Causal Comparative Study1.ppt

7/27/2019 Causal Comparative Study1.ppt

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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 (calledthe effect) because the

independent variable (called the

cause) has already occurred orcannot be manipulated


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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 ingroups 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 studiesattempt to identify relationships


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involve two (or more) groups and one

independent variable

correlational studiestypically involve two (or more)

variables and one group


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involve making comparisons

correlational studies

involve establishing relationships


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Differences in causal-comparison and

experimental studies


individuals are not randomly selected but

selected because they belong to groups

experimental studiesindividuals are randomly selected and

assigned to two (or more) groups


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the researcher cannot manipulate the


experimental studiesthe researcher manipulates the



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the independent variable has already

occurred and cannot be manipulated

experimental studiesthe researcher manipulates the

independent variable to determine itseffects


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the random sample is selected from two

already-existing populations

experimental studiesthe random sample is selected from a

single population


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types of controlrandom assignment of participants to

groupspair-wise matchingcomparing homogeneous groups

comparing homogeneous subgroupsfactorial analysis of varianceanalysis of covariance


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random assignment of participants to

groupsnot possible in causal-comparative

studies because the groups already

exist and have already received thetreatment


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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 groupdoes not have a suitable match, the

participant is eliminated from the study


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comparing homogeneous groups

control for extraneous variables thatare homogeneous with respect to the

extraneous variableslimitation: 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 independentand 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 interactionbetween 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 forinitial 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 ofdescriptive and inferential statistics:

mean

standarddeviation

t-test

analysis ofvariance

chi squared


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mean

the descriptive statistic indicating theaverage performance of an individual

or group on a measure of some

variable


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standard deviation

the descriptive statistic indicating thespread of a set of scores around the

mean


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t-test

the inferential statistic indicatingwhether the means of two groups are

significantly different from one

another


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analysis of variance (ANOVA)

the inferential statistic indicating thepresence of a significant difference

among the means of three or more

groups


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chi squared (2)

the inferential statistic indicating thatthere is a greater than expected

difference among group frequencies

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