Terra Populus: Workflows for Integrating and Harmonizing Geospatial Population and Environmental Data

The Terra Populus project (TerraPop) addresses a variety of data management, curation, and preservation challenges with respect to spatiotemporal population and environmental data. In this article, we describe our approaches to these challenges, with a particular focus on geospatial data workflows and associated provenance metadata. The goal of TerraPop is to enable research, learning, and policy analysis by providing integrated spatiotemporal data describing people and their environment. To do so, TerraPop is assembling a globe-spanning and temporally extensive collection of high-quality population and environmental data, ensuring good documentation, and developing a Web-based data access system that enables users to assemble customized integrated data sets drawing on a variety of data sources and formats. We describe TerraPop's collection strategies, detail the geospatial workflows involved in preparing data for ingest into the project database and those used to transform data across formats for dissemination, and discuss the system used to capture and manage provenance metadata throughout the project. A key aspect of the project is the development of global current and historical administrative unit boundaries that can be linked to census data. These boundaries serve as the linchpin of TerraPop's data integration strategy, and constitute an important data set in their own right.     

Urban fifth graders’ connections-making between formal earth science content and their lived experiences

Earth science education, as it is traditionally taught, involves presenting concepts such as weathering, erosion, and deposition using relatively well-known examples—the Grand Canyon, beach erosion, and others. However, these examples—which resonate well with middle- and upper-class students—ill-serve students of poverty attending urban schools who may have never traveled farther from home than the corner store. In this paper, I explore the use of a place-based educational framework in teaching earth science concepts to urban fifth graders and explore the connections they make between formal earth science content and their lived experiences using participant-driven photo elicitation techniques. I argue that students are able to gain a sounder understanding of earth science concepts when they are able to make direct observations between the content and their lived experiences and that when such direct observations are impossible they make analogies of appearance, structure, and response to make sense of the content. I discuss additionally the importance of expanding earth science instruction to include man-made materials, as these materials are excluded traditionally from the curriculum yet are most immediately available to urban students for examination.     

