of purpose. Use writing centers for grammar and syntax checks only.

Writing

In all of your writing, I personal prefer and recommend using a classic style. The basic idea with classic style is that you treat the reader as an equal, and present truth to them like an equal, as if engaged in a conversation with a colleague. You write as if you have something you want to show your reader; you are sure it is true and independently verifiable; your reader is just as competent as you are; your writing must get out of the way so that your reader can see the truth. With this mentality, you are trying to get out of the way so that your reader can appreciate your ideas directly rather than going through you to get them.

The best metaphor I’ve heard is this: imagine you and your friend are hiking. You are 20 meters ahead, have reached the top of the hill, and can see down into the valley. Your friend asks, “what do you see down there?” You respond, “There is a small cluster of houses at the base on the mountain at the far side. There is a highway that runs through the middle, which exits from the valley about three miles Northwest. People are riding their bikes and strolling around together, and some are picnicking”.

The response you gave your friend here is classic style. Your friend would see the same thing if she were to catch up and stand next to your side. Had she arrived first, she would have given an identical description to you (pragmatically speaking). You and your friend are equals; you just have a vantage point that she cannot appreciate because she is not standing where you are. Furthermore, the truth is not exclusive to you and your friend; anyone who walks up will see what you see in the valley. Pretty much any nature documentary is written or spoken in classic style.

Tell me, which of these passages do you find more compelling and informative to read?

In the contemporary discourse surrounding epistemological frameworks, it becomes increasingly pertinent to delineate the multifaceted interactions between ontological presuppositions and methodological approaches. The dialectic of constructivist theories vis-à-vis positivist paradigms necessitates a nuanced examination of the interstitial dynamics that underpin theoretical synthesis. Consequently, scholars must engage in rigorous hermeneutic analysis to unravel the complex interplay of these divergent yet interrelated conceptual paradigms.

To understand where knowledge comes from, we need to look at the basic ideas behind different theories of knowledge. Some theories, like constructivism, suggest that knowledge is shaped by our experiences. Others, like positivism, argue that knowledge comes from observable facts. By comparing these ideas against each other, we can better understand how they influence each other and how they help us make sense of the world.

Can you guess which passage is written in classic style? Can you guess which statement of purpose would be more likely to be memorable, enjoyable, and thus more likely to be set aside for consideration? Did it seem like any meaningful information was lost in the second example? (I apologize for typos, and I also want to make clear that I completely made up these examples on the fly. So don’t quote me!).

I’m not expert enough to advise on writing beyond advocating for this general attitude toward writing (so definitely check out this great advice). Just start each attempt at writing by saying it out loud at first. Imagine that the thing you are describing is over the hill, and you are orating to the audience what lies on the other side. Have a conversation with your reader, not a lecture or litmus test.

Completing Your Application

The amount of time it takes to complete an application is longer than you think. One application takes, on average, at least an entire 9-5 day to complete. Filling out these applications, along with writing and revising your statements of purpose, CV, and diversity statements, will basically be a part-time job during your Fall semester, and you should absolutely treat it that way. Every day, schedule a block of time to work on your applications and their materials. Seriously. Start earl, and plan to submit your application two weeks before the deadline to give yourself plenty of time.

Also note that submitting your application is not the end of matters. It takes a few days to process, so do not submit the night of the deadline. Also, after you submit each application, be sure to email each PA you applied to and let them know that you submitted your application, that you thank them for your time and assistance during the application process (regardless of how much they actually helped), and let them know that you look forward to the chance to hear more about their work during an interview.

Interview Conduct

Note that I am shy, so this perspective is written with a bias toward people who will tend to not speak up for themselves.

First, have a 20-30 second “elevator pitch” that cleanly and simply summarizes your research in a way your grandmother could understand. When someone asks, “what are you interested in?”, this is what you will tell them. And believe you me, you will be asked this like a million times so write it and practice it ahead of time. And believe me, if someone wants to hear more about it, they will ask you for details. If they don’t, then you just spared them from hearing a bunch of crap they don’t want to hear but hey, now they know what you’re into.

