Please check this website periodically for announcements regarding PSYCH 710.

"Without replication, all results should be taken as preliminary." -- Gary Marcus, Cleaning Up Science, The New Yorker News Desk, December 24, 2012

• The course outline can be obtained here.
• Lectures will be held onn Tuesday afternoon, 3:30-5:30 PM, in room ABB-270. The first lecture is on September 4th.
• You will be assigned to one of two statistics labs that will be held on Thursdays, 10:30AM-12:30PM or 12:30PM-2:30PM, in PC-154. The first labs will be on September 13th.
• The textbook for this course, Designing Experiments and Analyzing Data: A model comparison perspective (3rd ed., Maxwell & Delaney), can be obtained in the McMaster bookstore.
• The statistical softwared used in this course is R, which can be downloaded here.

• Office Hour:
  
  • TAs will hold weekly office hours at a time that will be announced shortly.
  • You can arrange to meet with me by sending me an email (with PSYCH 710 in the subject line).

• Review of basic aspects of descriptive and inferential statistics: PSYCH 710 assumes that students have taken an undergraduate course in inferential statistics. Some of the important concepts taught in such a course are reviewed in the following documents.
  • Maxwell & Delaney's basic statistics tutorial. This file also can be found on the CD that came with the textbook.
  • PJB's notes on z and t-tests [updated 10:55AM, September 9, 2014]
  • Instructions for getting the data sets that accompany A Beginner's Guide to R can be found here.
  • A brief handout describing how to extract subsets of data from R variables can be found here.
  • Here is a very useful discussion about using colourmaps to visualize data.

• The following items review important issues regarding significance testing:
  • The ASA's Statement on p-Values
  • Goodman, S (2008). A Dirty Dozen: Twelve P-Value Misconceptions, Seminars in Hematology, 45(3), 135-40.
  • Cohen, J (1994). The earth is round (p<.05). American Psychologist, 49(12), pp. 997-1003.
  • Lykken, D.T. (1968). Statistical significance in psychological research. Psychological Bulletin, 70(3), pp. 151-159.
  • Gelman, A (2013). P values and statistical practice. Epidemiology, 24(1), 69-72.
  • Loftus, G. (1996). Psychology will be a much better science when we change the way we analyze data. Current Directions in Psychological Science, 5, 161-71.
  • Krantz, D (1999). The null hypothesis testing controversy in Psychology. J. American Statistical Association, 44(448), 1372-81.
  • Significant
  • Please don't ever try to boost your results.

• R links
  • The Comprehensive R Archive Network (CRAN)
  • Introductory Tutorials Covering Miscellaneous Topics in R
  • R For Beginners
  • R for Data Science
  • Some Free R Manuals, Tutorials, etc.
  • Jonathan Baron's R reference card
  • tidyverse: A collection of several useful R packages that share common philosophies and are designed to work together.
  • R bloggers
  • Quick-R
  • NY Times article discussing why R is taking over the world
  • ITWorld article describing growth of R users relative to other statistical platforms.
  • Edinburgh Psychology R-users
  • A blog post illustrating how to do a paired-sample t test is here.
  • R Tutorial:
    • Learn how to do a chi-squared test of independence here.
    • A comparison of two proportions.
    • t tests for paired and independent samples.
    • Easy alternatives to bar charts in native R graphics.

• Interesting websites that deal with statistical issues
  • The following items review important issues regarding replication:
    • Five ways to fix statistics.
    • Cleaning Up Science [by Gary Marcus]
    • Reproducibility Project (Psychology)
    • How Reliable Are Psychological Studies (The Atlantic, Aug 27, 2015)
    • The Replicability of Cognitive Psychology in the OSF-Reproducibility-Project
    • The Bayesian Reproducibility Project
    • Mini Meta-Analysis of Your Own Studies
  • The 20% Statistician [Daniel Lakens blog]
  • Statistical Modeling, Causal Inference, and Social Science [An interesting and sometimes-entertaining blog about statistics]
  • Neuroskeptic
  • Flowing Data [Data Visualization]
  • Decision Science News
  • The Hardest Science
  • Peg's Blog [Blogs on on psychology, psychometrics, and statistics that help me to remember what I've read and done and think]

• Course notes on chapters from the textbook: (N.B. These notes may be modified during the term.)
  • If you need a review of basic statistics, please read my notes on testing differences between means. [updated 11-Sep 5:30 PM]
    
    • I created two R scripts that illustrate the central limit theorem. The scripts show the distribution of sample means when the scores are drawn from a uniform distribution and a log-normal distribution. To use the scripts you must download them to your computer and then execute them using R's source command. More detailed instructions, as well as a description of what you will see when the script executes, are provided in the script files (which can be read with any text editor or word processor). If you do open/edit the files with a word processor, make sure to save them as ascii text files.

    • R tutorial for conducting t tests for paired and independent samples.

  • Chapter 3 (One-way Between-Subjects ANOVA) [updated 03-Oct-2018 10:30 AM]
    
    • A script that contains the R commands that I used in class can be obtained here here.
    • A handout that provides an example of how to do a one-way ANOVA in R can be found here.
    • Several 1-way ANOVA practice questions can be found here.
  • Chapter 4 (Individual Comparisons Among Means)
  • Chapter 5 (Multiple Comparisons of Means)
    • Illustrations of potential problems associated with doing multiple comparisons can be found here, here, and here.
  • Chapter 6 (Trend Analyses) [updated on 28-Sept-2015, 4:00 PM]
  • Chapter 7 (Between-Subjects Factorial Designs)
    • Here is a paper describing common erroneous analyses of interactions.
  • Chapter 9 (Designs with Covariates)
  • Chapter 10 (Designs With Random or Nested Factors) [updated on 2-Dec-2014, 7:45 PM]
    • Errors and corrections for Table 10.10 and an equation on page 509.
  • Chapter 11 (One-way Within-Subjects Designs: Univariate Approach)
  • Chapter 12 (Higher-Order Designs with Within-Subjects Factors)

• Lab/Tutorial assignments

  Labs (eventually) will be listed here so that you can peek ahead to see what we are going to do. However, please note that the labs may be changed to reflect changes in the material that is covered during our lectures.

  • Lab 1: Introduction to R [handout; answer key]
    You can download the Squid dataset here.
  • Lab 2: Graphs, Confidence Intervals, t-tests [handout; answer key]
  • Lab 3: One-way ANOVA [handout; answer key]
  • Lab 4: Linear Contrasts and Trend Analysis [handout; answer key].
  • Lab 5: Multiple Comparisons [handout; answer key].
  • Lab 6: Factorial ANOVA (part 1) [handout; answer key].
  • Lab 7: Factorial ANOVA (part 2) and ANCOVA [handout; answer key].
  • Lab 8: Random and Nested ANOVA [[handout; answer key]
  • Lab 9: Within-Subjects ANOVA [handout; answer key ].
  • Lab 10: Factorial Within-Subjects ANOVA and Split-plot ANOVA [handout; answer key].