Interactions Are Critical

Although we agree with Theobold and Freeman (2014) that linear models are the most appropriate way in which to analyze assessment data, we show the importance of testing for interactions between covariates and factors.To the Editor:Recently, Theobald and Freeman (2014) reviewed approaches for measuring student learning gains in science, technology, engineering, and mathematics (STEM) education research. In their article, they highlighted the shortcomings of approaches such as raw change scores, normalized gain scores, normalized change scores, and effect sizes when students are not randomly assigned to classes based on the different pedagogies that are being compared. As an alternative, they propose using linear regression models in which characteristics of students, such as pretest scores, are included as independent variables in addition to treatments. Linear models that include both continuous and categorical independent variables are often termed analysis of covariance (ANCOVA) models. The approach of using ANCOVA to control for differences in students among treatments groups has been suggested previously by Weber (2009) . We largely agree with Theobald and Freeman (2014) and Weber (2009) that ANCOVA models are an appropriate method for situations in which students cannot be randomly assigned to treatments and controls. However, in describing how to implement linear regression models to examine student learning gains, Theobald and Freeman (2014) ignore a fundamental assumption of ANCOVA.ANCOVA assumes homogeneity of slopes (McDonald, 2009 ; Sokal and Rohlf, 2011 ). In other words, the slope of the relationship between the covariate (e.g., pretest score) and the dependent variable (e.g., posttest score) is the same for the treatment group and the control. This assumption is a strict assumption of ANCOVA in that violations of this assumption can result in incorrect conclusions (Engqvist, 2005 ). For example, in Figure 1, both pretest score and treatment have statistically significant main effects in a linear model with only pretest score (F(1, 97) = 25.6, p < 0.001) and treatment (F(1, 97) = 42.6, p < 0.01) as independent variables. Therefore, we would conclude that all students in the class with pedagogical innovation had significantly greater posttest scores than those students in the control class for a given pretest score. Furthermore, we would conclude that the pedagogical innovation led to the same increase in score for all students in the treatment class, independent of their pretest scores. Clearly, neither of these conclusions would be justified.Researchers must first test the assumption of the homogeneity of slopes by including an interaction term (covariate × treatment) in their linear model (McDonald, 2009 ; Weber 2009 ; Sokal and Rohlf, 2011 ). For example, if we measured student achievement in two courses with different instructional approaches in a typical pretest/posttest design, then the interaction between students’ pretest scores and the type of instruction must be considered, because the instruction may have a different effect for high- versus low-achieving students. If multiple covariates are included in the linear model (see Equation 1 in Theobald and Freeman, 2014 ), then interaction terms need to be included for each of the covariates in the model. If the interaction term is statistically significant, this suggests that the relationship between the covariate and the dependent variable is different for each treatment group (F(1, 96) = 25.1, p < 0.001; Figure 1). As a result, the effect of the treatment will depend on the value of the covariate, and universal statements about the effect of the treatment are not appropriate (Engqvist, 2005 ). If the interaction term is not statistically significant, it should be removed from the model and the analysis rerun without the interaction term. Failure to remove an interaction term that was not statistically significant also can lead to an incorrect conclusion (Engqvist, 2005 ). Whether there are statistically significant interactions between the “treatment” and the covariates in the data set used by Theobald and Freeman (2014) is unclear.Open in a separate windowFigure 1.Simulated data to demonstrate heterogeneity of slopes. Pretest values were generated from random normal distributions with mean = 59.8 (SD = 18.1) for the treatment course and mean = 59.3 (SD = 17.0) for the control course, based on values given in Theobald and Freeman (2014) . For the treatment course, posttest values were calculated using the formula posttest_i = 80 + 0.1 × pre-test_i + Ɛ_i, where Ɛ_i was selected from a random normal distribution with mean = 0 (SD = 10). For the control course, posttest values were calculated using the formula posttest_i = 42 + 0.5 × pre-test_i + Ɛ_i, where Ɛ_i was selected from a random normal distribution with mean = 0 (SD = 10). n = 50 for both courses.In addition to being a strict assumption of ANCOVA, testing for homogeneity of slopes in a linear model is important in STEM education research, as slopes are likely heterogeneous for several reasons. First, for many instruments used in STEM education research, high-achieving students score high on the pretest. As a result, their ability to improve is limited due to the ceiling effect, and differences between treatment and control groups in posttest scores are likely to be minimal (Figure 1). In contrast, low-achieving students have a greater opportunity to change their scores between their pretest and posttest. Second, pedagogical innovations are more likely to have a greater impact on the learning of lower-performing students than higher-performing students. For example, Beck and Blumer (2012) found statistically greater gains in student confidence and scientific reasoning skills for students in the lowest quartile as compared with students in the highest quartile on pretest assessments in inquiry-based laboratory courses.Theobald and Freeman (2014, p. 47) note that “regression models can also include interaction terms that test whether the intervention has a differential impact on different types of students.” Yet, we argue that these terms must be included and only should be excluded if they are not statistically significant.     

"It's cheesy when they smile:" what girl athletes prefer in images of female college athletes

Building on previous research in which we provided an opportunity for female college athletes to construct their own photographic portrayals, this study explored young female athletes' perceptions of the college athlete photographs. Fifty-two girls participated in focus group interviews where they viewed and discussed the images. The young athletes particularly liked images they perceived to show authentic athletes (e.g, in athletic settings, with appropriate sport attire), images they could relate to due to personal experiences, and images that reflected competent and passionate sportswomen. Images perceived as revealing a lack of motivation, poor sporting attitudes, and nonathletic poses generally were disliked. Images depicting multiple social identities (e.g., an athlete in a dress) were controversial and generated much discussion.     

The application of transformational leadership theory to parenting: questionnaire development and implications for adolescent self-regulatory efficacy and life satisfaction

We draw upon transformational leadership theory to develop an instrument to measure transformational parenting for use with adolescents. First, potential items were generated that were developmentally appropriate and evidence for content validity was provided through the use of focus groups with parents and adolescents. We subsequently provide evidence for several aspects of construct validity of measures derived from the Transformational Parenting Questionnaire (TPQ). Data were collected from 857 adolescents (M(age) = 14.70 years), who rated the behaviors of their mothers and fathers. The results provided support for a second-order measurement model of transformational parenting. In addition, positive relationships between mothers' and fathers' transformational parenting behaviors, adolescents' self-regulatory efficacy for physical activity and healthy eating, and life satisfaction were found. The results of this research support the application of transformational leadership theory to parenting behaviors, as well as the construct validity of measures derived from the TPQ.