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.”

Last week, I discussed whether you should do an internship during graduate school. Today, I want to share a list we compiled of internships related to Human Development and Family Sciences. Many of these internships are likely also relevant to graduate students in related disciplines such as psychology, sociology, public health, and communications.
 
As with my lists of postdoctoral positions and graduate fellowships, I make no claims that any information, including information about citizenship requirements, is accurate.
 
Also as with the other lists, it’s a broad list. Internship sites include government agencies like the National Institutes of Health, research institutes like RAND, and nonprofits like UNICEF. The topic areas vary widely, and include public health, sexual health, nutrition, statistics/psychometrics, policy, education, ethnic/racial minority families, adulthood and aging, and child development (detailed in a column). Locations are around the United States and the world (detailed in a column). Some are summer internships and others can occur during the academic year. They are as short as 8 weeks or as long as 7 months. There are both paid and unpaid internships (and a column that details whether paid, and if so how much). 
 
Find it here. 
 
If you know of any others, please share them with me and I will add them. If you find errors or broken links, please let me know.
 
“40+ internships related to Human Development & Family Sciences first appeared on Eva Lefkowitz’s blog on May 28, 2019.”

Leavitt, C. E., Waterman, E. A., & Lefkowitz, E. S. (in press). The role of sexual mindfulness in sexual wellbeing, relational wellbeing, and self-esteem. Journal of Sex & Marital Therapy.
 
Interest in mindfulness as a way to handle stress and improve day-to-day wellbeing has increased in recent years. We set out to understand sexual mindfulness in particular – the ability to be in the present moment, and not judgmental, during sexual experiences. Some prior research has considered associations between trait mindfulness and individual, sexual, and relational wellbeing. However, trait mindfulness is a necessary but not sufficient indicator of ability to be mindful in sexual situations. In my earlier work, I’ve described how positive parent-child relationships are likely necessary but not sufficient for the ability to have positive parent-child conversations about sex. Similarly, people need the ability to be mindful in general to be able to be mindful in sexual situations. However, even individuals who are mindful in their daily routine may experience obstacles to mindfulness during sexual experiences, such as being overly goal-oriented, self-critical, or sexually anxious. Thus, we developed a measures of sexual mindfulness to consider its role in sexual satisfaction, relational satisfaction, and self-esteem in a sample of 194 midlife men and women (ages 35-60). We found that more sexually mindful individuals tended to be more satisfied with their relationships, more satisfied with their sex lives (particularly for women), and have better self-esteem. Some of these associations occurred even after controlling for trait mindfulness. We believe that these findings have important implications for researchers, interventionists, and clinicians who work with couples and individuals to address sexual wellbeing, relational wellbeing, and individual wellbeing. 
 
“Sexual mindfulness matters for individual, relational, and sexual wellbeing first appeared on Eva Lefkowitz’s blog on May 23, 2019.”

There are certain things that I think the majority of graduate students should do. Apply for external funding. Attend conferences. Publish. Do a post doc (that one less so, but still, the majority).  My advice on doing an external internship is much more of “it depends” advice than some of these other topics.
 
To clarify, I am not referring to clinical internships. If you are in a program like MFT or clinical psychology, your program and licensing has specific requirements for internship hours that of course you must meet. But I am talking about internships that are not fulfilling clinical hours, but instead are about getting experience in a non-university setting such as a research institution, non-profit organization, or government agency.
 
If you know that you want a career as a professor, then an internship may not be the best use of your time. The main accomplishments for acquiring a faculty position are relatively straightforward: publish; perhaps pursue funding, particularly if an R1 university is your dream job; perhaps get teaching experience, particularly if a more teaching-focused university is your dream job. The best use of your summers, in order to meet the goal of a tenure track faculty position, is to write. 
 
The two main reasons why you should consider doing an internship are if you do not plan to go into an academic career, or if you do not yet know what type of career you want. Here are some benefits of doing an internship, whether summer or academic year, and whether paid or unpaid:

• Learn about a field/career: Most graduate students have a relatively strong sense of what it’s like to be a professor – you spend a lot of time with professors. However, for other careers, an internship can really help you get a sense of whether you are interested in that type of institution, and/or that type of position.
• Networking: Networking in general is helpful for anyone, but for faculty positions, networking rarely leads to a job materializing just for you. The availability of new tenure track positions generally relies on many factors out of the control of an individual faculty members (or department heads!) going all the way up to the provost. However, in many other organizations, networking can result in a fast track to a position. Internships are an excellent way to make those connections – I know many people who eventually secured a full time position at the place they did their internship.
• Gain transferable skills: Even if you do not eventually secure a job at your internship site, the skills you develop there will better position you for other alt-academic positions when you do go on the job market. 

