1. Overview

South Asia is a flashpoint for natural disasters with profound societal impacts for the region and globally. Half the worlds population depends on the regions great rivers, the Indus, Ganges, and Brahmaputra. The frequent occurrence of floods, combined with large and rapidly growing populations, ongoing cross-border conflicts, high levels of poverty, and unstable governments, make South Asia highly susceptible to humanitarian disasters. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, and the 2010 flooding of the Indus River in Pakistan exemplify disasters on scales almost inconceivable elsewheredisasters devastating local residents and communities while posing significant threats to the U.S.s and other nations security interests and assets in the region. The challenges of mitigating such devastating disasters are exacerbated by limited flood forecast capability, lack of forecast use and sharing in and between countries, and the transboundary nature of the hazard.

At the same time, the South Asia situation poses an appropriate and valuable context for the interdisciplinary study of how technical and social factors at multiple levels and scales positively or negatively impact societal vulnerability and resilience to hazardous events. Despite past flood-related disasters, high risk of more severe disasters, and the increasing availability of forecasting, only limited advances have been achieved in improving forecasting leading to risk mitigation. Many national meteorological and hydrological agencies in South Asian countries provide at most a 1-3 day forecast of streamflow and potential flooding, often with no warning at the upstream border (examples include Nepal/India and India/Bangladesh). This is in part due to a lack of streamflow data sharing among the different countries. Studies undertaken using ensemble weather forecasts have begun to address technological gaps in meeting specific, regional flood vulnerability problems (i.e. data sharing, and forecast lead time) such as for Bangladesh (Hopson & Webster, 2010; Webster et al., 2010). Consequently, flood prediction partnerships are suggested as a means to bridge the gap between the existing, global scale, long lead time weather prediction, and actual implementation and use of the resulting much-enhanced flood prediction capability (Webster 2013), since it is clear better flood discharge prediction will not on its own result in effective outcomes (Syvitiski & Brakenridge, 2013). There are major gaps in understanding how to ensure forecasting and monitoring improvements result in enhanced mitigation of flood risk and flood damage. In such circumstances, trust is especially important for successful policy implementation and related behavior changes (e.g., Leach & Sabatier, 2005; Schlager, 1995; Zafonte & Sabatier, 2004) because individual decision makers and policy makers are negotiating uncertain contexts compounded by the delivery of probabilistic forecasts, which are inherently uncertain.

Our interdisciplinary research will increase understanding of these interconnected, technical, social, and policy-related dynamics. It will take an essential step in developing sustainable approaches to mitigating hazards in policy contexts where new technologies are being implemented to advance flood-forecasting science. Specifically, in response to the catastrophic threat posed by flooding in South Asia and the intellectual challenges they pose to understand and mitigate them, we have formed the South Asia Flood Prediction Partnership (SAFPP)an interdisciplinary team of researchers from the University of Nebraska, U.S. National Center for Atmospheric Research (NCAR), and the Dartmouth Flood Observatory at the University of Colorado (DFO). Our partnership brings together a long history of advancing knowledge in water policy and behavioral changes; trust studies (including research related to water resource and hazards); multilevel and latent variable statistics needed to expand the capabilities of social science and policy scientists' to simultaneously model multilevel influences; and geoscience, hydrological modeling, flood forecasting, and remote sensing. Together the SAFPP proposes linked activities to mitigate the impact of severe floods in the Brahmaputra, Ganges, and Indus basins, while advancing a new framework and methodologies for examining policy implementation and trust at multiple levels (e.g., micro, meso, and macro levels). Our work will determine the technical, psychosocial, and policy impacts of forecast improvements and warning-related societal communications and responses. Specifically, we hypothesize trust is a key indicator of policy uptake and successful implementation. We predict trust will affect how forecast information is used, or actively resisted, and in turn shape what policy options at different levels are feasible and considered worthwhile. Our proposed work will leverage our interdisciplinary teams prior NSF-, DOE-, NASA-, and USAID-funded research on flood forecasting, near real time flood remote sensing, measurement and mapping, social science of trust, and multilevel, structural equation and latent variable statistics for contextual and multilevel data. As indicated in our letters of partnership from the Flood Forecasting Division of the Pakistan Meteorological Department (PMD), the Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES), and the World Bank, these agencies and organizations will enable access to the decision-making processes, support, and data we require to conduct our inquiry.

2. Objectives

By accomplishing the following specific objectives we will test our central hypotheses that: a) innovative data assimilation approaches will advance big river flood forecasting, b) public availability of such forecasts will alter societal reaction, including national and state water policy responses, and c) successful policy reform, including needed changes in the use of flood forecasts in decisions affecting tens of thousands of inhabitants, depends on the presence of specific patterns of trust at multiple-levels.

Objective 1: Implement long-lead, public-access flood forecasting systems for the Brahmaputra, Ganges, and Indus basins and quantify the benefits of data assimilation of satellite-derived river discharge estimates on improving forecasting skill. The Climate Forecasting Applications for Bangladesh (CFAB) river flow forecasting system (Hopson & Webster, 2010), currently operational only for Bangladesh, will be extended into India and implemented basin-wide for the Indus. We will then improve existing forecast modeling through assimilation of satellite microwave remote sensing of river discharge, creating an enhanced lead-time (10-15 day) probabilistic river flow forecasting scheme. This can provide operational updates of the forecast models in-stream flows and soil moisture conditions. We will test the skill gains provided by each data assimilation component compared to the initial forecast system as a function of watershed, spatial-scale, and forecast lead-time. We will also investigate the predictability improvements by using multi-model weather forecasting fields in the hydrologic forecasting system, allowing us to compare and contrast uncertainty reductions from data assimilation systems to those of improvements in weather forecasting.

Objective 2: Transform forecast discharge values (flood peak discharges) into inundation extent maps, as derived from archival analysis of microwave and optical sensor imagery monitoring actual inundation extent along the rivers. A unique enhancement to river flow forecasting will be to transform modeled forecast discharge peaks into inundation extent maps captured from historic flood monitoring imagery. Importantly, this provides accurate geolocation for disaster relief efforts in complicated terrain, where numerical inundation modeling would normally fail and is in any case computationally challenging even when data such as channel bathymetry is available. This will be accomplished using analogue approaches to select pairings of archived, remote sensing mapping of inundation extent matched with discharges similar to model predictions.

