Paternal exposures (and other non-maternal factors) around pregnancy could have important effects on offspring health. One challenge is that data on partners are usually from a subgroup of mothers with data, potentially introducing selection bias, limiting generalisability of findings. We aimed to investigate the potential for selection bias in studies using partner data.

We characterise availability of data on father/partner and mother health behaviours (smoking, alcohol, caffeine and physical activity) around pregnancy from three UK cohort studies: the Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford and the Millennium Cohort Study. We assess the extent of sample selection by comparing characteristics of families where fathers/partners do and do not participate. Using the association of parental smoking during pregnancy and child birthweight as an example, we perform simulations to investigate the extent to which missing father/partner data may induce bias in analyses conducted only in families with participating fathers/partners.

In all cohorts, father/partner data were less detailed and collected at fewer timepoints than mothers. Partners with a lower socio-economic position were less likely to participate. In simulations based on ALSPAC data, there was little evidence of selection bias in associations of maternal smoking with birthweight, and bias for father/partner smoking was relatively small. Missing partner data can induce selection bias. In our example analyses of the effect of parental smoking on offspring birthweight, the bias had a relatively small impact. In practice, the impact of selection bias will depend on both the analysis model and the selection mechanism.

The human sciences should seek generalisations wherever possible. For ethical and scientific reasons, it is desirable to sample more broadly than ‘Western, educated, industrialised, rich, and democratic’ (WEIRD) societies. However, restricting the target population is sometimes necessary; for example, young children should not be recruited for studies on elderly care. Under which conditions is unrestricted sampling desirable or undesirable? Here, we use causal diagrams to clarify the structural features of measurement error bias and target population restriction bias (or ‘selection restriction’), focusing on threats to valid causal inference that arise in comparative cultural research. We define any study exhibiting such biases, or confounding biases, as weird (wrongly estimated inferences owing to inappropriate restriction and distortion). We explain why statistical tests such as configural, metric and scalar invariance cannot address the structural biases of weird studies. Overall, we examine how the workflows for causal inference provide the necessary preflight checklists for ambitious, effective and safe comparative cultural research.

Small businesses employ more than half of the entire workforce, account for more than sixty percent of new jobs created in the United States, and are responsible for about fifty percent of private domestic gross product. It is noteworthy, however, that small business owners in credit markets, in particular minority owners, have difficulty in securing sources of capital for their business operation. The literature on credit market discrimination shows consistent results that can be interpreted as evidence that minority owners are discriminated against compared to their counterparts (i.e., White owners) in obtaining loans, which may be caused by lenders’ discrimination, although such behavior is prohibited under current fair-lending laws. This paper uses pooled cross-sectional data from the Survey of Small Business Finances (1993, 1998, and 2003) and a bivariate probit model based on James J. Heckman’s approach to deal with sample selection bias for those choosing to apply for loans. Those who didn’t apply for loans have been ignored in analyses of credit markets for small business owners. This paper adds to the small business lending market literature by 1) combining cross sectional data from the Survey of Small Business Finances (SSBF) for 1993, 1998, and 2003 to get more precise estimates and test statistics with more power; 2) conducting regression analyses with different model specifications to show the robustness of the empirical results; and 3) dealing directly with problems of sample selection based on Heckman’s approach with particular attention to the assumptions required to justify the identification of the effect (i.e., exclusion restrictions).

The analysis confirms previous results, suggesting that minority owners are discriminated against in credit markets. These conclusions are supported in a variety of model specifications.

Many popular books and articles that purport to explain how people, companies, orideas succeed highlight a few successes chosen to fit a particular narrative. Weinvestigate what effect these highly selected “success narratives”have on readers’ beliefs and decisions. We conducted a large, randomized,pre-registered experiment, showing participants successful firms with foundersthat all either dropped out of or graduated college, and asked them to makeincentive-compatible bets on a new firm. Despite acknowledging biases in theexamples, participants’ decisions were very strongly influenced by them.People shown dropout founders were 55 percentage points more likely to bet on adropout-founded company than people who were shown graduate founders. Mostreported medium to high confidence in their bets, and many wrote causalexplanations justifying their decision. In light of recent concerns about falseinformation, our findings demonstrate how true but biased information canstrongly alter beliefs and decisions.

Political elites increasingly express interest in evidence-based policymaking, but transparent research collaborations necessary to generate relevant evidence pose political risks, including the discovery of sub-par performance and misconduct. If aversion to collaboration is non-random, collaborations may produce evidence that fails to generalize. We assess selection into research collaborations in the critical policy arena of policing by sending requests to discuss research partnerships to roughly 3,000 law enforcement agencies in 48 states. A host of agency and jurisdiction attributes fail to predict affirmative responses to generic requests, alleviating concerns over generalizability. However, across two experiments, mentions of agency performance in our correspondence depressed affirmative responses – even among top-performing agencies – by roughly eight percentage points. Many agencies that initially indicate interest in transparent, evidence-based policymaking recoil once performance evaluations are made salient. We discuss several possible mechanisms for these dynamics, which can inhibit valuable policy experimentation in many communities.

