This research note briefly describes the levels of measurement of variables and their applications in the quantitative analysis of survey data. It first presents the concept of the measurement of variables. Second, the four levels of measurements, namely, nominal, ordinal, interval, and ratio, with examples are offered. Then, the application of these measurement levels to the statistical analysis of data at the univariate (descriptive statistics), bivariate, and multivariate (e.g., binary logistic and multiple linear regression) levels are discussed. This note is expected to be useful to the beginning (naïve) scholars for real-world application of statistical tools to analyze survey data.

Small and medium farmers across the USA face multifarious challenges for survival. Agritourism has been an alternative to diversify their income streams. Researchers have examined various factors determining the profitability of agritourism operations that range from the operators’ socio-demographic and firmographic to geographic characteristics. However, the understanding of the extent to which the number of visitors and other factors influence the profitability of agritourism operators in Maryland, USA, is limited. This paper examines various factors associated with (a) the profitability of and (b) the number of visitors to agritourism operations. Using data collected from Maryland in 2022, the multivariate results reveal that the number of visitors to the farm increases the odds of profitability. However, the relationship was mediated by the number of employees, which influenced the number of attractions and visitors. Moreover, the number of employees independently and significantly affected the profitability. Further results reveal that the length of operation, the number of employees, and operating during the fall season significantly influence the number of visitors. These findings imply that policies should consider ways to facilitate labor hiring as well as other factors to attract farm visitors and increase farm profitability.

Heirs’ property is a form of collective land ownership arising from intestate succession, often resulting in clouded titles, fractional ownership, and legal vulnerability. This tenure system is especially prevalent among African American landowners in the Southern United States and poses significant challenges for sustainable land use, agricultural development, forest management, and conservation. This paper presents an interdisciplinary review, research, and analysis encompassing legal studies, environmental policies, and rural social science to examine how heirs’ property status leads to diminished productivity, land underutilization, disinvestment, and involuntary land loss. Key issues include barriers to accessing USDA and NRCS programs, an inability to implement long-term land management plans, and an increased risk of partition sales and tax foreclosures. This review also examines demographic trends, regional concentration, and the broader socio-environmental impacts of insecure land tenure. Current policy responses, such as the Uniform Partition of Heirs Property Act (UPHPA), USDA land access provisions, and community-based legal interventions, are assessed for their effectiveness and limitations. The article concludes with policy and programmatic strategies to support title clearing, promote equitable land retention, and enhance participation in conservation and climate resilience initiatives. By highlighting the intersection of property law, racial equity, and environmental sustainability, this review contributes to a growing body of research aimed at securing land tenure for historically marginalized communities.

Scholars worldwide are in dialogue about using Artificial Intelligence (AI) in various fields of research. It has supported enhancing work efficiency, including social science research. Although ethical use of AI is still fuzzy, the use of AI in visual anthropological and sociological research that entails the Interpretive Approach raises several questions. This editorial highlights three key questions: Are researchers satisfied with the interpretation (the meaning created) by AI, i.e., the authenticity of the interpretation? Can AI reach the depth of the details of the visual object being interpreted? Thirdly, what ethical issues would AI-based research encounter if AI were highly supportive? Answering these questions, however, is not easy. Since a detailed analysis of these components needs rigorous research work, we consider issues that will be the basis for further research in this editorial note. Hence, the purpose of this note is to bring the research agenda to the forefront of researchers for further investigation rather than answering specific research questions mentioned here.

Authorship and contributorship are among the most important, sensitive, and complex areas of academia and thus demand critical awareness among the authors, editors, and readers.