I check email a lot – more than I should I am sure. I have colleagues who are skilled at limiting their email checking – only before noon. Only after noon. Never after 9:00 PM. Not on weekends. I fail on all of these accounts, even when I try. In the past, I never, across 365.25 days a year, took a break from email for more than a few hours.
 
What I have done for the past 20+ years, though, is use my vacation message. When I went out of town, whether for work or for a conference (oops, I meant work or vacation), I put my vacation message on. I love that satisfying moment of setting it up. I find the vacation message very liberating, because I can choose whether to reply to someone – they know I am away and so it lowers the expectation of an immediate response. I particularly love that at UConn, if someone emails me from within UConn using their UConn account in Outlook, once they start the email they can see I have a vacation message, and they are, I believe, less likely to send their message. But even when I was at Penn State, I would often, while away, receive messages and then a follow up, never mind, I see you’re away, and I figured it out. Basically, I love the moment I set that vacation message and the corresponding decreasing sense of responsibility to swiftly respond to each message.
 
In the past though, I always continued to check email across every break, even if my vacation message was on. I often didn’t reply, but sometimes I did if it seemed important, or quick. But more critically, if it was an annoying email, or about a problem, even if I didn’t reply, it was in my head. I would be thinking about it, or feeling an increase in stress levels, because that message existed.
 
The summer after my first year as department head, my husband and I took a 4-night vacation to Bermuda to celebrate our 15-year anniversary. It was our first real trip without the kids since they were born. And after much deliberating and stressing about it, I decided not only to turn on the vacation message, but also not to check my work email. I still checked my Gmail account, and so people in the office knew if it was an emergency they could contact me. My vacation message explained I wouldn’t be checking email, and whom to contact instead. I moved my work email icon on my phone off of the first screen so it wasn’t always in front of me. And, for about 5 days, I never checked my work email. Seriously, would you want to be checking work email while here?:

Guess what? Everyone survived. Nothing came up that couldn’t wait. And, when I returned I felt a lot more rested and ready to tackle the email and work in general.
 
Last summer our family went to Spain for about a week, and I did the same thing. Again, nothing went terribly wrong, no one ever had to contact me there, and I dealt with everything when I returned. Can’t really tell in the photo, but I was rather relaxed:

And most recently, for about 9 days in June, my family was in the UK, and I put up this vacation message:

Thank you for your message. I am out of the office and will be doing my best to recharge before returning to the office on June 26th. During this time, I will be using all of my will power not to check my email. If this is an emergency, please contact one of the following:

[DETAILS]

Otherwise, I will certainly respond to you as soon as I can when I return.

In the past, I have used variants of this message, with lines such as “I hope that you also find times to disconnect and recharge this summer.” This trip was a bit longer, but again, nothing burned down in my absence. And, with jet lag, I was up at 5:00 AM my first morning back and had sorted through all of the email by about 7:00 AM, even though of course I didn’t respond to everything in 2 hours. Instead of checking email, I was taking time to really appreciate the scenery, like at this moment after climbing 212 stairs in Bath Abbey (thank goodness for all of my stairs walking at work):

If you haven’t ever really, truly disconnected from your email, I highly recommend you find a time to do so this summer. Let me know if you find it as freeing as I do. 
  
“The joy of the vacation message first appeared on Eva Lefkowitz’s blog on July 2, 2019.”

If I convinced you that grant writing matters for many different post-PhD jobs, you may be wondering how in the world you can get grant funding. Trying to get external funding can be a daunting task, particularly for a new faculty member who has never pursued such funding before, and is juggling research, teaching, and service. Even knowing where to apply for funding can be confusing at the start. Most people know about NIH as a potential funding source, but there are many other possible sources that people may not as quickly consider. I wanted to review these sources today – and also discuss the differences between grants and contracts.
 
These sources apply both to faculty, and to research associates in a range of different positions discussed in my recent post. 

