Statistical Foundations

Statistical Foundations is designed to provide graduate students with a practical, applied approach to the application of fundamental behavioral and educational research design and statistical principles. Students will learn how to differentiate and appropriately select the best statistical methods for use in various research designs and analytical problems.

Note: This course was historically called “Research Design and Analysis I”


This course will mostly focus on basic statistical techniques and several forms of the ANOVA model, which can be used by themselves or serve as building blocks for more advanced techniques in other courses.

Students will also learn how to:

  1. Use the R statistical programming environment (via the R Studio IDE) to analyze data and

  2. Interpret and communicate the results of analyses (including creating reproducible research reports with R Markdown).


TEXTBOOK: Explaining Psychological Statistics, 4th edition, by Barry Cohen

EXAMPLES: Encyclopedia for Quantitative Methods in R

YOUTUBE: Sarah Schawrtz Stats LINK

Downloadables: PDFs for references

Chapters Topics Slides Worksheets
Intro to the Course
Cohen’s Textbook, 4th edition PDF or PPTX
APA Format Pertaining to Quantitative Analysis PDF or PPTX
Intro to R HTML
1 Variables, Scales, Rounding, & Summation HTML HW 1
2 Exploration of Data with Plots HTML HW 2
3 Summarizing Data with Descriptive Statistics HTML HW 3
4 Standardized Scores & The Normal Distribution HTML HW 4
5 Intro to Hypothesis Testing: 1 Sample z-test HTML HW 5
6 Confidence Interval Estimation: The t Distribution HTML HW 6
7 Independent Samples t-Test for Means PPTX HW 7
8 Statistical Power & Effect Size PPTX HW 8
9 Linear Correlation HTML HW 9
10 Linear Regression HW 10
11 Matched t-Test HW 11
12 1-way Independent Groups ANOVA HW 12
13 Multiple Comparisons HW 13
14 2-way ANOVA HW 14
15 Repeated Measures ANOVA HW 15
16 2-way Mixed Design ANOVA HW 16
19 The Binomial Distribution
20 Chi-Squared Tests