"Without replication, all results should be taken as preliminary." -- Gary Marcus, Cleaning Up Science, The New Yorker News Desk, December 24, 2012
- The first lecture for PSYCH 710 will be held on Tuesday, September 5 (12:30-2:00 PM).
- The first stats labs will be held on Thursday, September 7 (10:30-noon, 12:30-2:00). Assignment to the first or second lab section will be made during the first lecture.
- I will be updating the material on this website during August 29-31 (as well as throughout the term).
- 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.
- 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.
- Interesting websites that deal with statistical issues
- Lecture slides
- Review of statistical inference Part 1 [PDF]
- Review of statistical inference Part 2 [PDF]
- One-way ANOVA [PDF]
- Linear contrasts, trends, and multiple comparisons [PDF]
- Factorial ANOVA I [PDF]
- Factorial ANOVA II [PDF]
- ANCOVA [PDF]
- Random, Mixed, & Nested ANOVA [PDF]
- 1-Way Within-Subjects ANOVA [PDF]
- Higher-Order Within-Subjects ANOVA [PDF]
- R demos
- Central Limit Theorem: drawing samples from uniform and log-normal distributions
- distribution of p values when H0 is true and when H0 is false [R script]
- 1- and 2-tailed confidence interavals [ R script]
- 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 25-Sep-2021 11:33 AM]
- An R script containing the commands in my notes is here.
- 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.
- This on-line chapter by Daniel Lakens discusses philosophical and methodological issues concerning the use of p values to test hypotheses.
- This R script demonstrates some coverage properties of 2-tailed and 1-tailed confidence intervals.
- Fact: p values are distributed uniformly when the null hypothesis is true. [R script demo]
- Chapter 3 (One-way Between-Subjects ANOVA [Rscript]) [updated 03-Oct-2021 4:35 PM]
[Added a equations 18 and 19 in Section 3.3.6 and added Section 3.3.7]
- A script that contains the R commands that I used in class can be obtained here here.
- Chapter 4 (Individual Comparisons Among Means [Rscript]) [updated 25-Sep-2021 11:33 AM]
- Chapter 5 (Multiple Comparisons of Means [Rscript]) [updated 26-Sep-2022 9:10 AM]
[Modified Section 5.4.1 to include dunnettT3Test in the PMCMRplus package.]
- Chapter 6 (Trend Analyses [Rscript]) [updated 25-Sep-2021 11:33 AM]
- A brief comparison of trend analysis and regreression is presented here.
- Chapter 7 (Between-Subjects Factorial Designs [Rscript]) [updated 09-Oct-2021 09:37 PM]
[Added Sections 7.8.1, 7.10.1, and 7.11.2.]
- Here is a paper describing common erroneous analyses of interactions.
- Here is an illustration of a problem that can occur with a linear model that violates marginality.
- This handout shows how factorial ANOVA can be thought of as a set of orthogonal contrasts performed on a one-way design.
- Some useful R commands for summarizing data from factorial designs.
- This handout gives an example of analyzing 3-way interactions.
- Chapter 9 (Designs with Covariates [Rscript]) [updated 14-Nov-2023 12:07 PM]
[Replaced references to "textbook" with citations to Maxwell et al., 2004.]
- Chapter 10 (Designs With Random or Nested Factors) [Rscript]) [updated 23-Nov-2023 3:20 PM]
[Fixed minor typesetting errors.]
- Chapter 11 (One-way Within-Subjects Designs: Univariate Approach) [Rscript]) [updated 28-Nov-2023 10:42 AM]
[Fixed minor typographical errors.]
- Examples of mixed-model analyses. [HTML]
- Chapter 12 (Higher-Order Designs with Within-Subjects Factors) [Rscript]) [updated 27-Nov-2022 9:05 PM]
[Fixed some typographical errors and made very minor changes to Section 12.1.4]