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Ethical data management

1/30/2015

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In Professional Development, we discussed violations of ethical data management, using some high profile examples as case studies (e.g., Hauser, Stapel, Woo-Suk). Instead of having all students read about each case, I assigned each student to read an article about their own case study (see syllabus for exact readings). I think it led to good discussion, where each student could present their case to their classmates, rather than my lecturing about each case, or everyone coming in with the same knowledge base. Many of the cases we discussed were rather glaring/obvious, with entire datasets fabricated or manipulated. But we also talked about fuzzier cases and where one might draw the line. 

Then we talked about best practices in data management. The goal was two-fold – first, recognizing how to avoid violating ethical data management principles. Second, even when you are doing everything ethically, making sure you do it in a way where no one could suspect you of unethical practices. I should be clear that we didn’t try to tackle IRB/human subjects ethical issues this week – we really focused on the data management end of things.

I suggested the following best practices:

Know your collaborators well. This point is important whether you’re talking about mentors, mentees, or collaborators on the same level. In some of the cases we discussed, there were collaborators who were getting or hearing about great data that turned out to be problematic. I’m not saying that the collaborators/students should have recognized the situation sooner – many people have been tricked in similar ways. But the more closely you work with someone, look at data together, and know the person you work with, the easier it might be to recognize problems. In many of these cases, people who had nothing to do with the fabricated or massaged data had publications that were rescinded and thus disappeared from their CVs. That’s a huge deal for someone junior, and you do not want that to happen to you.

Know your own data.
Before you run analyses, look at your data, your means and standard deviations, get a sense of what you have. I don’t mean start running analyses to test hypotheses at the start, but running descriptives, identifying problems with scales or measures… doing these things early can prevent problems later.

Clean data before analyses. No data are perfect. Data have outliers. Data have inconsistent responses. But when do you address these issues? Don’t wait until your results are not significant to poke around and look for data to eliminate. Instead, before any analyses, clean your data. Sometimes participants report they’ve had 20,000 sex partners, or that they’ve had sex 9000 times in the past 3 months. Other times participants answer “1” to every item on a 7 point scale even though some items are reverse scored. It’s acceptable to clean data, or even to throw out improbable data, as long as your decision rules are logical, consistent, pre-determined, and not decided after you test your hypotheses.

Make data cleaning decisions openly.
Be open about the data cleaning process. Don’t make these decisions on your own, and then lose all of the raw data (pesky fires!). Make the decision process public and based on group consensus about how to handle these issues.

Document data cleaning decisions.
Document decision rules, and any cases that were changed from the raw data. Be ready to show someone your decision rules, raw data, and cleaned data, if asked.

Save syntax. All syntax you ever write. One of my students, Rose Wesche, wrote a whole blog post on this point recently, so you can just read what she said.

Analyzing partial datasets.
This is a tough one. There are times when analyzing a partial dataset is highly useful. You want to submit for a conference but your data aren’t all in. I did my job talk on the first half of my dissertation data. If I hadn’t, I wouldn’t have had a job talk. But a risk here is analyzing partial data repeatedly until the results duck under the magical p < .05, and then ending data collection. If you really must analyze a partial dataset, be sure you know what your final N will be, and don’t deviate from it.

Archive data post-publication.
APA says 5 years after the publication. Because many of us publish for many years after publication, it’s important to archive data for many years.

Students generated the following additional ideas:

Write a clear methods section,
so that others can replicate your methods.

Imagine your worst enemy over your shoulder.
Apparently my husband shared this point last year in their methods class – nice to know they listened/retained it. That is, when making data cleaning and analysis decisions, make sure they’re justifiable.

General transparency.

Change the publish or perish culture.
Students were concerned that many ethical violations occurred because of intense pressure to publish in order to succeed and/or obtain tenure. They thought a culture shift would decrease the prevalence of such incidents.

Stress management. Related to the prior point, as individuals, we might not be able to change the culture, but we could work on our own management of the pressures of academia, so that we can make wise/ethical decisions.

What did we miss? What’s important to teach students about being future scientists/researchers?

“The post Ethical issues in data management first appeared on Eva Lefkowitz’s blog on January 30, 2015.”

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This week in adolescent development: puberty 2

1/23/2015

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This week we talked about puberty. I posted some things last year, so I'm trying not to be repetitive but here are some things that we newly discussed or didn't bother to share last year.

