scholarly journals Variance & Homogeneity of Facial Trait Space Across World Regions [PSA001 Secondary Data Analysis]

2020 ◽  
Author(s):  
Sally Y Xie ◽  
Eric Hehman
Keyword(s):  
Data Analysis ◽  
Secondary Analysis ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Psychological Science ◽  
World Regions ◽  
Trait Impressions ◽  
Trait Space

This preregistration is part of the PSA secondary analysis challenge. We investigate how the facial 'trait space' shifts across countries and world regions, using the PSA_001 dataset shared by the Psychological Science Accelerator. The facial trait space refers to the interrelationships between many of the trait impressions that people infer from faces. Here, we examine whether this trait space is more homogeneous (or less differentiated) in some cultures than others.

scholarly journals Exploring youth information-seeking behaviour and mobile technologies through a secondary analysis of qualitative data

2018 ◽  
Vol 50 (3) ◽  
pp. 322-331 ◽  
Author(s):  
Leanne Bowler ◽  
Heidi Julien ◽  
Leslie Haddon
Keyword(s):  
Data Analysis ◽  
Information Seeking ◽  
Qualitative Data ◽  
Secondary Analysis ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Mobile Technologies ◽  
The European Union ◽  
Data Set ◽  
Information Behaviour

This paper examines issues associated with secondary analysis of qualitative data and their implications for information behaviour scholarship. Secondary data analysis poses a range of potential challenges for data creators, but also opportunities, including the ability to expand theory to a wider context, strengthen the reliability and validity of existing theory, gain access to populations that may be difficult to access, and to promote data archiving. The paper uses as a case study of secondary data analysis the results from our re-examination of data gathered previously in the European Union project Net Children Go Mobile, drawing from the interview transcripts from the 34 children in the UK data set. Our approach to secondary analysis was reanalysis, applying a new interpretive lens to the data that necessitated new questions in order to reveal hidden layers in the data. The data was analysed for evidence of information behaviour in order to understand how mobile technologies may be changing the way that young people seek and use information. The reanalysis of the data set supported existing models of information behaviour but revealed new ways of information seeking based on the affordances of screen size and data plans.

scholarly journals Conducting secondary analysis of qualitative data: Should we, can we, and how?

2017 ◽  
Vol 18 (1) ◽  
pp. 81-97 ◽  
Author(s):  
Nicole Ruggiano ◽  
Tam E Perry
Keyword(s):  
Data Analysis ◽  
Best Practices ◽  
Qualitative Data ◽  
Secondary Analysis ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Ethical Problems ◽  
Current State ◽  
Critical Interpretive Synthesis ◽  
Recommended Guidelines

While secondary data analysis of quantitative data has become commonplace and encouraged across disciplines, the practice of secondary data analysis with qualitative data has met more criticism and concerns regarding potential methodological and ethical problems. Though commentary about qualitative secondary data analysis has increased, little is known about the current state of qualitative secondary data analysis or how researchers are conducting secondary data analysis with qualitative data. This critical interpretive synthesis examined research articles (n = 71) published between 2006 and 2016 that involved qualitative secondary data analysis and assessed the context, purpose, and methodologies that were reported. Implications of findings are discussed, with particular focus on recommended guidelines and best practices of conducting qualitative secondary data analysis.

scholarly journals Secondary Data Analysis: Lessons and perspective of a research parasite

2021 ◽  
Author(s):  
Ayush Raman
Keyword(s):  
Data Analysis ◽  
Signal To Noise Ratio ◽  
Secondary Analysis ◽  
Scientific Progress ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Research Directions ◽  
Award Recipient ◽  
Junior Research ◽  
New Research

Secondary data analysis refers to re-analyzing publicly available datasets to investigate the questions that original scientists had not posited. This helps in scientific progress by paving the path to more reliable and robust analyses and new research directions without any considerable expense. However, these datasets are anything but perfect, and researchers must investigate and assess the signal to noise ratio robustly to extract meaningful information. These efforts of rigorous secondary analysis are further recognized and supported by the Research Parasite Awards. As the 2020 Junior Research Parasite Award recipient, I share my journey and perspective of a research parasite in this commentary article.

