- Dec 2017
-
crumplab.github.io crumplab.github.io
-
In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.
For the most part this section of Ecperimental Design was an easy read and had very few confusing parts. I was able to follow along and understand all the components of an Experimental Design
-
To demonstrate this problem, he asked participants to rate two numbers on how large they were on a scale of 1-to-10 where 1 was “very very small” and 10 was “very very large”. One group of participants were asked to rate the number 9 and another group was asked to rate the number 221 (Birnbaum 1999). Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this difference is because participants spontaneously compared 9 with other one-digit numbers (in which case it is relatively large) and compared 221 with other three-digit numbers (in which case it is relatively small).
This example to give us an idea of the concept was intriguing and enjoyable to read. It wasn't a boring experiment, and I was actually quite interested while reading it.
-
There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others.
Gave me a good understanding of how counterbalancing works and what it does
-
Clearly, a between-subjects design would be necessary here.
It is not very clear to me as to why a between-subject design is automatically necessary here
-
Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition.
Good explanation of random assignment
-
This matching is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.
-
- Oct 2017
-
crumplab.github.io crumplab.github.io
-
hapter 8 Control Problems
Honestly, I thought this chapter was written really well. There were a lot of key concepts when it came to Control Problems and the examples really help the reader understand each concept as well. I enjoyed the witty and humorous remarks about Bugs Bunny as well because it made the rest of the information packed reading more enjoyable.
-
tching is often attempted in research with special populations. For example, research comparing the effect of brain damage to a specific region of the brain on some ability often first measures performance among a group of subjects with a specific pattern of brain damage, and then finds matched control subjects who do not have brain damage, but are as similar as possible on other dimensions as each of the impaired subjects.
Is there a limitation of how big the matching can be in regards to the population can it be a small population or does it have to be a large one
-
The aim of matching is the same, to create equivalent groups of subjects in each experimental condition.
Maybe mention if the matching can be done at random? Because it was something I questioned myself
-
There can also be interactions between the two.
can there also be an effect between the two?
-
“All other things being equal”, or “All other conditions remaining the same”
Maybe an example of how this could better be understood, because I was a little confused as to what exactly this meant.
-
Bugs Bunny is a metaphor for all of the control problems that can plague experiments.
good and understandable metaphor that allows the reader to understand/get an idea of what Control Problems are.
-
know, someday these scientists are gonna invent something that will outsmart a rabbit —Bugs Bunny Hewwo! Acme Pest Contwol? Weww I have a pest I want contwolled. —Elmer Fudd
I like how the chapeter begins with something witty like this because then it grabs the readers attention and allows them to want to continue to keep reading.
-