Author Aliza Lipman is OPEN's Student Liaison and a graduate student in the Portland State University Applied Psychology Program Community Track. Her main research interest is childhood trauma prevention, and her current research examines trauma experienced by juvenile offender populations. In Spring 2021, she led a discussion with OPEN's student members around converting a CV to a resume for program evaluation. 

Photo by Etienne Boulanger on Unsplash

Many graduate students are expected to catalogue their accomplishments in a curriculum vitae (CV) which, in the world of academia, is your ID and passport. For students who intend to work in the applied world, the CV, while expansive, just isn’t what employers are looking for. What employers are looking for is a resume: a document carefully crafted with the listed position and company in mind.

Below is a table showing some of the key differences in goal, content and formatting between a CV and a resume.

Curriculum Vitae	Resume
Gives future employer detailed knowledge of your entire academic career and work history Inclusive of all accomplishments Does not emphasize any particular component Experiences listed in chronological order 2-5 pages in length	Tells future employer the ways in which you are qualified and what sets you apart from other applicants Crafted with specific job type or organization in mind Includes recent or relevant academic history and work experience Only includes accomplishments pertinent to the job Approximately 1 page in length

via GIPHY

But this doesn't mean you have to start from scratch! A CV is a great document to pull from when making a resume. As we said earlier, it includes all academic accomplishments. The craft is knowing what to pull from the CV and what is superfluous information. 

Here are a few tips for making a resume from a CV:

1. Read through the job application for buzzwords and key tasks/experiences they are looking for in an employee. 
   
2. Search for other available materials about the job or the company including websites and social media accounts.
   
3. Scan your CV highlighting or pulling out experiences and skills that are relevant to the job listing and company.
   
4. Check the language in your newly formed resume. Try to use similar language to what is used in the job listing.  
   

Formatting is also important when crafting a resume. Employers expect a resume to:

• Use a small font like 11 or 12 point
• Use the past tense for work completed in the past and present tense for current work
• Avoid first-person when describing actions or accomplishments
• Use spaces and indentations to aid in legibility
• Use italics, bold, and underlining to aid in legibility

via GIPHY

For more information about building a resume from a CV, see the sites listed below:

• Career Profiles: Converting Your CV to a Resume
  
• ICC at UC Davis: Converting a Curriculum Vitae to Resume
  
• UPENN Career Services: Converting Your CV to a Resume

In addition to writing this piece, author Diego Catalán Molina led an OPEN event on 12 May 2021 about data analysis tips and tricks using R. Diego holds a PhD in Human Development at the University of California (UC), Davis, where he studied how, when, and for whom socioemotional school interventions work. He previously worked in Chile as a school counselor at a low income school, an advisor to school counselors and psychologists at network of charter schools, and an evaluator for the national agency that monitors education quality across the country. He has served on the OPEN leadership team since 2020.

Have you started using R and found obstacles to efficiently move from running analyses to reporting results? I know I have...

I started using R around 3 years ago. The first few months, I was frustrated often because I couldn't even read and manipulate data efficiently, when the REAL work was analyzing the data. And when I had analyzed it, I didn't know how to transfer the results efficiently into my documents and presentations.

Here’s what I tried:

• Manually type results into a word document--it’s really easy to make mistakes doing this!
• Copying and pasting the results from the console to a word document or a spreadsheet: This is a terrible strategy because the formatting of the results is not the same once pasted, and it’s really difficult to work this way when you have to re-run your analyses multiple times!

Looking back, I think most of my confusion had to do with me not understanding some of the core ideas behind doing data analysis in R.

Core ideas

After doing research and evaluation in R for almost 3 years, I realized there were a few principles or core ideas behind doing data analysis in R. On May 12th, I shared the following two ideas with OPEN members and friends:

1. Running analyses is like lazy shopping at the grocery store.
   

2. You need to tidy up your stuff to avoid wasting time.
   

Lazy shopping

You don’t realize it in the beginning, but running analyses in R is like throwing all kinds of groceries into a shopping cart.

In R language, your shopping cart is called a list. A list is an object that contains smaller objects within it, so it's just a "container". When you run any analysis, R will fill your list with the results of your analyses (and other stuff too).

If you are running analyses in R, you were probably taught to see the results by using functions like summary. However, this is the most inefficient way to use your results if you want to put them in a presentation or document.

Tidy up your mess

If you want to use your results efficiently, you need to learn how to clean up the mess that is the list in which these results live.

Here are simple steps to use your results more efficiently:

Do you want to learn more about efficient analyses in R and beyond?

Let's chat!