Objective 3: Test a model of trust, expand capacity for contextual/ecological modeling through the use of multilevel and latent variable statistics, and investigate policy uptake and implementation as we integrate the forecast information into reservoir regulation, transboundary water information sharing, and national and regional disaster planning. We will test our multilevel model of trust in policy uptake and implementation contexts, at the same time as we provide in-country education, capacity building, and technological advancements to ongoing flood forecasting systems, and investigate ways in which the projects forecast information and technological development can be incorporated into ongoing forecasting systems and decision-making strategies in Bangladesh, India, and Pakistan. To achieve this objective, we will conduct interviews, focus groups, and surveys over the same time period as we are providing in-country input and training workshops for hydrologic engineers and scientists on the forecasting technologies developed during this project. We will analyze the data to test the utility and feasibility of competing statistical methods for testing multilevel social and policy models.

3. Expected Significance

Intellectual merit. Through this proposal, our South Asia Flood Prediction Partnership (SAFPP) will significantly advance the technical and social effectiveness capability of long-lead time flood forecasting for the Indus, Ganges, and Brahmaputra basins. The SAFPP will further develop and deploy real-time flood-related hazards monitoring and forecasting which will, for the first time, assimilate satellite-based measurements of river discharge and reservoir storage status into model-based predictions. We also will assess and reduce potential barriers to policy uptake in the impacted regions that would interfere with using this vital forecast information to reduce societal vulnerability.

Specifically, the proposed research is transformative in that it combines advances in weather forecasting and remote sensing, into a cohesive framework for successfully integrating and advancing flood forecasting capacity and subsequent use. Development of data assimilation approaches to incorporate remotely-sensed discharge into coupled hydrometeorological forecasting systems can advance river forecasting, in South Asia, the U.S. and globally. Evaluating the most beneficial pathways to assimilate remotely-sensed discharge into these systems will reduce forecast uncertainty. Doing so places this work at the forefront of research into river monitoring and forecasting technology development in data-poor global basins and thus defines future research trajectories in this field.

At the same time and integrated into the forecasting advances, we will advance knowledge about trust and its impact on policy uptake and implementation. Within the social science of trust, there is a need to develop a comprehensive, multilevel framework to guide the study of institutional trust and confidence (Li, 2012). Nowhere is this need greater than in the context of building resilience to disasters and creating the preconditions for sustainable development. Thus, in this project, we will advance the understanding of conceptualization, measurement, and development or emergence of trust within and across levels of decision making and within society (Kozlowski & Klein, 2000). Such understanding is especially important in policy contexts involving multiple actors facing a potential set of action options through which they may collectively produce outcomes (e.g., reduce vulnerability to disaster and increase sustainability) on behalf of others. Related, our proposed research provides a distinctive opportunity to explore the conceptual considerations underpinning policy change leading to sustainability by leveraging our work on trust in institutions. One of the most pressing considerations facing modern society is how policy makers confront the myriad sources and forms of uncertainty which could derail the effectiveness of their preferred alternatives (Baumgartner & Jones, 2009). Hauser and Benoit-Barne (2002) suggest issues of trust often arise out of uncertainty. Consequently, we propose to investigate, a critical yet under investigated dynamic: How do different levels and dispersion patterns of different forms of trust impact group processes and outcomes in contexts of high uncertainty? While there is a growing literature on the publics trust in government, there is little research on how trust at different levels impacts collective decisions involving local and state inter-organizational collaborations. In an age in which authority is divided among agencies and across jurisdictions, members of organizations must engage in collaborative, cross-agency initiatives to fulfill their own organizations mandate (Michaels, Goucher, & McCarthy, 2006). Thus, we will study the conceptualization, measurement, and development of trust government officials have in each other and the collective decision making or advisory groups and processes in which they participate. Throughout this research, we will advance the statistical methodology and capabilities of social and policy scientists to assess and model multilevel and latent variable influences.

Broader impacts. On a local level, project results will benefit millions of people exposed to flooding risk in the study region by significantly improving the warnings (in terms of accuracy, lead time, accessibility and use) vulnerable residents receive. Indeed, if a facility such as the one operating at NCAR had been online for the Indus River in July 2010, making discharge/flood forecasts and allowing community access, it could have made a tremendous difference. The Kosi, in 2008 along the India/Nepalese border, represents another avoidable catastrophe: The flood flow that broke the levee was not a very large one. The levee could have been reinforced if the area had the kind of warning that is now possible and can be readily implemented as proposed in this project. This is what, in part, this proposal is requesting funding to accomplish.

We know in order for successful implementation of technologies, there needs to be acceptance and use. The proposals social science components are intended to assure successful technical and policy uptake. We will investigate the extent to which the capacity to couple discharge forecasts to direct historical observational imagery provides forecasts of the range of possible inundation extent in a form accessible to decision makers while still conveying realistic, probabilistic estimates. This project will provide all data in a convenient database accessible to researchers, practitioners and members of the public, and research results will inform ongoing efforts in the Indus, Brahmaputra, and Ganges to effectively engage key stakeholders in emergency management, hazard mitigation and economic development policy and implementation. But we do not presume that increased forecast skill and accessibility is enough. We expect the necessity of understanding the role of trust in the policy making and implementation process in which they engage to enhance inter-organizational collaboration in disaster planning. Achieving the full benefits of scientific and technological breakthroughs driving the quickly evolving field of flood forecasting requires the buy-in and cooperation of experts in disaster/emergency planning and response and robust, regional economic planning. Policy outcomes of our successful work will include developed protocols for manipulating reservoir water levels, for example, intended for rapid execution in circumstances with little opportunity for reflection. Through participating in this project, local communities could even be empowered to directly access relevant flood hazard information.

Importantly, we will work with our partners to assure successful transfer of the flood forecasting technology to vulnerable communities in our target sites. We are cognizant of the challenges in successful technology transfer, both as a matter of technology as well as politics. We will work closely with our partners who have extensive expertise in achieving sustainability of technology transfer. For example, one of our key partners, RIMES, already is working with communities in Bangladesh on improving access to flood forecasting warnings. The enhanced model we develop will be disseminated to other communities with which RIMES presently is working. Beyond South Asia, our research will be of value to many communities struggling to make the transition from vulnerability to resilience and groups engaged in emergency preparedness and mitigation.

