scholarly journals Augmenting Social Science Research with Multimodal Data Collection: The EZ-MMLA Toolkit

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 568
Author(s):  
Bertrand Schneider ◽  
Javaria Hassan ◽  
Gahyun Sung
Keyword(s):  
Data Collection ◽  
Social Science Research ◽  
Science Research ◽  
Sensor Data ◽  
Easy Access ◽  
Classroom Context ◽  
Multimodal Data ◽  
Hand Motion ◽  
Multimodal Sensing ◽  
Sensing Technology

While the majority of social scientists still rely on traditional research instruments (e.g., surveys, self-reports, qualitative observations), multimodal sensing is becoming an emerging methodology for capturing human behaviors. Sensing technology has the potential to complement and enrich traditional measures by providing high frequency data on people’s behavior, cognition and affects. However, there is currently no easy-to-use toolkit for recording multimodal data streams. Existing methodologies rely on the use of physical sensors and custom-written code for accessing sensor data. In this paper, we present the EZ-MMLA toolkit. This toolkit was implemented as a website and provides easy access to multimodal data collection algorithms. One can collect a variety of data modalities: data on users’ attention (eye-tracking), physiological states (heart rate), body posture (skeletal data), gestures (from hand motion), emotions (from facial expressions and speech) and lower-level computer vision algorithms (e.g., fiducial/color tracking). This toolkit can run from any browser and does not require dedicated hardware or programming experience. We compare this toolkit with traditional methods and describe a case study where the EZ-MMLA toolkit was used by aspiring educational researchers in a classroom context. We conclude by discussing future work and other applications of this toolkit, potential limitations and implications.

Making Sense of Sensor Data: How Local Environmental Conditions Add Value to Social Science Research

2020 ◽  
pp. 089443932092060
Author(s):  
Ned English ◽  
Chang Zhao ◽  
Kevin L. Brown ◽  
Charlie Catlett ◽  
Kathleen Cagney
Keyword(s):  
Social Science ◽  
Environmental Conditions ◽  
Low Cost ◽  
Social Science Research ◽  
Science Research ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Federally Funded ◽  
Physical And Mental Health ◽  
Making Sense

Recent advances in computing technologies have enabled the development of low-cost, compact weather and air quality monitors. The U.S. federally funded Array of Things (AoT) project has deployed more than 140 such sensor nodes throughout the City of Chicago. This article combines a year’s worth of AoT sensor data with household data collected from 450 elderly Chicagoans in order to explore the feasibility of using previously unavailable data on local environmental conditions to improve traditional neighborhood research. Specifically, we pilot the use of AoT sensor data to overcome limitations in research linking air pollution to poor physical and mental health and find support for recent findings that exposure to pollutants contributes to both respiratory- and dementia-related diseases. We expect that this support will become even stronger as sensing technologies continue to improve and more AoT nodes come online, enabling additional applications to social science research where environmental context matters.

scholarly journals Reliability and validity of measuring social support networks by web and telephone

2006 ◽  
Vol 3 (2) ◽  
Author(s):  
Tina Kogovšek
Keyword(s):  
Data Collection ◽  
Social Science Research ◽  
Reliability And Validity ◽  
Science Research ◽  
Order Effect ◽  
Personal Network ◽  
Convenience Sample ◽  
Second Year ◽  
The University ◽  
The Relationship

Egocentered networks are common in social science research. Here, the unit of analysis is a respondent (ego) together with his/her personal network (alters). Usually, several variables are measured to describe the relationship between egos and alters. In this paper, the aim is to estimate the reliability and validity of the averages of these measures by the multitrait-multimethod (MTMM) approach. In the study, web and telephone modes of data collection are compared on a convenience sample of 238 second year students at the Faculty of Social Sciences at the University of Ljubljana. The data was collected in 2003. The results show that the telephone mode produces more reliable data than the web mode of data collection. Also, method order effect was shown: the data collection mode used first produces data of lower reliability than the mode used for the second measurement. There were no large differences in validity of measurement.

scholarly journals SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 408
Author(s):  
Jonas Chromik ◽  
Kristina Kirsten ◽  
Arne Herdick ◽  
Arpita Mallikarjuna Kappattanavar ◽  
Bert Arnrich
Keyword(s):  
Data Collection ◽  
Observational Studies ◽  
Electrodermal Activity ◽  
Real Life ◽  
Sensor Data ◽  
Rigorous Approach ◽  
Additional Information ◽  
Multiple Sensors ◽  
Multimodal Sensing ◽  
Home Based

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.

scholarly journals On the Same Page: A Formal Process for Training Multiple Interviewers

2018 ◽  
Author(s):  
Carolyn Sattin-Bajaj
Keyword(s):  
Data Collection ◽  
Training Program ◽  
Qualitative Data ◽  
Social Science Research ◽  
Science Research ◽  
Training Models ◽  
Multi Stage ◽  
Data Collection And Analysis ◽  
Qualitative Methodologies ◽  
Research Assistants

The increased utilization of qualitative methodologies as part of mixed-method health and social science research has highlighted the need for training procedures for every stage of qualitative data collection and analysis. Yet, few group training models exist for collecting reliable, valid qualitative interview data. This article presents a multi-stage, collaborative interview training process for a large team of research assistants. The training program combines insights and techniques used in both structured and semi-structured interviewing. It also includes ongoing instruction and feedback prior to and during data collection in an effort to ensure consistency and reliability. In the article, I describe each stage of the training program in detail, review some of the challenges encountered during implementation, and conclude with a discussion of how researchers and course instructors might adapt the methods to fit their particular needs.

scholarly journals Using Twitter for Demographic and Social Science Research: Tools for Data Collection and Processing