The interview is not rocket science. You’ll want to overthink it - don’t! Be curious, and speak up when you have a thought you think is interesting and that you’d like to see what people think about it. When people ask you a question about yourself, just tell them! Academics are nerds, and many are awkward, so do not necessarily expect that they will always be good at interviewing you. This no doubt hurts some people’s pride but academia definitely has its own niche culture and anyone who says otherwise is probably up to something.

Importantly, you will want to use the interview as an opportunity to make sure that you and the PA are a good fit, temperamentally speaking. If you be yourself, you will get a more accurate picture of what your interactions with the PA will generally be like - and same for them. It’s useful for both of you to just be honest and yourselves - but professional too!

With that said, let me be clear about what I mean by “be yourself”. What I don’t mean is “be unprofessional”. You definitely want to have a professional persona of sorts, which is somewhat an extension of your elevator pitch. This does not mean “be fake”. I personally believe that people have multiple genuine sides to them, and your professional persona - which you will partly discover over time - will probably become a favorite aspect of yourself if you do this whole academia thing right. But practice developing a poise and response to the professional atmosphere - even though career centers usually emphasize the private sector, sign up for some practice interviews. You’ll learn about yourself.

In general, let me give you an important piece of advice that, for some reason, is controversial (?): be likable. Remember, this PA will have to spend five to six years with you, and believe it or not, PhD students can be insufferable to work with - that road goes both ways. Even if it has nothing to do with your qualifications, fair or unfair, PAs will judge you based on abstract intuitions about intellectual fit, personality, lab culture, and general simpatico-ness. So no posturing, no trying to outsmart the next person, no preaching at people, or whatever. You know the drill, but it’s worth reminding about.

Lastly, your competition will be fierce, and will be hanging out with you while you all try to pretend like the situation is not what it is. This will make you nervous. Do not let that tempt you into feeling like you need to be posturing all the time to prove how smart you are. Seriously, nobody likes it, and you will not want to work for 5-6 years alongside those who do. Your PA is already impressed with you - that’s why you are there. The interview is only about assessing fit - not skill, qualifications, but fit.

In general, follow this advice:

• Be confident in sharing your ideas.

• When you do share your ideas, it should come from a place of:

  • genuine passion for the subject

  • eagerness to share with others

  • an enthusiasm for hearing what other people have to say about your ideas, even if they might disagree.

• Ask questions when you are curious.

• Listen to other people, and try to show genuine curiosity about what they are saying.

• Be familiar with a few recent papers of each interviewee, least of not which you primary and secondary PAs.

  • Ask questions about their work - where you going next? What do you make of this other paper?

  • But don’t try too hard to feign interest. It’s OK if you’re not interested - just provide your platitudes and get on with your life.

• Usually, there will be a round an interviewing, often taking the form of a speed-dating like thing. At the end of these interviews, have an interesting question planned before you enter.

  • It should not be “will I have to teach” “Will I get health insurance” “how much will I get paid” etc. Don’t do that during the interview, do that during the off times or during your time talking to e.g., the students.

  • One good question to end with is: “is there a question that you think I should have asked, but did not ask?” Every person I’ve asked this has given a thoughtful response, and I usually learned a lot from. It’s a hit!

Vetting Your PAs

The first thing I’ll say is that if your potential PA seems to tolerate, encourage, or god-forbid enjoy it when students do that posturing crap, let that be a red flag. This is a very real thing and a sign of a toxic environment. You want a supportive environment that encourages curiosity, professionalism, rigor, and an appreciation toward the competitiveness of the field (i.e., job market), but not one that supports competition among students or between students and advisors. That is a real thing indeed, more common than you think, and something you will want to keep an eye out for. Some other advice:

• Check the PA’s CV: does it consist of all first-authored papers? If so, consider removing those PAs from your short list. You want your PA to be a good mentor that helps you develop ideas, not someone who is going to use you as free labor on their own work while taking all of the credit. Job applications will want evidence of first-authored papers, so make sure you will be allowed to get them.