There's a good map from an old Steinberg text (I don't have the latest edition so I don't know if it's still in there) showing average age of menarche across countries. But great illustration of cross-national difference in menarche, which led to a good discussion of the reasons why.

We also talked about the time trend in menarche.




Picture
http://www.ncbi.nlm.nih.gov/books/NBK10786/?report=reader
We talked about measurement, though I'm feeling too lazy to link to all of my good measurement links. Okay fine, here:

Petersen et al.'s (1988) seems to be the most widely used measure. Find it here.

Morris & Udry (1980) have a good example of figure ratings.

And the Tanner Scale, which is often physician assessed.

We discussed Chastiotis et al. (1998), in which they found that mothers' age at menarche was associated with daughters' age in West Germany but not in East Germany during the unification, but were not associated in East Germany. They interpret this finding as due to the fact that in West Germany they experience more continuity in context across generations, whereas in East Germany, there was stress/discontinuity across generations.

And during the break my students showed me relevant youtube videos from helloflo. Both cute, though I laughed harder at this one than at this one.

“The post This week in adolescent development: Puberty 2 appeared on Eva Lefkowitz’s blog on January 23, 2015.”



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Grad seminar in professional development and ethics

1/19/2015

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I'm excited this semester to be teaching, for the first time, a graduate seminar in professional and ethical issues in HDFS. You can find the syllabus here. This course is required for all of our 2nd year graduate students, and partially meets their PSU Grad School SARI requirements (scholarship and research integrity).

My biggest challenge in putting together the syllabus is that I had way too much material. There is so much to cover, and 15 weeks at 1.25 hours per week could not hold it all. I decided that some topics we already cover during orientation and/or their first year orientation seminar, and so those topics would be assumed to be understood (e.g., plagiarism, human subjects & IRB issues). There are also topics, like grant writing and going on the job market, that are very important, but that it's probably too early in their grad school careers to cover in detail, so we have overviews on those topics. I'm trying to line up a grant writing class in future years, and I would definitely consider offering a job market course, although there are multiple constraints in doing so.

In addition, I included in the syllabus a list of additional readings on topics we don't have time to cover.

I gave 6 assignments, which may seem high for a 1.5 credit course. But the students do not have to write any large paper or take any exams, nor do they have to write weekly reaction papers. I designed assignments that are super practical, and that students should be doing anyway, like writing a CV, doing informational interviews with alumni, looking at job listing to start thinking about tailoring themselves for the job market, writing a manuscript review, and setting up a website.

A big thank you to Claire Kamp Dush whose work on putting together similar syllabi gave me ideas for readings for mine.

I still have an open week at the end, so if you have an ethical or professional development issue that you think should be included, let me know!

“The post Grad seminar in professional development and ethics first appeared on Eva Lefkowitz’s blog on January 19, 2015.”

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Grad Seminar in Adolescent Development 2015

1/14/2015

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I'm teaching my grad seminar in adolescent development again this semester. You can find the syllabus here.  Here are the topics and readings for my contemporary issues assignment.


It hasn't changed that much from last year. I've updated some readings.  The biggest change is I'm trying something new with the final project. The past few times I've taught the course, I've had a big final project, and a manuscript review assignment earlier in the semester. This time, I decided, at the advice of students from last year's class, to have students peer review each others' final projects instead. I struggled with how to do this assignment in a way that would be helpful for everyone. I didn't want to overwhelm students if they received feedback from 2 classmates in addition to feedback from me. So, I'm going to try modeling it on the peer review process. Each student will anonymously review 2 other students' projects. They will submit the reviews to me, and then as part of the grading feedback, I will serve as "editor" providing the student who wrote the final project with feedback that incorporates their classmates' reviews. My goals with this assignment are to provide students with:
1. Process of writing peer reviews
2. Feedback from multiple sources
3. Experience of going through peer review, with feedback from an editor and 2 reviewers

Students who choose to revise their paper for an improved grade will have to also respond to the editor and reviewers' reviews. We will see how it all goes.

I love teaching this course. It provides an opportunity for me to talk about adolescent development, to hear from students with a range of interests and perspectives, and to keep up to date on contemporary work in the area.

“The post Grad Seminar in Adolescent Development 2015 first appeared on Eva Lefkowitz’s blog on January 14, 2015.”


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    Eva S. Lefkowitz

    I write about professional development issues (in HDFS and other areas), and occasionally sexuality research or other work-related topics. 

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