With low interest in this week’s tutorial, I have decided to cancel the session.
Please visit https://oacstats.blog/2018/05/01/s18-spss-workshop/ to review the different topics of the SPSS workshop.
If you have any questions, please feel free to book an appointment to go over them – http://oacstats.youcanbook.me
Thank-you and sorry for the inconvenience,
Oh yes! It is that time of year again 🙂 I have to admit that I love fall – my favourite season. The time for so many new beginnings. With this all in mind, the new schedule for F19 OACStats workshops is now open for registration at https://oacstats_workshops.youcanbook.me/. Workshops will be approximately 3 hours long with breaks and hands-on exercises – so bring your laptops with the appropriate software installed. Please note that the workshops are being held in Crop Science Building Rm 121B (room with NO computers) and will begin at 8:30am.
September 10: Introduction to SAS
September 17: Introduction to R
October 15: Getting comfortable with your data in SAS: Descriptive statistics and visualizing your data
October 29: Getting comfortable with your data in R: Descriptive statistics and visualizing your data
November 5: ANOVA in SAS
November 15: ANOVA in R
I am also trying something new this semester – to stay with the theme of new beginnings 🙂 Tutorials! These will be held on Friday afternoons from 1:30-3:30 – sorry only time I could get a lab that worked with all the schedules. They will be held in Crop Science Building Rm 121A (room with computers). Topics will jump around a bit with time to review and work on Workshop materials. To register for these please visit: https://oacstatstutorials.youcanbook.me/
September 13: Saving your code and making your research REPRODUCIBLE
Cancelled: September 20: Introduction to SPSS
September 27: Follow-up questions to Intro to SAS and Intro to R workshops
October 18: More DATA Step features in SAS
October 25: More on Tidy Data in R
November 1: Open Forum
November 15: Questions re: ANOVAs in SAS and R
November 29: Open Forum
I hope to see many of you this Fall!
One last new item – PODCASTS. I’ll be trying to record the workshops and tutorials. These will be posted on the new page and heading PODCASTS. I will also link to them in each workshops post.
Welcome back and let’s continue to make Stats FUN
The W19 lineup of workshops are now available for viewing and for registration. Please note that the workshops will all be held on Wednesday mornings from 9-11am in the Crop Science Building – Rm 121A. Please review the list of workshops and visit the online registration page to sign up for the workshops that you are interested in.
- January 9, 2019: Managing your Research Data – Collecting your data, getting it organized and ready for statistical analysis.
- January 16, 2019: Managing your Research Data – Storing your data, backing it up, and future access.
- January 23, 2019: SAS- Introduction and Getting Comfortable with the interface and your data
- January 30, 2019: R – Introduction and Getting Comfortable with the interface and your data
- February 6, 2019: SPSS – Introduction and Getting Comfortable with the interface and your data
- February 27, 2019: SAS- Wrangling and Visualizing your data
- March 6, 2019: R- Wrangling and Visualizing your data
- March 13, 2019: SAS- Overview of GLIMMIX – Mixed model analysis and Regression
- March 20, 2019: R- Overview of aov(), lmer(), nlme() – Mixed model analysis and Regression
- March 27, 2019: SAS- Special topics – please send requests
- April 3, 2019: SAS- Special topics- please send requests
Have a great holiday break and hope to see you in the New Year!
Many statistical procedures test specific hypotheses. Principal Component Analysis (PCA), Factor analysis, Cluster Analysis, are examples of analyses that explore the data rather than answer a specific hypothesis. PCA examines common components among data by fitting a correlation pattern among the variables. Often used to reduce data from several variables to 2-3 components.
When running a PCA, you need to consider a couple of questions: How many factors/components should be used, and how do you interpret the factors/components?
Before running a PCA, one of the first things you will need to do is to determine whether there is any relationship among the variables you want to include in a PCA. If the variables are not related then there’s no reason to run a PCA. The data that we will be working with is a sample dataset that contains the 1988 Olympic decathlon results for 33 athletes. The variables are as follows:
run100m: time it took to run 100m
longjump: distance attained in the Long Jump event
shotput: distance reached with ShotPut
highjump: height reached in the High Jump event
run400m: time it took to run 400m
hurdles110m: time it took to run 110m of hurdles
discus: distance reached with Discus
polevault: height reached in the Pole Vault event
javelin: distance reached with the Javelin
run1500m: time it took to run 1500m
score: overall score for decathlon
Download the data in an excel spreadsheet here.
For this workshop we will conduct the same analysis in the 3 commonly used statistical packages: SPSS, SAS, and R. We will stat with SPSS then progress to SAS and finally to R.
If you are using SAS, please download the SAS program.
If you are using R, please download the R Script file.