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Pearson’s correlation is widely used to test for an association between two variables and also forms the basis of several multivariate statistical procedures including many latent variable models. Spearman’s ρ\rho is a popular alternative. These procedures are compared with ranking the data and then applying the inverse normal transformation, or for short the normrank transformation. Using the normrank transformation was more powerful than Pearson’s and Spearman’s procedures when the distributions have less than normal kurtosis (platykurtic), when the distributions have greater than normal kurtosis (leptokurtic), and when the distribution is skewed. This is examined for testing if there is an association between two variables, identifying the number of factors in an exploratory factor analysis, identifying appropriate loadings in these analyses, and identifying relations among latent variables in structural equation models. R functions and their use are shown.

How much people believe that they understand information, so-called metacomprehension, is important for education. This ability to discriminate between well-learned versus not well-learned information is important to allow the student to decide which areas need further understanding. Feedback can affect metacomprehension and is important for guiding student self-regulated learning. The effects of taking an assessment and finding out the score on self-rated understanding, interest, and knowledge were measured. Participants sampled via MTurk were randomly allocated to one of three groups using Qualtrics in-built randomizer, with the restriction of equal numbers in conditions. The three groups were asked metacomprehension questions: (a) after reading a passage but before taking an assessment (Pre-Test group); (b) after taking an assessment but before being told their score (Post-Test group); and (c) after hearing their score (Post-Feedback group). Responses for understanding and interest substantially decreased between those asked these questions before receiving the feedback versus those asked after receiving the feedback. These self-ratings were also used to predict scores on a later assessment and were similar in their diagnostic value for all conditions.

Introduction: There is a growing concern about the threat of distractions in online learning environments. It has been suggested that mindfulness may attenuate the effects of distraction. The extent to which this translates to academic performance is under investigation. We aimed to investigate the relationship between task-irrelevant visual distraction, time pressure, and mindful self-regulated learning in the context of a low-stake computer-based assessment. Methods: The study sampled 712 registered users of Prolific.co who were prescreened, current undergraduate university students. After data quality screening, 609 were retained for analyses. A 2 x 2 between-subjects design was used. Participants were randomly assigned to the following groups: (1) a control condition, (2) a distract condition, (3) a time pressure condition, or (4) a distract and time pressure condition. All participants completed reading comprehension questions, demographic questions, and the Mindful Self-Regulated Learning Scale. Results: Presenting a visual distraction increased self-reported distraction and having a clock present increased self-reported time pressure. The distraction did not have a statistically significant effect on test performance. Mindfulness was negatively correlated with test performance, self-reported distraction, and self-reported time pressure. Discussion: Continuous task-irrelevant visual distractions may not be distracting enough to influence low-stakes testing performance, but they do influence self-perceptions. [ABSTRACT FROM AUTHOR]

Background: Educational and developmental psychologists often examine how groups change over time. Two analytic procedures -- analysis of covariance (ANCOVA) and the gain score model -- each seem well suited for the simplest situation, with just two groups and two time points. They can produce different results, what is known as Lord's paradox. Aims: Several factors should influence a researcher's analytic choice. This includes whether the score from the initial time influences how people are assigned to groups. Examples are shown, which will help to explain this to researchers and students, and are of educational relevance. It is shown that a common method used to measure school effectiveness is biased against schools that serve students from groups that are historically poor performing. Methods and results: The examples come from sports and measuring educational effectiveness (e.g., for teachers or schools). A simulation study shows that if the covariate influences group allocation, the ANCOVA is preferred, but otherwise, the gain score model may be appropriate. Regression towards the mean is used to account for these findings. Conclusions: Analysts should consider the relationship between the covariate and group allocation when deciding upon their analytic method. Because the influence of the covariate on group allocation may be complex, the appropriate method may be complex. Because the influence of the covariate on group allocation may be unknown, the choice of method may require several assumptions.

There is much discussion about and many policies to address achievement gaps in education among groups of students. The focus here is on a different gap and it is argued that it also should be of concern. Speed gaps are differences in how quickly different groups of students answer the questions on academic assessments. To investigate some speed gaps, response times from approximately 75,000 untimed online assessments were compared by English language learning proficiency, student gender, and ethnicity. Also examined were the relationships between response time and accuracy for these groups. The differences observed lead to recommendations for assessment accommodations and teaching strategies for taking assessments.

Amphiphilic block copolymers can assemble into a variety of structures on the nanoscale in selective solvent. The micelle blending protocol offers a simple unique route to reproducibly produce polymer nanostructures. Here we expand this blending protocol to a range of polymer micelle systems and self-assembly routes. We found by exploring a range of variables that the systems must be able to reach global equilibrium at some point for the blending protocol to be successful. Our results demonstrate the kinetics requirements, specifically core block glass transition temperature, Tg, and length of the block limiting the exchange rates, for the blending protocol which can then be applied to a wide range of polymer systems to access this simple protocol for polymer self-assembly.

