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.”

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.”

If you are in an undergraduate research program, such as your university’s honors college, at some point you likely need to identify a research mentor. It is a huge commitment for you, because if you are doing a thesis, you will be spending about a year of your life reading, writing, and researching for this one paper. But keep in mind it’s also a big commitment on the part of the faculty member. I love mentoring undergraduate students on research projects, but I end up spending significant time across 9-12 months when I do so, so I only commit to it if I think it will be a good fit.
 
How do you identify an undergraduate research mentor? Here are some things to consider.
 

• What are your research interests? Think about your general interests, and what you might want to spend a year reading and writing about.
• Visit your department’s faculty research page. Read about the different faculty’s research interests, and see who is doing research that interests you.
• Ask around. Are there other students who are doing research this year? Perhaps you’re a junior and you know some seniors working on honors theses this year. Ask them for advice on whom to work with.
• Talk to your honors (or other) adviser. If you chat to them about your research interests, it may help you to narrow down whom you might work with. 
• Keep in mind that some faculty require that you work on their research projects at least one semester BEFORE you begin your independent research project like a thesis. Therefore, even if you do not plan to graduate in the next couple of semesters, you should consider getting involved in research earlier. I’ve previously discussed other reasons you should get involved in research.
• Once you have identified one or more potential research mentors, you will need to email them. This email is very important because it is the professor's first impression of you. Please consider my advice when sending this particular email, and also when sending emails more generally:
  
  1. The greeting.
  You cannot go wrong by starting the email "Dear Dr. Smith" (well, unless you're writing to Dr. Jones; obviously you need to put in the faculty member's correct name). Some faculty are comfortable with being addressed more casually, but others are not. But no one will be offended if you're too formal, and someone could easily be offended by being too casual. Don't risk it with "Dear Firstname," "Hey," "Hi Firstname" or launching right into the message. You might think that “Hey professor” or “Hi professor” sounds respectful, but how would you feel if your professor emailed you with the greeting “Hey student”? The important thing to remember: Most faculty were students and professionals in a time before texting, Instagram, Snapchat, and yes, even Facebook (and for many of us, before email), and are used to more formality in writing. Avoid Mr./Ms./Mrs./Miss. They are almost never appropriate.
  
  2. Briefly introduce yourself, and why you're writing.
  
  3. Make it clear that you've done your homework before sending the email. Say something about the work of the person you're emailing, such as "I am very interested in your research on how interactions with parents relate to adolescents' mental health."
  
  4. Don't assume that someone will immediately agree to be your research mentor. Most of the time, faculty will want to meet you in person, talk more about your interests, and see if it's a good fit.
  

 
5. Don’t assume that the professor can meet with you in the next few days. Or at one specific time that would be most convenient to you. Ask about general availability to meet, and be flexible about when you can meet.

6. You may also have to be flexible if you have very specific interests, and no one in the department studies that topic. If you email someone and start by saying "I want to do a research paper on sexual predators on social media" and that's pretty far from the professor's own research, s/he may be less responsive than if you describe general interests in social media, or sexuality, and then meet with him/her to discuss it further. Remember, too, that your research paper does not have to be on exactly what you want to do for the next 10 years. You will gain extensive experience and knowledge by working with your research mentor, so be open to other ideas as well so that the mentor relationship can benefit you both.

7. Reread and spellcheck your email! Demonstrate in your first communication that you are a careful and serious student who writes well.
 
These steps will not guarantee that the person you ask will say no. But they will increase your likelihood of a positive response, and make a good impression in the process.
 
“How to identify and secure an undergraduate research mentor first appeared on Eva Lefkowitz’s blog on November 13, 2018.”

Finding articles can be challenging, but sometimes it's even harder to know what to do with them once you find them. How do you go from a 10-page dense article to a few lines in your own literature review or introduction to a study? Although I’ve previously written about what those brief summaries should look like in introductions, today I’m going to provide advice on how to read, make sense of, and summarize for yourself, the content of the articles you find.

Reading the article
Make sure you fully understand the major sections of the article, which generally have an hourglass shape:

a. Introduction. In this section, the authors are describing past work, and how their study fits into past work. When you are writing your own literature review, if you are reading a paper by Brown and Jones, and they cite work by Smith, you should not summarize the authors' summary of Smith and then cite Brown and Jones. If Smith sounds relevant to the paper you're writing, track down Smith and read it directly. When you are reading a journal article for your own literature review, you can use the introduction to find other articles, but you shouldn’t use their summaries of past work for your summary. What eventually will go into your literature review or introduction will focus on the authors’ findings. But, including their conceptual framing and theoretical perspective in your summary for yourself is often helpful.

b. Methods. A description of the sample, procedures, and measures. This section is very useful for fully understanding what the authors did, although you’re unlikely to write much about it in your literature review.

c. Results. An explanation of the statistics performed, and what the authors found. This section is important for learning the main findings of the article. However, many articles use very complicated statistics, and sometimes a reader can get lost in those details. Don't worry if you can't understand everything they did. Focus instead on their explanation of what they found.

d. Discussion. In this section, authors summarize their findings, interpret their findings, link their findings back to the literature, and discuss limitations, future directions, and implications. The summaries in the discussion can be particularly useful if you had trouble following the details in the results section.
 
e. References: This section is very useful for tracking down articles the authors refer to, that are relevant to your own work.

