SubjectBook

Taamneh, Salah; Dcosta, Malcolm; Kwon, Kyeong-An; Pavlidis, Ioannis

doi:10.1145/2851581.2892338

Cited by 4 publications

(3 citation statements)

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“…In view of the increasing richness and complexity of data structures obtained from, for example, sensors (e.g., psychophysiological measures), audio and video recordings, appropriate data visualization for effective combination of multimodal data and communication of essential scientific results becomes even more a challenge. In this respect, more sophisticated visualization tools such as SubjectBook ( Taamneh et al, 2016 ), 5 ChronoViz ( Fouse et al, 2011 ) 6 and ELAN ( Wittenburg et al, 2006 ) 7 may turn out helpful.…”

Section: Discussionmentioning

confidence: 99%

“…Data visualization is an important means to communicate scientific results ( Anscombe, 1973 ; Tufte, 2001 ; Duke et al, 2015 ; Taamneh et al, 2016 ). Continuous quantitative data are often visualized in the form of summary statistics, with a measure of central tendency (e.g., arithmetic mean) being displayed together with a measure of dispersion (e.g., SD, CI).…”

Section: Introductionmentioning

confidence: 99%

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Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial

Loffing

2022

Front. Psychol.

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Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial

Loffing

2022

Front. Psychol.

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“…The outcome is a web-based interface which we developed for re-organizing the outputs of T-pattern detection in a relational database where their management is much more effective and transparent, and they can also be exported into ELAN or other tools for further analysis. Similarly to the Subjectbook project (Taamneh et al, 2016), it is a crossplatform interface for exploring and analyzing behavioral data, and in the narrower context of our particular field of interest, it can be considered as a supplementary component of the Data Exchange Platfrom (Pattern Vision, 2019). Theme users already have a software and an exchange annotation format (Schmidt et al, 2009) to import behavioral data for T-pattern detection from various data collection tools, and now, we would like to facilitate access to data in the opposite way: import back the resulting T-patterns for post-analysis.…”

Section: Introductionmentioning

confidence: 99%

Post-processing T-patterns Using External Tools From a Mixed Method Perspective

Szekrényes

2019

Front. Psychol.

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Researchers of behavioral science often work with time-aligned annotation data based on video and/or audio recordings. Various platforms are available to process these data, offering various kinds and ways of data analysis. It often happens that one would wish to use one platform for a certain kind of analysis, and another platform for another kind. It may also happen that one would keep the results of the first analysis and continue processing the data using another platform—all as a chain of analyses on the way to discovery. When it comes to T-pattern analysis, the task of further analyzing already identified patterns across platforms requires a general framework within which virtually any kind of data can be processed in a cross platform environment: that of a database. Data (including patterns) from one platform are then imported into this database, where these patterns are further processed to uncover further properties, then the patterns can be exported to another platform, including the one the data originated from. This contribution aims at introducing a new methodology and a tool implemented as a web-based service for these purposes.

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Dimension Hopper: A Tool for Exploring Multidimensional Data in Design Research

Dritsa,

Houben

2023

Proceedings of the 2nd International Conference of the ACM Greek SIGCHI Chapter

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SubjectBook

Cited by 4 publications

References 11 publications

Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial

Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial

Post-processing T-patterns Using External Tools From a Mixed Method Perspective

Dimension Hopper: A Tool for Exploring Multidimensional Data in Design Research

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