Analysis of Survey Data for Establishing the “Best Medical Survey Instrument” Using Text Mining

2012 ◽  
pp. 233-249
Author(s):  
Jeremy LaMotte ◽  
Ruth Moore ◽  
Sanjay Thomas ◽  
Chris Jenkins ◽  
Linda A. Miner
Keyword(s):  
Text Mining ◽  
Survey Data ◽  
Survey Instrument

More connected, less attached: Factors shaping junior ranks’ perceptions of the Australian media’s coverage of the Australian Defence Force

2016 ◽  
Vol 159 (1) ◽  
pp. 94-107
Author(s):  
Kevin Foster ◽  
Jason Pallant
Keyword(s):  
Survey Data ◽  
Media Coverage ◽  
Media Use ◽  
Good Deal ◽  
Survey Instrument ◽  
Australian Defence Force ◽  
Original Survey ◽  
The Military

This article seeks to understand what Australian Defence Force (ADF) junior ranks think about the Australian media’s coverage of defence issues and ADF operations. Through the analysis of original survey data, it considers how patterns of media use and the opinions of superiors, family and friends influence their views. This article compares these data with the findings from an earlier survey, which used the same survey instrument to examine what mid-ranking officers in the ADF thought about media coverage of the military. It explores the contrasts and continuities between the differing survey cohorts and considers what these suggest about the origins of and influences on their opinions. It concludes that while the surveys reveal a good deal about what ADF personnel think about the media’s coverage of their operations, it raises questions about why they think as they do that can only be answered through further studies.

Chapter 7: An Investigation Into Sports Coaches’ Twitter Use

2020 ◽  
Vol 39 (4) ◽  
pp. 481-490 ◽  
Author(s):  
Stephen Harvey ◽  
Obidiah Atkinson ◽  
Brendon P. Hyndman
Keyword(s):  
Text Mining ◽  
Survey Data ◽  
Online Survey ◽  
Qualitative Data ◽  
Access To Information ◽  
Quantitative Survey ◽  
Sports Coaches ◽  
Gain Access

Purpose: To investigate sports coaches’ Twitter use. Methods: Coaches (N = 310) from 22 countries and a range of sports completed an online survey. Quantitative survey data were analyzed descriptively and triangulated with qualitative data using Leximancer (Brisbane, Queensland, Australia) text mining software. Results: Most participants reported using Twitter for ≥3 years and accessed the platform multiple times per day. More than half participants agreed that using Twitter had positively impacted both their own confidence as a coach and their athletes/players/team’s performance. The strongest overall themes from the qualitative data revealed that Twitter helped sports coaches improve their practices through the sharing of information, connecting with other coaches, and building positivity into their interactions when supporting players. Discussion/Conclusion: Sports coaches perceive Twitter to be a highly valuable platform to network, collaborate, gain access to information, and share ideas and resources.

scholarly journals Text Mining in Survey Data

2019 ◽  
Vol 12 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Christine P. Chai
Keyword(s):  
Text Mining ◽  
Survey Data

scholarly journals An empirical analysis Of survey data about comprehension Of collaboration

2020 ◽  
Vol 10 (4) ◽  
pp. 7-11
Author(s):  
Ralph Ferguson ◽  
Efosa C. Idemudia
Keyword(s):  
Social Media ◽  
Information Systems ◽  
Survey Data ◽  
Empirical Analysis ◽  
Market Economy ◽  
Survey Instrument ◽  
Business Analytics ◽  
Ethical Misconduct ◽  
Teamwork Skills ◽  
Academic Assignments

Statement of Problem: Collaboration a Means to Chaos Likert Type Survey Instrument: Collaborating with other students on academic assignments without having permission from your professor. A large Southwest university examines five years of data about the distinction that may contribute to ethical misconduct in the classroom and misguided actions as a future professional. The classroom models the evolution of technological shifts, which requires broader interaction across disciplines to manage projects. The encouragement to build teamwork skills through class assignments benefits preparation to enter the market economy to lead in e-commerce, social media, information Systems, and Business Analytics. The survey data indicates that collaboration with or without professorial approval is acceptable.

scholarly journals A Two-Component Dust Model for the Wolf-Rayet Ring Nebula RCW 58

