Terminating Sequential Delphi Survey Data Collection

The Delphi survey technique is an iterative mail or electronic (e-mail or web-based) survey method used to obtain agreement or consensus among a group of experts in a specific field on a particular issue through a well-designed and systematic multiple sequential rounds of survey administrations. Each of the multiple rounds of the Delphi survey administration is augmented with continuous summary feedback of aggregated responses from the same group of experts. Statistical methods to analyze data from the Delphi surveys to make decisions for terminating subsequent Delphi data collection are needed to ensure that (a) stability of the responses of the panel of experts is reached; and (b) termination of the rounds of the Delphi survey administration is based on sound statistical results. The present study presents an overview of the parametric and nonparametric statistical methods that can be used to analyze the structured Delphi survey data to make decisions about terminating the sequential Delphi survey data collection. Accessed 9,961 times on https://pareonline.net from January 23, 2012 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

A conceptual and pragmatic review of regression analysis for predictive analytics

Regression analysis and modeling are powerful predictive analytical tools for knowledge discovery through examining and capturing the complex hidden relationships and patterns among the quantitative variables. Regression analysis is widely used to: (a) collect massive amounts of organizational performance data such as Web server logs and sales transactions. Such data is referred to as Big Data and (b) improve transformation of massive data into intelligent information (knowledge) by discovering trends and patterns in unknown hidden relationships. The intelligent information can then be used to make informed data-based predictions of future organizational outcomes such as organizational productivity and performance using predictive analytics such as regression analysis methods. The main purpose of this chapter is to present a conceptual and practical overview of simple- and multiple- linear regression analyses

Predictive analytics

Predictive analytics and modeling are analytical tools for knowledge discovery through examining and capturing the complex relationships and patterns among the variables in the existing data in efforts to predict the future organizational performances. Their uses become more common place due largely to collecting massive amount of data, which is referred to as big data, and the increased need to transform large amounts of data into intelligent information (knowledge) such as trends, patterns, and relationships. The intelligent information can then be used to make smart and informed data-based decisions and predictions using various methods of predictive analytics. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools of predictive analytics. The chapter provides a detailed coverage of some of the predictive analytics tools such as Simple and Multiple-Regression, Polynomial Regression, Logistic Regression, Discriminant Analysis, and Multilevel Modeling

Effectiveness of various innovative learning methods in health science classrooms: A meta-analysis

This study reports the results of a meta-analysis of the available literature on the effectiveness of various forms of innovative small-group learning methods on student achievement in undergraduate college health science classrooms. The results of the analysis revealed that most of the primary studies supported the effectiveness of the small-group learning methods in improving students’ academic achievement with an overall weighted average effect-size of 0.59 in standard deviation units favoring small-group learning methods. The subgroup analysis showed that the various forms of innovative and reform-based small-group learning interventions appeared to be significantly more effective for students in higher levels of college classes (sophomore, junior, and senior levels), students in other countries (non-U.S.) worldwide, students in groups of four or less, and students who choose their own group. The random-effects meta-regression results revealed that the effect sizes were influenced significantly by the instructional duration of the primary studies. This means that studies with longer hours of instruction yielded higher effect sizes and on average every 1 h increase in instruction, the predicted increase in effect size was 0.009 standard deviation units, which is considered as a small effect. These results may help health science and nursing educators by providing guidance in identifying the conditions under which various forms of innovative small-group learning pedagogies are collectively more effective than the traditional lecture-based teaching instruction

Effectiveness of small-group learning pedagogies in engineering and technology education: A meta-analysis

This study reports the results of a meta-analysis synthesizing the available literature on the effectiveness of various forms of small-group learning methods on the academic achievement of college students in undergraduate engineering and technology classrooms. The meta-analytic results showed that cooperative learning, collaborative learning, problem-based learning, and peer-led team learning pedagogies were studied in college technology and engineering classrooms. The results also revealed that most of the primary studies supported the effectiveness of the small-group learning methods in improving students’ academic achievement with an overall positive weighted average effect size of 0.45 in standard deviation units favoring small-group learning methods. The findings might help engineering and technology instructors and educators by providing guidance in identifying the conditions under which various forms of innovative small-group pedagogies are more effective than the traditional lecture-based teaching and individualized instruction