# Capella University and the Scientific Merit Review

Posted November 6, 2012

For the past 20 years Statistics Solutions’ mission is to help graduate students graduate.  One of the places students get stuck is with the writing aspects of Capella’s Scientific Merit Review (SMR).  Whether you go to Berkeley or Capella, students need help.  Students (“learner” always reminded me of Milgram’s 60’s Obedience to Authority study) at Capella however have a couple of things working against them.  First, they’re not on campus to get the help they need, and second, they’re paying tuition as the process continues.

My staff and I have worked with over 2,000 graduate students, and despite the resources at the universities, some still need help from an objective, non-evaluative professional.  We are such professionals!   When we work with students they typically get stuck in the same few places:  research questions, proposed data analysis, and the target population and participation selection.

Research questions are easy to handle: make sure the constructs (your measures) are obtainable and measure what you want to measure, AND you arrange these constructs in statistical language.  For example, if you have constructs A and B, and want to relate them (read “correlate” them), then say that.  If you are assessing whether A predicts B (read “regression”), then say predict, impact, or account for variability in B.

Capella’s Scientific Merit Review also asks for a data plan.  When it comes to data analysis plans, these plans are based on two things: the statistical language you used in the research questions and the level of measurement of your variables.  We have resources on our website or if you need more one-on-one help you can go to click here.  By the way, Capella will send you back for a round of revisions (tuition not included) if you don’t have this correct.  When I went to school 100 years ago, the IRB which we would have sent our SMR to, made sure that we didn’t hurt our participants but now they look at everything.  And let’s face it, the revision costs you both time and money.

Sample size is typically trickier still (even with the help of G-power).  There are two tricks: selecting the right analysis (see data plan above) and selecting the effect size.  Effect size can be derived by looking at past research using these constructs and analyses, then calculating or seeing the effect size used.  There’s also a realistic aspect too:  for dissertations—and I’ve seen 1000’s of them—large and medium effect sizes, requiring relatively small sample (under 100 participants),  is the norm.  Requesting a small effect size (small effect take a lot of people to detect) requires typically 300-500 participants—and this is just not reasonable for a dissertation student to obtain.  Here are a couple of resources (sample size tool; power analysis) to get you started.  I should note that the exception is when you are conducting EFA, CFA, path analysis, and structural equation modeling; these techniques typically require 150 or more participants.

I’m going to leave you with a Dissertation Template to look at.  It’s free and you may find some definition of terms helpful.

Good luck with your Scientific Merit Review and call us if you run into trouble.  Contact us at by clicking here or call us at (877) 437-8622 (M-F, 9-5 EST)

PS:  A Stanford Ph.D. student just called; their private stats consultant just took another job.  See, everybody needs help sometimes, even schools with lots of resources!

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