This is yet another reason we cannot assume that data is 'objective' because it is quantitative in nature. For example, when constructing an experiment "an extreme groups design (e.g., assigning participants to high or low conditions) maximizes the variances of the components of the product term, it also results in much more power with respect to the interaction effect than would the corresponding observational design" (Cortina 2002: 343). Conversely, doing an experiment 'in the field' is likely to yield a less statistically-significant impact because of the inability to control the extremity of the variables. A recent study of the statistical power of research in the social sciences revealed that only 40% of all MIS studies had adequate statistical power to ensure that the probability that the null hypothesis would be rejected correctly at all times (Baroudi & Orlikowski 1989: 87). Significance criteria, sample estimate, and effect size, can all influence...
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