One of our instructors–David Lillis–recently gave a talk in front of the Wellington R Users Group highlighting 15 Tips for using the R statistical programming language aimed at the beginner.
Below is a video recording of his presentation…
Karen Grace-Martin
R Programming Video: 15 Tips for The Beginner
March 25th, 2014 by Karen Grace-Martin
Analysis of Complex Sample Surveys Made Simple
March 19th, 2014 by Karen Grace-Martin
Complex Surveys use a sampling technique other than a simple random sample. Terms you may have heard in this area include cluster sampling, stratified sampling, oversampling, two-stage sampling, and primary sampling unit.
Complex Samples require statistical methods that take the exact sampling design into account to ensure accurate results.
This webinar, by guest presenter Dr. Trent Buskirk, will give you an overview of the common sampling techniques, and their effects on data analysis.
This webinar has already taken place. You can gain free access to a video recording of the webinar by completing the form below.
Statistically Speaking members can access this recording from the Analysis Toolbox Resources page at the Programs Center without signing up.
Member Training: Multicollinearity
March 1st, 2014 by Karen Grace-Martin
Multicollinearity isn’t an assumption of regression models; it’s a data issue.
And while it can be seriously problematic, more often it’s just a nuisance.
In this webinar, we’ll discuss:
- What multicollinearity is and isn’t
- What it does to your model and estimates
- How to detect it
- What to do about it, depending on how serious it is
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
Member Training: Discrete Time Event History Analysis
February 1st, 2014 by Karen Grace-Martin
What is the relationship between predictors and whether and when an event will occur?
This is what event history (a.k.a., survival) analysis tests.
There are many flavors of Event History Analysis, though, depending on how time is measured, whether events can repeat, etc.
In this webinar, we discussed many of the issues involved in measuring time, including censoring, and introduce one specific type of event history model: the logistic model for discrete time events.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
Effect Size Statistics
January 29th, 2014 by Karen Grace-Martin
Effect Size Statistics are all the rage.
Journal editors want to see them in every results section.
You need them for performing sample size estimates. (And editors want those too).
But statistical software doesn’t always give us the effect sizes we need.
In this webinar, we will go over:
- The difference between standardized and unstandardized effect size statistics
- An overview of effect size statistics for some common analyses (there seem to be so many!)
- How to calculate these when your software doesn’t give them to you
This webinar has already taken place. You can gain free access to a video recording of the webinar by completing the form below.
Statistically Speaking members can access this recording from the Analysis Toolbox Resources page at the Programs Center without signing up.
3 Tips to Make Interpreting Moderation Effects Easier
January 24th, 2014 by Karen Grace-Martin
Understanding moderation is one of those topics in statistics that is so much harder than it needs to be.
Here are three suggestions to make it just a little easier.
1. Realize that moderation just means an interaction
I have spoken with a number of researchers who are surprised to learn that moderation is just another term for interaction.
Perhaps it’s because moderation often appears with discussions of mediation. Or because we tend to think of interaction as being part of ANOVA, but not regression.
In any case, both an interaction and moderation mean the same thing: the effect of one predictor on a response variable is different at different values of the second predictor. (more…)