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In a time where new research methods are constantly being developed and science is evolving, researchers must continually educate themselves on cutting-edge methods and best practices related to their field. The second of three volumes, this Handbook provides comprehensive and up-to-date coverage of a variety of issues important in developing, designing, and collecting data to produce high-quality research efforts. First, leading scholars from around the world provide an in depth explanation of various advanced methodological techniques. In section two, chapters cover general important methodological considerations across all types of data collection. In the third section, the chapters cover self-report and behavioral measures and their considerations for use. In the fourth section, various psychological measures are covered. The final section of the handbook covers issues that directly concern qualitative data collection approaches. Throughout the book, examples and real-world research efforts from dozens of different disciplines are discussed.
The sciences have been perennially interested in understanding similarities and differences between the sexes. Among humans, both males and females seek to secure serially monogamous partnerships with kind and intelligent mates similar to themselves. However, the sexes differ in the relative value placed on resources and physical attractiveness, their willingness to engage in short-term liaisons, and jealousy in response to emotional and sexual infidelities. Consideration of cultural factors, modern relationship innovations, and diversity in sexual orientation and gender identity provides further complexity to our understanding of similarities and differences. Recommendations are made for future research in these areas, and the societal implications of evolutionary work on the sexes is discussed.
Item response theory (IRT) represents an alternative measurement approach to Classical test theory (CTT) that has been developed to address some of the key limitations in CTT. IRT utilizes a logistic function to jointly scale both person characteristics (e.g. ability) and task characteristics (e.g. difficulty) along a common metric, and is grounded upon the notion that different item sets should not result in different scaling solutions. This provides IRT with a number of advantages over CTT; namely that the performances of individuals who are administered different sets of tasks may still be justifiably compared. From this basis, the IRT approach has established its utility through its applications in such varied contexts as adaptive testing, cognitive diagnostic modeling, item difficulty modeling, and latent class analyses. The current chapter focuses on applied issues in measurement with IRT, with emphasis on its distinct advantages over traditional approaches.
In this chapter, the authors review concepts related to reliability and validity of measurement in the social sciences. Necessarily eschewing detailed reviews of statistical methods to examine reliability and validity of measurement, the authors focus on terse discussion on key concepts and elementary methods. In addition, given the immense literatures on topics the authors could only discuss in brief, the authors point to sources that provide helpful, focused treatments of undiscussed ideas and techniques. Authors conclude that, given reliability and validity of measurement is crucial to perform scientific research, social scientists ought to prioritize establishing evidence for reliability/validity of measurement for social scientists’ measures and experimental methods.