Section 3.2: Experiments in the Real World. Equal Treatment for All in Experiments. The experimenter must know exactly what treatments and responses he wants information about. The experimenter must provide all materials needed for the treatments and to measure the responses.
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Until the end of the study and the results are in, only the study’s statistician knows for sure who has the treatment and who has the placebo.
The cartoon brings us back
to non-sampling errors from
Chapter 2…the knowledge of
vocabulary is very
What will Agent B be doing
What will Agent Q be doing
What will Agent K be doing
B = treated differently
Q = non-adherer
K = dropout
Find print (magazine/newspaper) or online info regarding a clinical trial.
Print or photocopy entire article and bring to class on Friday. Make sure it has information about the procedures of the experiment.
Again, this is due in class on Friday.
Tomorrow, we will be passing back papers and passing out the books which we have. Those which do not get a book will get a photocopy of Chapter 3. I have been told books will be here this week (cross your fingers).
If you have borrowed a book, with permission or not, please bring it tomorrow so I can properly check it out.
Quiz for 3.1 is on block day.
#3.1A (state yes or no and give reason)
#3.6A (state the response variable)
#3.9B (state the first 5 rooms to the flat-rate group)
#3.14 (state 1 lurking variable)
Section 3.2: Experiments in the Real World
All subjects are randomly assigned to groups, and all groups are given different treatments.
So far the examples have had only one explanatory variable (ex., drug vs. placebo). A completely randomized design can have any numberof explanatory variables…
In this example, there are two explanatory variables to describe the durability
of fabric under repeated washings. The type of cleansing agent and the
temperature of the water are both explanatory variables which are being
tested. This produces 9 different treatments.
A combination effect is called an interaction.
Combines matching with randomization. It is an example of block designs.
Compares just two treatments.
Choose pairs of subjects as closely matched as possible. Assign one treatment to each subject by tossing a coin or reading odd and even digits from Table A. (every heads goes to group 1, every tails to group 2).
Sometimes a matched pair is one person who tests two items, one after the other.
A block is a group of experimental subjects that have some commonality that is known before the experiment that could affect the response to the treatments (could be divided by gender, age range, etc.).
In a block design, the random assignment of subjects to treatments is carried out separately within each block (like two, or more, randomized comparative experiments).
What type of sample design is a block design similar to?