scholarly journals Recommendations for Increasing the Transparency of Analysis of Preexisting Data Sets

2019 ◽  
Vol 2 (3) ◽  
pp. 214-227 ◽  
Author(s):  
Sara J. Weston ◽  
Stuart J. Ritchie ◽  
Julia M. Rohrer ◽  
Andrew K. Przybylski
Keyword(s):  
Data Analysis ◽  
Secondary Data ◽  
Open Science ◽  
Secondary Data Analysis ◽  
Data Sets ◽  
Science Practices ◽  
Advantages And Disadvantages ◽  
Analytic Methods ◽  
Technological Advances

Secondary data analysis, or the analysis of preexisting data, provides a powerful tool for the resourceful psychological scientist. Never has this been more true than now, when technological advances enable both sharing data across labs and continents and mining large sources of preexisting data. However, secondary data analysis is easily overlooked as a key domain for developing new open-science practices or improving analytic methods for robust data analysis. In this article, we provide researchers with the knowledge necessary to incorporate secondary data analysis into their methodological toolbox. We explain that secondary data analysis can be used for either exploratory or confirmatory work, and can be either correlational or experimental, and we highlight the advantages and disadvantages of this type of research. We describe how transparency-enhancing practices can improve and alter interpretations of results from secondary data analysis and discuss approaches that can be used to improve the robustness of reported results. We close by suggesting ways in which scientific subfields and institutions could address and improve the use of secondary data analysis.

scholarly journals Recommendations for increasing the transparency of analysis of pre-existing datasets

2018 ◽  
Author(s):  
Sara J Weston ◽  
Stuart James Ritchie ◽  
Julia Marie Rohrer ◽  
Andrew K Przybylski
Keyword(s):  
Data Analysis ◽  
Secondary Data ◽  
Open Science ◽  
Secondary Data Analysis ◽  
Science Practices ◽  
Advantages And Disadvantages ◽  
Analytic Methods ◽  
Technological Advances ◽  
Methodological Tool ◽  
Existing Data

Secondary data analysis, or the analysis of pre-existing data, can be a powerful tool for the resourceful researcher. Never has this been more true than now, when technological advances allow for easier sharing of data across labs and continents and the mining of large sources of “pre-existing data”. However, secondary data analysis is often ignored as a methodological tool, either when developing new open science practices or improving analytic methods for robust data analysis. In this paper, we hope to provide researchers with the knowledge necessary to incorporate secondary data analysis into their toolbox. Specifically, we define secondary data analysis as a tool and in relation to other common forms of analysis (including exploratory and confirmatory, observational and experimental). We highlight the advantages and disadvantages of this tool. We describe how engagement in transparency can improve and alter our interpretations of results from secondary data analysis and provide resources for robust data analysis. We close by suggesting ways in which subfields and institutions could address and improve the use of secondary data analysis.

scholarly journals Enabling Confirmatory Secondary Data Analysis by Logging Data Checkout

2019 ◽  
Vol 2 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Kimberly M. Scott ◽  
Melissa Kline
Keyword(s):  
Data Analysis ◽  
Standard Form ◽  
Secondary Data ◽  
Data Access ◽  
Secondary Data Analysis ◽  
Data Sets ◽  
Exploratory Research ◽  
Critical Elements ◽  
Library Book ◽  
Access To Data

As more researchers make their data sets openly available, the potential of secondary data analysis to address new questions increases. However, the distinction between primary and secondary data analysis is unnecessarily confounded with the distinction between confirmatory and exploratory research. We propose a framework, akin to library-book checkout records, for logging access to data sets in order to support confirmatory analysis when appropriate. This system would support a standard form of preregistration for secondary data analysis, allowing authors to demonstrate that their plans were registered prior to data access. We discuss the critical elements of such a system, its strengths and limitations, and potential extensions.

Answering Your Developmental Questions using Secondary Data

2018 ◽  
Author(s):  
Pamela Davis-Kean ◽  
Justin Jager ◽  
Julie Maslowsky
Keyword(s):  
Social Sciences ◽  
Data Analysis ◽  
Secondary Data ◽  
Population Data ◽  
Secondary Data Analysis ◽  
Scientific Integrity ◽  
Data Sets ◽  
National Data ◽  
Methodological Issues ◽  
Future Studies

Secondary data analysis of large longitudinal and national data sets is a standard method used in many social sciences to answer complex questions regarding behavior. This paper details the advantages of using these data sets to study important developmental questions across the lifespan. First, an overview of how using secondary data can increase the scientific integrity of your studies is provided. Then, information on where and how you can obtain data sets for use in answering your specific questions is provided. Finally, methodological issues related to using longitudinal, population data sets are discussed. The use of these data sets can enhance the science and theory testing of developmental psychologists by increasing the rigor and the generalizability of our research to the population. Secondary data analysis is therefore an important method to consider using for future studies.

scholarly journals Applied Open Science for Secondary Data Analysis and Meta-Analysis

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 568-568
Author(s):  
Jennifer Lodi-Smith
Keyword(s):  
Data Analysis ◽  
Meta Analysis ◽  
Secondary Data ◽  
Self Esteem ◽  
Open Science ◽  
Secondary Data Analysis ◽  
Sensitive Data ◽  
Complex Projects ◽  
Meta Analyses ◽  
The Relationship

Abstract This talk will provide guidance on the practicalities of open science for secondary data analysis and meta-analyses. Example studies will provide practical considerations for preregistering complex projects, insights into strategies for transparently reporting deviations from preregistrations, advice on deciding when and how to share sensitive data, and tips on transparent documentation of analysis code. Examples will be drawn from an ongoing meta-analysis of the relationship between self-concept clarity and self-esteem (https://osf.io/sa2bx/), the Rochester Adult Longitudinal Study (https://osf.io/ya4ph/), and the Aging and Autism Study (https://osf.io/g9c3e/). The pedagogical value of preregistration will be emphasized throughout the talk.

scholarly journals Sexual Assault: Indigenous Women’s Experiences of Not Being Believed by the Police

2021 ◽  
pp. 107780122110139
Author(s):  
Jodie Murphy-Oikonen ◽  
Lori Chambers ◽  
Karen McQueen ◽  
Alexa Hiebert ◽  
Ainsley Miller
Keyword(s):  
Data Analysis ◽  
Sexual Assault ◽  
Qualitative Study ◽  
Sexual Victimization ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Indigenous Women ◽  
Women’S Experiences ◽  
Women's Experiences ◽  
The Way

Rates of sexual victimization among Indigenous women are 3 times higher when compared with non-Indigenous women. The purpose of this secondary data analysis was to explore the experiences and recommendations of Indigenous women who reported sexual assault to the police and were not believed. This qualitative study of the experiences of 11 Indigenous women reflects four themes. The women experienced (a) victimization across the lifespan, (b) violent sexual assault, (c) dismissal by police, and (d) survival and resilience. These women were determined to voice their experience and make recommendations for change in the way police respond to sexual assault.

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