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Q methodology is a research method used in psychology and in social sciences to study people's "subjectivity"—that is, their viewpoint. Q was developed by psychologist William Stephenson . It has been used both in clinical settings for assessing a patient's progress over time (intra-rater comparison), as well as in research settings to ...
Difference in differences (DID [1] or DD [2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. [3]
In 1938 Richardson [2] introduced a choice method in which subjects reported the most alike pair of a triad and the most different pair. The component of this method involving the most different pair may be properly called "MaxDiff" in contrast to a "most-least" or "best-worst" method where both the most different pair and the direction of difference are obtained.
One of the most important requirements of experimental research designs is the necessity of eliminating the effects of spurious, intervening, and antecedent variables. In the most basic model, cause (X) leads to effect (Y). But there could be a third variable (Z) that influences (Y), and X might not be the true cause at all.
Mode effect is a broad term referring to a phenomenon where a particular survey administration mode causes different data to be collected. For example, when asking a question using two different modes (e.g. paper and telephone), responses to one mode may be significantly and substantially different from responses given in the other mode.
Traditionally, research in cognitive psychology has focused on causal relations when the cause and the effect are both binary values; both the cause and the effect are present or absent. [6] [7] It is also possible that both the cause and the effect take continuous values. For example, turning the volume knob of a radio (as the cause) increases ...
Additional, early, detailed modeling work showed that the generalized estimating equation (GEE) modeling approach used by Christakis and Fowler (and other groups) was quite effective for estimating social contagion effects and in distinguishing them from homophily; [20] this paper concluded, "For network influence, we find that the approach ...
Second, the difference-in-differences (DID) method with a parallel trend assumption (2 groups would show a parallel trend if neither of them experienced the treatment effect) is a useful method to reduce the impact of extraneous factors and selection bias. [3] The differential effect of treatments (DET) was explored using several examples and ...