CAUSAL-COMPARATIVE&

CORRELATIONALRESEARCH

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

The Three Types

• There are 3 types of causal-comparative research:– Exploration of Effects– Exploration of Causes– Exploration of Consequences

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

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.

Similarities of Causal Comparative to Correlational research

• Neither allow the researcher to manipulate the variables.

• Both attempt to explore causation.

Similarities of Causal comparative to Experimental research

• Both require at least one categorical variable.• Both compare group performances to

determine relationships.

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

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

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.

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

Data Analysis

• Construct frequency polygons.• Means and standard deviations (only if

variables are quantitative)• T-test for differences between means.• Analysis of covariance

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.

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.

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.

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.

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.

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

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.

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

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.