• Government agencies: These are often the ones that people think of when they think about pursuing external grants, and include some of the same ones you might pursue during graduate school or for a post doc, such as NIH, NSF, IES, NIJ, CDC, ACYF, USDA, FDA, DoD, and military branches.  One challenge with funding from these federal agencies is they sometimes have restrictions on citizenship status.
• State agencies: obviously which state agencies fund research varies by the state you live in. Several of my UConn HDFS colleagues have funding from CT state agencies, and it was not as frequent among my Penn State colleagues. I don’t know if this difference indicates different opportunities in the states, or different preferences among the faculty. But, sources in CT, as an example, include:
  • Department of Administrative Services/The Office for Workforce Competitiveness
  • Department of Children & Families
  • Department of Economic and Community Affairs
  • Department of Education and Education Connection
  • Department of Mental Health and Addiction Services
  • Department of Social Services
  • Judicial Branch
  • Office of Policy and Management
• Foundations and agencies:  Funding from foundations and agencies can be very different from funding from government, and in particular, federal agencies. Federal agencies almost always have specific forms to complete and specific deadlines for when you apply. Some foundations have similar procedures, but others work more one-on-one with potential grantees. More so than with federal agencies, knowing someone personally can often help with foundation funding. Sometimes, you cannot apply for foundation funding unless invited. Here are some examples of foundations that are often relevant to HDFS faculty and alumni:
  • American Cancer Association
  • American Heart Association
  • American Psychological Foundation
  • Bill & Melinda Gates Foundation
  • Foundation for Child Development
  • Kaiser Family Foundation
  • Robert Wood Johnson Foundation
  • Spencer Foundation
  • Social Science Research Council
  • Society for the Psychological Study of Social Issues
  • Templeton Foundation
  • WT Grant Foundation
• Community-based agencies: These tend to be smaller agencies than the foundations listed above, and are often doing more direct translational and applied research than theory testing. Often they will partner with local researchers to address a very specific question such as to assess efficacy of a particular intervention, to do a needs assessment, or to develop a new prevention program.
• Industry funding: Funding from companies/corporations is quite common in some areas of academia, such as in engineering. It tends to be less common in HDFS and other social/behavioral science programs. However, there are times when HDFS faculty may receive industry funding, for instance, from educational materials companies, from companies that develop health-related apps, or from food industries for nutrition researchers.

 
What’s the difference between a grant and a contract? It has taken me some time to determine the difference, but I found this chart from University of Pittsburgh very helpful:

[Source: http://www.research.pitt.edu/fcs-basics-federal-contracting#GrantvsContract]
 
In summary, grants are often investigator-initiated – you develop the research question. Contracts are often agency-initiated – they ask you to answer a specific question they want addressed, such as the efficacy of an existing program, or what are the effects of substance use among military personnel on their families. You have more flexibility and freedom with grants, both in terms of what you pursue, but also the ability to make changes (e.g., new constructs, new measures), less frequent reporting, and fewer restrictions on when you release your findings.
  
“Common sources of external funding for faculty/research associates first appeared on Eva Lefkowitz’s blog on June 27, 2019.”

In my professional development seminar, we have a week on grant writing. Of course, the topic of grant writing could be (and frequently is) a course in itself, so what I cover in 2.5 hours is really only an overview and primer on thinking about grants.
 
Research grants are not only relevant to faculty; they might fit into your career at several different stages.

[I got really excited about PowerPoint’s SmartArt feature this past semester]
 
In terms of graduate students, there are a range of grants/fellowships that grad students can apply for, which I’ve covered in an earlier post, though now I have a cool new graphic.

There are also different types of postdoctoral funding, which I also covered in an earlier post.
 
In terms of grants that one might apply for at the faculty/research associate level, there are a range of sources one might pursue for grant funding. I am going to write about this topic separately, soon.
 
First, though, I want to discuss different kinds of PhD-level jobs where either grant writing is part of your expected job responsibilities, or the skills you develop during grant writing are valuable as transferable skills for that position.
 

• Tenure-track faculty positions: Probably the most obvious. If you are in a tenure track position, particularly at an R1 university, there is usually an expectation that you will pursue external funding to support your research. At other universities, depending on their research-focus and the type of department you are in, there may or may not be such an expectation. If you are at a very teaching-oriented university, you may never have to pursue external funding. But keep in mind, often there are other reasons you may need to write proposals, such as applying for a Fulbright, or even applying internally for research leave. Having training/experience in grant writing may prepare you for these tasks.
• Faculty in medical schools/soft money faculty: Many faculty at medical schools are expected to bring in their own salaries. They can do so through teaching, research grants (whether their own or time on others’ research grants), and/or clinical hours. Grant writing is an integral part of such faculty members’ positions. I know people who submit multiple proposals to every NIH funding cycle.
•  University research positions: University-based research associates and similar non-faculty positions at universities are also often on soft money. They have to bring in their own salary through their own grants or by serving as an investigator on other people’s grants. Grant writing is a critical part of such positions.
• Research institute: Individuals who work as researchers at research institutes often pursue funding to support their salary/time. It will depend on the position, and depend on the institute, but grant writing is often a large proportion of such positions.
• Government and other funding agencies: If you work at NIH or a research foundation that provides grants to others, you may not be pursuing your own external funding. However, positions at such agencies usually involve being on the other end of the process: writing calls for proposals, giving advice to investigators, reviewing proposals, and managing external research grants. Thus, having grant writing experience can be really valuable for obtaining such a position.
• Non-profit organizations/NGO’s: If you work for a non-profit organization, chances are some of their funding comes from pursuing external grants or contracts. Grant writing skills are very valuable for obtaining such a job and for succeeding once you are there.
• Industry-based careers: If you have a job at a for-profit corporation, you may not need to write grant proposals for external funding. However, a student in my seminar this past semester pointed out that often in industry, you need to write proposals to supervisors for ideas you have for projects – you are essentially asking for internal funding for your internal research or idea. Grant writing skills and experience can well prepare you for such job responsibilities.