Thus, we will disseminate our research outcomes not only in South Asia, but also to vulnerable communities elsewhere in Africa, Asia, and so on, working in partnership with the organizations like RIMES, CARE-Bangladesh, INIH, FFD, and the World Bank until the technologies are fully adopted by the various Governments in these vulnerable nations. We will seek additional funds (e.g., from the World Bank, USAID, etc.) to support these efforts, as they go beyond the scope of science and technology development, but they are critical for long-term impact and sustainability.

The project also provides extensive interaction and knowledge sharing between U.S. and South Asian researchers and engineers, and student training in multi-disciplinary, hands-on operational forecasting systems, data collection techniques, data analysis, and presentations. Two postdoctoral fellows and GRAs and URAs will assist with the entire project. We also will request an REU so additional undergraduates can participate in varied research activities, such as conducting literature reviews, setting up surveys, coding, and analysis.

4. Research Plan

Our ambitious research objectives require four years to complete. Nonetheless, we are planning to achieve substantial progress and produce tangible accomplishments beginning in the first year. Arranged by objective, the detailed methods are provided below..

4.1. Implement long-lead flood forecasting systems.

Climate Forecasting Applications for Bangladesh (CFAB) has been producing operational flood forecasts for the Ganges and Brahmaputra Rivers in Bangladesh since 2003, on timescales from days to months (Hopson & Webster, 2010; Webster et al., 2010). The proposed methodology builds upon this effort and integrates adaptable hydrological streamflow multi-model, probabilistic meteorological/climate forecasts, and satellite and in situ data.

We will adapt the ensemble forecasting scheme developed by Hopson and Webster (2010) shown in Figure 1 for the major river basins in Bangladesh. This will make use of the recently available THORPEX Interactive Grand Global Ensemble (TIGGE[footnoteRef:1]) multi-center ensemble weather data, with focus on extreme precipitation[footnoteRef:2] in designing a fully-automated scheme for 1-15 day predications of river discharge forecasts for the South Asia region. The hydrological forecast model is a hydrologic multi-modeling system initialized by NASA and NOAA precipitation products (e.g., TRMM 3B42, Huffman et al., 2005, 2007; CMORPH, Joyce et al., 2004; NOAA HydroEstimator) whose states and fluxes are forecasted forward using TIGGE data products and conditionally post-processed to produce calibrated probabilistic forecasts of river discharge for key river reach locations. Already operational over the Brahmaputra and Ganges river basins, this system will be extended and calibrated for the Indus basin. [1: See: http://tigge.ecmwf.int/] [2: See: http://tparc.mri-jma.go.jp/TIGGE/tigge_extreme _prob.html]

(Figure 2. Collaborative partner RIMES-CEGIS selected pilot unions of Bangladesh in 2007, selected to receive CFAB long lead time flood warnings; CFAB 2007 10-day lead-time forecasts (right panel) showing strong likelihood of severe flooding: ensemble forecasts - colored lines; observed discharge solid black; danger level threshold black dashed.)Provide for Public Access. Although this is the last task represented in Figure 1, providing the public with truly beneficial flood warning information is a complex endeavor, and is the subject of much of this proposals research. However, our team can build from ongoing regional efforts. To enhance and evaluate the benefits of advanced warnings to vulnerable Bangladeshi communities living in flood-prone regions, RIMES, in early collaboration with the Center for Environmental and Geographic Information Services (CEGIS) and the Forecasting and Warning Centre (FFWC) of Bangladesh, created a direct dissemination network to local communities and individuals living in fifteen pilot unions (Fig. 2, left panel showing original six pilot sites in 2006) in Bangladesh (CEGIS, 2006). Participants receive flood hazard warnings based on CFAB model forecasts. The warnings are disseminated to the pilot unions during the monsoon season, and are tailored to be understandable to affected communities. For example, in July and September 2007, severe flooding occurred in the Brahmaputra basin (Fig. 2, right panel), and warnings were disseminated to the pilot sites days in advance of the flooding. This contributed to mitigating the disastrous consequences of the flood wave on lives and livelihoods. Post flood assessments are also carried out to assess the effectiveness and drawbacks of the flood forecast and dissemination system at the community level and individual level. This pilot dissemination and surveying program is now being managed and expanded by our collaborative partner, the Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES). These established pilot sites will be locations in which the University of Nebraska social science team conducts interviews and focus groups.

Forecast dissemination will also occur at national and international levels. Since the probabilistic (ensemble) forecasts can be presented in different ways, during the dissemination and input workshops, we will work with end-users and prospective end users to determine the most useful ways to present valuable and uncertain flood forecast information. The Dartmouth Flood Observatory (DFO) River Discharge Measurements web site is a model format on which we can build.[footnoteRef:3] River Watch 2 is an existing automated processor supported by NASA Earth Science Research and Applications Programs. In support of our proposed work, the University of Colorado is making available extensive space on River Watch 2 for South Asia-focused displays to provide a prototype portal for both satellite-based present status information and the model-based discharge prediction and flood warning information. Thus, Figs. 3 and 4 indicate what has been entirely missing but is now feasible for South Asia. It provides a subscene of the present DFO River Watch 2 map view (automated, updated daily) of current river flow severity status of monitored sites (Fig. 3), and also a sample time-series of the existing present and historic status output at one site (Fig. 4). We can adapt these displays to incorporate forecast and present-status information (as in Figs. 3 and 4) and create more appropriate map displays publishable at larger scale for the three river basins of interest on the DFO and partner organization websites. [3: See: http://floodobservatory.colorado.edu/CriticalAreas/DischargeAccess.html]

(Figure 5 Daily time series of observed river discharge (solid) and model nowcast (dash) based on the satellite-based river discharge estimates for Ganges River at Hardinge Bridge ground station in Bangladesh. Satellite-derived information at locations with distance ranging from 63 KM to 1828KM upstream Hardinge Bridge station were used. From (Hirpa et al., 2013).)Examine the benefits of data assimilation of satellite-derived river discharge estimates on improving forecasting skill. Bangladesh is a classic example of flooding issues exacerbated by international boundaries and lack of upstream river flow information. Consequently, members of our research team, in collaboration with the Global Disaster Alert and Coordination facility in Europe, have used satellite-based daily measurements of stream widths at multiple locations upstream of Bangladeshs borders (similar to those shown in Figs. 3 and 4, see also Fig. 7) to remove this limitation. At certain microwave wavelengths, there is very little interference from cloud cover (floods can be measured even when the ground surface is obscured from optical sensors such as MODIS). Using a strategy first developed for wide-area optical sensors (Brakenridge, Anderson, Nghiem, & Chien, 2005), such data can be used to measure river discharge changes (Brakenridge, Nghiem, Anderson, & Mic, 2007). As rivers rise and discharge increases, floodplain water surface area increases. Microwave emission over river measurement sites, observed from space, can monitor such changes. Our work has: 1) examined the capability of using these data to track the downstream propagation of flood waves through India, and 2) evaluated their use in producing river flow nowcasts (Fig. 5), and forecasts at 1-15 days lead time (Hirpa et al., 2013). The results have demonstrated the propagation of a flood wave along both river channels can be tracked reliably in near real time, and therefore could be incorporated into river flow predictive modeling.

