causal comparative research
TRANSCRIPT
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CAUSAL-COMPARATIVE&
CORRELATIONALRESEARCH
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What is causal-comparative research?• Known as “ex post facto” research. (Latin “after the
fact”).• Attempt to determine the cause or consequences of
differences that already exist between or among groups of individuals.
• To identify a causative relationship between an independent variable and a dependent variable. Usually a suggested relationship (not proven) as the researcher do not have complete control over the independent variable.
• When Independent variables can not be or should not be examined using controlled experiment
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The Three Types
• There are 3 types of causal-comparative research:– Exploration of Effects– Exploration of Causes– Exploration of Consequences
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Similarities to Causal Comparative to Correlational research
• Both lack manipulation• Both require caution in interpreting results (Causion is
difficult to establish)• Both are examples of associational research:
• Researchers seek to explore relationships among variables.
• Both attempt to explain phenomena of interest.• Both seek to identify variables that are worthy of later
exploration• Often provide guidance for later experimental studies.
• Result can lead to testable experimental hypothesis
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Differences
Causal-Comparative• Typically compare two or
more groups of subjects.• Cause & Effect• Involves at least 1
categorical variable.• Analyzes data by
comparing averages or uses cross-break tables.
Correlational• One Group• Requires a score on each
variable for each subject.• Association of variables• Investigate 2 or more
quantitative variables.• Analyzes data by using
scatter plots and/or correlation coefficients.
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Similarities of Causal Comparative to Correlational research
• Neither allow the researcher to manipulate the variables.
• Both attempt to explore causation.
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Similarities of Causal comparative to Experimental research
• Both require at least one categorical variable.• Both compare group performances to
determine relationships.
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Differences
Causal-comparative• No manipulation of the
variables.• Provide weaker
evidence for causation.• The groups are already
formed, the researcher must find them.
• Should not/is not/can not be manipulated
Experimental• The independent variable
is manipulated.• Provide stronger evidence
for causation.• The researcher can
sometimes assign subjects to treatment groups.
• Manipulation of independent variable
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The steps…
• Problem Formulation• Select the sample to be studied.• Instrumentation- achievement tests,
questionnaires, interviews, observational devices, attitudinal measures…there are no limits…
• Collection of data• Analysis of data
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The design
• The basic design is to select a group that has the independent variable and select another group of subjects that does not have the independent variable.
• The 2 groups are then compared on the dependent variable.
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Internal Validity
• Usually 2 weaknesses in the research:– Lack of randomization– Inability to manipulate an independent
variable• Threats–Oftentimes subject bias occurs– Location– Instrumentation– Loss of subjects
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Data Analysis
• Construct frequency polygons.• Means and standard deviations (only if
variables are quantitative)• T-test for differences between means.• Analysis of covariance
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Proceed with caution!!!
• The researcher must remember that demonstrating a relationship between 2 variables (even a very strong relationship) does not “prove” that one variable actually causes the other to change in a causal-comparative study.
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Limitations
• There must be a “pre-existing” independent variable–Years of study, gender, age, etc.
• There must be active variables- variables which the research can manipulate –The length and number of study
sessions, instructional techniques, etc.
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Examples
• Exploration of effects caused by membership in a given group.–Question: What differences in abilities are
caused by gender?–Hypothesis: Females have a greater amount
of linguistic ability than males.
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Examples
• Exploration of causes of group membership.–Question: What causes individuals to join a
gang?–Hypothesis: Individuals who are members
of gangs have more aggressive personalities than individuals who are not members of gangs.
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Examples
• Exploration of the consequences of an intervention.–Question: How do students taught by the
inquiry method react to propaganda?–Hypothesis: Students who were taught by
the inquiry method are more critical of propaganda than are those who were taught by the lecture method.
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The Relationship between Years of Experience and Job Satisfaction
CORRELATIONAL DESIGNAlternate- There is a relationship between years of
experience and job satisfaction among elementary school teachers.
Null- There is a no relationship between years of experience and job satisfaction among elementary school teachers.
Sample: Randomly selected one group of teachers
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Data analysis- Correlational
• Correlation (r) between two variables within the group to test null hypothesis.
• Direction (+/-) and magnitude (.01 to 1) determine nature of relationship between the variables.
• If null hypothesis is rejected than the alternate hypothesis is accepted.
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The Relationship between Years of Experience and Job Satisfaction
CAUSAL COMPARATIVE DESIGNAlternate- Teachers with high level of experience will
be more satisfied with their job than teachers with low level of experience.
Null- Teachers with high level of experience will be equally satisfied with their job when compared with the teachers with low level of experience.
Sample: Two groups of teachers with high-low experience as independent variable
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Data analysis- Causal Comparative• Independent variable- years of experience
with dependent variable- job satisfaction• Comparing variables with mean job
satisfaction scores using t-test, ANOVA or other tests for both the groups.
• Accepting or rejecting hypothesis based on the test results will lead to conclusion.