Welcome to the OSF home of the Fall 2021 section of PA652: Statistical Analysis! Herein, you will find copies of the course syllabus, PDf copies of books and book chapters, the homework assignments, and other supporting materials. Here's an excerpt of the course objectives from the syllabus: ## Course Objectives Broadly speaking, by the end of this course, students will be able to: * formulate testable research hypotheses, based on operational definitions of variables; * describe how various statistical analyses or strategies address different types of questions and hypotheses; * interpret statistical results; and * articulate rationale, strengths, and limitations of various statistical analytical methods. This will be an applied course wherein practical data analysis skills will be emphasized over formal mathematical proofs. New **R** users are welcomed. If you have never programed before and feel intimidated at the prospect, this class is for you! We’re in this together. The structure of this course is tailored for the data-analysis needs of contemporary behavior analysts. In the first part of the course, we will focus on introductory programming and data wrangling skills. Using what is called Ordinary Linear Regression (OLS), our first handful of statistical models will be relatively simple, and all will make the conventional normality assumption. By the middle of the course, we will begin using a more powerful class of models from within the Generalized Linear Model (GLM), which will allow us to model a larger range of data types (e.g., counts, categorical data). In the final third of the class, we will explore models that allow for many repeated measures using the full Generalized Linear Mixed Model (GLMM) framework. These will be taught using the free and powerful **R** computing environment, with an emphasis on open science principles such as transparency and reproducibility. Though it is this final class of statistical models that will be of greatest use for behavior analysts, the earlier frameworks will be necessary for building a strong foundation. As this is an introductory course, our coverage of the material will be far from exhaustive. However, if I am successful with my intentions, students will be on good footing to seek out more advanced or specialized tools on their own.
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