Few things in the sciences have the near-universal power to stoke the fires of contentious scholarly debates than the subject of null hypothesis significance testing – or NHST. Across many scientific disciplines NHST is the standard way in which we determine whether or not our research findings are worth talking about.Continue reading
Many students have a difficult time understanding standardization when starting out in learning statistics. Common questions often include:
- What does standardized mean?
- How do you standardize a score?
- Why should I give a damn?
The answers are fairly straightforward. Here’s a rundown for your statistical woes.
Welcome to another stupidly-long, but hopefully informative instructional on introductory statistical concepts. Today we tackle regression analysis. Use the menu links below to jump around if you need/want to get a quick bit of info on any topic:
1) Background: Correlation analysis [conceptually] explained
2) Correlation analysis and OLS linear regression
3) From guesses to predictions: The logic of using linear regression
— a) Building the equation
— b) Interpreting regression
— c) What is OLS?
4) Advanced Applications
5) Conclusion & Further reading
Not too long ago, I wrote an article here about advanced procedures for examining interactions in multiple regression. As I described some of the challenges researchers commonly face in trying to examine differences between people in a data set, I argued that when it comes to data analysis, splitting a continuous variable into a dichotomy (i.e. two categories) is kind of a dumb idea (MacCallum et al, 2002). Continue reading
During my undergraduate years I spent large segments of my working week learning SPSS. Much of it was trial and error (ok, mostly error), but in my trials I recall one consistent experience. An experience that is familiar to many other students, I’m sure.