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Sara J. Weston

Bio: Sara J. Weston is an academic researcher from University of Oregon. The author has contributed to research in topics: Personality & Big Five personality traits. The author has an hindex of 12, co-authored 43 publications receiving 535 citations. Previous affiliations of Sara J. Weston include Washington University in St. Louis & Northwestern University.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: For example, the authors found that personality traits are associated with the risk of developing disease, most notably the traits of conscientiousness, neuroticism, and openness, and used logistic regression to predict new disease diagnosis.
Abstract: While personality traits have been linked concurrently to health status and prospectively to outcomes such as mortality, it is currently unknown whether traits predict the diagnosis of a number of specific diseases (e.g., lung disease, heart disease, and stroke) that may account for their mortality effects more generally. A sample (N = 6,904) of participants from the Health and Retirement Study, a longitudinal study of older adults, completed personality measures and reported on current health conditions. Four years later, participants were followed up to see if they developed a new disease. Initial cross-sectional analyses replicated past findings that personality traits differ across disease groups. Longitudinal logistic regression analyses predicting new disease diagnosis suggest that traits are associated with the risk of developing disease—most notably the traits of conscientiousness, neuroticism, and openness. Findings are discussed as a means to identify pathways between personality and health.

109 citations

Journal ArticleDOI
11 Jun 2019
TL;DR: It is explained that secondary data analysis can be used for either exploratory or confirmatory work, and can be either correlational or experimental, and the advantages and disadvantages of this type of research are highlighted.
Abstract: 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.

102 citations

Journal ArticleDOI
TL;DR: This large, national study investigated whether conscientiousness and neuroticism were associated with smoking behavior after the onset of a disease, finding that high levels of neuroticism predicted less smoking when paired with high level of conscientiousness, a combination described as healthy neuroticism.

82 citations

Journal ArticleDOI
TL;DR: Meta–analytic summaries indicated that the fixed effects of personality change are somewhat heterogeneous and that the variability in trait change is partially explained by sample age, country of origin, and personality measurement method.
Abstract: This study assessed change in self-reported Big Five personality traits. We conducted a coordinated integrative data analysis using data from 16 longitudinal samples, comprising a total sample of over 60 000 participants. We coordinated models across multiple datasets and fit identical multi-level growth models to assess and compare the extent of trait change over time. Quadratic change was assessed in a subset of samples with four or more measurement occasions. Across studies, the linear trajectory models revealed declines in conscientiousness, extraversion, and openness. Non-linear models suggested late-life increases in neuroticism. Meta-analytic summaries indicated that the fixed effects of personality change are somewhat heterogeneous and that the variability in trait change is partially explained by sample age, country of origin, and personality measurement method. We also found mixed evidence for predictors of change, specifically for sex and baseline age. This study demonstrates the importance of coordinated conceptual replications for accelerating the accumulation of robust and reliable findings in the lifespan developmental psychological sciences.

68 citations

Journal ArticleDOI
TL;DR: Second-order latent growth models demonstrated both mean-level declines on purpose over time, as well as the capacity for inter-individual variability in change patterns for retired adults, in one of the first longitudinal investigations into how individuals' sense of purpose fluctuates in older adulthood.
Abstract: Objectives: Though cross-sectional research has suggested that sense of purpose declines into older adulthood, it remains unclear whether inter-individual variability occurs in these trajectories, ...

63 citations


Cited by
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Journal ArticleDOI
08 May 2019
TL;DR: The most common mistakes being to describe effect sizes in ways that are uninformative (e.g., using arbitrary standards) or misleading as mentioned in this paper, i.e., squa...
Abstract: Effect sizes are underappreciated and often misinterpreted—the most common mistakes being to describe them in ways that are uninformative (e.g., using arbitrary standards) or misleading (e.g., squa...

1,292 citations

Journal Article

676 citations

01 Jan 2016
TL;DR: The encyclopedia of research design is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you very much for reading encyclopedia of research design. Maybe you have knowledge that, people have look hundreds times for their favorite books like this encyclopedia of research design, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their desktop computer. encyclopedia of research design is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the encyclopedia of research design is universally compatible with any devices to read.

465 citations

Journal ArticleDOI
26 Aug 2021
TL;DR: This Primer provides an introduction to genome-wide association studies (GWAS), techniques for deriving functional inferences from the results and applications of GWAS in understanding disease risk and trait architecture, and discusses important ethical considerations when considering GWAS populations and data.
Abstract: Genome-wide association studies (GWAS) test hundreds of thousands of genetic variants across many genomes to find those statistically associated with a specific trait or disease. This methodology has generated a myriad of robust associations for a range of traits and diseases, and the number of associated variants is expected to grow steadily as GWAS sample sizes increase. GWAS results have a range of applications, such as gaining insight into a phenotype’s underlying biology, estimating its heritability, calculating genetic correlations, making clinical risk predictions, informing drug development programmes and inferring potential causal relationships between risk factors and health outcomes. In this Primer, we provide the reader with an introduction to GWAS, explaining their statistical basis and how they are conducted, describe state-of-the art approaches and discuss limitations and challenges, concluding with an overview of the current and future applications for GWAS results. Uffelmann et al. describe the key considerations and best practices for conducting genome-wide association studies (GWAS), techniques for deriving functional inferences from the results and applications of GWAS in understanding disease risk and trait architecture. The Primer also provides information on the best practices for data sharing and discusses important ethical considerations when considering GWAS populations and data.

299 citations