Use Of Data For Decision Research Articles

Establishing a baseline to measure change in political will and the use of data for decision-making in maternal and newborn health in six African countries

The Evidence for Action (E4A) program assumes that both resource allocation and quality of care can improve via a strategy that combines evidence and advocacy to stimulate accountability. The present paper explains the methods used to collect baseline monitoring data using two tools developed to inform program design in six focus countries. The first tool is designed to understand the extent to which decision-makers have access to the data they need, when they need it, and in meaningful formats, and then to use the data to prioritize, plan, and allocate resources. The second tool seeks the views of people working in the area of maternal and newborn health (MNH) about political will, including: quality of care, the political and financial priority accorded to MNH, and the extent to which MNH decision-makers are accountable to service users. Findings indicate significant potential to improve access to and use of data for decision-making, particularly at subnational levels. Respondents across all six program countries reported lack of access by ordinary citizens to information on the health and MNH budget, and data on MNH outcomes. In all six countries there was a perceived inequity in the distribution of resources and a perception that politicians do not fully understand the priorities of their constituents.

CONSTRUCT VALIDATION OF A MEASURE TO ASSESS SUSTAINABILITY OF SCHOOL-WIDE BEHAVIOR INTERVENTIONS

This study assessed aspects of construct validity of the School-wide Universal Behavior Sustainability Index–School Teams (SUBSIST), a measure evaluating critical features of the school context related to sustainability of school-wide interventions. Participants at 217 schools implementing School-wide Positive Behavior Support (SWPBS) were administered the measure. A two-step cluster analysis was performed to classify schools based on responses to the 39 SUBSIST items. A two-cluster solution was obtained, with schools in one cluster (n = 139) obtaining significantly higher scores on SUBSIST items than schools in the other cluster (n = 78). The most critical item that contributed to cluster formation was the use of data for decision making. Results also showed that higher sustainability scores were associated with increased frequency of school team meetings, presentation of data to school staff, access to an external coach/consultant, and greater number of years implementing SWPBS. These results are discussed with regard to implications for sustaining school-wide interventions.

Approaches to ensuring and improving quality in the context of health system strengthening: a cross-site analysis of the five African Health Initiative Partnership programs

BackgroundIntegrated into the work in health systems strengthening (HSS) is a growing focus on the importance of ensuring quality of the services delivered and systems which support them. Understanding how to define and measure quality in the different key World Health Organization building blocks is critical to providing the information needed to address gaps and identify models for replication.Description of approachesWe describe the approaches to defining and improving quality across the five country programs funded through the Doris Duke Charitable Foundation African Health Initiative. While each program has independently developed and implemented country-specific approaches to strengthening health systems, they all included quality of services and systems as a core principle. We describe the differences and similarities across the programs in defining and improving quality as an embedded process essential for HSS to achieve the goal of improved population health. The programs measured quality across most or all of the six WHO building blocks, with specific areas of overlap in improving quality falling into four main categories: 1) defining and measuring quality; 2) ensuring data quality, and building capacity for data use for decision making and response to quality measurements; 3) strengthened supportive supervision and/or mentoring; and 4) operational research to understand the factors associated with observed variation in quality.ConclusionsLearning the value and challenges of these approaches to measuring and improving quality across the key components of HSS as the projects continue their work will help inform similar efforts both now and in the future to ensure quality across the critical components of a health system and the impact on population health.

Differences in Principals' Data Use for Decision Making: An Administrative Probelm-Solving Perspective

As school leaders, principals must seek, use, and make sense of data and information. This study presents the findings of a survey study of 182 high school principals’ data use and examines the differences in the extent of principals’ data use for decision making in solving various dimensions of administrative problems. Results show principals used data frequently for decision-making in instructional leadership, organization operational leadership and school vision leadership, among which data use in instructional leadership was most frequent. Principals’ use of data was significantly less frequent in the leadership dimension of collaborative partnerships and larger-context politics. This article ends with implications with regard to improving school leadership programs in meeting the emergent need of preparing data driven decision makers and suggestions for research relevant to the central topic of data-driven decision making.

Improving the use of health data for health system strengthening

Background: Good quality and timely data from health information systems are the foundation of all health systems. However, too often data sit in reports, on shelves or in databases and are not sufficiently utilised in policy and program development, improvement, strategic planning and advocacy. Without specific interventions aimed at improving the use of data produced by information systems, health systems will never fully be able to meet the needs of the populations they serve.Objective: To employ a logic model to describe a pathway of how specific activities and interventions can strengthen the use of health data in decision making to ultimately strengthen the health system.Design: A logic model was developed to provide a practical strategy for developing, monitoring and evaluating interventions to strengthen the use of data in decision making. The model draws on the collective strengths and similarities of previous work and adds to those previous works by making specific recommendations about interventions and activities that are most proximate to affect the use of data in decision making. The model provides an organizing framework for how interventions and activities work to strengthen the systematic demand, synthesis, review and use of data.Results: The logic model and guidance are presented to facilitate its widespread use and to enable improved data-informed decision making in program review and planning, advocacy, policy development. Real world examples from the literature support the feasible application of the activities outlined in the model.Conclusions: The logic model provides specific and comprehensive guidance to improve data demand and use. It can be used to design, monitor and evaluate interventions, and to improve demand for, and use of, data in decision making. As more interventions are implemented to improve use of health data, those efforts need to be evaluated.

Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Utilization of data mining in software engineering has been the subject of several research papers. Majority of subjects of those paper were in making use of historical data for decision making activities such as cost estimation and product or project attributes prediction and estimation. The ability to predict software fault modules and the ability to correlate relations between faulty modules and product attributes using statistics is the subject of this paper. Correlations and relations between the attributes and the categorical variable or the class are studied through generating a pool of records from each dataset and then select two samples every time from the dataset and compare them. The correlation between the two selected records is studied in terms of changing from faulty to non-faulty or the opposite for the module defect attribute and the value change between the two records in each evaluated attribute (e.g. equal, larger or smaller). The goal was to study if there are certain attributes that are consistently affecting changing the state of the module from faulty to none, or the opposite. Results indicated that such technique can be very useful in studying the correlations between each attribute and the defect status attribute. Another prediction algorithm is developed based on statistics of the module and the overall dataset. The algorithm gave each attribute true class and faulty class predictions. We found that dividing prediction capability for each attribute into those two (i.e. correct and faulty module prediction) facilitate understanding the impact of attribute values on the class and hence improve the overall prediction relative to previous studies and data mining algorithms. Results were evaluated and compared with other algorithms and previous studies. ROC metrics were used to evaluate the performance of the developed metrics. Results from those metrics showed that accuracy or prediction performance calculated traditionally using accurately predicted records divided by the total number of records in the dataset does not necessarily give the best indicator of a good metric or algorithm predictability. Those predictions may give wrong implication if other metrics are not considered with them. The ROC metrics were able to show some other important aspects of performance or accuracy.

Sector differences in the role of institutional research in informing decision making and governance in higher education

The intent of the authors in preparing this paper is to compare and contrast the roles of Institutional Research in informing decision making and governance in higher education. Using a case study format the authors will provide an example of a research project conducted within each of the following sectors of United States higher education system: private 4-year not for profit, public major research institutions/private specialized institutions, and a pubic state university system. The first case study from the private sector will feature a mixed methodology study that used data from the National Survey of Student Engagement (NSSE) and student focus groups to explore student engagement at this institution and inform faculty development. The second case study will compare the use of data for decision making from the standpoint of large public research institutions and private specialized institutions. The third case study from a state university system focuses on recent initiatives in the public sector to inform the public, the institutions, the State, and the federal government using data from institutional research offices, student engagement surveys, and student learning assessments shared through consortia and national associations. These examples will be discussed within the structure of decision making and governance in higher education.

Cross-sector learning among researchers and policy-makers: the search for new strategies to enable use of research results

This paper assesses the preliminary results of a research funding strategy that alters the structure and process of research by requiring interaction between researchers and policy-makers. The five research teams focused on different aspects of expanding social protection in health in Latin America and the Caribbean. Preliminary results revealed negotiation of the research questions at the start of the process, influencing not only the project design, but the decision-makers' ways of thinking about the problem as well. As the projects advanced, turnover among government officials on four of the teams impaired the process. However, for the one team that escaped re-composition, the interaction has led to use of data in decision-making, as well as a clear recognition by both parties that different kinds of evidence were at play. The process highlighted the importance of stimulating systems of learning in which multiple kinds of knowledge interact. This interaction may be a more realistic expectation of such initiatives than the original goal of "transferring" research knowledge to policy and practice.

HIV Prevention Community Planning: Challenges and Opportunities for Data-Informed Decision-Making

The use of data in decision-making by the Massachusetts Prevention Planning Group (MPPG) was assessed using multiple methods: in-depth interviews, member surveys, directed observations, and archival review. Three factors known to influence group decision-making were of interest: (1) member characteristics, (2) group structure, and (3) data inputs. Membership characteristics were not related to reliance on data. However, group structure factors and data inputs were directly related to reliance on data. Most members accepted an advisory role and felt participation was worthwhile. About half were dissatisfied with decision-making processes, citing member conflicts and distrust. Incompleteness of data, inadequate presentation quality, and lengthy intervals between presentations and actual decision-making were identified as deficits. Although most members reported skills with HIV- and intervention-related data, most also reported deficiencies in interpreting evaluation and cost-effectiveness studies. Member trust and use of data in decision-making could be improved by clarifying decision-making structures and processes, assuring high-quality data presentations, and supporting or training members to better interpret and use data.

A Case Study of the Effects of Self-Graphing Reading Performance Data for a Girl Identified with Emotional/Behavioral Disorders

Effects of a student with severe emotional/behavioral disorders (SEBD) graphing her own performance data on rate of correct words read per minute were evaluated using a withdrawal of treatment case study design. The student recorded her daily performance using a desktop computer and standard spreadsheet software that automatically graphed each data point when the numeric value representing the daily reading rate was entered. Results indicate that the student's reading rate increased concurrent with the implementation of the self-graphing intervention to the point that the student's reading rate was commensurate with that recommended for her grade level. The discussion of this article centers on the ease of implementation and the potential of this intervention to enhance the use of data for decision making in classrooms for students with emotional/behavioral disorders.

Integrating quantitative and qualitative methods to assess the impact of child survival programs in developing countries: the case of a program evaluation in Ceara, Northeast Brazil.

In evaluating public health programs, the tradition has been to design quantitative approaches, relying on epidemiological and statistical techniques to determine if and to what extent a program has an effect on a predetermined targeted population. More recently, however, qualitative methods such as rapid ethnographic assessments and focus groups have been implemented more frequently. This article describes an outcome evaluation of a community health workers program that integrated quantitative and qualitative methods to assess the impact of child survival interventions in reducing infant mortality and inadequate weight gain in children among municipalities in the state of Ceara, Northeast Brazil. By using multiple methods that combine quantitative and qualitative components, researchers can broaden their understanding of complex public health issues and direct use of data for decision making.