Evaluating explanations and conclusion Spurious relationships Not all things

Evaluating explanations and conclusion spurious

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Evaluating explanations and conclusion Spurious relationships: Not all things that appear to be related are directly related. For example, lung cancer and the number of ashtrays a person owns are related. However, this relationship is spurious (i.e., misleading). Another variable directly related to each of these amount of cigarettes smoked produces an indirect relationship between ashtray and lung cancer. So when a researcher proposes a direct relationship between constructs, s/he should provide a convincing argument that there are no other variables producing this relationship. If then tests: These tests “are the workhorse of qualitative data analysis” (Miles & Huberman, 1994, p. 271). In the fuller version an if then test is a conditional sentence in the form of, If the hypothesis is true, then there should be a specific consequence. Every explanation based on data is a type of hypothesis, usually in the form of relationships among variables, underlying principles, or processes. The researcher tests his or her hypothesized explanation by predicting that some consequent would occur with a novel sample of people or set of events. The next two methods are much related to the if then test. Rival explanations: Eliminating competing explanations is a powerful way to add weight to a theoretical conclusion. The researcher formulates at least one plausible competing explanation and repeats the if then test. The explanation that best explains the data is the most plausible. The researcher can then report how the weaker explanations could not compete. However, the consumer must beware that the competing explanations offered are not straw men; that is, explanations that were not plausible in the first place easy to refute. This might occur if the researcher is so bent on her or his own explanation that s/he does not address more plausible hypotheses, but still wants to give the appearance that s/he has used this technique to gain credibility. Another caveat for the consumer is to not conclude that, just because the ompeting explanations were not as robust as the one proposed by the researcher, the proposed one is the best one. There might still be a better explanation than the one proposed, but it has not been discovered as of yet. In other words, the last person standing may not be the strongest. On a more practical note, the researcher must provide evidence that not only his or her explanations are better than the competition; they are also good in themselves. Replicating findings: This strategy is recognized by both qualitative and quantitative researchers as an excellent way to support hypotheses and theories. The more often the same findings occur despite different samples and conditions, the more confidence we can have in the conclusions. Hypothesized relationships that can only be supported by one sample of individuals in only one setting have little use in the practical world. Occasionally, a researcher will report several replications of the study in the same report. This is a good way to provide evidence for the robustness of his or her explanations.
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