Summarizing the articles
When I write a literature review, I find it helpful to summarize each article for myself in the same way, in one document, so that I can then work with my summaries, rather than the articles. I provide myself enough information so that I don't frequently need to go back to the article. When I do this, I make sure to PUT THINGS IN MY OWN WORDS so I can work from the summaries and know that I am using my words, not the authors'. This point is critical for avoiding unintentional plagiarism. If you think, I’ll just copy and paste, and change it later, when you come back to it a couple of months later, you may not remember you haven’t already put it in your own words.
I also want to make sure I have enough detail about the methods so I can easily write it into my paper e.g., I need to know if it was only female participants, only one ethnic group, what age the participants were, etc. Here's an example of one I did:

Once you have summarized all of your articles, you can then write your own literature review, using your article summaries. I like to start by creating an outline of the full literature review, so that as I add each article to my paper, I can make sure it goes in the correct place. This technique also helps me realize if there are sections of my paper where I have too few articles and need to find more.
 
Having these summaries makes the transition to writing the actual paper so much more straightforward, and saves you time in the long run. And I’m all about efficiency in any way possible.

“How to read and summarize research articles first appeared on Eva Lefkowitz’s blog on November 6, 2018.”

When did you master the successful journal article search? During the course of their undergraduate studies, many students have to write a course paper that involves finding primary sources such as journal articles, particularly during junior and senior year. Undergraduate honors students almost always need primary sources for writing their thesis. Graduate students obviously also need to hunt down journal articles for course papers, comprehensive exams, and theses/dissertations throughout their time in graduate school (and after), though some begin graduate school already having developed strong skills in this area.
 
I’ve learned over time that the preparation students receive from their instructor in advance of having to track down articles varies widely. Some instructors fully prepare students for this task, particularly in honors seminars. Others, particularly by graduate school, expect students to know how to perform a successful literature review. So, my advice on how to do an effective literature search may be useful for students toward the second half of their undergraduate career, or early in their graduate career.
 

• Starting with a textbook: If you’re early in the process – let’s say, trying to narrow down a topic for a review paper for a course or your thesis, looking at your textbook for that course, or other courses, can be useful. Textbook writers spend a lot of time tracking down articles on a huge range of topics.  You can see what articles the textbook cites, and track down those original sources. For instance, if you're interested in sexual behavior, you can look in the sexual behavior section of your adolescent development textbook and track down the papers that the textbook author cited.
• Review articles: In many areas of research, someone (or multiple someones) has published review articles or meta-analyses. Review articles can be very useful because they summarize a fair bit of past work. However, there is a lot of information in one paper, so you will have to spend a lot more time reading it than an empirical article that summarizes only one study. To find review articles, you can check the titles of articles during your searches for ones that include “meta-analysis" or "review" in the title. The reference list of the review article will help you find other relevant articles (though they obviously will be older than the review article). You can also look at some specific journals that carry review articles, including Psychological Bulletin, Psychological Review, Human Development, and Developmental Review. 
• Searching in databases: Use Psychinfo, Google scholar, or other journal database to find articles on a specific topic. Learning to choose the right search terms can take time. You may need to spend a fair bit of time practicing and trying out a range of different terms before you find the right articles. If a search gives you < 10 articles, you need broader search terms. If it gives you > 1000 articles, you probably need narrower terms. If you’re using google scholar, using quotation marks can be really important. Let’s use an example. We recently wrote a paper on long-distance relationships. Here are some things I could try:
  • LONG DISTANCE RELATIONSHIPS: Without quotation marks, there are 4,740,000 results. Obviously that won’t work. Though, when I look at the first page, it is clear that they are likely some of the most relevant ones.
    

• Snowballing: If an article you read cites another article to make a specific point relevant to your interests, look it up. Researchers use this approach a lot.
• Forward searches: This technique is one of my favorites, because to me it feels like detective work. In psychinfo and google scholar, you can find all the articles that cite a specific article. So, if you find a great article on your topic, you can find all the articles published subsequently that refer to the great article. Some may not be relevant to your topic, but many likely will be.
• Don’t stop at first page of results: When you do a search, go beyond the first page. I have had students come to me with a reference list where everything was published in the past 2 years, and some are only tangentially related to their topic. The student often then tells me that s/he couldn't find anything more relevant. But often the issue is that the student didn't look through enough of the results in his/her search. When I do a search, I generally look through every article that comes up. That's why you want your search to be specific enough that it produces < 1000. The most recent research may be great, and you want to include the most recent research, but if you're not going more than 2 years back, you're likely excluding a lot of other great research.

Finding the right articles is a critical step to an effective literature review, and will save you so much time later when you are actually trying to summarize others’ work or relate it to your own.  
 
“Good journal article searching habits first appeared on Eva Lefkowitz’s blog on October 30, 2018.”