Educational Research: Causal-Comparative Studies

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Educational Research
Causal-Comparative Studies
EDU 8603 Educational Research Richard M. Jacobs,
OSA, Ph.D.
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Research...

• The systematic application of a family of methods
  employed to provide trustworthy information about
  problems

an ongoing process based on many accumulated
understandings and explanations that, when taken
together lead to generalizations about problems
and the development of theories
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The basic steps of research...
Scientific and disciplined inquiry is an orderly
process, involving
? recognition and identification of a topic to
be studied (problem)
? description and execution of procedures to
collection information (method)
? objective data analysis
? statement of findings (results)
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Research methods...
Quantitative
collects and analyzes numerical data obtained
from formal instruments
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Quantitative methods...

• descriptive research (survey research)

• correlational research

• causal-comparative research (ex post facto
  research)

• experimental research

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• causal-comparative research (ex post facto
  research)

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
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Research variables...
Independent
an activity of characteristic believed to make a
difference with respect to some behavior
(syn.) experimental variable, cause, treatment
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dependent variable
the change or difference occurring as a result
of the independent variable
(syn.) criterion variable, effect, outcome,
posttest
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A causal-comparative study
a study in which the researcher attempts to
determine the cause, or reason, for pre-existing
differences in groups of individuals
called an ex post facto study because both the
effect and the alleged cause have already
occurred and must be studied in retrospect
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Differences in causal-comparison and
correlational studies

• causal-comparative studies

attempt to identify cause-effect relationships

• correlational studies

attempt to identify relationships
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• causal-comparative studies

involve two (or more) groups and one independent
variable

• correlational studies

typically involve two (or more) variables and
one group
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• causal-comparative studies

involve making comparisons

• correlational studies

involve establishing relationships
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Differences in causal-comparison and experimental
studies

• causal-comparative studies

individuals are not randomly selected but
selected because they belong to groups

• experimental studies

individuals are randomly selected and assigned
to two (or more) groups
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• causal-comparative studies

the researcher cannot manipulate the independent
variable

• experimental studies

the researcher manipulates the independent
variable
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• causal-comparative studies

the independent variable has already occurred
and cannot be manipulated

• experimental studies

the researcher manipulates the independent
variable to determine its effects
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• causal-comparative studies

the random sample is selected from two
already-existing populations

• experimental studies

the random sample is selected from a single
population
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Conducting a causal-comparative study
1. select the problem
2. select participants and instrument
3. design and procedure
4. data analysis and interpretation
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1. select the problem
the researcher starts with an effect and seeks
its causes
the independent variable cannot or should not be
manipulated
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2. select the participants and instrument
select samples representative of their
respective populations and similar with respect
to critical variables other than the independent
variable
called comparison groups
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3. design and procedure
the performance of the groups is compared using
some valid dependent variable measure
(instrument)
lack of randomization, manipulation, and control
are sources of weakness
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• control

the process by which the researcher attempts to
ensure that the findings are as free of
researcher bias and error as possible
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• types of control

random assignment of participants to groups
pair-wise matching
comparing homogeneous groups
comparing homogeneous subgroups
factorial analysis of variance
analysis of covariance
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• random assignment of participants to groups

not possible in causal-comparative studies
because the groups already exist and have already
received the treatment
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• pair-wise matching

first find a participant in the second (third,
fourth, etc.) group with the same or similar
score on the control (nonmanipulated) variable as
the participant in the first group
second if a participant in either group does
not have a suitable match, the participant is
eliminated from the study
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• comparing homogeneous groups

control for extraneous variables that are
homogeneous with respect to the extraneous
variables
limitation lowers the number of participants in
the study and, of course, limits the
generalizability of the findings
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• comparing homogeneous subgroups

form subgroups within each group that represent
all levels of the control (nonmanipulated)
variable
controls for the variable and also permits the
researcher to determine whether the independent
variable affects the dependent variable
differently at different levels of the control
(nonmanipulated) variable
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• factorial analysis of variance (FANOVA)

building the control (nonmanipulated) variable
into the research design
then use FANOVA to analyze the results to
determine the effect of the independent and
control (nonmanipulated) variable on the
dependent variable, both separately and in
combination
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FANOVA allows the researcher to determine if
there is an interaction between the independent
variable and the dependent variable such that the
independent variable operates differently at
different levels of the independent variable
building it into the research design
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• analysis of covariance (ANCOVA)

statistically adjusts initial group differences
on a dependent variable for initial differences
on some other variable related to performance on
the dependent variable
removes initial differences so that the results
can be fairly compared as if the two groups
started equally
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• symbolic representation of the basic
  causal-comparative design