One of the main sources of hydrologic prediction error is due to uncertainty in the model parameters, as well as routing errors due to lack of river cross-section information. These problems are often linked to the lack of reliable ground discharge observation used for model calibration. This problem can be mitigated by using discharge estimates derived from satellite-based river measurements at locations where there are limited ground observations. Additionally, another large source of hydrologic predictive uncertainty is lack of knowledge of current in-stream flows and catchment soil moisture states. Data assimilation can be used to improve both of these hydrologic uncertainty problems through optimally combining satellite-based river discharge estimates with hydrologic model flow states, as well as using the causal link between in-stream flows and catchment soil moisture. This approach then modifies a priori state of the model by taking into account the relative errors in the model simulations and the estimates. In the past few years, hydrologic data assimilation has become popular mechanism for reducing forecast uncertainty, in part due to the availability of a wide range of satellite-based soil moisture and river information. This proposals research is at the forefront ofexploring the use of satellite-based river flow measurements in this context.. Equally relevant for our hydrologic application are assimilation of in-situ discharge (e.g., Seo et al., 2009; Moradkhani et al., 2005; Weerts and El Serafi, 2006; Clark at al., 2008; and Lee at al., 2012), and satellite-based water elevation (e.g., Montanari et al., 2009).

In this project we propose to improve and extend the lead time of the near-real time riverflow prediction of the CFAB hydrologic model using data assimilation of upstream flow information provided by the remote sensing at several upstream locations. We intend to use sequential Monte Carlo data assimilation techniques (Arulampalam et al., 2002) for the assimilation at each model run time steps. Our recent work demonstrated the value of the remote sensing data assimilation for operational prediction (Hirpa et al., 2013) in basins with no ground upstream river discharge observation, strictly by tracking flood waves. By also assimilating this information into physical hydrologic models, however, we anticipate the overall forecast skill of the CFAB scheme could be improved significantly.

4.2. Transform forecast discharge values (flood peak discharges) into inundation extent maps.

(Figure 6. The DFO record of flooding in this portion of the Ganges Basin is shown as light blue (2000 to 20111), light red was flooding in the 10 days prior to map update date, and red was current flooding. The numbers indicate River Watch discharge measurement sites. This is a small subscene of the complete Surface Water Record display for this region.)NASAs orbital technology has been used at DFO extensively, since the launch of the twin MODIS sensors in early 2000 and 2002, to map flooding in South Asia. Unlike other remote sensing-based organizations active in flood response, DFO maintains a large and growing archive of such map data, in digital (GIS) format, and for use in making comprehensive regional displays indicating the history of inundation as well as on-going flooding (Fig. 6). The archival flood information is exceptionally valuable, providing as it does a view of flood hazard.

This large archive of such mapped inundation resident at DFO will allow production of an innovative flood prediction product. As illustrated in Fig. 7, past inundation extent can be matched to the corresponding remote (Figure 7. Left: MODIS imaging and mapping of 2003 flooding along the Ganges River between river measurement sites 200 and 201. At site 200 (uncalibrated) peak discharge was 8500 m3/sec. Right: mapping of 2004 flooding. The uncalibrated peak discharge here is only ~3500 m3/sec. ) sensing-derived discharge values (the same approach can be used for any ground station sites for which data output is available publicly). Linkage to the appropriate inundation map can be provided at the individual site displays: when a particular discharge and flood threshold is predicted, the user can call up the inundation that resulted, historically, from the same values. Mapping inundation maps to the ensemble of river forecasts produced by the CFAB model could then produce a range of possible inundation extent scenarios. Such capability highlights the importance of understanding how and why end users process this uncertain information. We hypothesize trust is an important consideration at the individual, group and intergroup level of interpretive understanding.

4.3. Test a model of trust, investigate policy uptake and implementation, and expand the capacity for contextual/ecological modeling through the use of multilevel and latent variable statistics.

Trust has long been considered a key variable in understanding cooperation in policy and international relation contexts (e.g., Hoffman, 2002), as well as in natural resource management contexts (e.g., Earle, 2010; Leahy & Anderson, 2008; Poortinga & Pidgeon, 2006; Stern, 2008; Tennberg, 2007; Winter & Cvetkovich, 2010). Kingdon (1984), Sabatier and Jenkins-Smith (1993), and Dye (1995) each propose a conceptualization of policy making involving the notion of policy communities to probe how the policy process is shaped by events and actors. Within the policy community are a core cadre of participants who make most of the policy domains routine decisions (Baumgartner & Jones, 2009; Birkland, 1997). These participants function in the policy process as individuals, members of groups, and members of networks or systems. In light of this, we hypothesize success in making policy decisions is based, in part, on patterns of trust that emerge at individual, group, and systems levels and the interactions between trust at these levels. Trust likely impacts the diversity of ideas and the different contributions individuals make, which then impacts the organizational capacity upon which policy-community members draw (Innes & Booher, 2003). At the same time, interpersonal relations are increasingly recognized as contributing to explaining human behavior in policy contexts (Sabatier & Weible, 2007). The social component of generating and expanding knowledge, including trust in institutions, is a function of collective reasoning (Fleck, 1979): Individuals assume and develop changed patterns of collective action (Heclo, 1974, p. 306) through exchanges with others. Potentially, these social practices are examined continuously and reformed as participants develop insights into the practices in which they are engaged (Giddens, 1990).