 
I won’t argue that every job post-PhD requires grant writing skills. However, many different careers benefit from such skills, so getting these experiences early on can be highly marketable.  
  
“Jobs where grant-writing matters first appeared on Eva Lefkowitz’s blog on June 25, 2019.”

The last two academic years I taught a large intro course, with 300 and 350 students. The last time I taught a course with 200 students was my first year as an assistant professor – 1998. The last time I had 100 students was 2000. So, I had some new learning to do about how to handle large classes, and also, how to teach large classes in 2019 (well, technically it was 2018).
 
One large management issue with 350 students is emails. I was fortunate to have three TAs each time I taught, so they honestly dealt with the majority of the emails. Even if students emailed me, I would skim the email and assuming it was one that should go to the TA, forward it to that student’s TA (they each had part of the alphabet). How should students know if they should email me or the TA? It’s in the syllabus, of course:

This information was my attempt to decrease overall emails and make it as easy as possible for students to know whom to email and when. It’s a challenge in a large course to appear both approachable when needed and not standoffish, but also not have every student email weekly with questions they could answer on their own. Despite these attempts, I received emails from students every single day, at least 90% of the time about topics they should have emailed their TAs (or no one) about. The TAs became very good at screenshotting the syllabus and sending it to students in their replies.
 
Unfortunately, a clear syllabus is not very useful unless students read it. And, it has become very challenging to get students to read the syllabus. There is little incentive, as far as I can tell, especially given how little effort it takes to email the instructor/TA with a question rather than look for the answer in the syllabus.
 
I tried to keep my syllabus as short as possible – it is five pages. Perhaps that seems long, and I have read about the one page syllabus, but note that the one-page syllabus usually has a number of appendices or addendums, at which point you are asking students to read multiple documents. I’d rather have it all in one place. I use bullet points and tables. And multiple colors.
 
Yes, I have a syllabus quiz. It is due one day after the end of add/drop period so that everyone in the course can take it. It is worth points toward their final grade. It is online so they can take it at home, with the syllabus in front of them. They can take it as many times as they would like until they get a score they are satisfied with. Most students did eventually get 100% (84% of them). But 4% never took it, and 8% scored 83% or lower, even with retakes.

The most recent time I taught the course, I also put an Easter egg in my syllabus. Any guesses on what percent of the class did the extra credit based on the Easter egg in the syllabus? I’ll wait.

11%. You might wonder, why would I reveal my Easter egg in a blog post? I figure if the students didn’t find it in the actual syllabus for the course they took, they are not going to find it buried in a blog post.
 
Want to see my whole syllabus? You can find it here.

Normally my blog posts provide advice for academia. I’m not sure this post could serve as best practices for getting students to read the syllabus, though, because, as you can see, I have not been particularly successful. I would love to hear what you have done to get students in large classes (any classes?) to read your syllabus.
  
“It’s in the syllabus  first appeared on Eva Lefkowitz’s blog on June 20, 2019.”

I am a huge fabric poster convert, and my goal is to convince everyone that their lives will be better if they use fabric posters. I do not have any investments in fabric poster companies, nor do I have affiliate links – I just think you will be happier if you too convert to fabric posters. Here are, in my opinion, the benefits of fabric posters:

• Goodbye poster tubes. No one enjoys carrying a poster tube through the airport, right? Or squeezing it into the overhead compartment? Or worrying that you will forget it on the plane? The only thing I will miss about poster tubes is easily being able to identify in the airport and at baggage claim who else is going to the same conference.
• Fold it and go. Perhaps related to the prior point, you can literally take a fabric poster, fold it, and put it in your carryon. I mean, you could put it in your checked bag too, but if they lose your checked bag, they lose your poster.
• Don’t worry about bending it. There is just no worry in your office or in the hotel room or anywhere else that someone will accidentally step on your poster and bend it. It can’t really get hurt.
• Iron if needed. If you really beat it up somehow, or you want zero creases, almost every hotel has an iron, and you can iron it.
• Reusable. It is much easier to save and reuse a fabric poster, if, for instance, you present it at your university after presenting it at your national conference.
• Cost. Hardest for me to believe was that fabric posters do not cost more than paper posters. The last poster – regular size – I ordered cost $16.20. Yes, I paid extra for expedited shipping, but that’s on me for submitting it last minute.
• Conversation starter. If most people are still using paper at the next conference you attend, lots of people will want to touch and ooh and aah over your fabric poster, and find out how you made it and how much it cost. Trust me.

