A critical aspect of rural research is carefully defining and describing the rural context. This is particularly important in rural special education research because different definitions of rural may influence resource allocation, grant funding eligibility, and/or research findings. In order to highlight the importance of operationalizing rural, we discuss the challenges of defining rural, provide descriptions of commonly used definitions to familiarize readers with standardized coding schemes, and summarize an empirical example demonstrating the implications different definitions can have on rural special education research and policy. We conclude by providing recommendations for both producers and consumers of research.

The purpose of this study was to explore what cultural mentoring looks like in practice in short-term study abroad courses, how frequently instructors engage in cultural mentoring, and what demographic and background variables might predict the extent to which faculty members engage in cultural mentoring. Using data from a survey of 473 faculty members from 72 U.S. colleges and universities who had recently taught short-term study abroad courses, we identified four types of cultural mentoring behaviors – Expectation Setting, Explaining the Host Culture, Exploring Self in Culture, and Facilitating Connections. We also identified key predictors of the frequency with which participants engaged in cultural mentoring, including rank, race/ethnicity, and discipline.

Moderation occurs when the magnitude and/or direction of the relation between two variables depend on the value of a third variable called a moderator variable. Moderator variables are distinct from mediator variables, which are intermediate variables in a causal chain between two other variables, and confounder variables, which can cause two otherwise unrelated variables to be related. Determining whether a variable is a moderator of the relation between two other variables requires statistically testing an interaction term. When the interaction term contains two categorical variables, analysis of variance (ANOVA) or multiple regression may be used, though ANOVA is usually preferred. When the interaction term contains one or more continuous variables, multiple regression is used. Multiple moderators may be operating simultaneously, in which case higher-order interaction terms can be added to the model, though these higher-order terms may be challenging to probe and interpret. In addition, interaction effects are often small in size, meaning most studies may have inadequate statistical power to detect these effects. When multilevel models are used to account for the nesting of individuals within clusters, moderation can be examined at the individual level, the cluster level, or across levels in what is termed a cross-level interaction. Within the structural equation modeling (SEM) framework, multiple group analyses are often used to test for moderation. Moderation in the context of mediation can be examined using a conditional process model, while moderation of the measurement of a latent variable can be examined by testing for factorial invariance. Challenges faced when testing for moderation include the need to test for treatment by demographic or context interactions, the need to account for excessive multicollinearity, and the need for care when testing models with multiple higher-order interactions terms.

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As testing programs transition from paper to online testing, they must study mode comparability to support the exchangeability of scores from different testing modes. To that end, a series of three mode comparability studies was conducted during the 2019–2020 academic year with examinees randomly assigned to take the ACT college admissions exam on paper or online. Unlike many prior mode comparability studies, examinees testing online earned higher scores on average than examinees testing on paper, especially on the English, reading, and writing tests. Much of the observed mode effects were accounted for by items near the ends of the tests. Subsequent examination of item omit rates, differential item functioning by mode, and change‐point analyses supported the notion that the observed mode effects were related to differential speededness. Specifically, examinees testing online were slightly less speeded, and were therefore able to perform better on items near the ends of the tests. Practical implications for linking across modes, test design, and test administration conditions are discussed.

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