Although much work remains, progress is being made in conceptualizing, defining, and measuring trust in institutions, defined most commonly as some combination of positive expectations and or leading to trust-relevant actions, such as willingness to be vulnerable to, comply with, and/or otherwise support the institution (e.g., as reviewed in McEvily & Tortoriello, 2011; Schoorman, Mayer, & Davis, 2007). The recent emphasis on advancing the theoretical and methodological foundations of trust in institutions offered by our interdisciplinary trust research team (Bornstein, Tomkins, Neeley, Herian, & Hamm, in press; Hamm et al., in press; Hamm, PytlikZillig, Herian, Tomkins, & Dietrich, 2012; Hamm et al., 2011; PytlikZillig, Tomkins, Herian, Hamm, & Abdel-Monem, 2012; Tomkins, PytlikZillig, Herian, Abdel-Monem, & Hamm, 2010) and by other researchers (e.g., Mayer, Davis, & Schoorman, 2006; Nannestad, 2008; Rousseau, Sitkin, Burt, & Camerer, 1998) has identified critical gaps in understanding how institutional trust develops and changes over time. As a key component of our contributions, we posit a sophistication model of trust development (see Fig. 8) that hypothesizes as an individual gains experiences with and knowledge about an institution (i.e., develops sophistication), the individual will shift from relying on general, trust dispositions, to more institution-particiularized and then commitment-relevant trust-constructs (Herian, Hamm, Tomkins, & PytlikZillig, 2012).

(Figure 8. Conceptual diagram of our sophistication model of trust in institutions)We have been researching our sophistication model in the contexts of courts, city government, and water regulatory agencies in the United States. We find the predictive ability of trust-related constructs has varied across samples (Hamm et al., in press; Hamm et al., 2011), potentially as a function of differing levels of sophistication. We are currently examining our sophistication hypothesis more rigorously in a multi-year longitudinal study of students who, at initial testing, had very little experience and knowledge of water regulation institutions. We have randomly assigned some students to read (over many months) and learn about water regulation institutions, and others to a control condition (reading about health-related regulatory activities). We expect sophistication to change most in the experimental students, and along with those changes, the bases of trust in water regulatory institutions to also change.

An important gap in our research and in the field of trust research at large concerns the potential influence and interaction of trust constructs as stakeholder sophistication increases and trust develops and emerges at different ecological or contextual levels (e.g., micro, meso, macro levels). The vast majority of trust research, including ours, generally examines trust at a single level (e.g., the individual, micro, or between-group, meso level). There is little, if any, research, however, investigating how trust at different levels might interact or jointly influence important outcomes (e.g., vulnerability, resilience, policy outcomes), though it has been argued that a multilevel approach is critical to understanding how trust impacts policy and other political behaviors (e.g., Hutchinson & Johnson, 2011). Filling this gap is important for more fully understanding trust in institutions, especially given that governance institutions operate in an inherently multilevel, policy context, in general, and especially when natural resource decisions cross national borders (e.g., Hoffman, 2002; Tennberg, 2007).

Moving toward a multilevel model of trust and sophistication. Examination of trust constructs within a multi-organization and multilevel context is an ideal place to test, refine, and expand our sophistication model because amounts and types of knowledge and experience with an institution (i.e., type of sophistication) are likely to vary across different levels of the institution and to be dependent on ones roles and relationships with that institution. For example, persons involved in the development of flood forecasts may be quite sophisticated regarding the science and information provided by the forecasts, but less sophisticated regarding the politics occurring between stakeholders that might use or be affected by the use (or non-use) of the forecast information. Similarly, the public could vary widely in their knowledge, opinions and appreciation for the complexities involved in decision making related to a developing forecasting system. Meanwhile, decision makers may have an added layer of interpersonal and potentially emotional experience (e.g., frustration, felt responsibility, discomfort with uncertainty) with the emerging forecast system. Although trust research has been conducted at levels ranging from the individual micro to the societal macro levels (e.g., for macro-level research see La Porta, Lopez-de-Silanes, Shleifer, & Vishny, 1997; You, 2012), research on trust constructs and processes that span multiple levels is rare. Thus, little is known about whether and how trust at one level (among forecast developers) might affect trust at another level (decision makers or the public), or the extent to which emergent processes (Kozlowski & Chao, 2012; Kozlowski & Klein, 2000) impacts trust at higher-levels.

(Figure 9. Our guiding multilevel trust conceptual model)Our guiding theoretical model (illustrated by Fig. 9) predicts that different forms and patterns of trust across multiple contextual levels will impact intra- and intergroup processes, and thereby affect the outputs (products) of that performance. At the highest level, we expect trust in the system as a whole to be impacted via emergent processes for insiders (e.g., emerging patterns of relationships with other insiders from within and outside of their identity groups), and via more direct (e.g., performance outcomes such as useful and accessible forecasts) processes for the public. Although the model is comprised of multiple parts, in the present research we will focus primarily on the central hypothesis that positive group processes, within and between insider groups (groups most directly involved in forecast development and use of forecasts in policy decisions), will be facilitated by optimal (not high or low) levels of and variability in trust and result in more positive outcomes and higher levels of overall public trust (likely mediated by perceptions of performance outcomes). The rationale for this hypothesis stems from research in the social sciences pertaining to group functioning and team performance. Within-group or intra-team trust refers to the levels and patterns of trust (or trust constructs) that occur among members of the same group. Average within-group (intra-team) trust refers to the average extent to which each member of the group trusts each other member (cf., intraentity trust, Fang, Palmatier, Scheer, & Li, 2008). Prior research has found that higher levels of within group trust may facilitate collaboration (e.g., resulting in high reciprocity and coordination between group members), but also to less adaptability to change (e.g., undermining monitoring, promoting features of group think) (Fang et al., 2008). Thus, we predict that that too high or low within-group trust will be detrimental to overall group functioning, especially in contexts that are uncertain, changing, and unpredictable, such as warning and emergency communication contexts. Variance in own group trust refers to the overall variability around the average level of trust in a group to which someone is a member. We hypothesize that different levels of individual trust within a group trust may serve different purposes (with the high-trustors facilitating cooperation and optimism, and low trustors facilitating monitoring of processes and tasks), and thus, that there may be an optimal amount of variance of trust in groups of which one is a part. Without enough variance, different roles and purposes will not be fulfilled, but too much variance could cause conflict and undermine group processes. We also will explore emergent processes and their relationships to variance in own group trust, such as whether the variance in extent to which people trust their whole group arises out of variance in dyadic trust within the group, and/or from differences in dispositional trust between individual members, and whether one source of variance is more beneficial than another over time. Between group trust refers to the levels and patterns of trust that occur between groups (cf. interorganizational trust, Fang et al., 2008). For example, between group trust could refer to the trust between two organizations, two working groups, a working group and a governing council, a larger group and a sub-group, and so on. Meanwhile, variance in between group trust refers to the variability in the extent to which trust exists between groups. Although variables at different levels (e.g., within vs. between groups) may not necessarily operate the same, it seems reasonable that, at least in highly interactive and overlapping groups (such as we propose to study), too much or too little trust or variance in trust may impact the extent to which groups effectively collaborate and monitor one anothers efforts.