Doubles as a blanket. If you get stranded in an airport, you have a large piece of fabric that you can use to keep warm in an emergency. What can you do with a paper poster in an emergency? A very quick fire for heat?

So, I convinced you, and now YOU want to make your own fabric poster. But how? The website I have used is Spoonflower, recommended to me by Rose Wesche. Thanks to Mackenzie Wink for figuring out the details of how to use it and helping it get set up in our department. So most of this information comes from them.
1. Create a login
2. Click upload- you will upload a JPEG image of the poster 
**PLEASE NOTE: THE POSTER MUST BE UPLOADED IN THE CORRECT FORMAT. It must be a JPEG not a PowerPoint or pdf. This link  explains how to convert it.
3. If you have to come back to the account before being able to checkout with the order, the poster will be in your 'Design Library'
4. Ensure that settings are correct-
a. Centered (the default is a repeated design meaning multiple posters would print repeatedly on the poster like a fabric pattern)
b. 48X36
c. Performance Pique fabric
d. 1 yard
5. Make sure that the poster looks exactly how you want it in the preview window (make sure you can see the whole thing, etc.). This preview will be exactly how it prints.
6. checkout
 
It’s actually quite straightforward. And I bet once you try it, you will be converted for life. Until we all go to digital interactive posters. 

  “Where I convert you to fabric posters first appeared on Eva Lefkowitz’s blog on June 18, 2019.”

Last week I wrote about best practices in data management. And then later realized that I previously blogged about it. Today I want to address issues related to analysis and publication. Which apparently I have also previously blogged about. But I think four years later, I have enough new things to say to make it worthwhile to write a new post.
 
Much as in the past few years there have been high profile cases around issues related to data management, there have been similar high profile cases around ethical issues in responsible reporting. Although we spend a fair bit of time in class reviewing different types of ethical issues, and discussing specific case studies, I am only going to briefly mention the pitfalls here, to focus instead on best practices. Particular ethical issues in publishing (some of which I discussed in that 2015 post) include (many of these points draw from readings that I cite in my syllabus):

• Plagiarism
• Self-plagiarism
• p-hacking/fishing for significance
• HARKing: Hypothesizing after the results are known
• Garden of forking paths
• Non-transparency in analyses
• Not correcting for Type I error
• Claiming that the difference between significant and not significant is meaningful
• Overstating meaningfulness, particularly with large sample sizes (ignoring practical significance)
• Ignoring meaningful results that may not be significant
• Viewing p < .05 as a magical cutoff
• Causal conclusions without causal evidence
• Not revealing conflict of interest/funding sources

 
So, here are some suggestions for best practices:
 
Don’t plagiarize or self-plagiarize: Yes, it seems obvious. I’ve talked about these issues in more detail in previous posts. But the main point is that accidental plagiarism, and self-plagiarism, are relatively common and you should follow best practices to avoid them.
 
Write hypotheses before running analyses: It is very common for people (not just students) to say, I’m really interested in how [broad construct A] relates to [broad construct B]. Then to run a bunch of correlations trying to see if indicators of A relate to indicators of B. Then to drop variables/analyses that don’t work very well, and then keep ones that do. Then create a story around these findings. If instead you formulate hypotheses first, you can commit to running specific things, and keeping all of those analyses in your paper. And, not HARKing, or coming up with post-hoc hypotheses for why A would relate to B in that way. You don’t have to table every unsuccessful analysis – you can for instance say, we expected there would be interactions with gender, but Step 3, in which we added interactions with gender was never significant, so we do not report those analyses in the table. And, you can still run follow up analyses if you find something you can’t quite explain – but just explain clearly that you ran those analyses to follow up on the unexpected finding, rather than pretending you had planned to run them all along.
 
