[arathbon]

I was expecting a pretty dull course. It wasn't

Mandatory for Kin students, this course covers the basics of research design and statistics. Topics included: What is the scientific method and the empirical-analytic tradition?, The Principle of Falsifiability, Differences in Data Types, Differences in Research Designs, External and Internal Threats to Validity, Type I vs. Type II errors, The Publication Process, Research Ethics, Measurements of Central Tendency, Measurements of Variability, Normal Distributions and Z-scores, Pearson r, Bivariate Regression, t-tests, chi-squared, the phi coefficient, inter-rater reliability and Spearman's rank-order.

We generally had two lectures a week and one tutorial, except for 3 weeks where we had an extra lecture and no tutorial. That part of the organization was sort of done in the fly. We had a tutorial scheduled in via solar, but they were switched to thursdays. Lectures generally explained the material and Tutorials worked through problem sets. They were very helpful and important. The textbook was a good reference, but I found the problem sets provided the best practice for the exam and for me were sufficient.

The course breakdown was as follows:

5% Online Tricouncil Policy Statement on Ethics in Research Involving Humans Tutorial-- This was a completion mark, but I understand a handful didn't take the time to do it. (It takes longer than it says it well). Unfortunately for them there were a handful of related questions on the midterm.

5% Lab Assignment on Standard Deviation, Correlation and Basic Research Design-- Make sure you ask about anything thing you are unsure about, it may clear up misconceptions about the boundaries between Basic and Applied research and other "fuzzy" concepts.

30% Midterm- Pretty Easy, Ethics questions were killer. MC, fill in the blanks/lists, short answer, and a few calculation questions. Formulas provided, but some of the math questions were conceptual (i.e., based on the variation in x, the variation in y and the covariability between the two calculate the pearson r). The fill in the blanks/lists were tricky for a lot of individuals.

Two 10% Lab Quizes- First one on T-Tests, second on nonparametric tests. Very application style. They give the formulas and have already calculated things like sums, means and standard deviations. Don't calculate your own standard deviation if given to you as you may choose the wrong formula and it wastes times.

40% Exam-- Non-cumulative but many concepts from before the midterm were required to be successful. Same format as midterm. Also asked you a very broad long answer question integrating everything you know from the course.

Dr. Lyons was a great prof. Used a lot of examples from Kinesiology and kept things real simple for people who didn't understand math. Also pretty darn funny, in a dry humour kind of way.

I'm going to start by saying that I was pleasantly surprised by this course! I know a lot of people found it extremely dull, but once I understood the material it was actually pretty interesting and extremely useful.

I found the lectures to be pretty boring, but mostly because I'm one of those people who doesn't understand anything math related right away and I would subsequently space out. I only went to lectures to fill out the blanks and to understand some of the more conceptual topics like hypothesis testing, internal/external validity, etc.

That being said, Dr. Lyons was a great prof. He's just genuinely a really nice guy (he was actually waiting in the IWC and wished everyone he recognized from class good luck on the exam), very helpful and made stats simple. I also thought his notes were very good, and the textbook was also good at clearing up anything from the notes that I didn't understand.

A very important part of this course is the weekly lab/tutorial! The long answer questions on the exam and midterm were the exact same style as the ones in tutorial, so go! They were also very helpful at clearing up some of the minor technicalities, and I didn't hear any complaints about any of the TA's.

If you're afraid that you'll do poorly in this class because you're not good at math, don't be! I honestly did not do very well in Math 1LS3 (which most people say is ridiculously easy), and managed to get a 12 in this course. This course is more about conceptually understanding when to use a certain equation, what it's measuring and what numbers to plug in.

This course will definitely help in understanding future case studies presented in other classes, and how such data are expressed...