week[4] - Time For Crab

A bit stressed out, but my boss had a birthday/tenure surprise party last night and that was super fun! It also meant I got to see a bunch of other grad students and some professors in a much more casual environment, which was cool.

Some goals for the week:

• Start the third stats homework. This course is getting more challenging now, but that's also pretty fun (as we progress, I learn new stuff, and I feel like I'm actually adding to my R toolkit!). On the flip side, I now understand why my students always skipped checking conditions and assumptions :) But you GOTTA DO IT

• Set up the new closet. You may remember this goal from last week... and the week before... running joke...

This is something I haven't done yet, but I thought it might be interesting to briefly summarize what I'm learning in various classes so you can get a taste for what I'm talking about in my coursework.

Math/Sci Ed Policy, PD & Teacher Prep

This class focuses largely on what policy around education looks like, what "reform" efforts look like and aim for, and our working model for how factors inside and outside the classroom inform curriculum & instruction.

This week, we looked at value-added models (a standardized, statistically-based means for assessing teachers based on student test performance) and the evidence for and against them. While the jury's still out, I consider myself convinced that their use is currently outkicking their coverage.

• Haertel, E. H. (2013). Reliability and Validity of Inferences about Teachers Based on Student Scores. William H. Angoff Memorial Lecture Series. Educational Testing Service. https://eric.ed.gov/?id=ED560957

• Johnson, S. M. (2012). Having It Both Ways: Building the Capacity of Individual Teachers and Their Schools. Harvard Educational Review, 82(1), 107–122.

• Leana, C. R., & Pil, F. K. (2006). Social Capital and Organizational Performance: Evidence from Urban Public Schools. Organization Science, 17(3), 353–366.

• Sanders, W. L., & Horn, S. P. (1998). Research Findings from the Tennessee Value-Added Assessment System (TVAAS) Database: Implications for Educational Evaluation and Research. Journal of Personnel Evaluation in Education, 12(3), 247–256.

Core I

This class (first of a two-semester series) focuses on what educational research is, our position and identity as researchers, and how equity and social justice can be emphasized throughout educational research. It also serves to develop our analytical and communication skills!

(yes, this class has a lot of reading)

This week, we focused on different epistemologies' notion of "quality," particularly in qualitative research, and standards by which research can be assessed. By far the best reading this week was O'Connor (2002), a thoughtfully-enacted study on resilience and risk across "historical time."

• Erickson, F., & Gutierrez, K. (2002). Comment: Culture, Rigor, and Science in Educational Research. Educational Researcher , 31(8), 21–24.

• Joyce, K. E., & Cartwright, N. (2020). Bridging the Gap Between Research and Practice: Predicting What Will Work Locally. American Educational Research Journal, 57(3), 1045–1082.

• Moss, P. A., Phillips, D. C., Erickson, F. D., Floden, R. E., Lather, P. A., & Schneider, B. L. (2009). Learning From Our Differences: A Dialogue Across Perspectives on Quality in Education Research. In Educational Researcher (Vol. 38, Issue 7, pp. 501–517). https://doi.org/10.3102/0013189x09348351

• O’Connor, C. (2002). Black Women Beating the Odds From One Generation to the Next: How the Changing Dynamics of Constraint and Opportunity Affect the Process of Educational Resilience. American Educational Research Journal, 39(4), 855–903.

• Solórzano, D. G., & Yosso, T. J. (2002). Critical Race Methodology: Counter-Storytelling as an Analytical Framework for Education Research. Qualitative Inquiry: QI, 8(1), 23–44.

• Tracy, S. J. (2010). Qualitative Quality: Eight “Big-Tent” Criteria for Excellent Qualitative Research. Qualitative Inquiry: QI, 16(10), 837–851.

General Linear Methods I (Stats)

This class is the second of a three-course sequence in basic statistical skills in R. It covers multiple linear regression, one-way analysis of variance (ANOVA), multiple comparison procedures, factorial ANOVA, Analysis of Covariance (ANCOVA), nested designs, and some other skills.

Like I said last week, we're mostly starting Multiple Linear Regression in R as we begin Homework 3 this week. While I don't have any pretty plots for you yet, I'll summarize by saying that if you've ever done simple linear regression with one explanatory variable and one response, this just expands on that by adding a number of response variables.