• Ask good questions:

  • What direction is their research heading in? Do they want to expand the scope of their lab? Or are they doubling down on their current research agenda? Do they want students who will bring fresh ideas or contribute something new? Or do they want someone to fall in line and facilitate what everyone is already working on? What is there funding situation - did they just get a 5-year, multi-million dollar grant to study X? If you are not interested in X, that could be a problem for you!

  • What is their mentoring philosophy? Or more precisely, what do they expect in their graduate students? Consider whether those expectations align with your personality, expectations, and goals.

  • Where are the previous students now? This will tell you two things: one, if students are getting good jobs, and two, if the students are actually finishing their PhD. You cannot ask the second one directly, because that will be like asking “are you a good mentor?” They are not going to volunteer the fact that none of their students ever tolerate them long enough to finish, but you can find out in other ways.

  • What is the lab culture like? Do people tend to collaborate? Do they have shared offices? Do they socialize outside of work together? These would be good signs. Be on the lookout for cutthroat type atmospheres, a high tolerance and responsiveness to pretentious posturing, or argumentative or defensive attitude.

Asking these same questions to the PA’s students. They will be more honest with you, but probably not completely honest seeings how they are often recruited to help judge you. They even rate you on forms! Rest assured, the students hate doing it. But you can get more accurate indicators from them about the PA’s work habits, personality, mentoring style and expectations, and ethics (yes, be on the lookout for ethical concerns! They are more common than you think).

Updating Google Drive File

Say you messed up the first draft, or have written some revisions yourself before your collaborators got a chance to start revising on Google Drive. Or whatever. The point is, you want to throw some more recent changes up to an extant Google Doc, which will add the changes while preserving anything that did not change. Easy, just use the code below. Beware: Google Doc’s track changes will not pick this up.

# to update an existing google drive file
trackdown::update_rmd(
    # leave out file extension (i.e., no ".Rmd" at the end)
    file = "name-of-your-R-Markdown-file", 
    gfile = "name-your-file-will-have-on-Google-Drive"
    ) 

Sending the File To A Particular Location in Google Drive

Simple: all you need to do is add the path = argument in upload_rmd():

# to send to google drive
trackdown::upload_rmd(
    # leave out file extension (i.e., no ".Rmd" at the end)
    file = "name-of-your-R-Markdown-file", 
    gfile = "name-your-file-will-have-on-Google-Drive",
    path = "blah/blah-blah/name-your-file-will-have-on-Google-Drive"
    )

View Contents of Google Drive in R Console

Easy. It even displays in a tidy tibble. Friggin sweet, right? Just use:

# view contents of google drive in a tibble
# "n = n" limits the output to the first n files it finds
# "pattern = 'pattern'" returns files with the name specified as the pattern
googledrive::drive_find()

Getting Crosstabs

fisher.results$crosstab.all %>% 
  # formatting for blog :)
  huxtable_print(add_colnames = FALSE) %>% 
  insert_row(after = 0, NA, rep("cyl", 3), NA) %>% 
  merge_cells(row = 1, col = 2:4) %>% 
  set_align(row = 1, col = 2:4, value = "center") %>% 
  set_bottom_border(row = 1, col = 3)

Table 7 shows the cross-tabulation between transmission type and number of cylinders, with percentages and counts (the latter in parentheses). You also get row and column totals, along with the sample total in the right-most bottom corner. You also get two other cross-tabulation, one with percentages calculated relative the row total; the other with percentages calculated relative to the column total. Here’s an example with row totals:

fisher.results$crosstab.row %>% 
  # formatting for blog :)
  huxtable_print(add_colnames = FALSE) %>% 
  insert_row(after = 0, NA, rep("cyl", 3), NA) %>% 
  merge_cells(row = 1, col = 2:4) %>% 
  set_align(row = 1, col = 2:4, value = "center") %>% 
  set_bottom_border(row = 1, col = 3)

Ah - with the row-wise percentages, you can see that the likely driver of statistical significance here is the inverse relationship between transmission type and cylinder. More than half of automatics have 8 cylinders, while more than half of manuals have only 4 cylinders. Looks like an interaction effect!