Barriers for homeless with dual diagnosis: lessons learned from intensive mobile psychosocial assertive community treatment program

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tara Rava Zolnikov ◽  
Michael Hammel ◽  
Frances Furio ◽  
Brandon Eggleston
Keyword(s):  
Data Analysis ◽  
Effective Treatment ◽  
Dual Diagnosis ◽  
Treatment Program ◽  
Assertive Community Treatment ◽  
Secondary Analysis ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Community Treatment ◽  
Content Type

Purpose Dual diagnosis is a term that describes the co-occurrence of mental health disorders or illness and substance use or abuse disorders. Because this co-occurrence results in multiple diseases, layers of treatment are often needed to successfully create positive change in the individual. The purpose of this study is to explore factors of treatment that could facilitate improvements in functionality and quality of life for those with a dual diagnosis. Design/methodology/approach A secondary data analysis, using both quantitative and qualitative data, was completed. Secondary analysis is an empirical exercise that applies the same basic research principles as studies using primary data and has steps to be followed, including the evaluative and procedural steps commonly associated with secondary data analysis. Documentation data from the intensive mobile psychosocial assertive community treatment program was gathered for this analysis; this program was used because of the intensive and community-based services provided to patients with a dual diagnosis. Findings The major findings from this secondary analysis suggested that significant barriers included “denial” (e.g. evasion, suspension or avoidance of internal awareness) of diagnoses, complicated treatment and other barriers related to housing. Ultimately, these findings provided greater insight into potential effective treatment interventions for people living with a dual diagnosis. Originality/value This study adds to the growing body of literature showing that patient-centered care allows for more effective treatment and ultimately, improved health outcomes.

Prevalence of leisure participation in older adults with and without pain: A secondary data analysis of the 2014 Health and Retirement Study

2020 ◽  
pp. 174239532094961
Author(s):  
Haley Feller ◽  
Nancy Baker
Keyword(s):  
Older Adults ◽  
Data Analysis ◽  
Leisure Activities ◽  
Secondary Analysis ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Health And Retirement Study ◽  
Leisure Participation ◽  
Using Data ◽  
Retirement Study

Objectives The purpose of this study is to describe the prevalence of leisure activities in adults ages 50 and over and to examine if pain changes that prevalence. Methods We completed a secondary analysis using data from the 2014 Health and Retirement Study Leave-Behind Questionnaire (n = 7,541) to analyze frequency and 95% confidence intervals of leisure participation and its relationship to pain. Results The majority of respondents reported regular participation in half of the 10 leisure activities analyzed. Watching television (98.0%) and using the computer (64.5%) had the highest reported regular participation, while volunteering and attending a club or meeting warranted less than 10% regular participation. Of the 7,541 respondents, 39.1% reported pain. For those with pain, regular participation was significantly lower than those without pain in five leisure activities, with exercise having the greatest difference. Discussion The results of our secondary data analysis indicate that older adults may not be regularly participating in different types of leisure activities and that they more regularly participate in passive activities, such as watching television. Older adults with pain have significantly lower reported rates of participation in leisure activities than those without pain, especially in relation to exercise.

scholarly journals Secondary Data Analysis in Nursing Research: A Contemporary Discussion

2020 ◽  
Vol 29 (5) ◽  
pp. 279-284
Author(s):  
Siobhan O’Connor
Keyword(s):  
Data Analysis ◽  
Analytical Method ◽  
Quantitative Data ◽  
Secondary Analysis ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Nursing Research ◽  
Nursing Science ◽  
Qualitative And Quantitative ◽  
Contemporary Research

This editorial provides an overview of secondary data analysis in nursing science and its application in a range of contemporary research. The practice of undertaking secondary analysis of qualitative and quantitative data is also discussed, along with the benefits, risks and limitations of this analytical method.

Using an Existing Data Set to Answer New Research Questions: A Methodological Review

2009 ◽  
Vol 23 (3) ◽  
pp. 203-215 ◽  
Author(s):  
Daniel M. Doolan ◽  
Erika S. Froelicher
Keyword(s):  
Data Analysis ◽  
Research Question ◽  
Secondary Analysis ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Data Set ◽  
Research Questions ◽  
Conducting Research ◽  
New Research ◽  
Existing Data

The vast majority of the research methods literature assumes that the researcher designs the study subsequent to determining research questions. This assumption is not met for the many researchers involved in secondary data analysis. Researchers doing secondary data analysis need not only understand research concepts related to designing a new study, but additionally must be aware of challenges specific to conducting research using an existing data set. Techniques are discussed to determine if secondary data analysis is appropriate. Suggestions are offered on how to best identify, obtain, and evaluate a data set; refine research questions; manage data; calculate power; and report results. Examples from nursing research are provided. If an existing data set is suitable for answering a new research question, then a secondary analysis is preferable since it can be completed in less time, for less money, and with far lower risks to subjects. The researcher must carefully consider if the existing data set’s available power and data quality are adequate to answer the proposed research questions.

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