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 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 Protecting against researcher bias in secondary data analysis: challenges and potential solutions

2022 ◽  
Author(s):  
Jessie R. Baldwin ◽  
Jean-Baptiste Pingault ◽  
Tabea Schoeler ◽  
Hannah M. Sallis ◽  
Marcus R. Munafò
Keyword(s):  
Data Analysis ◽  
Secondary Data ◽  
Evidence Base ◽  
Secondary Data Analysis ◽  
Questionable Research Practices ◽  
Administrative Records ◽  
Alternative Approaches ◽  
Secondary Data Sources ◽  
Existing Data ◽  
Researcher Bias

AbstractAnalysis of secondary data sources (such as cohort studies, survey data, and administrative records) has the potential to provide answers to science and society’s most pressing questions. However, researcher biases can lead to questionable research practices in secondary data analysis, which can distort the evidence base. While pre-registration can help to protect against researcher biases, it presents challenges for secondary data analysis. In this article, we describe these challenges and propose novel solutions and alternative approaches. Proposed solutions include approaches to (1) address bias linked to prior knowledge of the data, (2) enable pre-registration of non-hypothesis-driven research, (3) help ensure that pre-registered analyses will be appropriate for the data, and (4) address difficulties arising from reduced analytic flexibility in pre-registration. For each solution, we provide guidance on implementation for researchers and data guardians. The adoption of these practices can help to protect against researcher bias in secondary data analysis, to improve the robustness of research based on existing data.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document