Statistically test the difference between two analyses: If two variables are significantly correlated but two others are not, don’t describe them as meaningfulness different findings. Or, if two variables are correlated for one group but not another, don’t describe it as X matters for group A but not group B. For instance, if you’re interested in whether body image has similar associations with girls’ sexual behavior and boys’, you can run interactions with gender rather than separate analyses. If you do the latter, and one correlation is significant and the other is not, it could be meaningful, or it could be noise that pushed one correlation slightly about p < .05 and the other below it, or it could be that the correlations are identical but one group was slightly larger. But if you run the analyses separately, you can’t conclude that the associations are meaningfully different, even if one reaches statistical significance and the other does not.
 
Describe correlational results without causal language. It is so easy to write that X predicted Y – that’s even the way we talk about variables statistically in regression. But, unless you have manipulated something, avoid using causal language. Explain associations, but don’t say that one led to the other, even if you have longitudinal data. Helpful ways to write about non-causal associations in the results section:

• Body image was positively associated with number of sexual partners
• Young men who had more positive body image tended to have more sexual partners

In the discussion, you can be more speculative, but be clear that you’re being more speculative, and explain alternative explanations. An example:

• Having better body image may lead men to feel more confident sexually, which in turn leads to opportunity for more sexual partners. However, it is important to note that these results are correlational. It is also possible that having more sexual partners leads men to feel better about their bodies, or that something else explains this association.  For instance, it may be that men with better self-esteem both feel better about their bodies, and have more sexual partners.

 
Consider effect size: If you have an enormous sample, it is easy to get a significant correlation, even at r = .10. But a correlation of .10 means that you explained 1% of the variance, which is not particularly meaningful. Be aware of the practical significance of your results.
 
Reveal all potential conflicts of interest and funding sources. Enough said.
 
Consider pre-registration: there are arguments for and against, as I discussed in my 2015 post. But do know that pre-registration is becoming increasingly common, including some journals that will review your paper before you run analyses so that, if accepted, whether you find significant results or not, they agree to publish it.
 
Somehow this list does not feel very comprehensive, perhaps because some of the best practices in data management also apply here, and also, because I wrote about the topic 4 years ago. But, if you add “avoid the bullet points at the top of this post” you have a pretty good list of good practices to follow.
 
“Best practices in responsible reporting first appeared on Eva Lefkowitz’s blog on June 13, 2019.”

Data in a dissertation can come from different sources. The six primary sources I can think of are (1) using your advisor’s pre-existing data, (2) getting involved in your advisor’s data collection and adding some research questions or measures to that project; (3) using data from another faculty member at your university – maybe another faculty member in your department whom you have worked with; (4) finding publically available secondary data; (5) collecting your own data; (6) some combination of the above. I believe that I have had students do all of these possible options at some point or another. I will discuss some of the advantages and disadvantages of each option, with examples, below.
 
Using your advisor’s data: My favorite part about being a professor is mentoring graduate students, and a large part of such mentoring is research. Advisors often love when students work with data they already have. I think all faculty feel as though we have mounds of data and we wish someone would just write it up for us. When students use their advisor’s data, life is easier for the advisor because then advising converges with the faculty member’s own research agenda. The advisor feels very knowledgeable about the topic because it is within their own research area. And, your advisor likely has high quality data, perhaps data that you could not collect on your own. You cannot, for instance, collect a 20-year longitudinal study for your dissertation, but perhaps your advisor already did. Whatever the data, using your advisor’s data is efficient and can lead to the quickest time from dissertation start to finish. However, if you use your advisor’s data, you have constraints on the research questions you can ask. You must work within these constraints, whether they are the sample not being exactly what you might like, the measures being a bit of a stretch for the research questions you want, or perhaps another student is already working on a project with those data that overlaps with some of your research questions. Nevertheless, you can have a very successful dissertation with your advisor’s data. For instance, one of my former students, Sara Vasilenko, did a multi-paper dissertation using all data my colleague and I had previously collected (though Sara was there during some of the data collection). She has multiple papers based on these data, including this paper on how daily affect varies with sexual behavior, as well as a conceptual chapter from her introduction in a volume we co-edited.
 
Adding questions to advisor’s ongoing project: Some advisors may provide an opportunity to add questions to an ongoing research project, particularly a longitudinal project where you add questions to one or more timepoints. This option is more likely if you have worked with this mentor for a while and contributed to the project already in other ways – you have shown an investment. It is beneficial for your advisor because they have an opportunity for more publications from their project. It benefits you because you can now ask more tailored questions or have measures more tailored to your interests if you simply used your advisor’s existing data. But, it may also give you access to larger or better populations than you would have the time or funding to collect on your own. However, you are unlikely to be able to add every question or every measure you might want if designing your own study – your advisor probably has limits on how many questions could be added. In addition, you still do not have control over the sample or data collection techniques. Despite possible limitations, this option can be very fruitful. My former student Meghan Gillen, was very interested in body image. We were collecting data for a longitudinal study, and she ended up selecting body image measures to add to the project. She ended up writing her master’s thesis and dissertation from these data, and published six first authored papers using aspects of these data (here’s one on the freshman 15).
 
