Забыли?

?

# Design Supplemental

код для вставкиСкачать
```Design Supplemental
Design
вЂў Completely randomized
вЂў Randomized block design
вЂ“ Latin-Squares
вЂ“ Greco-Latin Squares
вЂў Repeated measures
вЂ“ Pre & post-test designs
вЂў Mixed designs (Between subjects factors and
repeated measures)
вЂ“ Split-plot
вЂў Random effects models
Completely randomized
вЂў The typical situation with only between-subjects
factors (IVs)
вЂў Participants are randomly assigned to treatment
groups
вЂў DonвЂ™t trust yourself to do this, peeps is bad at
randomizing
вЂў Example in R
вЂ“ x=c(1,2,3,4)
вЂ“ y=c(.25,.25,.25,.25)
пѓџ groups 1-4
пѓџ long run probability of
occurrence
вЂ“ sample(x, 80, replace=TRUE, prob = y)
Randomized block design
вЂў Suppose an effect is present that may influence results, but for
which you may not be interested in
вЂ“ Nuisance variable
вЂў We can extend the randomization process to control for a variety of
factors
вЂў Randomized Block Design
вЂ“ An extension of the completely randomized design in which a single
extraneous variable that might affect test unitsвЂ™ response to the
treatment has been identified and the effects of this variable are isolated
by blocking out its effects.
вЂў Examples:
вЂ“ Researchers collecting the data
вЂў Probably a relationship between who is collecting and outcome
вЂ“ Experimenter/participant expectations
вЂ“ Age
вЂў Create blocks of similarly aged individuals
Randomized block design
вЂў In such a situation we want to randomly
assign to treatment groups within each
blocking level
вЂў Example:
Treatment A
вЂ“ Experimenter 1: condition 1 condition 2 condition 3
вЂ“ Experimenter 2: condition 1 condition 2 condition 3
вЂ“ Experimenter 3: condition 1 condition 2 condition 3
Randomized Block Design
Independent Variables
Blocking variable
Control:
no music
Mornings and
afternoons
Evening hours
Experimental
treatment
slow music
Experimental
treatment:
fast music
Randomized block design
вЂў Partitioning the sums
of squares
вЂў F-statistic is now
MSb/t/MSresidual
вЂў This will be a more
powerful test,
although less so as
SSresdiual approaches
SSw/in
вЂ“ i.e. less effect due to
blocking variable
SStotal
SSb/t
SSw/in
SSresidual
SSblocks
Randomized block design
вЂў Conditions:
вЂ“ Correlation between blocking variable and DV
вЂ“ Treatment and blocking levels > 2
вЂ“ Random assignment within each blocking unit
вЂў Goal is to see homogeneous groups w/in each blocking level and
treatment levels will look similarly with respect to one another
вЂў Methods of implementation
вЂ“ Subject matching (age example)
вЂ“ Repeated measures (everyone gets all conditions, subjects
serve as their own block)
вЂ“ Other matched pair random assignment (e.g. twins, husbandwife)
вЂў Latin-squares and Graeco-Latin squares extend the blocking to 2
and 3 nuisance variables respectively
вЂ“ Consult Winer or Kirk texts
Latin Square Design
вЂў A balanced, two-way classification scheme that attempts
to control or block out the effect of two or more
extraneous factors by restricting randomization with
respect to the row and column effects
вЂ“ Randomly assign treatment levels according to levels of the
nuisance variable such that each level of the treatment variable
is assigned to the levels of the nuisance variables
вЂў Can help with sequence effects with RM variables
вЂў This special sort of balancing means that the systematic
variation between rows, or similarity between columns,
does not affect the comparison of treatments.
Latin Square
вЂў Example controlling for previous therapy experience and
therapist (A is the treatment, e.g. type of therapy)
Therapist
Previous therapy (none, yes
but no drugs, yes w/ drugs)
1
2
3
1
A1
A2
A3
2
A2
A3
A1
3
A3
A1
A2
Example: 3 groups receive Therapy A1
Group1- therapist1, no previous
Group2- therapist2, drugs
Group3- therapist3, no drugs
вЂў LS design has limited utility in behavioral sciences
вЂ“ Not too many cases with factors all with the same number of
levels
вЂ“ Kirk has suggested that with less than 5 levels itвЂ™s not very
practical due to few degrees of freedom for error term (except for
when several levels of treatment are assigned to each cell e.g. in
a mixed design situation)
вЂў Nevertheless, we see we have various ways to control
for a number of effects that might be present in our data
but not of interest to the study
Repeated measures design
вЂў Subjects participate in multiple levels of a
factor
вЂў Simplest is the paired t-test situation,
however вЂ�repeated measures designвЂ™ often
implies 3 or more applications of some
treatment
вЂў Example: pre and post-test
Mixed design
вЂў Between subjects factors with repeated
measures
вЂў Simple but common example is controltreatment, pre-post
вЂў Subjects are randomly assigned to control
or treatment groups and tested twice
Pre
Control
Treatment
Post
Mixed design
вЂў This example may appear a straightforward method for
dealing with data
вЂў However we could analyze it in two additional ways
вЂў ANCOVA
вЂ“ Test for differences at time 2 controlling (adjusting) for
differences seen at time 1
вЂў A simple t-test on the gain scores from pre to post
вЂ“ Same result as mixed but w/ different (simpler) output
вЂў One method may speak more to the exact nature of your
research question (e.g. only interested in time 2 or the
rate of improvement from time 1 to 2)
Mixed design
вЂў Consider also the possibility of an effect in posttest scores (or effect
of treatment) due solely to exposure to the pretest
вЂ“ E.g. with no prior training GRE scores would naturally be
assumed to increase the second time around more often than
not
вЂў Solomon four-group design
вЂў Split-plot factorial controls for a nuisance variable in mixed designs
вЂў WeвЂ™ll take a more in depth look at repeated measures and mixed
designs later
Random effects models
вЂў In most ANOVA design we are dealing with fixed effects
вЂў Random effects
вЂ“ If the levels included in our analysis represent a random
sample of all the possible levels, we have a random-effects
ANOVA.
вЂў With fixed effects the same levels of the effect would be used
again if the experiment was repeated.
вЂў With random effects different levels of the effect would be used.
вЂў The conclusion of the random-effect ANOVA applies to all the
levels (not only those studied).
Random effects models
вЂў
вЂў
вЂў
вЂў
Were the individual levels of the factor selected
due to a particular interest, or were they
chosen completely at random?
Will the conclusions be specific to the chosen
levels, or will they be applied to a larger
population?
If the experiment were repeated, would the
same levels be studied again, or would new
levels be drawn from the larger population of
possible levels?
Random effects models
вЂў Some examples:
вЂў Persuasiveness of commercials
вЂў Effect of worker on machine output
вЂў Vehicle emissions impact on the
environment
Random effects models
вЂў Determination of fixed vs. random effects affects
the nature of the tests involved, how the Fstatistic will be calculated (sometimes), and the
conclusions to be drawn
вЂў Some designs will incorporate both fixed and
random effects
вЂ“ Mixed effects designs
вЂў DonвЂ™t confuse with previous mixed design
factorial discussed previously
```
###### Документ
Категория
Презентации
Просмотров
21
Размер файла
167 Кб
Теги
1/--страниц
Пожаловаться на содержимое документа