Coordinated Data Analysis: A New Method for the Study of Personality and Health

Abstract

A majority of research by personality psychologists examining health has utilized publicly available datasets, for good reason. These resources are often the only available datasets large enough to detect expected effect sizes and may contain biological or genetic data that is difficult to obtain. However, researchers tend to examine only one large dataset at a time. Given recent meta-research on the robustness and replicability of "established" findings, all researchers should take greater care to evaluate the evidentiary value of their findings and seek methods to increase their robustness. Personality and aging psychologists who use publicly available datasets have a unique tool at their disposal in order to achieve this goal, namely, more publicly available datasets. More specifically, psychologists may use coordinated analysis (Hofer and Piccinin, 2009; Piccinin and Hofer, 2008) to examine relationships across several large datasets and, using the tools of meta-analysis, identify generalizable effect sizes and examine heterogeneity across countries and methods. This chapter describes the motivation for coordinated analysis, the process of using this method, and details several examples.

Trajectories of Big Five Personality Traits: A Coordinated Analysis of 16 Longitudinal Samples

“…One goal of this project was to apply techniques designed to enhance replicability of results to longitudinal research on personality development across adulthood using existing data. Researchers across many fields are currently focused on enhancing replicability and reproducibility, and encouraging open science practices (Condon et al, 2017; Nelson, Simmons, & Simonsohn, 2018; Vazire, 2018; Weston, Graham, & Piccinin, 2019). However, many of the shifts in research practices have focused on experimental sub‐fields of psychology, leaving longitudinal researchers with more limited options regarding how to enhance the replicability of their findings.…”

Cross-sectional and prospective association between personality traits and IADL/ADL limitations.

“…cohort samples in a pooled analysis allows for more robust and replicable results (Hofer & Piccinin, 2009;Weston et al, 2020) because the associations are tested across samples that differ in age, nationality, and measures of IADLs/ADLs and personality. Eight samples were identified that were comparable in overall design, with personality traits and IADLs/ADLs assessed at baseline, and IADLs/ADLs assessed again years later.…”

Coordinated Data Analysis: Knowledge Accumulation in Lifespan Developmental Psychology

“…A key consideration during this phase is the complexity of the research question being posed. As we suggest in Weston, Graham, and Piccinin (2019) it is often best to choose a simple yet foundational question. With complex questions and models that are increasingly common in aging research, many datasets are unlikely to fulfill highly specialized variable, design, or data requirements required to test them (Kelly et al, 2016).…”

“…Lifespan development, health psychology, and aging are cases in point: replication is a core scientific value, yet, replications are difficult to accomplish where complex longitudinal or timeintensive data are needed (Hofer & Piccinin, 2009, 2010Piccinin & Hofer, 2008;Weston, Graham, & Piccinin, 2019) or where participants are followed for long periods until a certain event occurs such as onset of a disease, a relationship event (like divorce), or death. Thus, fields that rely heavily on long-term longitudinal data, such as aging research, lifespan development, some subareas of health psychology, behavioral and social epidemiology, and others, face unique challenges when trying to adopt open, reproducible, and replicable research practices.…”