Data from another faculty member: Frequently my students work with my colleagues to gain experience with different types of data and different mentoring styles. I have had students use data from a colleague in the same department, a colleague in a different department at the same university, and a colleague in another state. In such situations, you may find data better suited to your research interests than data your advisor already has. It provides you an opportunity to learn about new topics, new data collection techniques, or new mentoring styles. And, it provides you with another mentor who can support you and also can be part of your larger network. However, your advisor may be less invested in a project that does not use their data. There may be more negotiation of roles, both mentoring and authorship, when you use someone else’s data. So, the process may be more complicated than simply using only your mentor’s data. Although I’m not sure I’ve ever had a student use a colleague’s data in their dissertation per se, I have had several students work with and publish papers based on colleagues’ data. For instance, Rose Wesche worked both with my then colleague in sociology Derek Kreager to publish multiple papers, such as this one I recently blogged about, as well as another paper on casual sexual experiences with my colleague at Kent State, Manfred Van Dulmen.
 
Publically available secondary data: Some students find publically available secondary data to address research questions that they cannot address with their mentor’s data. This option has many of the same advantages and disadvantages as using data from another faculty member. A further potential challenge can be getting access to such data. Sometimes there are a number of hurdles required before you are allowed to use such data. In addition, sometimes you need particular conditions, such as a secure computer without internet access in a locked office. Rose also used Add Health partner data for one of three papers in her dissertation. There were some harrowing moments waiting for all of the right permissions to come through for her to be able to access the data, though in the end it arrived in time.
 
Collecting your own data: One thing I love about mentoring students in research is that they take me in directions that I may not have gone without them, but I learn lots of new things and sometimes develop my own new interest in these areas. Of course, it’s great when students primarily work with data I have. However, I also enjoy when a student takes initiative on a new project and I learn at least as much as they do along the way. When you collect your own data, you have a lot more control over the research questions, design, and sample. It is the most obvious way to do exactly what you want to do. However, it is also costlier – it can cost money to collect good quality data, and it certainly takes more time to collect your own data than to use data someone else already collected. Your funds may not allow you to collect a large enough sample to insure publication, and as discussed already, you are unlikely to be able to collect longitudinal data (other than very short term longitudinal data). And, depending on the culture in your department, your advisor may be hesitant to support you collecting your own data. When I was in grad school and decided to collect my own data (an intervention, with video observations, and four total visits), my advisor was rather resistant. But I persisted, collected my own data, and ended up with five publications from those data, including one in Child Development. My first student – Tanya Boone-Holladay, received an F31 to collect her own dissertation data. Her research had a design based off of my own dissertation data collection. Her dissertation pre-dated the department’s multi-paper dissertation option. We published one paper together from these data, and she published subsequent papers after she graduated. In contrast, Chelom Leavitt collected data on a topic much further removed from my own research. Chelom came to me with an incredibly ambitious dissertation idea – she wanted to collect data on midlife adults (not an easy to capture population), in married relationships (more constraints), in three different countries speaking three different languages (the translation!). Using mostly measures of constructs I had never measured. She did it. And her first paper from her dissertation is now in press.
 
A combination: In one way or another, many of my students, particularly students who did multi-paper dissertations, chose a combination of these options. With a combination, you can maneuver around many of the challenges described above – you have access to high quality data you may not be able to collect on your own, but can also design your own smaller study to address more specific questions exactly as you’d like to. The challenge might be that you make things more complicated for yourself, in that you may introduce some of the challenges of using secondary data, or multiple collaborators, or getting your mentor’s buy-in. Emily Waterman used data from one of my projects, and also collected her own small-scale data to address more specific questions, getting the best of both worlds. 
 
Which option you choose depends on a number of factors, some in your control and some not. Your research questions, your timeline, the data your mentor has, the culture in your department, and your advisor’s preferences among others. When making a decision, consult with your advisor and other informal mentors to figure out the best option for you. 
 
“Where should your dissertation data come from? first appeared on Eva Lefkowitz’s blog on June 11, 2019.”

Wesche, R., Kreager, D. A., Feinberg, M. E., & Lefkowitz, E. S. (2019). Peer acceptance and sexual behaviors from adolescence to young adulthood. Journal of Youth and Adolescence, 48, 996-1008.

