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If we are to examine the role of “controls” in different experimental settings, it is appropriate that the word be defined carefully. The Oxford English Dictionary (Second Edition) defines the verb “control” in the following manner: “To exercise restraint or direction upon the free action of; to hold sway over, exercise power or authority over; to dominate, command.” So the word means something more active and interventionist than is suggested by it’s colloquial clinical usage. Control can include such mundane things as ensuring sterile equipment in a chemistry lab, to restrain the free flow of germs and unwanted particles that might contaminate some test.

The use of behavioral insights and experimental methods has recently gained momentum among health policy-makers. There is a tendency, however, to reduce behavioral insights applications in health to “nudges,” and to reduce experiments in health to “randomized controlled trials” (RCTs). We argue that there is much more to behavioral insights and experimental methods in health economics than just nudges and RCTs. First, there is a broad and rich array of complementary experimental methods spanning the lab to the field, and all of them could prove useful in health economics. Second, there are a host of challenges in health economics, policy, and management where the application of behavioral insights and experimental methods is timely and highly promising. We illustrate this point by describing applications of experimental methods and behavioral insights to one specific topic of fundamental relevance for health research and policy: the experimental elicitation and econometric estimation of risk and time preferences. We start by reviewing the main methods of measuring risk and time preferences in health. We then focus on the “behavioral econometrics” approach to jointly elicit and estimate risk and time preferences, and we illustrate its state-of-the-art applications to health.

Bayesian A/B inference (BABI) is a method that combines subjective prior information with data from A/B experiments to provide inference for lift – the difference in a measure of response in control and treatment, expressed as its ratio to the measure of response in control. The procedure is embedded in stable code that can be executed in a few seconds for an experiment, regardless of sample size, and caters to the objectives and technical background of the owners of experiments. BABI provides more powerful tests of the hypothesis of the impact of treatment on lift, and sharper conclusions about the value of lift, than do legacy conventional methods. In application to 21 large online experiments, the credible interval is 60% to 65% shorter than the conventional confidence interval in the median case, and by close to 100% in a significant proportion of cases; in rare cases, BABI credible intervals are longer than conventional confidence intervals and then by no more than about 10%.

The results of standard lab experiments have long been questioned because of the convenience samples of subjects they typically employ and the abstract nature of the lab settings. These two characteristics of experimental economics, it is argued, are the key factors that endanger the external validity of experiments.

Researchers have tried to address these issues by bringing the lab to non-traditional subjects including participants in remote locations, and/or by moving the setting of experiments closer to reality by using real goods and/or settings that are not stripped of context.

While field experiments might help experimental economists to increase the external validity of their investigations, these potential benefits might come at costs that can be considerable. Specifically, going into the field can dramatically increase the demands on, and challenges to, experimental control. This is particularly true for experiments in small-scale societies in remote locations on which I focus in this article.

Digital gaming has become one of the largest entertainment sectors worldwide, increasingly turning the medium into an attractive vehicle for the communication of advertising messages. As a result, the incorporation of products or brands in digital games or in-game advertising (IGA) is expected to grow steadily over the course of the following years. However, much work is still needed to determine and optimize the effectiveness of IGA. The goal of the chapter is to advance IGA effectiveness research by investigating the influence of three aspects of the context in which a game is played and the player’s involvement in response to this context on brand awareness.

To this purpose, three experiments were set up. The first experiment (between-subjects, N = 121) investigated the impact of the social setting in which a game is played, the second experiment (within-subjects, N = 31) examined the effect of the game controls that are used, and the third experiment (between-subjects, N = 62) analyzed the influence of the game story.

The findings of the experiments show that the game context can significantly influence the way in which people recall and recognize brands that are included within its environment and that examining the player’s involvement in response to this context can provide useful information regarding the processes underlying this effect.

These findings contribute to the knowledge of when, how, and in which games advertising can be incorporated in order to achieve games’ full potential as an advertising medium.

This paper covers different types of research designs, like, longitudinal, cross-section and sequential design, experimental design (including factorial experimental design), and correlational design, with illustrative examples. This paper will help a scholar to know how choice of research design depends on a number of parameters, highlighted by author.

There are several ways to define words. One is to ascertain the formal definition by looking it up in the dictionary. Another is to identify what it is that you want the word-label to differentiate.

While classical exchange theorists excluded bargaining from the scope of their theories, most contemporary theorists have done the opposite, concentrating exclusively on negotiated exchanges with binding agreements. This chapter describes the theoretical logic and empirical results of a new program of research comparing the effects of reciprocal and negotiated forms of exchange. As the work shows, fundamental differences between the two forms of exchange affect many of the processes addressed by current theories. Reciprocal exchanges produce weaker power use, greater feelings of trust and affective commitment, and stronger perceptions of the partner’s fairness than equivalent negotiated exchanges. I discuss the implications of this work for theories of exchange and social interaction, and outline future directions for the next phase of the research program.

A heated debate surrounds the significance of reproducibility as an indicator for research quality and reliability, with many commentators linking a “crisis of reproducibility” to the rise of fraudulent, careless, and unreliable practices of knowledge production. Through the analysis of discourse and practices across research fields, I point out that reproducibility is not only interpreted in different ways, but also serves a variety of epistemic functions depending on the research at hand. Given such variation, I argue that the uncritical pursuit of reproducibility as an overarching epistemic value is misleading and potentially damaging to scientific advancement. Requirements for reproducibility, however they are interpreted, are one of many available means to secure reliable research outcomes. Furthermore, there are cases where the focus on enhancing reproducibility turns out not to foster high-quality research. Scientific communities and Open Science advocates should learn from inferential reasoning from irreproducible data, and promote incentives for all researchers to explicitly and publicly discuss (1) their methodological commitments, (2) the ways in which they learn from mistakes and problems in everyday practice, and (3) the strategies they use to choose which research components of any project need to be preserved in the long term, and how.