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Chapter 8

PSYB01H3 Chapter Notes - Chapter 8: Developmental Psychology, Selection Bias, Quasi

Course Code
David Nussbaum

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Chapter 8: Quasiexperimental and Nonexperimental Designs
What allows us to label single and multifactorial experiments as true = randomization
oBetween subject - means randomly assigning participants to different conditions
or levels of the independent variables
oWithin subject - means participants receive all levels or conditions of the
independent variables in a randomized or counterbalanced order
Quasiexperimental Design
Gender, race, age, ethnicity, socio-economic status (SES), locale, diagnosis, personality
traits, and personal history are just some examples of independent variables that are not
possible for an experimenter to directly manipulate
When particular individual characteristics are used for the bases of selecting research
participants, an experimenter is often interested in studying the effects of these subject
variables on a dependent measure
oHere a subject variable such as gender is treated as a type of independent
When exposures to events, situations, or settings that emanate from the 'real world'
define how participants are selected, we refer to this type of independent variable as a
natural treatment
Independent variables of true experiments are completely controlled ad directly
manipulated by the experimenter
Subject variables and natural treatments belong to a distinct class of independent
variables that many behavioural researchers term "quasi-independent"
oA quasiexperiment is one that investigates the effects of quasi-independent
variable on a dependent variable
oAn experimenter can directly control and manipulate one or more of the
independent variables
oQuasiexperiments offer a fertile research designs for investigating some of the
most important and creative questions in psychology
Nonequivalent-control-group designs: nonequivalent-control-group designs have
experimental and comparison groups that are designated before the treatment occurs and
are not created by random assignment
Before-and-after designs: before-and-after designs have a pretest and posttest but no
comparison group. In other words, the participants exposed to the treatment serve, at an
earlier time, as their own controls
Natural Treatments as Quasi-Independent Variables
Natural treatments fall into the class of quasi-iandependent variables that cannot be
directly manipulated;
oResearcher can provide only an "after-the-fact" or ex post facto analysis
Subject Variables as Quasi-Independent Variables
Mixed factorial designs combines both between and within subjects factors
Random assignment provides us with the best chance of ensuring that the groups would
be equivalent at the beginning of the experiment before the treatment or the manipulation
of the independent variable
Groups are selected on the basis of pre-existing, immutable subject characteristics or
exposure to some kind of natural treatment
The control group can never be considered to be equivalent to the experimental group
Pre-existing differences cannot be equalized by random assignment as would be the case
in a true experiment
oThe most common quasiexperiment is referred to as a nonequivalent control
group design
Have experimental and control groups that have been predetermined or
predesignated by either an existing subject characteristics or an already
occurred natural treatment that are not created by random assignment

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Quasiexperimental studies with nonequivalent control or comparison groups often receive
high marks for their external validity in general, and their ecological validity in particular
oAre often high in generalizability and high in realism
Two methods of selection of a control group can be used: individual matching and
aggregate matching
oIndividual matching: individual cases in the treatment group are matched with
similar individuals in the control group
Can create a control group that is very similar to the experimental group
When rndom assignment is not possible, the second method of matching makes mores
sense: identifying a comparison group that matches the treatment group in the aggregate
rather than trying to match individual cases
oReferred to as aggregate matching: it means finding a comparison group that
has similar distributions as key variables
A researcher matches on a variable that is highly related to the dependent variable
oA researcher can collect pretest measures and then match participants either
individuals or in the aggregate on pretest scores
regression artifact - can occur anytime a pretest measure is used for matching
When retested on that same ability earn sores that are closer to the mean of their group
than were the original scores - regression to the mean
oMay be confounding factor for interpreting results of studies that match groups
on pretest measures
oIs important to consider whenever random assignment is not or cannot be used
and groups are matched on a pretest measure
Before and After Designs
Most common feature - absence of a comparison group
All cases are exposed to the experimental treatment
oBasis for comparison is provide by comparing the pretreatment to the posttest
oSimples types = fixed sample panel design (one pretest and one posttest)
Interrupted time series design is often used in quasi-experimental research to examine
observations before and after a naturally occurring treatment
oSimplest time series design - there is a single experimental group for which we
have obtained multiple observations before and after a naturally occurring treatment
Multiple group before and after design
oSeveral before and after comparisons are made involving the same independent
and dependent variables but with different groups
Another type - involves multiple pretest and posttest observations of the same group
oRepeated measures panel designs which include several pretest and posttest
Are stronger than simple before and after panel designs because they
allow the researcher to study the process by which an intervention or treatment
has an impact over time
oTime series designs
Compare the trend in the dependent variable up to the date of the
intervention or event whose effect is being studied and the trend in the
dependent variable after the intervention
Particularly useful for studies of the impact of new laws or social programs
that affect everyone and that are readily assessed by some ongoing
Correlational Relationships
Quasi-experiments lack design features that improve internal validity, thus lessening
coincidence in causal conclusions
Researchers are often interested in examining the relationship between variables
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