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The chapter considers the ethical problems engendered by random assignment and privacy concerns in randomised controlled experiments and cluster randomised trials. The particular focus is on procedural, legislative and technical approaches to reducing or avoiding the problems. Examples are given from a variety of disciplines including health and education, though the main emphasis is on research in crime and delinquency.

In the past decade, microfinance institutions (MFIs) have experienced a boom in innovations of lending products, partly fueled by donors who see microfinance as the next promise to alleviate poverty. Examples of these new products are the combination of credit with health or life insurance, business and health education, savings products, and the adoption of (or conversion to) individual loan liability. The add-on features generally aim at reducing the vulnerability of clients while contributing to asset creation, hence improving repayment rates and the sustainability of the service. The product innovations typically result from organizations striving to extend outreach, increase impact, and promote sustainability. As in other industries, MFIs typically decide whether to adopt new strategies based on other MFIs’ success with the innovations. Many new microlending products and approaches continue to be developed. However, MFIs must generally rely on qualitative and descriptive case studies and anecdotal evidence on the effectiveness of these innovations to decide whether to implement the new strategies. The usual case study approach does not provide tangible evidence that can enable other organizations to know what changes can be expected if they were to adopt similar product changes.

In recent years, a wide range of psychosocial health interventions have been implemented among military service members and their families. However, there are questions over the evaluative rigor of these interventions. We conducted a systematic review of this literature, rating each relevant study (k = 111) on five evaluative rigor scales (type of control group, approach to participant assignment, outcome quality, number of measurement time points, and follow-up distality). The most frequently coded values on three of the five scales (control group type, participant assignment, and follow-up distality) were those indicating the lowest level of operationally defined rigor. Logistic regression results indicate that the evaluative rigor of intervention studies has largely remained consistent over time, with exceptions indicating that rigor has decreased. Analyses among seven military sub-populations indicate that interventions conducted among soldiers completing basic training, soldiers returning from combat deployment, and combat veterans have had, on average, the greatest evaluative rigor. However, variability in mean scores across evaluative rigor scales within sub-populations highlights the unique methodological hurdles common to different military settings. Recommendations for better standardizing the intervention evaluation process are discussed.

Misinterpretation of a negative test results in health screening may initiate less preventive effort and more future lifestyle-related disease. We predict that misinterpretation occurs more frequently among individuals with a low level of education compared with individuals with a high level of education.

The empirical analyses are based on unique data from a randomized controlled screening experiment in Norway, NORCCAP (NORwegian Colorectal Cancer Prevention). The dataset consists of approximately 50,000 individuals, of whom 21,000 were invited to participate in a once only screening with sigmoidoscopy. For all individuals, we also have information on outpatient consultations and inpatient stays and education. The result of health behaviour is mainly measured by lifestyle-related diseases, such as COPD, hypertension and diabetes type 2, identified by ICD-10 codes.

The results according to intention-to-treat indicate that screening does not increase the occurrence of lifestyle related diseases among individuals with a high level of education, while there is an increase for individuals with low levels of education. These results are supported by the further analyses among individuals with a negative screening test.

Though we have recently witnessed the “exponential production of digital data to measure, analyze, and predict educational performance” (Salajan & Jules, this volume), there has not been sufficient attention given to the quantitative methods that are used to process and transform this data in order to arrive at findings related to “what works”. This chapter addresses this gap by discussing a range of constraints that affect the main methods used for this purpose, with these methods being known as “impact evaluation.” Specifically, this chapter addresses its purpose, first, by making explicit the methodological assumptions, technical weaknesses, and practical shortcomings of the two main forms of impact evaluation—regression analysis and randomized controlled trials. Although the idea of Big Data and the ability to process it is receiving more attention, the underlying point here is that these new initiatives and advances in data collection are still dependent on methods that have serious limitations. To that end, not only do proponents of Big Data avoid or downplay discussion of the methodological pitfalls of impact evaluation, they also fail to acknowledge the political and organizational dynamics that affect the collection of data. To the extent that such methods will increasingly be used to guide public policy around the globe, it is essential that stakeholders inside and outside education systems are informed about their weaknesses—methodologically and in terms of their inability to take the politics out of policymaking. While the promises of Big Data are seductive, they have not replaced the human element of decision making.

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.

The purpose of this chapter is to review how well walking interventions have increased and sustained walking, and to provide suggestions for improving future walking interventions. A scoping review was conducted of walking interventions for adults that emphasised walking as a primary intervention strategy and/or included a walking outcome measure. Interventions conducted at the individual, community, and policy levels between 1990 and 2015 were included, with greater emphasis on recent interventions. Walking tends to increase early in interventions and then gradually declines. Results suggest that increased walking, and environmental-change activities to support walking are more likely to be sustained when they are immediately followed by greater economic benefits/time-savings, social approval, and/or physical/emotional well-being. Adaptive interventions that adjust intervention procedures to match dynamically changing environmental circumstances also hold promise for sustaining increased walking. Interventions that incorporate automated technology, durable built environment changes, and civic engagement, may increase cost-efficiency. Variations in outcome measures, study duration, seasons, participant characteristics, and possible measurement reactivity preclude causal inferences about the differential effectiveness of specific intervention procedures for increasing and sustaining walking. This review synthesises the effects of diverse walking interventions on increasing and sustaining walking over a 25-year period. Suggestions are provided to guide future development of more effective, sustainable walking interventions at the population level.

A major challenge in teaching medicine in a rural setting is that long geographical distances separate students, instructors, and educational resources. Clinical schools within the University of Sydney Medical Program are geographically dispersed and face similar challenges. As a result, a virtual ophthalmology clinic (VOC) was developed (Succar et al., 2013) and it is being delivered online to enable equitable access and consistency in the foundations of ophthalmology education for rural-based students. The program allows students to sharpen their clinical reasoning skills by formulating a diagnosis and treatment plan on virtual patients with simulated conditions. To evaluate the educational effectiveness of VOC, a randomized controlled trial was conducted with the University of Sydney medical students (n = 188). The pre- and post-test and student satisfaction questionnaire were administered. Twelve months later, a follow-up test was conducted to determine the long-term retention rate of graduates. On the basis of a statistically significant improvement in academic performance and highly positive student feedback, it became clear that the online delivery of VOC can serve as a model for higher education institutions creating an all-inclusive learning environment experienced by rural students and staff regardless of location and distance, while making a positive impact on learning.