Adolescents who are liked by their peers are more likely to have sex, which could place them at higher risk for negative outcomes such as STIs and unwanted pregnancy. However, less is known about whether peer acceptance in adolescence is associated to longer term sex-related outcomes in young adulthood. We used longitudinal sociometric data from the PROSPER study, which followed youth from early adolescence into young adulthood. During adolescence, well-liked individuals were more likely to have sexual intercourse by age 16. In young adulthood (age 19), well-liked individuals were more likely to have sexual intercourse (a normative behavior by this age) but less likely to be diagnosed with an STI (a risky outcome). For boys (but not girls), being well liked in adolescence was associated with having more past-year partners in young adulthood. Peer acceptance was not associated with other potentially risky outcomes like sex without a condom or casual sex. The findings suggest that well-liked adolescents experience healthy sexual development into young adulthood, even though they are more likely to be sexually active by age 16.
 
 “Being well liked in adolescence linked to healthy sexual development in young adulthood first appeared on Eva Lefkowitz’s blog on June 6, 2019.”

*Note: after writing this post, I realized that I had written on a similar topic more than 4 years ago (thanks memory!). But, I think the new one has enough new points to be worth including it as a new post (or, I just don't want to delete content I already created!).

As always, I loved teaching my graduate seminar on professional and career development this past semester. So, I hope this summer to blog about some of the topics we cover in that course.
 
In the past few years there have been several high profile cases of faculty members who were found to have made egregious errors in data management. In many of these cases, we are talking about blatant offenses like fabricating data, creating an entire dataset out of thin air, or purposefully throwing out participants because they do not support the narrative desired. In other cases, there is manipulation of data. I am not going to name names here, though there are news stories about many of the cases we discuss in my reading list. Note that just because an article is on my reading list, doesn’t mean that I use it as an example of a blatant error – some are subtle or ones that may not be errors at all.
 
To be clear, there can also be ethical issues at the analysis and publication stage related to plagiarism, fishing, writing up, authorship issues, peer review, etc. I am distinguishing those issues (which I hope to write about another time) from ethical issues in data management.
 
My suggestions about best practices are in part to make sure you and your collaborators are engaging in ethical practices. But they also help to insure that if you are ever falsely accused of data misconduct, you can defend yourself.
 
How do you avoid these ethical issues in data management? The short and easy answer is, “don’t falsify or unethically manipulate your data.” But obviously there are a number of other guiding principles for engaging in best practices in ethical data management. These include (with lots of caveats):