Researchers have several options available to analyze data from interventions when participants have not been randomly allocated into conditions. Among these are the gain score, ANCOVA, and propensity matching procedures. Each of these attempts to account for pre-treatment differences among the conditions, but they do so differently. These procedures are reviewed and methods for estimating them in R are shown. The choice of which of these procedures to use can be difficult. Different situations are shown where they perform differently. The primary conclusion of this paper is that models should be hypothesized for how the data may arise, data simulated for these models, and the properties of statistical procedures evaluated. A goal of this paper is to show these procedures without extensive mathematics in order to allow a broad read-ership to use these methods in their situations.

Trust in science is important, and Open Education Studies aims to publish trusted research. Two issues are addressed here: access to the data on which the research is based and how these data are analyzed. Some guidelines from other entities are discussed. As a new journal our guidelines should be influenced by the opinions of readers and authors, and as such we welcome discussion of how to ensure trust in the research OES publishes.

Multiple choice questions (MCQs) are becoming more common in UK psychology departments and the need to assess their reliability is apparent. Having examined the reliability of MCQs in our department we faced many questions from colleagues about why we were examining reliability, what it was that we were doing, and what should be reported when examining the reliability of MCQ exams. This paper addresses the most frequently asked questions. (Contains 1 table, 4 figures, and 5 footnotes.)

Although urbanization fundamentally alters water and energy cycles, contemporary land surface models (LSMs) often do not include key urban vegetation processes that serve to transfer water and energy laterally across heterogeneous urban land types. Urban water/energy transfers occur when rainfall landing on rooftops, sidewalks, and driveways is redirected to lawns or pervious pavement and when transpiration occurs from branches overhanging impervious surfaces with the corresponding root water uptake takes place in nearby portions of yards. We introduce Noah‐MP for Heterogenous Urban Environments (Noah‐MP HUE), which adds sub‐grid water transfers to the widely used Noah‐MP LSM. We examine how sub‐grid water transfers change surface water and energy balances by systematically increasing the amount of simulated water transfer for four scenarios: tree canopy expanding over pavement (Urban Tree Expansion), tree canopy shifting over pavement (Urban Tree Shift), and directing impermeable runoff onto surrounding vegetation (Downspout Disconnection) or into an engineered pavement (Permeable Pavement). Even small percentages of sub‐grid water transfer can reduce runoff and enhance evapotranspiration and deep drainage. Event‐scale runoff reduction depends on storm depth, rainfall intensity, and antecedent soil moisture. Sub‐grid water transfers also tend to enhance (reduce) latent (sensible) heat. Results highlight the importance not only of fine‐scale heterogeneity on larger scale surface processes, but also the importance of urban management practices that enhance lateral water transfers and water storage–so‐called green infrastructure–as they change land surface fluxes and, potentially, atmospheric processes. This work opens a pathway to directly integrate those practices in regional climate simulations. Key Points: We develop an urban land surface model representation of impervious to pervious runon and canopy overhanging impervious surfacesUsing idealized land use, we systematically examine the effects of lateral transfers on water and energy budgets over warm seasonsWe found large changes in runoff generation, water balances, and energy partitioning when lateral transfers are simulated [ABSTRACT FROM AUTHOR]

When people remember together, what one person says can affect what others report. The size of this effect is dependent on characteristics of the people and how they express their beliefs. The power relationship among people affects much of their social cognition, including the size of this memory conformity effect. However, some research has shown people conform more to high power individuals and some research shows the opposite. The proposed research identified what we believe is an important difference in these studies in the type of power that was manipulated: evaluation versus managerial power. The proposed research will examine these using a 2 × 2 factorial design, plus a control group. The study is designed to be like how people learn new vocabulary in an education context. [ABSTRACT FROM AUTHOR]

When students respond rapidly to an item during an assessment, it suggests that they may have guessed. Guessing adds error to ability estimates. Treating rapid responses as incorrect answers increases the accuracy of ability estimates for timed high-stakes summative tests like the ACT. There are fewer reasons to guess rapidly in non-timed formative tests, like those used as part of many personalized learning systems. Data from approximately 75 thousand formative assessments, from 777 students at two northern California charter high schools, were analyzed. The accuracy of ability estimates is only slightly improved by treating responses made in less than five seconds as incorrect responses. Simulations show that the advantage is related to: whether guesses are made rapidly, the amount of time required for thoughtful responses, the number of response alternatives, and the preponderance of guessing. An R function is presented to implement this procedure. Consequences of using this procedure are discussed.

Many education policies require estimating whether students in different grades are on track for achieving certain educational standards. One approach for constructing these cut scores is to estimate the values on tests that predict reaching targets on subsequent tests. Whether a student is deemed on target can affect the student's course counselling and aggregate statistics can affect school closures and funding and teacher employment. Seven different regression procedures for estimating cut scores are compared with 15 different data scenarios. In some situations, all the methods provided fairly accurate estimates, but in other situations, some estimates were poor. The choice of which regression procedure to use can make a difference. Overall, a method based on a loess regression performed well.

