Chapter 10
Finding Relationships Among
Variables:
Non-Experimental Research
Non-experimental research
Non-experimental approaches are used
when the researcher is unable to control
the variables or the nature of the question
is not causal.
 In general, a non-experimental design
includes research where:

– the researcher does not manipulate an IV,
– has limited or no control over the nature or
timing of the treatment, or
– when causal relationships are not the primary
focus of the research.
Quasi experiments

Quasi experiments
– Designs that look a lot like true experiments and
that are statistically analyzed in similar ways.
– The researcher can compare groups but does
not control the nature and/or the timing of the
treatment or comparison variable. Or the
treatment may be a participant variable.
– Causal interpretations cannot be made.

Participant variables – variables associated with
the participants themselves (e.g.. gender, a
treatment the participant chose).
Time series designs

Time series design
– A quasi-experimental design where participants
who have been exposed to a treatment are
tested both before and after the introduction of
that treatment.
– The researcher does not control the nature of
the treatment or the time that it was
introduced.
– Usually involves several pre and posttest
measures and may include a comparison group;
both are methods to try to control extraneous
variables.
Time series designs

Interrupted time series design
– The researcher takes several pretest measures
and several posttest measures.
– Measuring behavior at different times allows
us to determine the natural fluctuation in
scores and better assess any post treatment
changes.
– Because there is no control group, we can’t
tell if something else was responsible for the
changes in scores (other than the treatment).
Time series designs

Multiple time series design
– Like an interrupted time series design but
includes a control group that was not exposed
to the treatment.
– Even with a control group be cautious about
interpretations. An alternative explanation
may explain the outcome (i.e.. the outcome
may not have resulted because of the
treatment).
Non-equivalent groups designs

Non-equivalent groups designs
– Used when researchers want to compare
groups that they know, or suspect, are
different at the outset of the study.
– Compares changes in behavior between the
groups.
– By comparing changes you control for initial
group differences.
Longitudinal research

Longitudinal research
– Involves studying a group of individuals over
a long period of time to determine how
characteristics measured earlier in life relate
to behavior later in life.
– Difficult and expensive to conduct.
– Attrition (loss of participants) can be a major
problem and may effect the internal validity of
the study.
Cross-sectional research

Cross-sectional research
– Used to study groups of people who are
different ages.
– Cohort effect – variables that are confounded
with age.

Sequential research
– Combines cross-sectional and longitudinal
research by selecting a cross section of ages
over a number of years. Cohort effects are
controlled by following a number of age cohorts
(cross-sectional) over time (longitudinal).
Cross-sectional research

Microgenetic method
– Involves carefully observing behavior during
periods when rapid change is occurring and
collecting both quantitative and qualitative
information.
– Used to study the process of change.
Case studies

Case studies
– In depth studies of a single individual.
– Used when a researcher is interested in
studying a single individual on many variables
and is not assessing a treatment.
– The objective is to describe the characteristics
of the individual case, not to generalize to a
population of similar cases.
– Often qualitative, but can be more
quantitative.
Correlational research

Correlational research
– Used to study relationships between variables.
– Variables that are systematically related are
said to be correlated.
– Often used to research topics that would
otherwise be unethical to study.
– Correlation does not infer causation!
– Confounding variables may affect the results.
Correlational research

Multiple regression
– A powerful technique that allows us to look at
the relationship between a number of
predictor variables and a single criterion
variable.
– Results tell us if the predictor variables are
positively or negatively correlated with the
criterion variable. Also shows the relative
importance of each variable in determining
behavior.