• Choose collaborators carefully. Some of the news stories, and some personal stories I know, happened when one researcher fabricated or manipulated data and the collaborators/co-authors didn’t realize it. So, think about who you plan to collaborate with and whether you believe they are trustworthy. If someone approaches you with an idea too good to be true… it just might be.
• Know your data. Part of knowing your data relates to your collaborators. In some of the stories of fabricated data, someone asked a research question, and magically, perfect data to address the research question appeared in a short period of time. It reminds me a bit of the college cheating scandal and the alleged innocence of some of the kids. Specifically the students who received high SAT scores they wouldn’t have gotten on their own (if your scores show up and they are much better than any of your practice exams, wouldn’t you wonder?). If a collaborator suddenly has amazing data, ask to look at it together. Don’t just trust a table in MS Word with results.
• Understand your data. Another aspect of knowing your data is true even when you collect your own data or are working directly with data. Understand the data you work with. Run means and standard deviations on all of your data. Make sure you don’t have any miscoded data, or miscoded missing data (those -99’s that aren’t correctly coded as missing is a great example of how to mess up analyses). I once had a student come to me with some correlations she ran on a large sample (about 700 participants). The first thing I noticed when I looked at the output was that the n’s in the correlation table were hovering around 30. It turned out that she hadn’t recoded some variables for which certain questions weren’t asked based on skip patterns. But she had not noticed before bringing the output to me. Obviously all of those analyses were useless as they were based on less than 10% of the sample. Probably she would have caught it eventually, but what if she hadn’t and tried to publish it? Understand your own data.
• Data cleaning before analysis. As I said, understanding your data is important. And often when you look at your data there are things you need to clean. One way to avoid unethical data cleaning is to clean the data, and make cleaning decisions, before you run analyses. If you wait until after, you risk the scenario where you go – hey, that didn’t turn out how I wanted it to, let me see if there are any outliers… okay, let’s just drop these three people, and, viola!, now my hypothesis is supported. Instead, look for outliers before you do any analyses. You can identity outliers without bias in your decision about whom to include. Similarly, you can make decisions about recoding. For instance, we have sometimes recoded large numbers in a frequency count – like number of lifetime sexual partners, or number of times attending religious services in a year – with a cap. But again, we do that in advance. It would be less ethical to, say, run correlations, realize it wasn’t significant, look at the scatterplot and identify outliers, and only then decide how to cap certain participants’ values. You risk making decisions that support the findings you want to have.
• Document data cleaning decisions. When you clean and change any data, document all of those decisions. If it ever comes back to you (e.g., someone accuses you of manipulating data), you will have a clear record of any recoding you did and the reasons you did so.
• Make data cleaning decisions public. Do not make these decisions in isolation by yourself. Try to make them with a group (your advisor, your student, others on your grant/project) so that there is open discussion and consensus on decisions. In one of my longitudinal studies, we had a sizeable number of “born again virgins” – participants who reported having sex in their lifetime at one data collection point, and at a later data collection point reported that they never had sex in their lifetime. We met as a group and made decisions about how to handle these cases; looked carefully at the data for each participant; and made decisions rules that we could apply to all participants. We documented everything during this very public process.
• Save your syntax. When I first ran analyses in SPSS, as a full time research assistant on a large project, we didn’t have menus in SPSS. Heck, my computer didn’t even have a mouse so there was no way to point and click. Instead, I had to write all of the syntax myself (anyone reading this post as old as me? Remember assigning every variable name and label in the syntax? One missing comma, and it wouldn’t run?). Now, it is very easy to run analyses from menus without ever having a record of the syntax. Which seems easy, until you later try to recreate the analyses because a reviewer told you to drop a few participants for some reason. And you cannot run analyses that match your original results. Tears may spill. Don’t let that happen. If you use menus, make sure that you paste all analyses into syntax files and save them. You can annotate your syntax files so that they are easy to follow – e.g., explain each analysis and why you did it. Future-You will be very grateful to Today-You.
• Don’t analyze partial datasets. It is very tempting to “just check” how things are going when you have part of your data collected but haven’t completed data collection. There is little good that can come of such actions. What if nothing is significant? You aren’t going to stop data collection, are you? What if you find something significant, but when you finish data collection, it is no longer significant? Resist the temptation.
• Archive raw data: Make sure that you keep your raw data (and all your syntax) for a number of years after publication. Some societies or journals have guidelines on how long. If you collected physical data, you will need to keep the files. If you collected only electronic data, it is not hard to keep the data. Even when you clean the data and change variables, make sure you keep a version of your file with the original raw data before any cleaning or recoding.

 
Obviously, there are times you may not be able to follow every single rule here. When I went on job interviews I was halfway through dissertation data collection. I couldn’t exactly show up to job talks with no data to report on my dissertation. So, I ran the planned analyses on the half of the dataset that I had collected. I presented these preliminary results with a lot of caveats and apologies. But in that moment, the practical outweighed the best practice. Some of these guidelines, though, are true in any scenario. I can’t ever think of a good – or at least ethical – reason to not save your raw data. Then, some day, if someone accuses you of falsifying it or manipulating it, you can prove them wrong.
 
“Best practices in ethical data management first appeared on Eva Lefkowitz’s blog on June 4, 2019.”

Waterman, E. A., Wesche, R., Leavitt, C. E., & Lefkowitz, E S. (in press). Fraternity membership, traditional masculinity ideologies, and impersonal sex: Selection and socialization effects. Psychology of Men and Masculinities.
 
Rates of sexual aggression are higher among fraternity members than among other college men. Fraternity culture often engenders traditional masculine ideologies and risky sex-related attitudes that may reinforce sexual aggression. However, the process of how men internalize these ideologies is not well understood. It may be a process of selection, whereby men with more traditional masculine ideologies choose to join fraternities. Or, it may be a process of socialization, whereby being in a fraternity teaches men to adopt more traditional attitudes about gender.
 
In this paper, we used two longitudinal data sets to explore these selection and socialization effects. We found that men who more strongly endorsed male role norms about status and the sexual double standard were more likely to join fraternities, suggesting that men with more traditional attitudes about masculinity chose to join fraternities. We found little evidence to support the hypothesis that fraternities lead to more traditional ideologies about masculinity.
 
Many universities target fraternities as a context for training and intervention around sexual aggression. This intervention may be important given higher rates in these settings. However, our findings suggest that these young men may have preexisting attitudes that present risk for sexual aggression before joining fraternities. Thus, more work to target young men’s risky attitudes about masculinity and sexuality before students enter college may be particularly important in reducing rates of sexual violence on college campuses.
 
“Men with more traditional masculine ideologies choose to join fraternities first appeared on Eva Lefkowitz’s blog on May 30, 2019.”