Moderate to high severity wildfire can abruptly alter watershed properties and enhance extreme hydrologic responses such as debris flows and floods. The compounding effects of wildfire on flood hazard, represented here via flood frequency analysis (FFA; e.g., 100‐year flood) are of growing importance. Standard statistical FFA approaches are ill‐suited to examining this issue because wildfire‐affected flood peak observations are limited in number and violate the assumption of independent and identically distributed events. Here, we developed a process‐based FFA framework that integrates a stochastic rainfall generator, wildfire simulation, inverse modeling, and a physics‐based hydrological model to directly simulate the impacts of wildfire on FFA. We applied this framework in the upper Arroyo Seco watershed in Southern California, which experienced Moderate to high burn during the 2009 Station Fire. An FFA analysis, performed with simulated peak flows from the first year since fire demonstrates the 100‐year flood can be three times larger than simulations that only consider peak flows in non‐fire‐affected years. On the other hand, coupling process‐based FFA with stochastically simulated wildfire events and watershed's time‐varying hydrologic recovery yields "fire continuum FFA", a concept introduced here for the first time. Fire continuum FFA accounts for multiple wildfires within very long synthetic time series. Variability in upper tail flood peaks is substantially higher in fire continuum results as compared with pre‐wildfire FFA. This result highlights the importance of wildfire inter‐arrival time and post‐wildfire recovery processes, both of which are expected to change as a result of climatic change and evolving fire management strategies. Key Points: A process‐based flood frequency analysis (FFA) framework that integrates flood drivers and wildfire impactsA novel approach for quantifying compounding wildfire impacts on flood frequencyFire continuum FFA, a concept introduced here for the first time [ABSTRACT FROM AUTHOR]

Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US. Plain Language Summary: Fire danger index (FDI), a measure used to assess the risk and severity, relies on a combination of weather and fuel conditions. Multiple FDIs have been frequently used to predict and manage the risk of wildfire. However, it is unclear how well these indices work at different scales, causing uncertainty in predicting the likelihood of fire ignition and the potential size of a fire. Here, we analyzed four commonly used FDIs and conducted sensitivity analysis (SA) to determine their most important drivers. SA results indicate that daily minimum relative humidity, precipitation, and wind speed as the most important drivers. In addition, FDIs highly correlate with total fire size at annual and conterminous United States (CONUS) scale and such correlation decreases at finer spatial and temporal scales. We also used a regional climate model simulation to derive FDIs for current and future climate conditions. Our results suggest an overall increase in fire potential and a prolonged wildfire season in the future climate. Moreover, the enhanced fire frequency is projected to occur in spring and summer over the southwest US whereas in summer and fall over the northern and eastern CONUS. Key Points: The correlation between fire danger indices (FDIs) and observed wildfire size are scale dependentSensitivity analysis indicates that the daily minimum relative humidity and wind speed are the most important drivers of fire dangerCoupling FDIs with regional climate model simulation show higher fire potential in US in future climate [ABSTRACT FROM AUTHOR]

In the arid and semiarid southwestern United States, both cool- and warm-season storms result in flash flooding, although the former storms have been much less studied. Here, we investigate a catalog of 52 flash-flood-producing storms over the 1996-2021 period for the arid Las Vegas Wash watershed using rain gauge observations, reanalysis fields, radar reflectivities, cloud-to-ground lightning flashes, and streamflow records. Our analyses focus on the hydroclimatology, convective intensity, and evolution of these storms. At the synoptic scale, cool-season storms are associated with open wave and cutoff low weather patterns, whereas warm-season storms are linked to classic and troughing North American monsoon (NAM) patterns. At the storm scale, cool-season events are southwesterly and southeasterly under open wave and cutoff low conditions, respectively, with long duration and low to moderate rainfall intensity. Warm-season storms, however, are characterized by short-duration, high-intensity rainfall, with either no apparent direction or southwesterly under classic and troughing NAM patterns, respectively. Atmospheric rivers and deep convection are the principal agents for the extreme rainfall and upper-tail flash floods in cool and warm seasons, respectively. Additionally, intense rainfall over the developed low valley is imperative for urban flash flooding. The evolution properties of seasonal storms and the resulting streamflows show that peak flows of comparable magnitude are "intensity driven" in the warm season but "volume driven" in the cool season. Furthermore, the distinctive impacts of complex terrain and climate change on rainfall properties are discussed with respect to storm seasonality. [ABSTRACT FROM AUTHOR]

Students are often placed in groups to facilitate learning. Understanding the cognitive processes involved when students learn from others is important for creating group situations that facilitate learning. Research from cognitive psychology predicts who will learn most from whom and in what situations. It also provides a set of methods that education researchers can use to further explore "Learning from Others." However, because the goals of cognitive psychologists and educators often differ, it is important to apply cognitive psychology research cautiously within an educational context.