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      Prioritizing and overcoming biomass energy barriers: Application of AHP and G-TOPSIS approaches

      , , , , ,
      Technological Forecasting and Social Change
      Elsevier BV

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          How to make a decision: The analytic hierarchy process

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            Application of multi-criteria decision making to sustainable energy planning—A review

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              An Inconvenient Truth: Arbitrary Distinctions Between Organizational, Mechanical Turk, and Other Convenience Samples

              Sampling strategy has critical implications for the validity of a researcher's conclusions. Despite this, sampling is frequently neglected in research methods textbooks, during the research design process, and in the reporting of our journals. The lack of guidance on this issue often leads reviewers and journal editors to rely on simple rules of thumb, myth, and tradition for judgments about sampling, which promotes the unnecessary and counterproductive characterization of sampling strategies as universally “good” or “bad.” Such oversimplification, especially by journal editors and reviewers, slows the progress of the social sciences by considering legitimate data sources to be categorically unacceptable. Instead, we argue that sampling is better understood in methodological terms of range restriction and omitted variables bias. This considered approach has far-reaching implications because in industrial–organizational (I-O) psychology, as in most social sciences, virtually all of the samples are convenience samples. Organizational samples are not gold standard research sources; instead, they are merely a specific type of convenience sample with their own positive and negative implications for validity. This fact does not condemn the science of I-O psychology but does highlight the need for more careful consideration of how and when a finding may generalize based on the particular mix of validity-related affordances provided by each sample source that might be used to investigate a particular research question. We call for researchers to explore such considerations cautiously and explicitly both in the publication and in the review of research.
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                Author and article information

                Journal
                Technological Forecasting and Social Change
                Technological Forecasting and Social Change
                Elsevier BV
                00401625
                April 2022
                April 2022
                : 177
                : 121524
                Article
                10.1016/j.techfore.2022.121524
                4dd003ea-d308-4b4a-872a-49853bf6c099
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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