Causal-comparative research aims to identify potential causes of existing differences between groups by comparing them without manipulation. It is used when experimental manipulation is not possible. Threats to internal validity like lack of randomization make causation difficult to infer. Analysis of covariance can statistically control for initial group differences, while frequency tables and t-tests are commonly used to analyze data. Results always require cautious interpretation due to limitations of the design.

2. PURPOSE OF
CAUSAL – COMPARATIVE
RESEARCH
 To determine cause of differences that
already existed between or among groups
then attempts to determine the reason, or the
results, of the differences
 Used when independent variables cannot
or should not be examined using controlled
experiments
 A common design in educational research
studies

3. CAUSAL - COMPARATIVE
&
CORRELATIONAL RESEARCH
 Lack of manipulation
 Requires caution interpreting
results
 causation is difficult to infer
 Both can support subsequent
experimental research

4. CAUSAL - COMPARATIVE
VERSUS
CORRELATIONAL RESEARCH
CAUSAL – COMPARATIVE
 CORRELATIONAL
 does not to understand cause
Attempts attempt to understand
and effect effect
cause and
 investigates at least independent
 investigates two or more
one variable
variables
 two or more groups, comparing
 one groupof subjects a score on
these groups / requires
each variable for each subject use
compare averages or
 analyzes tablesusing scatterplots
crossbreak data when analyzing
data or correlational coefficients
and/

5. CAUSAL - COMPARATIVE
&
EXPERIMENTAL RESEARCH
 requires at least one
categorical variable (group
membership)
 both compare performances
(average scores) to determine
relationships
 compares separate groups of
subjects

6. CAUSAL - COMPARATIVE
VERSUS
EXPERIMENTAL RESEARCH
 EXPERIMENTAL
CAUSAL - COMPARATIVE
 causalcomparisons
group group comparisons
 individuals already in groups
Individuals randomly assigned to
before research begins
treatment groups
 independent not manipulated
variable
manipulated by the researcher
 cannot
 should not
 is not

7.  NON – MANIPULATED
VARIABLES
 Age
 Sex
 Ethnicity
 Learning Style
 Socioeconomic Status
 Parental Educational Level
 Family Environment
 Preschool Attendance
 Type of School

8. INSTRUMENTATION
Any of these devices can be used:
 Achievement Tests
 Questionnaires
 Interview Schedules
 Attitudinal Measures
 Observational Devices

9. DESIGN
 Select two groups that differ
on some independent
variable
I. One group possesses some
characteristics that the
other does not
II. Each group possesses the
characteristic but in
differing amounts

10. ONE GROUP
POSSESSES SOME CHARACTERISTICS
THAT THE OTHER DOES NOT
INDEPENDENT DEPENDENT
GROUP VARIABLE VARIABLE
C O
I (Group possesses (Measurement)
Characteristic)
-C O
II (Group does (Measurement)
not possess
characteristic)

11. EACH GROUP POSSESSES THE
CHARACTERISTIC BUT IN DIFFERING
AMOUNTS
INDEPENDENT DEPENDENT
GROUP VARIABLE VARIABLE
C1 O
I (Group possesses (Measurement)
characteristic 1)
C2 O
II (Group possesses (Measurement)
characteristic 2 )

12. THREATS TO
INTERNAL VALIDITY
IN CAUSAL COMPARATIVE
RESEARCH
WEAKNESSES:
Lack of randomization
Inability to manipulate
an independent variable

13. WAYS OF CONTROLLING
EXTRANEOUS VARIABLES
Matching of Subjects
Finding or Creating
Homogeneous Subgroups
Statistical Matching

14. OTHER THREATS
Loss of Subjects
Location
Instrumentation

15. THREATS UNDER INSTRUMENTATION
Instrument Decay
Data Collector Characteristics
Data Collector Bias

16. EVALUATING THREATS TO
INTERNAL VALIDITY
• Step 1: ask: What specific factors either
are known to affect or may logically be
expected to affect the variable on
which groups are being compared?
• Step 2: ask: What is the likelihood of
the comparison groups differing on
each factor?
• Step 3: Evaluate the threats on the
basis of how likely they are to have an
effect.

17. SUBJECT CHARACTERISTICS
Socioeconomic Level of the Family
Gender
Ethnicity
Marketable Job Skills

18. DATA ANALYSYS IN CAUSAL
COMPARATIVE STUDIES
•The first step in a data analysis of a causal-
comparative study is to construct frequency
polygons.
•Means and standard deviations are usually
calculated if the variables involved are
quantitative.
•The most commonly used test in this study is a
t-test for differences between studies.
•Analysis of covariance is particularly useful in
this study.
•The results of causal-comparative studies
should always be interpreted with caution,
because they do not prove cause and effect.

19. ASSOCIATIONS BETWEEN
CATEGORICAL VARIABLES
Grade level and gender of teachers
(hypothetical data)
GRADE LEVEL MALES FEMALES TOTAL
Elementary 40 70 110
Junior High 50 40 90
Senior High 80 60 140
TOTAL 170 170 340

20.  Such data can be used for purposes of
prediction and with caution in the search
for cause and effect. Knowing that a person
is a teacher and male, for example, we can
predict with some degree of confidence
that he teaches either junior or senior high
school, since 76% of males who are
teachers do so.
 40/170 is the probability of error in this
table.
 In this example, the probability that gender
is a major cause of teaching level seems
quite remote.
 Further, prediction from cross break tables
is much less precise than the scatter plots.

21. ANALYSIS OF COVARIANCE
 Is used to adjust initial group differences
on variables used in causal-comparative
and experimental research studies.
 Analysis of covariance adjusts scores on a
dependent variable for initial differences
on some other variable related to
performance on the dependent.
 Suppose we were doing a study to
compare two methods, X and Y, of
teaching fifth graders to solve math
problems.
 Covariate analysis statistically adjusts the
scores of method Y to remove the initial
advantage so that the results at the end of
the study can be fairly compared as if the
two groups started equally.

22. DATA ANALYSIS
AND
INTERPRETATION
 Analysis of data involves a variety of
descriptive and inferential statistics.
 The most commonly used descriptive
statistics are
(a) the mean, which indicates the
average performance of a group on
some measure of a variable, and
(b) the standard deviation, which
indicates how spread out a set of scores
is around the mean, that is, whether the
scores are relatively homogeneous or
heterogeneous around the mean.

23.  The most commonly used inferential
statistics are:
(a) the t test, used to determine
whether the means of two groups are
statistically different from one
another;
(b) analysis of variance, used to
determine if there is significant
difference among the means of three
or more groups; and
(c) chi square, used to compare
group frequencies, or to see if an
event occurs more frequently in one
group than another.

24. LACK OF RANDOMIZATION
 Lack of randomization, manipulation,
and control factors make it difficult to
establish cause-effect relationships
with any degree of confidence.
 However, reversed causality is more
plausible and should be investigated.
 It is equally plausible that
achievement affects self-concept, as it
is that self-concept affects
achievement.

25.  The way to determine the correct order
of causality-which variable caused
which- is to determine which one
occurred first.
 The possibility of a third, common
explanation in causal-comparative
research is plausible in many situations.
 One way to control for a potential
common cause is to equate groups on
that variable.
 To investigate or control for alternative
hypotheses , the researcher must be
aware of them and must present
evidence that they are not in fact the
true explanation for the behavioral
differences being investigated.

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