Transcript Non-Experimental Design

Non-Experimental Design
Where are the beakers??
Cautions
A relationship between two variables
does NOT mean one causes the other
(Think about the correlation between reading
achievement and body weight)
Correlation ≠ Causation
Cautions
Lack of variability in scores (e.g.
everyone scoring very, very low;
everyone scoring very, very high; etc.)
makes it difficult to identify relationships
Large sample sizes and/or using many
variables can identify significant
relationships for statistical reasons and
not because the relationships really exist
(Avoid shotgun approach)
Correlational Designs
Guidelines for interpreting the size of
correlation coefficients
– Much larger correlations are needed for
predictions with individuals than with
groups
Crude group predictions can be made
with correlations as low as .40 to .60
Predictions for individuals require
correlations above .75
Correlational Designs
Guidelines for interpreting the size of
correlation coefficients
– Exploratory studies
Correlations of .25 to .40 indicate the
need for further research
Much higher correlations are needed to
confirm or test hypotheses
How would you study…
The effect of smoking on student
achievement?
Whether children from abusive parents
have lower self-esteem than children of
non-abusive parents?
The differences in work ethic between
students of high, middle, and low socioeconomic status?
Why does most educational
research use non-experimental
designs?
There are ethical and logistical
considerations that often impede the use
of experimental studies.
What is the purpose of
non-experimental designs?
Describe current existing characteristics
such as achievement, attitudes,
relationships, etc.
There is no manipulation of an
independent variable
Causal-Comparative Design
A study in which the researcher attempts to
determine the reason for pre-existing
differences in groups of individuals
At least two different groups are compared on
a dependent variable or measure of
performance (called the “effect”) because the
independent variable (called the “cause”) has
already occurred or cannot be manipulated
Causal-Comparative Design
A “kissing cousin” to correlational
research design.
Causes studied after they have exerted
their effect on another variable.
Causal-Comparative Design
Drawbacks
– Difficult to establish causality based on
collected data.
– Unmeasured variables (confounding
variables) are always a source of potential
alternative causal explanations.
Causal-Comparative Example
Green & Jaquess (1987)
– Interested in the effect of high school
students’ part-time employment on their
academic achievement.
– Sample: 477 high school juniors who were
unemployed or employed > 10 hours/wk.
To Summarize…
Can non-experimental research claim causality?
NO!
Why?