Default T-Test in R

The t.test() function, which is built in to base-R along with other mainstream tests, has messy output. It also doesn’t provide the mean difference, the standard error of the difference, or Cohen’s . As an example of its use, I’ll conduct a -test to see whether automatic or manual transmissions are associated with greater miles per gallon. This data can be found in the mtcars dataset (from the datasets package, also available in base-R):

# t.test(dv ~ iv, data)
# am = automatic or manual
# mpg = miles per gallon
t.test(mpg ~ am, mtcars)

    Welch Two Sample t-test

data:  mpg by am
t = -3.7671, df = 18.332, p-value = 0.001374
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -11.280194  -3.209684
sample estimates:
mean in group 0 mean in group 1 
       17.14737        24.39231 

In this dataset, vehicles with automatic transmissions are coded as 0, and manuals as 1. The dependent variable is miles per gallon. The mean miles per gallon for vehicles with automatic transmissions (17.15) is quite a bit lower than for vehicles with manual transmissions (24.39). This difference is statistically significant, (that’s less than an alpha of .05), meaning the means are different enough that we might interested in investigating a bit further. At this point, you’d usually calculate Cohen’s .

T-Test Using my.t()

my.t(mtcars, iv = "am", dv = "mpg") %>% 
  huxtable_print() %>% 
  set_align(everywhere, value = "center") 

The output in Table 1 above provides all of the relevant statistics, clearly labeled, with Cohen’s included, and in a tidy format that can easily be exported to a .csv file, copied-and-pasted into Word, or printed within an R Markdown as a table as I have done here (using the huxtable package).

Output Formatting

Currently the output is in wide format, meaning that each statistic is its own column with the values for those statistics contained in the table row. If you prefer a different format, I have also added the long = argument that can optionally display output in long format, with all of the values in a single column with the statistic indicated by the row:

my.t(mtcars, iv = "am", dv = "mpg", long = TRUE) %>% 
  huxtable_print() %>% 
  set_align(col = 1, value = "left") %>% 
  set_align(col = 2, value = "center")

Personally, I like the long format, because usually I am running more than one -test. For example, say that in my car-shopping, miles per gallon was one of just several criteria I were using to decide what kind of car to buy. In addition to miles per gallon, I might compare automatic vs manual transmissions in their level of displacement, which roughly describes “how fast a vehicle gets up and goes” (my dad built engines for 40 years, so I hope he will forgive me if this description is less than accurate). Displacement is measured in cubic inches. Below is a table with the output of both tests side-by-side:

# conduct the tests
mpg.test <- my.t(mtcars, iv = "am", dv = "mpg", long = TRUE)
disp.test <- my.t(mtcars, iv = "am", dv = "disp", long = TRUE)
# relabel the columns with the name of the dv
mpg.test <- mpg.test %>% rename(MPG = Value)
disp.test <- disp.test %>% rename("Disp." = Value)
# combine into one dataframe
my.results <- left_join(mpg.test, disp.test, by = "Statistic")
# print the table
my.results %>% 
  huxtable_print()  %>% 
  set_align(col = 1, value = "left") %>% 
  set_align(col = 2, value = "center")

As shown in Table 3, the mean difference in displacement between automatics and manuals is 146.85, meaning that automatic transmissions are associated with an extra 146.85 cubic inches of displacement on average. So while you might lose ~7 miles to the gallon by going with an automatic transmission, you can also expect to gain ~147 cubic inches of displacement.

OK, but what the heck does that mean? Is that an impressive number? It’s hard to tell for those who don’t spend time dealing with vehicle displacement. At least with miles per gallon, I can readily discern about how much money I would save if my vehicle got an extra 7 miles to the gallon. Displacement is a black box to me - I can’t tell if that mean difference in displacement is meaningful or not, especially not compared to how meaningful the difference in miles per gallon was.