Study 1. Initial insider measure development and context description. The objective of Study 1, which will be conducted during months 1-6 of the project, is to develop measures and establish a foundational understanding of the context in which our model testing will take place. This study will be part 1 of an exploratory, sequential design (Creswell & Plano Clark, 2007; Small, 2011), and will be primarily qualitative in nature. Data sources will include structured notes from focus groups, and interviews (which will also be audiotaped in case of the need for clarification). Participants in the focus groups and interviews will include engineers and scientists currently producing weather and flood forecasts, those directly involved in the ongoing efforts to put the sophisticated flood forecasts to use in planning for disasters and regional economic development, including World Bank planners, NGO specialists working in the field on mitigation strategies, government policy makers, and residents.

Our procedures will involve traveling to the study areas early in Year 1, and conducting approximately 3 focus groups (one per country), and up to 15 semi-structured interviews of different stakeholders (5 per country) in order to sample a range of participants from each country and watershed, to explore the elements of our model (Fig. 9) and initial drafts of our measures of model elements. We will recruit relevant stakeholders for interviews and focus groups with assistance from CARE-Bangladesh, INIH, FFD, World Bank, and RIMES. Initial drafts of measures of the trust and process variables will be adapted from prior work (e.g., De Jong & Dirks, 2012; Fang et al., 2008; Hamm et al., 2011), while measures of the products (individual and team performance, performance outcomes, such as related to the accessibility and utility of the forecasts) and system sustainability will be developed as part of this research, with input from stakeholders. We expect interviews to last about 1 hour, and focus groups to last 2 hours. During the workshops, focus groups and interviews, in the tradition of Glaser and Strausss (1967) grounded theory, we will use generative questions (Trochim, 2005) to discuss each of the model components (intra and intergroup trust, group processes, products/performance, and system sustainability) and subcomponents (e.g., the subcomponents of trust, the importance of expertise and competence assessments, the impact of uncertainty), as they relate to key aspects of the forecasts and events related to flood forecasts (e.g., actual flooding). The in-depth, semi-structured nature of our protocols will give respondents the opportunity to provide detailed explanation and clarification (Lewis, 2003), and allow us to probe a consistent set of issues while hearing a range of perspectives (Berg, 1998; Hughes, 2002), thereby revealing strengths and weaknesses in our emerging theoretical model in this specific context. Note that the strategy of developing new variables dependent on what we learn from the focus groups and interviews, as well as our observations of dissemination workshops, makes protocol development an ongoing feature of the research rather than an activity we complete at the outset. Study 1 will begin this process, which will be continued in Studies 3 and 4. During the interviews we will also use cognitive interview techniques (Davison, Vogel, & Coffman, 1997) to better understand participant thoughts and reactions to the survey measures we develop, and for validation and fine tuning. A trained researcher will be present at each focus group to take notes. S/he also will listen to and take structured notes from the audio recordings of the interviews, and these notes, along with the notes from the flood forecast use dissemination workshops, will form the qualitative data base.

Analyses of data from the Study 1 focus group and interview notes will be conducted throughout Year 1, using Atlas.ti or other qualitative coding software. Two coders will use a draft coding protocol to analyze a subset of the notes for themes relating to the model components as well as additional themes that may become apparent during analyses. Coders will then meet with other research team members to expand, revise, and refine the codes as needed. Each document in the corpus will be coded by at least one coder, with a subset of the documents coded by two coders to estimate intercoder reliability.

Study 2. Testing and refinement of insider measures. Study 2 will be conducted at the end of Year 1 of the project (at about the time of the dissemination workshops) and is part 2 of the exploratory sequential design. The objective will be to gather data pertaining to the statistical/psychometric and construct validity of the measures (developed as part of Study 1) and to provide baseline data for Study 4 (Specific Aim 5). Participants will include all participants in the 3-day dissemination workshops (Type A participants, n 30, about 10 persons per country) which will include engineers and scientists directly involved in the ongoing efforts to put the sophisticated flood forecasts to use in planning for disasters and regional economic development; and other members of involved institutions who are both knowledgeable and not about the use of sophisticated flood forecasting but not directly involved in the workshop activities (Type B participants, n 120, approximately 40 per country), including RIMES experts, World Bank specialists, scientists and engineers currently producing weather and flood forecasts but not attending the workshops, and social scientists and NGO specialists working in the field on mitigation strategies. Participants will be recruited using multiple strategies with the assistance of our in-country collaborators (i.e. CARE-Bangladesh, etc.) and, if relevant, snowball sampling where those more centrally involved invite colleagues who are interested and have varied levels of knowledge about the use of sophisticated flood forecasting in developing disaster preparedness and regional economic plans).

To ensure efficient and appropriate measurement, surveys will be customized depending on the participants role or relationship to using sophisticated flood forecasting in developing disaster preparedness and regional economic plans (e.g., specific questions for workshop participants or relevant group members will differ from questions for more marginally involved participants). The survey will include measures of multilevel trust, process perceptions, and perceptions of team performance outcomes and sustainability of the system as whole and designed to take approximately 10-15 minutes. Initial quantitative analyses will include item analyses, scale reliabilities, exploratory factor analyses, correlation, and multiple regression analyses designed to examine both scale reliability/validity and hypothesized relationships between variables. Power analyses indicate that approximately 150 participants will be sufficient in our model to find effects as small as .22 at a power level of .8 and p < .05 (Soper, 2013). In addition, these data will be used in the more sophisticated investigations in Study 6.

