RESEARCH DESIGNS - QUANTITATIVE

CAUSAL-COMPARATIVE RESEARCH

It is a type of descriptive research since it describes conditions that already exist. It is a form of investigation in which the researcher has no direct control over independent variable as its expression has already occurred or because they are essentially non-manipulable. It also attempts to identify reasons or causes of pre-existing differences in groups of individuals i.e. if a researcher observes that two or more groups are different on a variable, he tries to identify the main factor that has led to this difference. Another name for this type of research is ex post facto research (which in Latin means ―after the fact‖) since both the hypothesised cause and the effect have already occurred and must be studied in retrospect. Causal-comparative studies attempt to identify cause-effect relationships, correlational studies do not.

Causal-comparative studies involvcomparison, correlational studies involve relationship. However, neither method provides researchers with true experimental data. On the other hand, causal-comparative and experimental research both attempt to establish cause-and-effect relationships and both involve comparisons. In an experimental study, the researcher selects a random sample and then randomly divides the sample into two or more groups. Groups are assigned to the treatments and the study is carried out. However, in causal-comparative research, individuals are not randomly assigned to treatment groups because they already were selected into groups before the research began. In experimental research, the independent variable is manipulated by the researcher, whereas in causal-comparative research, the groups are already formed and already different on the independent variable. Inferences about cause-and-effect relationships are made without direct intervention, on the basis of concomitant variation of independent and dependent variables. The basic causal-comparative method starts with an effect and seeks possible causes. For example, if a researcher observes that the academic achievement of students from different schools. He may hypothesise the possible cause for this as the type of management of schools, viz. private-aided, private-unaided, or government schools (local or state or any other). He therefore decides to conduct a causal-comparative research in which academic achievement of students is the effect that has already occurred and school types by management is the possible hypothesised cause. This approach is known as retrospective causal-comparative research since it starts with the effects and investigates the causes.

In another variation of this type of research, the investigator starts with a cause and investigates its effect on some other variable. i.e. such research is concerned with the question ‗what is the effect of X on Y when X has already occurred?‘ For example, what long-term effect has occurred on the self-concept of students who are grouped according to ability in schools? Here, the investigator hypothesises that students who are grouped according to ability in schools are labelled ‗brilliant‘, ‗average‘ or ‗dull‘ and this over a period of time could lead to unduly high or unduly poor self-concept in them. This approach is known as prospective causal-comparative research since it starts with the causes and investigates the effects. However, retrospective causal-comparative studies are far more common in educational research.

Causal-comparative research involves two or more groups and one independent variable. The goal of causal-comparative research is to establish cause-and-effect relationships just like an experimental research. However, in causal-comparative research, the researcher is able to identify past experiences of the subjects that are consistent with a ‗treatment‘ and compares them with those subjects who have had a different treatment or no treatment. The causal-comparative research may also involve a pre-test and a post-test. For instance, a researcher wants to compare the effect of ―Environmental Education‖ in the B.Ed. syllabus on student-teachers‘ awareness of environmental issues and problems attitude towards environmental protection. Here, a researcher can develop and administer a pre-test before being taught the paper on ―Environmental Education‖ and a post-test after being taught the same. At the same time, the pre-test as well as the post-test are also administered to a group which was not taught the paper on ―Environmental Education‖. This is essentially a non-experimental research as there is no manipulation of the treatment although it involves a pre-test and a post-test. In this type of research, the groups are not randomly assigned to exposure to the paper on ―Environmental Education‖. Thus it is possible that other variables could also affect the outcome variables. Therefore, in a causal-comparative research, it is important to think whether differences other than the independent variable could affect the results. In order to establish cause-and-effect in a causal-comparative research, it is essential to build a convincing rational argument that the independent variable is influencing the dependent variable. It is also essential to ensure that other uncontrolled variables do not have an effect on the dependent variable. For this purpose, the researcher should try to draw a sample that minimises the effects of other extraneous variables. According to Picciano, ―In stating a hypothesis in a causal comparative study, the word ―effect‖ is frequently used‖.