Independent
Dependent Group Variable
Variable (E) (X)
O (C) O Where E (experimental group)
C (control group) X (independent variable) O
(dependent variable)
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Independent
Dependent Group Variable
Variable (E) (X1) O (C)
(X2) O Where E (experimental group) C
(control group) X (independent variable) O
(dependent variable)
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4. Data analysis and interpretation
researcher uses a variety of descriptive and
inferential statistics
t-test
mean
analysis of variance
standard deviation
chi squared
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• mean

the descriptive statistic indicating the average
performance of an individual or group on a
measure of some variable
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• standard deviation

the descriptive statistic indicating the spread
of a set of scores around the mean
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• t-test

the inferential statistic indicating whether the
means of two groups are significantly different
from one another
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• analysis of variance (ANOVA)

the inferential statistic indicating the
presence of a significant difference among the
means of three or more groups
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• chi squared (?2)

the inferential statistic indicating that there
is a greater than expected difference among group
frequencies
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Mini-Quiz

• True and false

causal-comparative studies attempt to identify
the cause-effect relationships correlational
studies do not
True
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causal-comparative studies typically involve two
(or more) groups and one independent variable,
whereas correlational studies typically involve
two (or more) variables and one group
True
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causal-comparative studies involve relation,
whereas correlational studies involve cause
False
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oftentimes, causal-comparative research is
undertaken because the independent variable could
be manipulated but should not
True
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one of the most important reasons for conducting
causal-comparative research is to identify
variables worthy of experimental investigation
True
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lack of control means that the researcher can
and should manipulate the independent variable
False
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each group in a causal-comparative study
represents a different population
True
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the more similar two groups are on all relevant
variables except the independent variable, the
stronger the study is
True
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there is random assignment to treatment groups
from a single population in causal-comparative
studies
False
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lack of randomization, manipulation of the
independent variable, and control are all sources
of weakness in a causal-comparative design
True
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matching, comparing homogenous groups or
subgroups, and covariate analysis are strategies
that enable researchers to overcome problems of
initial group differences on an extraneous
variable
True
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interpretation of the findings in a
causal-comparative study requires considerable
caution because the cause may be the effect and
the effect may be the cause
True
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extraneous variables or confounding factors may
be the real cause of both the independent and
dependent variables
True
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• Fill in the blank

groups selected for a causal-comparative study
which differ on some independent variable and
comparing them on some dependent variable
comparison groups
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• Fill in the blank

unexplained variables that influence a dependent
variable
confounding factors
extraneous variables
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• Fill in the blank

a method for controlling extraneous variables by
comparing groups that are homogeneous with
respect to the extraneous variable
comparing homogeneous groups
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• Fill in the blank

a method for controlling extraneous variables by
forming subgroups within each group that
represent all levels of the control variable
comparing homogeneous subgroups
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• Fill in the blank

a statistical tool to determine the effects of
the independent variable and the control variable
on the dependent variable, both separately and in
combination
factorial analysis of variance
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• Fill in the blank

a statistical tool to adjust initial group
differences on variables
analysis of covariance
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• Fill in the blank

the descriptive statistic indicating the average
performance of a group on a measure of some
variable
mean
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• Fill in the blank

the descriptive statistic indicating how
clustered or spread out around the mean a set of
scores is
standard deviation
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• Fill in the blank

the inferential statistic determining whether
there is a significant difference between the
means of two groups
t-test
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• Fill in the blank

the inferential statistic determining whether
there is a significant difference between the
means of three or more groups
analysis of variance
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• Fill in the blank

the inferential statistic determining whether
there is a greater than expected difference among
group frequencies
chi squared
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• Fill in the blank

activities by which a researcher endeavors to
ensure that the results of a causal-comparative
study are not tainted by extraneous variables
control
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This module has focused on...
causal-comparative studies
which identify the cause, or reason, for
existing differences in the behavior or status of
groups
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The next module will focus on...
experimental studies
...which test hypotheses to establish
cause-and-effect relationships