CAUSAL-COMPARATIVE RESEARCH Prepared for: Eddy Luaran Prepared by: Nur Hazwani Mohd Nor ( ) Noriziati Abd Halim ( ) Noor fadzilah binti Adnan ( ) Abdul Aqib Iqbal bin Abdul Aziz ( ) Muhammad Azizan bin Rozman( )

What is Causal-Comparative Research? O Determined cause or consequences to the existing research. O Something referred as ‘ex post facto’ O Two different type: O can be manipulated O manipulate

O example of type: O exploration of effects O exploration of causes O exploration of consequent O Similarity & differences between correlational research and experimental research to causal-comparative research

involved in CAUSAL COMPARATIVE RESEARCH STEP

Interest i.Identify ii. Define Consider i.Causes ii.Consequences

STEP EXAMPLE Interest in Student Creativity 5W 1H Questions 1.Who is the target person? 2.What cause the creativity? 3.Why do only certain student got the creativity while other don’t? 4.When the students are creative? 5.How do they show their creativity

STEP INSTRUMENTATION No limits of using instrumentation Example : 1.Questionnaires 2.Achievement Test 3.Interview Schedule 4.Attitudinal Measures 5.Observational Devices

STEP DESIGN i.Select 2 or more group ii.Do comparison

Group Independent Variable - C Non Art Student Dependent Variable O Level of Creativity Group Independent Variable - C Non Dropout Student Dependent Variable O Level of Creativity STEP Example of Basic Causal Comparative Design : Group 1 Independent Variable C Dropout Student Dependent Variable O Level of Creativity Group 2 Independent Variable C Art Student Dependent Variable O Level of Creativity

Threats to Internal Validity in Causal- Comparative Research O Divided into two threats: O Subject Characteristics O Other threats O Have two weaknesses: O Lack of randomization – since the groups are already formed. O Inability to manipulate an independent variable – the groups have already been exposed to the independent variable.

O Subject Characteristics: O The major threats to the internal validity of a causal-comparative study O The researcher has had no say in either the selection or formation of the comparison groups, there is always the likelihood that the groups are not equivalent on one or more important variables other than the identified group membership variable. O Three types of procedures can be use to reduce the chance of this threats which is: O Matching of Subjects O Finding or Creating Homogeneous Subgroups O Statistical Matching

O Matching of Subjects: O To control for an extraneous variable is to match subjects from the comparison groups on that variable. O Pairs of subjects, one from each group, are found that are similar on that variable. O Eliminate/reduced the particular subject if match cannot be found.

O Finding or Creating Homogenous Subgroups: O Create groups that are relatively homogenous on that variables – to control for an extraneous variable. O Find two groups that have similar subject – form subgroups that represent various levels of the extraneous variable (eg. high, middle, low) – compare the comparable subgroups.

O Statistical Matching: O To control for an important extraneous variable. O Adjusts scores on a posttest for initial differences on some other variable that assumed to be related to performance on the dependent variable.

O Other Threats: O Depends on the type of study being considered. O Eg. In non invention studies, If the persons who are lost to data collection are different from those who remain (as is often probable) and if more are lost from one group than the other(s), internal validity is threatened. O If unequal numbers are lost, an effort should be made to determine the probable reasons.

O Conclusion: O Subject Characteristics: O Deal with only four – socioeconomic level of the family, gender, ethnicity, and marketable job skills.

Evaluating threats to internal Validity in Causal-Comparatives Studies O -involves a set of steps similar for experimental studies O Step 1: the researcher need to be concerned with factors unrelated to what is being studied. O Step 2 : What is the likelihood of comparison groups differing on each of these factors? (that different between group cannot be explained away by factor that is the same for all group) O Step 3 : Evaluate the threats on the basis of how likely they are to have an effect and plan to control for them.

Subject characteristics O Ex: -gender -ethnicity Mortality Step 1:probable refusing to be interview is related the hypothesis causal variable Step 2: more student in the dropout refuse to interview Step 3: likelihood of having an effect unless control:high

Instrumentation Instrument decay O Step 1 -this study means interview fatigue O Step 2 -the fatigue could be different for the two groups O Step 3 -likelihood of having an effect unless control:moderate Data Collector characteristics O Step 1-Can be expected to influence the information obtained on the hypothesis causal variable O Step 2 -Interview should be balance across the two groups O Step 3 - Likelihood of having an effect unless control :moderat Data collector bias O Step 1 -bias might be related to information obtained on the hypothesis O Step 2 -bias might differ for the two groups O Step 3 - likelihood of having an effect unless control: high

Other treats O -implementation, history, maturation, attitudinal and regression threats O -trick to identifying threats to internal validity in causal study O -based on evidence or experience O -can be greatly reduced if causal comparative are replicated

Data Analysis Causal-comparative research

Analyzing data First step in analyzing data in causal comparative study is : O To construct frequency polygons and then calculate the mean and standard deviation of each group. O Means and standard deviation are usually calculated if the variables involved are quantitative.

O Commonly used test in causal-comparative studies is a : O t –test : its for differences between means. O When more than 2 groups are used, then either an analysis of variance or an analysis of covariance is the appropriate test.

Analysis of covariance -The Analysis of Covariance (generally known as ANCOVA) is a technique that sits between analysis of variance and regression analysis. Particularly helpful in causal-comparative research. Its provide a way to match group on such variable as age, socioeconomic, status and so on.

O Before analysis of covariance can be used the data involved need to satisfy certain assumptions. O The result must be interpreted with caution. O Causal-comparative studies are good at indentifying relationship between variable but do not prove cause and effect.

2 ways to strengthen the interpretability of casual-comparative studies O First, alternative hypothesis should be formulated and investigated. O Second, if the dependent variable involved are categorical the study should be examined using the technique of discriminant function analysis. O The most powerful way to check on possible causes is perform an experiment.

Steps Involved in Causal- Comparative Research O Problem Formulation O The first step is to identify and define the particular phenomena of interest and consider possible causes O Sample O Selection of the sample of individuals to be studied by carefully identifying the characteristics of select groups O Instrumentation O There are no limits on the types of instruments that are used in Causal-comparative studies O Design O The basic design involves selecting two or more groups that differ on a particular variable of interest and comparing them on another variable(s) without manipulation (see Figure 16.1)

Threats to Internal Validity in Causal-Comparative Research O Subject Characteristics O The possibility exists that the groups are not equivalent on one or more important variables O One way to control for an extraneous variable is to match subjects from the comparison groups on that variable O Creating or finding homogeneous subgroups would be another way to control for an extraneous variable O The third way to control for an extraneous variable is to use the technique of statistical matching.

Does a Threat to Internal Validity Exist?

Other Threats O Loss of subjects O Location O Instrumentation O History O Maturation Data collector bias Instrument decay Attitude Regression Pre-test/treatment interaction effect