Elements of Research Design

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Elements of Research Design
For the purpose of explanation and prediction, scientific research finds it helpful to think in terms of cause and effect relationship. There is a procedure for identification of such relationship. In normal terms, a cause is something that makes some other thing to happen or change. For example, a stone thrown at the window pane will cause the pane of glass to shatter. In this situation, throwing of stone is the cause while the shattered glass is the effect. Such an implicit understanding of causality is found in everyday life. But the meaning of causality in scientific research rests on the assumption that one can observe a constant or stable association between the occurrence of events and from such an association a causative connection can be inferred. Causal relationships are generally regarded as the heart of scientific understanding and thus it is important to understand the bases of causal interferences. Social scientists generally require at least three kinds of evidence to establish causality. These requisites are association, direction of influence and nonspuriousness. Association: For one variable to be a cause of another, the variables must be statistically associated. If the pattern of changes in one variable is not related to changes in another, then the former is not the cause of the latter. Associations, of course, are almost never perfect. Perfect associations may be expected only under pure theoretical inference but not in the real world of observations. This is so because in real social situations it is not possible to eliminate completely the influence of extraneous factors. In actual practice, thus, causal relationships are determined in terms of strong or weak associations. For instance statistical measures only indicate the association of two variables. Cause and effect influence/deducation is based on the research design's formulation and theoretical premises. In social sciences, causal relationships are sometimes implied from comparatively weak associations. One reason for this is that many measurements in social sciences are imprecise. The

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primary reason though is that in explaining human action, it is assumed that there can be multiple causes operating independently or jointly to produce the same or similar effects. Direction of influence: The second criterion necessary to establish a causal relationship between events is that cause must precede its effect. At least the direction of influence should be from cause to effect. In other words, changes in the causal factor or independent variable must influence changes in the effect, or dependent variable; however, not vice versa. For example, the formation of clouds is always expected to precede the rains. The requirement of determining direction of influence has two major implications for designing a research study – a) hypothetical relationships should always specify the direction of influence among variables; and b) whenever the direction cannot be established theoretically it should be tested empirically. However, this task is relatively easier in experiments but often difficult in other types of social research. Nonspuriousness: When a correlation or association is produced by an extraneous third factor and none of the variables involved in the correlation has influenced the other then the relationship is called as a spurious relationship. The idea of nonspuriousness in research means that to infer a causal relationship from an observed correlation there should be good reasons to believe that there are no hidden factors which could have created an accidental or spurious relationship. To infer nonspuriousness, the researcher ideally is in a position to show that the relationship between the variables is held as constant. However, circumstances seldom allow research to control all variables. The greater the number of variables that is controlled without altering a relationship, the greater is the likelihood that the relationship is not spurious.

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