In Table 3, the Cohen’s statistics for miles per gallon and displacement are conveniently side-by-side. Both are very large effect sizes - quite a bit larger than 0.8, the rule of thumb for a large. Although the sign for displacement is negative (indicating that manual transmissions are associated with lower displacement than automatics), the absolute value is very close to that of miles per gallon. It seems like whatever gas I’d save with a manual transmission, I’d lose an equally-meaningful amount of displacement per cubic inch. That is, with Cohen’s , I can see that the magnitude of the differences between between automatic and manual transmissions (effect sizes of 1.53 and -1.49, respectively) are pretty similar, even though the mean differences themselves (7.24 miles per gallon and -146.85 cubic inches of displacement, respectively) are not comparable numbers.

Of course, miles per gallon and displacement might be too conceptually different to be compared meaningfully without added context. These tests would be most helpful if you also knew what adding 146.85 of displacement per cubic inch would feel like in terms of pushing your back against the seat as you accelerate in your new car. Maybe you would need, say, twice as much displacement (or 293.7 cubic inches) in order for a gain in displacement to be noticeable. But in the absence of better information, it’s nice to know for the sake of car shopping that the magnitude of the difference between the groups is basically the same for miles per gallon and displacement.

Paired Samples T-Tests

library(datasets)
anxiety <- ToothGrowth %>% 
  mutate(time.point = recode(supp, "OJ" = "before", "VC" = "after")) %>% 
  rename(anxiety = len)
levels(anxiety$time.point) <- c("before", "after")

my.t(anxiety, iv = "time.point", dv = "anxiety", paired = TRUE) %>% 
  huxtable_print() %>%  
  set_align(everywhere, value = "center") 

The function can handle repeated-measures data. For example, say we had data from a study that looked to see whether anxiety decreases following a round of exercise. I have two columns in my dataset: anxiety score, and time.point (before or after exercise). This would call for a paired samples t-test.

All you have to do is tack on the paired = TRUE argument to my.t:

Because the after group had a lower anxiety score than the before group, the mean difference and Cohen’s are positive. The reason is that the default behavior of the function is to subtract in the order of the level of the dv, which in this case is:

levels(anxiety$time.point)

[1] "before" "after" 

In this case, the “before” time point is the first level, so the mean difference and Cohen’s is based on mean.before - mean.after. But this is an odd order if you’re trying to interpret the mean difference as the mean change in anxiety over time. If thinking that way, the mean difference is really the mean change, which was a decrease in anxiety, which means the mean difference should be negative. Just change the order of the levels:

# change order of levels
anxiety$time.point <- factor(anxiety$time.point, levels = c("after", "before"))
# re-run t-test
my.t(anxiety, iv = "time.point", dv = "anxiety", paired = TRUE) %>% 
  huxtable_print()  %>% 
  set_align(everywhere, value = "left") 

The mean difference and Cohen’s are now negative to reflect that anxiety decreased after exercise.

Mentorship Beyond the Classroom

Quantitative Reasoning Fellowship (Fall 2021 - Present)

At the CUNY School for Labor and Urban Studies Learning Center.

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Brooklyn College

Mentorship Beyond the Classroom

My role: provide mentorship outside of the classroom including training and supervising research assistants, conducting weekly or bi-weekly journal clubs with research assistants, meeting with students during office hours or after class, advising students in one-on-one meetings, or responding to emails seeking advice. About 50% of my student mentorship has been advising students on applying to graduate school (four so far have been accepted into graduate programs in psychology and a related field).

Quantitative Support at the SLU Learning Center

My role: build and sustain an academic support system for students and faculty, specifically with the aim of facilitating quantitative pedagogy through one-on-one student support, the design and administration of quantitative support workshops, and in-class visitations and tutorials.

Since Summer 2020, I have been carrying out an appointment as a Quantitative Reasoning Fellow at the CUNY School of Labor and Urban Studies Learning Center.