Study 3. Development of public measures. During Years 1 and 2, measures of public trust in the flood forecast and warning systems will also be developed based on our prior measures. Participants in this study will be students from area universities who will serve as proxies for the general public (N 300 total, 100 per country). Procedures will be primarily survey-based. After a draft of the survey is developed and translated into the appropriate languages, at the end of Year 1 (around the time of the first workshops) we will conduct 3 cognitive interviews per country with students to ensure the understandability and appropriateness of the questions. The students will be recruited through our in-country collaborators and chosen for their interest in flood and forecasting topics. Because they may not know much about our teams efforts, we will prepare background information about the ongoing activities and flood forecasting in general for students to read before they complete the survey (which will assess trust and perceptions of team performance outcomes and of the system as whole, and will be designed to take about 10-15 minutes) and then engage with a researcher in a cognitive interview for approximately 30-45 minutes. They will be paid a $25 incentive for their time and effort. After refining the questions and background materials based on the student interviews, the reading and questions will finalized into both a paper and web-based survey. During Year 2, additional students (N 300; 100 per country) will be recruited from courses related to weather, climate and forecasting. These students will read background material, varying in detail and depth, about our project prior to completing the measures. The varied background given to the students, as well as natural variation in student knowledge and experience with the topics, will simulate differences in sophistication that we expect to find in the general public. We will conduct confirmatory factor analyses on the items to examine their dimensionality and to determine replication of prior findings. Initial analyses will assess scale reliability, using model-based reliability estimates (omega), and construct relationships, using structural equation modeling. Power analyses indicate that 300 participants will be more than sufficient to detect effects as small as .15 in our model (Soper, 2013). In addition, these data will be used in the more sophisticated investigations in Study 6.

Study 4. Simultaneous, explanatory, mixed-method, longitudinal study. This study uses an explanatory, mixed-method design in which qualitative study of the model conceptually outlined in Figure 1 is designed to help explain and understand (as well as triangulate) quantitative data pertaining to the model (Small, 2011). In addition, the study will continue the work started in Study 1 to construct our ultimate outcome variables (e.g., final assessments of perceptions of key events that occur during the project time period). This study will examine our previously-described optimal trust working hypothesis. Participants will include the same target groups (i.e., Types A and B) as described for Study 2. Our procedures will include the use of surveys developed during Studies 1 and 2 administered at the end of Year 1 (as part of Study 2), and again at the end or Years 2 and 3 (in conjunction with the Year 3 workshops, which will include some of the same participants as involved at the end of Year 1). Initial analyses of surveys will include longitudinal multilevel modeling procedures that take into account the highly nested nature of the data (time points nested within individuals nested within groups and organizations); but once again, these data will also be investigated in Study 6.

To obtain the explanatory qualitative data we will conduct twelve focus groups (one with each Type A/B participants in each country at the end of Year 1 (six focus groups) and likewise at the end of Year 3 (six more groups), with approximately 8-10 participants per group) as well as approximately 15 interviews (5 per country) conducted and spaced out between the two sets of focus groups. Focus groups will be conducted as part of the workshops; interviews will be conducted using distance methods (e.g., with the assistance of an in-country partner trained to conduct the interview, and a research co-interviewer in attendance via Skype or conference call). For the focus groups and interviews (which also will be audiotaped), we will use semi-structured protocols to examine how decision process outcomes result from initial conditions. This will provide important context for understanding changes in trust in the process of development and use of the forecasts. As was the case in Study 1, a trained research assistant will use a structured note taking protocol to gather data while attending the focus groups and from the audio tapes of the focus groups and interviews. The coding protocol for the qualitative data will build upon findings from Study 1 but also will be adapted as needed to take into account events taking place during the study.

Study 5. Public survey study. A large-scale survey will investigate public perceptions of and trust in flood forecast systems, science, and forecast information during Years 2 and 4 of the study. This survey, initially developed as part of Study 3, will take approximately 10-15 minutes. Participants will be a sample of the general public (100 per country, across 3 sites per country or 9 sites total) who will be asked their opinions about utility of flood forecasting in mitigating devastation from floods (as in Study 3, N = 300+ will allow us to achieve the power we need for our initial analyses). Survey procedures will be used, and measures will include perceptions of team performance outcomes and of the system as whole. Survey participants will have the opportunity to (but will not be required to) read or hear background information about the project previously developed for and tested with students. Initial analyses of the survey data will aim to replicate those used with Study 3 data, but now with data from the general public. Once again, however, we expect to also use these data in our Study 6, discussed next.

Study 6. Evaluation of methods for statistical modeling of multilevel trust. Multilevel research efforts are often hindered by an inability to simultaneously and statistically consider multiple levels of ecological and/or environmental influence (Bovaird, 2007). Ecological models such as the posed model of trust are often theoretically unified, but tested in a piecemeal fashion. Innovative modeling methods that overcome these limitations and are applicable to studying such multilevel determinants are needed. Multilevel structural equation modeling (MSEM) presents a statistical modeling framework that synthesizes multilevel modeling (MLM) appropriate for considering the effects of complex sampling, with broader techniques appropriate for integrating latent variables and measurement assumptions into multivariate linear models, thus enabling simultaneous evaluation of comprehensive ecological or contextual systems (Bovaird, 2007). The goals of this study are to: 1) continue to expand the capacity of current MLM and SEM frameworks to allow designation of complex levels of influence; 2) determine the sufficient data conditions necessary for implementing both complex univariate MLM models and complex MSEM; and 3) evaluate recently developed commercial and research software to enable implementation of the methodologies evaluated in this study. The first goal of this study will be achieved by implementing a statistical modeling framework that allows for consideration of more than two levels of an ecological MSEM for the multilevel theory of trust developed and evaluated in Studies 1-5. The data obtained in Studies 1-5 will be integrated to provide substantively valid estimates of population parameters based on relationships posed in the model of multilevel trust. In pursuit of the second goal, Monte Carlo simulation methods will be used to validate the statistical modeling framework with simulated data. Based on parameter estimates from empirical data collected in studies 1-5, Monte Carlo simulation methods will then be used to determine the data conditions necessary to confidently apply substantively relevant MLM and MSEM models. Finally, the third goal will be achieved by evaluating accessible software for implementing the expanded MSEM in the Mplus, SAS, WinBUGS, and R computing environments. This study will provide a significant expansion of the MSEM framework to allow consideration of complex realistic multilevel ecological models. The methods developed and evaluated in this study will be applicable to all research areas in social sciences that consider ecological or contextual models of behavior or development. This work is essential for answering the call for multilevel rather than single level approaches to understanding how trust impacts policy and other political behaviors (Hutchinson & Johnson, 2011).