2015 ◽  
Vol 46 (3) ◽  
pp. 390-421 ◽  
Author(s):  
Tyler H. McCormick ◽  
Hedwig Lee ◽  
Nina Cesare ◽  
Ali Shojaie ◽  
Emma S. Spiro
Keyword(s):  
Data Collection ◽  
Social Science ◽  
Large Scale ◽  
Social Science Research ◽  
Science Research ◽  
Demographic Information ◽  
Twitter Data ◽  
Data Collection Strategy ◽  
Twitter Users ◽  
Media Data

Despite recent and growing interest in using Twitter to examine human behavior and attitudes, there is still significant room for growth regarding the ability to leverage Twitter data for social science research. In particular, gleaning demographic information about Twitter users—a key component of much social science research—remains a challenge. This article develops an accurate and reliable data processing approach for social science researchers interested in using Twitter data to examine behaviors and attitudes, as well as the demographic characteristics of the populations expressing or engaging in them. Using information gathered from Twitter users who state an intention to not vote in the 2012 presidential election, we describe and evaluate a method for processing data to retrieve demographic information reported by users that is not encoded as text (e.g., details of images) and evaluate the reliability of these techniques. We end by assessing the challenges of this data collection strategy and discussing how large-scale social media data may benefit demographic researchers.

scholarly journals LEARNS: A creative approach to analysing and representing narrative data incorporating photo-elicitation techniques

2018 ◽  
Vol 23 (4) ◽  
pp. 334-343
Author(s):  
Wendy Marsh ◽  
Jen Leamon ◽  
Ann Robinson ◽  
Jill Shawe
Keyword(s):  
Data Collection ◽  
Thematic Analysis ◽  
Research Study ◽  
Social Science Research ◽  
Science Research ◽  
Analytical Framework ◽  
Photo Elicitation ◽  
Multiple Data ◽  
Insight Into ◽  
Collection Methods

Background Diversity exists in how storied data gathered in narrative inquiry is analysed and represented, more so when there is a need to combine multiple data collection methods, including photographs. Aim This paper discusses the use of an analytical framework entitled LEARNS developed as part of a PhD study that has potential to fill this gap. Results The step-by-step framework presented in this paper was developed in order to analyse the data collected in this research study and gives understanding and insight into the experience of mothers whose babies are removed at birth. The LEARNS framework provides transparency and credibility; it also negates the need to restrict findings to broad themes via content/thematic analysis. Conclusions LEARNS could offer other researchers a reliable framework to use for future social science research.

Usefulness of Netnography in the Digital Banking Context

2022 ◽  
pp. 280-294
Author(s):  
Irina Dimitrova ◽  
Peter Öhman
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Social Science ◽  
Real Time ◽  
Research Method ◽  
Social Science Research ◽  
Science Research ◽  
Customer Feedback ◽  
Research Areas ◽  
Financial Research

This chapter discusses the usefulness of netnography as a research method in the digital banking context. Netnography has become a relative attractive data collection and data analysis method in some social science research areas but is still relatively unknown in financial research. Compared with other research methods, netnography seems to have some advantages in the digital banking world, such as real-time customer feedback. Moreover, virtual observations can be used not only by researchers but also by bank representatives to, for example, find out how bank customers can contribute to value co-creation.

Video-Conferencing Interviews in Qualitative Research

2015 ◽  
pp. 140-157 ◽  
Author(s):  
Kimberly Nehls ◽  
Brandy D. Smith ◽  
Holly A. Schneider
Keyword(s):  
Data Collection ◽  
Qualitative Data ◽  
Video Conferencing ◽  
Computer Mediated Communication ◽  
Social Science Research ◽  
Science Research ◽  
Mediated Communication ◽  
Advantages And Disadvantages ◽  
Computer Mediated ◽  
Qualitative Data Collection

This chapter synthesizes the literature on real-time, synchronous, video interviews as a qualitative data collection method. The authors specifically focus on the advantages and disadvantages of this method in social science research and offer conceptual themes, practical techniques, and recommendations for using video-interviews. The growing popularity of computer-mediated communication indicates that a wider audience will be willing and able to participate in research using this method; therefore, online video-conferencing could be considered a viable option for qualitative data collection.

scholarly journals Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data

2020 ◽  
pp. 089443932094411
Author(s):  
Sebastian Bähr ◽  
Georg-Christoph Haas ◽  
Florian Keusch ◽  
Frauke Kreuter ◽  
Mark Trappmann
Keyword(s):  
Social Science Research ◽  
Science Research ◽  
Third Party ◽  
Sensor Data ◽  
Future Research ◽  
German Population ◽  
Missing Measurements ◽  
Error Sources ◽  
Measurement Quality

As smartphones become increasingly prevalent, social scientists are recognizing the ubiquitous data generated by the sensors built into these devices as an innovative data source. Passively collected data from sensors that measure geolocation or movement provide an unobtrusive way to observe participants in everyday situations and are free from reactivity biases. Information on day-to-day geolocation could provide valuable insights into human behavior that cannot be collected via surveys. However, little is known about the quality of the resulting data. Using data from a 2018 German population-based probability app study, this article focuses on the measurement quality of geolocation sensor data, with a strong focus on missing measurements. Geolocation sensor data are an example of an available data type that is of interest to social science research. Our findings can be applied to the wider subject of sensor data. In our article, we demonstrate (1) that sensor data are far from error-free. Instead, device-related error sources, such as the manufacturer and operating system settings, design decisions of the research app, third-party apps, and the participant, can interfere with the measurement. To disentangle the different influences, we (2) apply a multistage error model to analyze and control the error sources in the specific missingness process of geolocation data. We (3) raise awareness of error sources in geolocation measurement, such as the use of GPS falsifier apps, or device sharing among participants. By identifying the different error sources and analyzing their determinants, we recommend (4) identification strategies for future research.

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