 
An important point to note about internships. Although some organizations advertise that they have internships available, not all do. If you are interested in a particular place for an internship, reach out and ask. It is helpful if you have some type of connection to someone at that organization, such as an alum of your program, a contact of your mentor, etc. Internships can be an excellent opportunity for setting yourself up for a future career outside of academia.
 
 
“Should you do an internship during graduate school? first appeared on Eva Lefkowitz’s blog on May 21, 2019.”

HDFS Graduate seminar in professional and career development
 
My grad seminar on professional and career development may be my favorite course to teach (I had to temper this statement a bit this year, as two of my students this semester TAed for me in my general education lifespan class, where I told 350 students that class was my favorite to teach). But perhaps my favorite position to date was director of the graduate program, and one of the most fulfilling aspects of my 20+ years as a professor is mentoring graduate students. Teaching this course feels like mentoring more than teaching.
 
My learning objectives have not changed much since I previously taught this course. However, I have updated the readings, played with the order of topics (there is often a chicken/egg issue of whether to cover, for instance, “how to” related to publishing and peer review, or ethics of publishing and peer review, first), and added one new assignment.
 
Here is the full syllabus:
syllabus_5095_2019-sp.pdf

 
“HDFS Graduate seminar in professional and career development first appeared on Eva Lefkowitz’s blog on January 31, 2019.”

Somewhere in my house, I have my new year’s resolutions from probably 1984, when I was in high school. I wrote them on a piece of paper and taped them behind a wall clock in my bedroom so no one else would see them. If I had to guess what it said, I would guess, “lose weight” and “be kinder to people.” Okay, I am going to try to find it.

Look at that – I was correct about the year. I had appearance concerns, but not weight. I had a version of be kinder (forgive the spelling error). And we can all laugh that teenaged Eva wanted to be more organized. How organized was she? At the end of the year, she gave herself checks and X’s for her prior year’s resolutions (3 out of 5 ain’t bad, but it’s no A).
 
I know there’s a body of research on New Year’s resolutions, and I know generally people are really bad at keeping them. My track record isn’t good. I have done the general (lose weight a common one). The specific (pack lunches to work or cook and freeze meals for family – works through January). The “realistic” (eat more vegetables). The important (be more patient with children). My former workout buddy and I used to lament the gym crowds in January and find relief in March when we didn’t have to wait for our cardio machines anymore, so I know I’m not the only one with trouble sticking to it.
 
I’ve noticed for the past couple of years that some friends have gone with one-word intention for the new year rather than a resolution. And apparently (I just looked) it’s now a thing on Twitter, too #OneWord2019). Some people are even having their classes do it. So I decided I would try it this year. My daughter and I thought maybe others would want to try it, too, so we set up a board for others to join us on New Year’s Eve, even including a bowl of inspirational words if people felt limited.

(Yes, someone did write Pokémon. I promise he’s under 10).
 
It took me a while to settle on my own word. I juggled some like joy and shine. But I have a fair bit of joy, I just don’t always notice it when it’s there. So I played with appreciate, but it didn’t stick. I cycled through some like peace, reflect, and Zen. But who is kidding who, I’m never going to be Zen or fully at peace. They didn’t feel realistic or sit well with me. And then it came to me, and stuck all day.
 
PAUSE.
 
I think that’s my word. It reminds me a bit of what we teach kids in socioemotional learning (SEL). Take a moment. Breathe. Pause. Then respond. When my kids were in kindergarten they had an SEL curriculum where they learned to turtle – take a moment and physically turtle instead of immediately reacting. For a while when the kids were little, if I felt overwhelmed as a parent, I would physically turtle to remind them of the technique and the fact that we have strategies other than yelling to deal with emotions.
 
I’m not going to turtle in a faculty meeting (though I did once in front of my 350-student class, but only as a demonstration). But I can work to take a moment before reacting. And I can do the same thing with my family members. Pause in the moment before reacting.
 
And I think of it more generally, too. Pause before deciding the best thing in this moment is to go on Facebook or to check my work email in the middle of watching a movie with the kids. Pause to listen to the kids’ story (even if it’s about what happened while playing Minecraft) instead of staring into space. Appreciate the quiet moments, or the loud moments, for what they are, before trying to alter them.
 
I’m not going to dramatically change my personality overnight. I’m not going to become Zen-master Eva. But if I can find more moments to pause, I think that will help. At least until February.
 
 
“One-word intention for 2019 first appeared on Eva Lefkowitz’s blog on January 1, 2019.”