Both the empirical and simulation phases of this study will compare model estimates from the following softwares: the recently released Mplus version 7 (Muthn & Muthn, 1998-2012) which now allows complex hierarchical nesting; a two-step approach using a combination of Mplus version 7 and procedures/packages from the SAS 9.3 and R software environments; and WinBUGS for implementing Bayesian methods through Markov Chain Monte Carlo (MCMC). Of particular interest is the newly-released Mplus version 7 which has the expanded ability to model three-or-more nested hierarchical levels or cross-classified non-nested levels with multivariate latent variable models. Mplus 7 utilizes a Bayesian approach through MCMC, as does WinBUGS, to implement such complex multilevel models; however, the software has only been available for a limited time (as of November 2012) and has not been thoroughly evaluated. Prior to the release of this version of Mplus, there were limited options (i.e. only two levels) for estimating complex multivariate latent variable model such as required in simultaneous estimation of complex multilevel ecological models. Thus, the proposed work is timely and has the potential for broad impacts across the social and behavioral sciences.

Also of particular focus in this studyparticularly goal 2 are the methodological limitations posed by multilevel ecological research pertaining to sample size and geographical nesting. Generally speaking, a large sample size (often j = 50-100 macro level units) is required for multilevel modeling and particularly MSEM primarily because of the model complexity and estimation methods used to obtain parameter estimates. Obtaining a large sample size at the micro/individual level is often not a problem, but obtaining a sufficiently large sample size at the macro level is. In the current study, the micro-level population is relatively large and accessible, while the macro-level populations (regions and countries) are finite and small. Song and Lee (2004) have shown that Bayesian methods similar to those implemented in Mplus and WinBUGS can be particularly effective for estimating structural models with small samples. In addition, geographically-based macro-level units vary greatly in population size, making it difficult if not impossible to have a balanced design.

5. Relation to Results of Prior NSF Research, Other Research, & Long-Term Goals

The proposed research brings the Colorado teams (NCAR and DFO) prior research on flood forecasting together with the Nebraska teams prior NSF-funded research on trust and stakeholder engagement. In doing so we are able to advance the fields of flood forecasting and trust in the context of technical and policy acceptance and implementation. The inclusion of the sophisticated statistical modeling and latent variable analysis components of this project address the challenge of assessing trust and policy uptake at different scales. Thus, as a team, we will work towards our long-term, shared goals of reducing flood vulnerability through advancing flood forecasting and its use by agencies and groups, on behalf of vulnerable individuals.

We will leverage our successful NASA-funded research feasibility project designed to better define the pathway to be taken to full and sustainable implementation of a flood mapping processor, a merger between an automated, near real time (NRT), MODIS sensor-based flood map product and a complementary, radar frequency, Envisat ASARbased global flood mapping processor (Kleuskens, et al., 2011; Westerhoff et al., 2010). Also, in collaboration with end users, the research team is implementing, on a trial basis, coupled flood discharge and inundation forecasting for Bangladesh. During our dissemination and input workshops with end users we will determine, for example, which forecast locations would likely provide the earliest demonstration of specific enhancements to end-user capabilities and operations. This will contribute to increasing local capacity to plan for the flood threat. Building on the advances in flood forecasting by NCAR and DFO, we are developing a valuable product to provide decision makers, within and outside of government, with maps indicating anticipated extent of flood plain inundation. Such work and our comparative examination of trust in multiple organizations and among individuals integral to the successful use of flood forecasts is made possible by leveraging our exceptional access to and positive relationships with them made possible through our relationships with CARE-Bangladesh, INIH, PMD, World Bank, and the World Food Programme.

As key parts of each of our NSF-funded projects relating to trust (Testing a Three-Stage Model of Institutional Confidence across Branches of Government (SES-1061635); Law and Social Science Postdoctoral Fellowship: Trust and Confidence (SES-1228559); Understanding the Role of Trust in Cooperating with Natural Resource Institutions (SES-1154855); SBES: Medium: Investigating the Role of Distrust in Unauthorized Online Activities Using an Integrated Sociotechnical Approach; IGERT: Resilience and Adaptive Governance in Stressed Watersheds (DGE-0903469)), we have been developing reliable and valid measures of trust-related constructs and testing and refining an interdisciplinary theory of the development of institutional trust. In the cybersecurity project, we are examining the relevance of the construct of distrust and its impact on hacking behaviors. The theory-testing and measures development of the trust measures is currently ongoing in all the projects. Relevant publications include: Bornstein et al. (in press); Hamm et al. (in press); Hamm et al. (2011); PytlikZillig et al. (2012); Tomkins et al. (2010). Relevant papers include: Hamm et al. (2012); Tomkins, PytlikZillig, Herian, and Hoppe (2012). The proposed project will build on this prior work, allowing us to assess and refine our sophistication model in a dynamic, real-world, societally-important, policy context. It also provides us with the opportunity to go beyond the individual or micro level and test the potential influence and interaction of trust constructs at the meso and macro levels. At the same time, the proposed research allows us to explore further the role of time, as we study trust constructs as they evolve, thus providing us another opportunity to assess sophisticationnot simply as a static, point-in-time construct measured cross-sectionallybut also as a longitudinal and developmental phenomenon.

The present work also builds on our NSF-funded work in public engagement (Developing a Social-Cognitive, Multilevel, Empirically-Based Model of Public Engagement for the Shaping of Science and Innovation Policy (SBE-0965465)) which uses U.S. students as research participants and experimentally investigates methods for enhancing public engagement and the publics use of complex science-related information in their policy recommendations concerning nanotechnology development and regulation. This research has included examination of trust in science and scientists and has involved about 1,000 science majors in considering ethical, legal and social issues related to nanotechnology (PytlikZillig & Tomkins, 2011; PytlikZillig et al., 2011; PytlikZillig et al., 2013). The current proposal will allow us to build on our experiemental work in real-world, international contexts. We have also conducted NSF-Funded work in water contexts (Knowledge Discovery and Information Fusion Tools for Collaborative Systems to Adaptively Manage Uncertain Hydrological Resources (IIS-0535255). This project studied complex hydrologic data and decision making in the U.S., and provided the basis for studying water resource institutions, along with considering aspects of trust and social justice in this policy arena (Bornstein et al. (2009)). The proposed research takes that research into international contexts.

Finally, the proposed project will build on the Department of Education-funded and related research that has advanced statistical research on multilevel modeling (Bovaird, 2007; Bovaird & Embretson, 2008; Bovaird & Koziol, 2012; Locker, Hoffman, & Bovaird, 2007) and latent variables (Little, Bovaird, & Widaman, 2006) as well as modeling contextual effects in longitudinal studies (Little, Bovaird, & Card, 2007).

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