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Mediating effects are often tested using hierarchical multiple regression (HMR) procedures. Typical of the HMR-based strategies is the very frequently cited and widely used procedure described by Baron and Kenny (1986). Unfortunately, there are several important problems with it. More specifically, as we demonstrate below, it: (a) is of virtually no value for buttressing claims of mediating effects for data from non-experimental research; (b) produces erroneous inferences about the existence of mediating effects for misspecified mediation models; and (c) is incapable of providing credible evidence of such effects in a large proportion of cases, even for properly specified mediation models. We detail a number of important implications of our analyses.

This essay is a review of the recent literature on the methodology of economics, with a focus on three broad trends that have defined the core lines of research within the discipline during the last two decades. These trends are: (a) the philosophical analysis of economic modelling and economic explanation; (b) the epistemology of causal inference, evidence diversity and evidence-based policy and (c) the investigation of the methodological underpinnings and public policy implications of behavioural economics. The final output is inevitably not exhaustive, yet it aims at offering a fair taste of some of the most representative questions in the field on which many philosophers, methodologists and social scientists have recently been placing a great deal of intellectual effort. The topics and references compiled in this review should serve at least as safe introductions to some of the central research questions in the philosophy and methodology of economics.

In this chapter, the author argues that Austrians are perhaps uniquely placed to be effective practitioners of causal inference techniques on observational data. This is because, while the methods are easy to implement, their validity and value lies in a detailed, “analytical/historical” narrative to accompany the findings. This is true for several reasons. (1) all the models have identifying assumptions (e.g., no spillovers and parallel trends) that are best addressed by an exposition of the institutional/economic/historical milieu in place before and after the treatment under study; (2) determination of external validity also requires detailed institutional and historical knowledge; and (3) researchers often want to know the mechanisms producing the reduced form result that comes out of most causal inference studies. Here again, institutional and historical learning is crucial. My conclusion is that Austrians should add the tools of causal inference with observational data to their arsenal of analysis. This would be good both for their publication prospects and for the profession at large.

This review investigates the effects of temporal aggregation and systematic sampling on time-series analysis, focusing on their influence on data accuracy, interpretability and statistical properties. The purpose of the study is to synthesise existing literature on the topic and offer insights into the trade-offs between these data reduction techniques.

The research methodology is based on an extensive review of theoretical and empirical studies covering univariate and multivariate time series models, focusing on unit roots, ARIMA, GARCH, cointegration properties and Granger Causality.

The key findings reveal that while temporal aggregation simplifies data by emphasising long-term trends, it can obscure short-term fluctuations, potentially leading to biases in analysis. Similarly, systematic sampling enhances computational efficiency but risks information loss, especially in non-stationary data, and may result in biased samples if sampling intervals coincide with data periodicity. The review highlights the complexities and trade-offs involved in applying these methods, particularly in fields like economic forecasting, climate modelling and financial analysis.

The originality and value of this study lie in its comprehensive synthesis of the impacts of these techniques across various time series properties. It underscores the importance of context-specific applications to preserve data integrity, offering recommendations for best practices in the use of temporal aggregation and systematic sampling in time-series analysis.

Tests of assumed mediation models are common in research in many disciplines, including managerial psychology, industrial and organizational psychology, organizational behavior, and organizational theory. Thus, the purpose of this paper is to detail experimental design options for conducting such tests in a manner that has the potential to yield results that have high levels of internal and construct validity.

The paper presents a logical analysis of strategies for testing mediation models so as to insure valid inferences about causal relations between variables.

The most appropriate strategy for testing assumed mediation models is research that uses randomized experimental designs.

Managers should base their actions on valid evidence about phenomena. More specifically, managerial actions should be predicated on research results that have high levels of internal, construct, and statistical conclusion validity. Thus, this paper encourages managers to base decisions about organizational policies and practices on well‐designed experimental research.

This paper addresses a number of points about issues involving internal and construct validity in tests of assumed causal models that have not been covered in previous work.

Strategy researchers typically avoid using data more than a few years old for estimation of cross-sectional models. However, problems that might be caused by older data generally reflect more basic weaknesses in research design. This chapter develops criteria for evaluating the importance of the age of data used in cross-sectional research and indicates ways that better research design may be more effective than the substitution of newer data sets.

Given the complexities in improving safety in the construction industry globally, which is exacerbated by the complex safety environment in developing countries (DCs), prevention through design (PtD) has been established to improve occupational safety and health (OSH) where applied. However, it has received very little attention in DCs and the extant literature with limitations. Using Nigeria as a case study, the current study advances the understanding of PtD in DCs by investigating the critical barriers to PtD and other potential OSH responsibilities of designers in the construction industry.

Twenty-eight semi-structured interviews and e-interviews of architects, builders and civil engineers in Nigeria were analysed using the six-phase thematic analysis. To improve the trustworthiness of the research, triangulation, peer-debriefing, refining the interview protocol and thick detailed description were done.

The study's findings question the extant general knowledge and understanding of PtD among clients and designers and its technical aspect among designers where it is skewed to structural safety and omitted in universities' curricula. This explains the inconclusive findings of existing studies on why there is a high level of awareness of PtD but a low level of implementation. There is little client support for PtD, and designers have limited influence on clients in terms of it. The fear of liability from PtD is exacerbated by the limited legal system and lack of adequate legislation in the country. The demotivating attitudes of clients and contractors towards designers in terms of PtD are also reported, just as there are project delivery barriers such as traditional procurement not supporting PtD as design and build procurement does.

For the sustainable growth in the practice of PtD, the increased and improved quality of education and awareness of PtD is needed but this must focus on instilling a robust understanding of it among designers based on the local context. This educational requirement can be supported by statute.

Through qualitative data, the findings explain and offer insight into the inconclusive findings in the extant studies on PtD in Nigeria. Also, it contributes to improving health and safety by advancing the understanding of the critical barriers to PtD and other potential OSH responsibilities of designers in D Nigeria's construction using qualitative data.

This study estimates returns to higher education across different fields in Thailand for 2019 and 2021, accounting for field selection endogeneity. The comparison offers insights into the impact of the pandemic and other economic shocks on the returns.

The study applies an econometric causal framework, integrating economic theory with empirical analysis using data from Thailand’s socioeconomic surveys in 2019 and 2021. A multinomial treatment effects model with sample selection correction is used to estimate the impact of different fields of study on income, accounting for selection both into higher education in different fields and into employment, addressing potential biases from ability sorting and sample selection.

The study finds variations in returns to education across fields. In 2019, teaching offered the highest returns on average, followed by healthcare. Social sciences, business and computer-related fields showed moderate returns, while the combined group of science, agriculture, engineering and architecture had non-significant returns, indicating a low weighted average across these diverse fields. In 2021, healthcare exhibited the highest return due to pandemic-driven demand. Across both years, controlling for occupation reduced the estimated returns by approximately 50%, highlighting the role of occupational status in mediating educational returns.

This study uniquely applies an econometric causal framework to analyze returns to higher education by field of study in Thailand. It offers insights for policymakers to align educational programs with labor market demand and emphasizes the importance of data-driven decisions in responding to disruptions.

Recent labor market research has called into question whether social capital effects are causal, or are spuriously due to the influence of social homophily. This essay adopts the demand-side perspective of organizations to examine the causal status of social capital. In contrast with supply-side approaches, we argue that homophily is a key mechanism by which organizations derive social capital. We develop an approach to bolster inferences about the causal status of social capital, and illustrate these ideas using data from a retail bank.