In terms of what I actually do, this position is a lot like supervising a research methods or senior project course, only less grading! Most of the degrees at SLU require a senior capstone project, and about 50-60% of the visits are with respect the student’s capstone project. During our sessions, we work together to devise concrete strategies for acquiring data, analyzing it, and reporting it. Students usually consult with me several times throughout the semester across various stages of their projects.

Since my first year to date, I have provided over 100 hours of one-on-one consultation. The projects students come to me span a surprisingly wide range of disciplines and methods, ranging from economics to sociology to psychology to public health. I’ve consulted on quantitative analyses of economic and other public data; survey and prevalence data; and on analysis of qualitative data (e.g., case studies, thematic content analysis).

I have also developed and administered several workshops designed to guide students on different aspects of the empirical research process. So far, I have designed and conducted four 75-minute workshops designed to help students refine and concretize different stages of the research process: refining research questions into testable quantitative hypotheses, operationalizing variables and ensuring sound measurement and assessment; constructing a data analysis plan, and analyzing data with jamovi using a codebook. At the end of each workshop, students received fillable PDF documents that can be used on their own projects. These guides are designed to systematize those “soft”-skill aspects of the research process that are often mysterious and intractable for new researchers. They are meant to be self-contained versions of my workshops and are works in progress. They are slightly geared toward the content area of labor and urban studies, but anyone who may find them useful can access them here:

• Refining (Operationalizing) Your Research Question
• Measuring Your Variables and Acquiring Data
• Planning Your Data Analysis
• Analyzing Your Data With Jamovi (with GIFs!)

Brooklyn College Center for Achievement in Science Education (CASE)

My role: lead 45 hours of instruction in statistical programming in R, which provides core knowledge of the R programming language, practice in the a tidy approach to data manipulation, and application of the statistical knowledge that students gained through other aspects of the workshop within R.

In early Spring of 2021, I reached out to the leadership at the Brooklyn College Center for Achievement in Science Education (CASE) to ask how I might contribute to Brooklyn College’s diversity initiatives. Later in the year, I was invited to join as a workshop leader for the SURGE program, an NIH-funded academic support program designed to equip Brooklyn College freshman from underrepresented backgrounds with the kinds of scientific skills that make for strong PhD candidates.

The general idea is that if you want to diversify the PhD pool, you have to catch students early, give them a sense of belongingness in the scientific community, spur their curiosity toward the science, and most importantly provide the opportunity to experience life as a scientist. Students should get these kinds of exposure early enough to either a) run for the hills before they spend more time than they need to on something they don’t like, or b) get started on the preliminaries of pursuing graduate school early to maximize preparedness. The idea is to diversify through recruitment and to provide access to important skills that more advantaged students tend to already have easier access to (on average).

While there is a lot that a student can doon their own to prepare themselves - indeed, I have compiled quite a large list myself - the fact of the matter is that many students simply do not have equal opportunity. This program addresses that.

As a guest speaker in the workshop once put it:

Coding is freedom, independence, and power.

Lecturer On Record, Graduate Course, Special Topics in Experimental Psychology (Statistical Coding with R)

My role: instruct students in the Master’s in Experimental Psychology program at Brooklyn College in the use of R, covering base R to project management using RStudio projects, tidyverse principles including dplyr and ggplot2, using R Markdown to render attractive summary reports, and communicating statistical results to both experts and (hypothetical) non-expert stakeholders in an accessible and responsible way.

I was invited to teach this course literally one week before the first class after the sudden departure of the previous instructor. I created the coursework and in-class group projects on the fly.

• The R Markdown File
• The Rendered PDF

Lab Instructor, Statistical Methods in Psychological Research, Brooklyn College

Fall 2018, Spring 2019, Summer 2019

My role: instruct two statistics lab sections per semester. Topics covered included introductions to descriptive statistics, probability theory, prediction, ordinary least squares, between- and within-subject ANOVA models, statistical inference, consumption of statistical information, plotting, using Excel and SPSS statistical software, and principles of plotting.