scholarly journals Characterizing the intelligence analysis process: Informing visual analytics design through a longitudinal field study

2011 ◽  
Author(s):  
Youn-ah Kang ◽  
John Stasko
Keyword(s):  
Field Study ◽  
Visual Analytics ◽  
Intelligence Analysis ◽  
Longitudinal Field ◽  
Analysis Process

Characterizing the intelligence analysis process through a longitudinal field study: Implications for visual analytics

2012 ◽  
Vol 13 (2) ◽  
pp. 134-158 ◽  
Author(s):  
Youn-ah Kang ◽  
John Stasko
Keyword(s):  
Field Study ◽  
Visual Analytics ◽  
Task Analysis ◽  
System Development ◽  
Intelligence Analysis ◽  
Work Processes ◽  
Target Domain ◽  
Primary Target ◽  
Analysis Process ◽  
And Task

While intelligence analysis has been a primary target domain for visual analytics system development, relatively little user and task analysis has been conducted within this area. Our research community’s understanding of the work processes and practices of intelligence analysts is not deep enough to adequately address their needs. Without a better understanding of the analysts and their problems, we cannot build visual analytics systems that integrate well with their work processes and truly provide benefit to them. In order to close this knowledge gap, we conducted a longitudinal, observational field study of intelligence analysts in training within the intelligence program at Mercyhurst College. We observed three teams of analysts, each working on an intelligence problem for a 10-week period. Based on the findings of the study, we describe and characterize processes and methods of intelligence analysis that we observed, make clarifications regarding the processes and practices, and suggest design implications for visual analytics systems for intelligence analysis.

Investigating the Unified Motive Scales

2020 ◽  
pp. 1-5
Author(s):  
Martin Bettschart ◽  
Marcel Herrmann ◽  
Benjamin M. Wolf ◽  
Veronika Brandstätter
Keyword(s):  
Field Study ◽  
Predictive Validity ◽  
Selection Process ◽  
Police Department ◽  
Longitudinal Field ◽  
Explicit Motives ◽  
Achievement Motive

Abstract. Explicit motives are well-studied in the field of personality and motivation psychology. However, the statistical overlap of different explicit motive measures is only moderate. As a consequence, the Unified Motive Scales (UMS; Schönbrodt & Gerstenberg, 2012 ) were developed to improve the measurement of explicit motives. The present longitudinal field study examined the predictive validity of the UMS achievement motive subscale. Applicants of a police department ( n = 168, Mage = 25.11, 53 females and 115 males) completed the UMS and their performance in the selection process was assessed. As expected, UMS achievement predicted success in the selection process. The findings provide first evidence for the predictive validity of UMS achievement in an applied setting.

Positive and negative self-efficacy effects revisited: A longitudinal field study

2010 ◽  
Author(s):  
Shuhua Sun ◽  
Zhaoli Song ◽  
Vivien Kim Geok Lim ◽  
Don J. Q. Chen ◽  
Xian Li
Keyword(s):  
Field Study ◽  
Self Efficacy ◽  
Longitudinal Field

Cognitive development and tonal systems of Greek language: A comparative longitudinal field study

2011 ◽  
Author(s):  
Thalis N. Papadakis ◽  
Evdokia Lagakou ◽  
Christina Terlidou ◽  
Dimitra Vekiari ◽  
Ioannis K. Tsegos
Keyword(s):  
Cognitive Development ◽  
Field Study ◽  
Longitudinal Field ◽  
Greek Language

scholarly journals Preprocessing Profiling Model for Visual Analytics

2020 ◽  
Author(s):  
Alessandra Maciel Paz Milani ◽  
Fernando V. Paulovich ◽  
Isabel Harb Manssour
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Visual Analytics ◽  
Data Preprocessing ◽  
Interview Study ◽  
Raw Data ◽  
Important Stage ◽  
Analysis Process

Analyzing and managing raw data are still a challenging part of the data analysis process, mainly regarding data preprocessing. Although we can find studies proposing design implications or recommendations for visualization solutions in the data analysis scope, they do not focus on challenges during the preprocessing phase. Likewise, the current Visual Analytics processes do not consider preprocessing an equally important stage in their process. Thus, with this study, we aim to contribute to the discussion of how we can use and combine methods of visualization and data mining to assist data analysts during the preprocessing activities. To achieve that, we introduce the Preprocessing Profiling Model for Visual Analytics, which contemplates a set of features to inspire the implementation of new solutions. In turn, these features were designed considering a list of insights we obtained during an interview study with thirteen data analysts. Our contributions can be summarized as offering resources to promote a shift to a visual preprocessing.

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