ABSTRACT
The OO ex post facto hypothesis is generated after the data have been collected, so it is not possible to disconfirm it (Babbie, 2010: 462). Classifying into dichotomous groups can be OO problematic. There is the difficulty of interpretation and the OO danger of the post hoc assumption being made, that is believing that because X precedes O, X causes O. As the researcher attempts to match groups on key OO variables, this leads to shrinkage of sample (Spector, 1993: 43). (Lewis-Beck (1993: 43) reports an example of such shrinkage from a sample of 1,194 to 46 after matching had been undertaken.) It often bases its conclusions on too limited a sample OO or number of occurrences. It frequently fails to single out the really significant OO factor or factors, and fails to recognize that events have multiple rather than single causes. As a method it is regarded by some as too flexible.OO It lacks nullifiability and confirmation.OO
15.6 Designing an ex post facto investigation We earlier referred to the two basic designs embraced by ex post facto research – the co-relational (or causal) model and the criterion group (or causal-comparative) model. As we saw, the causal model attempts to identify the antecedent of a present condition and may be represented thus:
Although one variable in an ex post facto study cannot be confidently said to depend upon the other as would be the case in a truly experimental investigation, it is nevertheless usual to designate one of the variables as independent (X) and the other as dependent (O). The left to right dimension indicates the temporal order, though having established this, we must not overlook the possibility of reverse causality. In a typical investigation of this kind, then, two sets of data relating to the independent and dependent variables respectively will be gathered. As indicated earlier in the chapter, the data on the independent variable (X) will be retrospective in character and as such will be prone to the kinds of weakness, limitations and distortions to which all historical evidence is subject. Let us now translate the design into a hypothetical situation. Imagine a secondary school in which it is hypothesized that low staff morale (O) has come about as a direct
result of reorganization some two years earlier, say. A number of key factors distinguishing the new organization from the previous one can be readily identified. Collectively these could represent or contain the independent variable X and data on them could be accumulated retrospectively. They could include, for example, the introduction of mixed ability and team teaching, curricular innovation, loss of teacher status, decline in student motivation, modifications to the school catchment area or the appointment of a new headteacher. These could then be checked against a measure of prevailing teachers’ attitudes (O), thus providing the researcher with some leads at least as to possible causes of current discontent. The second model, the causal-comparative, may be represented schematically as:
Using this model, the investigator hypothesizes the independent variable and then compares two groups, an experimental group (E) which has been exposed to the presumed independent variable X and a control group (C) which has not. (The dashed line in the model shows that the comparison groups E and C are not equated by random assignment.) Alternatively, she may examine two groups that are different in some way or ways and then try to account for the difference or differences by investigating possible antecedents. These two examples reflect two types of approach to causal-comparative research: the ‘cause-to-effect’ kind and the ‘effect-tocause’ kind. The basic design of causal-comparative investigations is similar to an experimentally designed study. The chief difference resides in the nature of the independent variable, X. In a truly experimental situation, this will be under the control of the investigator and may therefore be described as manipulable. In the causal-comparative model (and also the causal model), however, the independent variable is beyond her control, having already occurred. It may therefore be described in this design as non-manipulable.