Conducting a Causal-Comparative Study

Although the independent variable is not manipulated, there are control procedures that can be exercised to improve interpretation of results. Design and Procedure The researcher selects two groups of participants, accurately referred to as comparison groups. These groups may differ in two ways as follows:

(i) One group possesses a characteristic that the other does not.

(ii) Each group has the characteristic, but to differing degrees or amounts.

(iii) Definition and selection of the comparison groups are very important parts of the causal-comparative procedure.

(iv) The independent variable differentiating the groups must be clearly and operationally defined, since each group represents a different population.

(v) In causal-comparative research the random sample is selected from two already existing populations, not from a single population as in experimental research.

(vi) As in experimental studies, the goal is to have groups that are as similar as possible on all relevant variables except the independent variable.

(vii) The more similar the two groups are on such variables, the more homogeneous they are on everything but the independent variable.

Control Procedures

Lack of randomization, manipulation, and control are all sources of weakness in a causal-comparative study.

Random assignment is probably the single best way to try to ensure equality of the groups.

A problem is the possibility that the groups are different on some other important variable (e.g. gender, experience, or age) besides the identified independent variable.

Matching

      Matching is another control technique.

      If a researcher has identified a variable likely to influence performance on the dependent variable, the researcher may control for that variable by pair-wise matching of participants.

      For each participant in one group, the researcher finds a participant in the other group with the same or very similar score on the control variable.

      If a participant in either group does not have a suitable match, the participant is eliminated from the study.

      The resulting matched groups are identical or very similar with respect to the identified extraneous variable.

      The problem becomes serious when the researcher attempts to simultaneously match participants on two or more variables.

Comparing Homogeneous Groups or Subgroups

ü  To control extraneous variables, groups that are homogeneous with respect to the extraneous variable are compared.

ü  This procedure may lower the number of participants and limit the generalisability of the findings.

ü  A similar but more satisfactory approach is to form subgroups within each group that represent all levels of the control variable.

ü  Each group might be divided into two or more subgroups on the basis of high, average, and low levels of ‗Independent variable‘.

ü  Suppose the independent variable in the study is students‘ IQ. The subgroups then will comprise of high, average, and low levels of IQ. The existence of comparable subgroups in each group controls for IQ.

ü  In addition to controlling for the variable, this approach also permits the researcher to determine whether the independent variable affects the dependent variable differently at different levels of the control variable.

ü  The best approach is to build the control variable right into the research design and analyze the results in a statistical technique called factorial analysis of variance.

ü  A factorial analysis allows the researcher to determine the effect of the independent variable and the control variable on the dependent variable both separately and in combination.

ü  It permits determination of whether there is interaction between the independent variable and the control variable such that the independent variable operates differently at different levels of the control variable.

The Value of Causal-Comparative Research:

In a large majority of educational research especially in the fields of sociology of education and educational psychology, it is not possible to manipulate independent variables due to ethical considerations especially when one is dealing with variables such as anxiety, intelligence, home environment, teacher personality, negative reinforcement, equality of opportunity and so on. It is also not possible to control such variables as in an experimental research. For studying such topics and their influence on students, causal-comparative method is the most appropriate.

The Weaknesses of Causal-Comparative Research:

There are three major limitations of causal-comparative research. These include,

(a) lack of control or the inability to manipulate independent variables methodologically,

(b) the lack of power to assign subjects randomly to groups and

(c) the danger of inappropriate interpretations.

The lack of randomization, manipulation, and control factors make it difficult to establish cause-and-effect relationships with any degree of confidence. The statistical techniques used to compare groups in a causal-comparative research include the t-test when two groups are to be compared and ANOVA when more than two groups are to be compared. The technique of ANCOVA may also be used in case some other variables likely to influence the dependent variable need to be controlled statistically. Sometimes, chi square is also used to compare group frequencies, or to see if an event occurs more frequently in one group than another.

 Use of Analysis of Covariance (ANCOVA)

It 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 sixth standard students to solve mathematical 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.