Residential energy efficiency programs play an important role in combating climate change. More precise quantification of the magnitude and timing of energy savings would bring large system benefits, allowing closer integration of energy efficiency into resource adequacy planning and balancing variable renewable electricity. However, it is often difficult to quantify the efficacy of an energy efficiency intervention, because doing so requires consideration of a hypothetical counterfactual case in which there was no intervention, and randomized control trials are often implausible. Although quasi-experimental econometric evaluation sometimes works well, we find that for a set of energy efficiency rebate programs in Northern California, a naïve interpretation of econometric measurement finds that rebate participation is associated with an average increase in electricity consumption of 7.2% [4.5%, 10.1%], varying in magnitude and sign depending on the type of appliance or service covered by the rebate. A subsequent household survey on appliance purchasing behavior and analysis of utility customer outreach data suggest that this regression approach is likely measuring the gross impact of buying a new appliance but fails to adequately capture a counterfactual comparison. Indeed, it is unclear whether it is even possible to construct a suitable counterfactual for econometric analyses of these rebate programs using data generally available to electric utilities. We view these results as an illustration of a limitation of econometric methods of program evaluation and the importance of weighing engineering modeling and other imperfect methods against one another when attempting to provide useful evaluations of real-world policy interventions.

Detrital zircon geochronology can help address stratigraphic- to lithospheric-scale geological questions. The approach is reliant on statistically robust, representative age distributions that fingerprint source areas. However, there is a range of biases that may influence any detrital age signature. Despite being a fundamental and controllable source of bias, handpicking of zircon grains has received surprisingly little attention. Here, we show statistically significant differences in age distributions between bulk-mounted and handpicked fractions from an unconsolidated heavy mineral sand deposit. Although there is no significant size difference between bulk-mounted and handpicked grains, there are significant differences in their aspect ratio, circularity and colour, which indicate inadvertent preferential visual selection of euhedral and coloured zircon grains. Grain colour comparisons between dated and bulk zircon fractions help quantify bias. Bulk-mounting is the preferred method to avoid human-induced selection bias in detrital zircon geochronology.

A primary challenge for researchers that make use of observational data is selection bias (i.e. the units of analysis exhibit systematic differences and dis-homogeneities due to non-random selection into treatment). This article encourages researchers in acknowledging this problem and discusses how and – more importantly – under which assumptions they may resort to statistical matching techniques to reduce the imbalance in the empirical distribution of pre-treatment observable variables between the treatment and control groups. With the aim of providing a practical guidance, the article engages with the evaluation of the effectiveness of peacekeeping missions in the case of the Bosnian civil war, a research topic in which selection bias is a structural feature of the observational data researchers have to use, and shows how to apply the Coarsened Exact Matching (CEM), the most widely used matching algorithm in the fields of Political Science and International Relations.

China’s small-scale agricultural producers face many challenges to increasing productivity and efficiency. In recent years, the Chinese government has provided support for farmer professional cooperatives (FPCs) to connect small farms with upstream and downstream processes in the food supply chain. This study combines propensity score matching and sample selection-corrected stochastic production frontier analysis to estimate the impacts of FPC participation by greenhouse vegetable producers on technical efficiency and income. Results indicate that FPCs help participants improve returns to scale and marginal returns to land and labor, increase technical efficiency, and obtain ¥4,460 (18%) greater income per greenhouse than nonparticipants.

Innovation is essential for agricultural and economic development, especially in today's rapidly changing global environment. While farmers have been recognized as innovation generators, many innovation studies continue to consider them as recipients or adopters of externally promoted innovations only. Based on household data from Ghana, this study, in contrast, investigates the innovation-generating behavior among rural farmers. Inspired by two innovation theories—induced innovation and innovation systems—we specifically focus on how to build the capacity of farmers to generate innovations. Controlling for selection bias, we show that participation in farmer field fora (FFF), a participatory extension approach, can play a positive role in strengthening farmers' innovation-generating capacity. Specifically, we show that FFF participants have about 27% higher probability of generating innovations than non-participants, and FFF participation appears to increase the number of innovation-generating practices implemented by a farm household by about 49%. However, we do not find significant spillover effects of FFF on the innovation-generating capacity of non-participants, which has implications for the cost-effectiveness of the FFF program. The results also indicate that education and risk preference are important drivers of farmers' innovations. We conclude that policies for the generation of innovations among farmers should focus on building innovation capacity through institutional arrangements that permit interactions and learning between stakeholders.