1991 ◽  
Vol 143 ◽  
pp. 421-421
Author(s):  
J.P. Cassinelli ◽  
J.S. Mathis ◽  
K.A. Van Der Hucht ◽  
T. Prusti ◽  
P.R. Wesselius
Keyword(s):  
High Resolution ◽  
Survey Data ◽  
Survey Instrument ◽  
Ring Nebula ◽  
Two Component ◽  
Infra Red ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Analysis System ◽  
Rayet Star

The spectacular ring nebula RCW58 around the Wolf-Rayet star WR40 (HD 96548, WN8) has been observed by the IRAS-Survey instrument and by the IRAS Chopped Photometric Channel (CPC) to study its IR flux and morphology. The survey data were examined with the Groningen Exportable Infra-red High-resolution Analysis system (GEISHA) software, and analysed in terms of line and dust contributions.

Survey Forms for Data Collection

2022 ◽  
pp. 171-188
Author(s):  
Annie Bangtegan Domede ◽  
Autumn Dinkelman
Keyword(s):  
Data Collection ◽  
Focus Groups ◽  
Survey Data ◽  
Data Gathering ◽  
Document Analysis ◽  
Survey Instrument ◽  
Survey Method ◽  
Oral Histories ◽  
Survey Form ◽  
Accurate Interpretation

Data collection is done through various methods including but not limited to surveys, interviews, observations, document analysis, focus groups, and oral histories. Each of these methods employs data gathering tools in order to facilitate the collection of information or data. In this section, the survey method, particularly the survey instrument or survey form, is discussed. Specifically, this chapter will focus on the fundamental factors to consider when developing the form to ensure coming up with relevant, unbiased, and focused questions, which will yield relevant and appropriate answers. In addition, considerations to take into account for proper administration of a survey form will be covered as well as the guidelines for a better and more accurate interpretation of survey data.

An Investigation Into the Reasons Physical Education Professionals Use Twitter

2018 ◽  
Vol 37 (4) ◽  
pp. 383-396 ◽  
Author(s):  
Stephen Harvey ◽  
Brendon Hyndman
Keyword(s):  
Social Media ◽  
Physical Education ◽  
Text Mining ◽  
Survey Data ◽  
Research Directions ◽  
Education Professionals ◽  
Classification Framework ◽  
Media Research ◽  
Social Media Research

Purpose: To date, there have been limited investigations relating to physical education (PE) professionals’ engagement in the use of Twitter. Consequently, the aim of the study was to investigate the reasons PE professionals use Twitter, with questions underpinned by Casey, Goodyear, and Armour’s three-level conceptual classification framework of Pedagogies of Technology. Method: The application of Leximancer text mining software was uniquely employed to text mine the survey data to determine the key themes and concepts. Results: It was discovered that PE professionals perceived the Twitter platform to be highly valuable to connect with others in the profession, learn from others, and share ideas (both within schools and more broadly) via a convenient, usable form of technology. Discussion/Conclusions: Understanding the reasons PE professionals use Twitter can provide a broader understanding for those contemplating the utilization of this platform and inform future Twitter/social media research directions for the field of PE.

scholarly journals OPTIMASI MESIN PENCARI BUKU FIKSI BERDASARKAN PADA SEMANTIK IMPRESI

2021 ◽  
Vol 5 (1) ◽  
pp. 1-8
Author(s):  
Rengga Asmara ◽  
◽  
Nur Rasyid Mubtadai ◽  
Varidh Bimantara
Keyword(s):  
Text Mining ◽  
Survey Data ◽  
Error Rate ◽  
Special Interest ◽  
Cosine Similarity ◽  
Application System ◽  
Final Project ◽  
Search Results ◽  
Similarity Algorithm

Fiction books are one of the most popular types of books in Indonesia. There are five most popular genres in fiction books, namely fantasy, mystery, romance, sci-fi, and thriller. Each genre gives a different impression and special interest for readers. It has become a common habit when people choose a fiction book based on the title, author, or publisher of the book. However, it does not provide precise search results. In this final project, an application system was developed to find out fiction books based on semantic impressions on the cover of the fiction book. The impression on each book cover is obtained through a survey of fiction book lovers in Indonesia. To get the results of the closeness between the user search and the impression survey data obtained through text mining, as well as the cosine similarity algorithm to calculate the most precise proximity value to the impression the user expects. The results of this system display a fiction book that has a closeness value with an error rate of 3.93% based on the impression expected by the user.

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