Challenges and solutions for data analysis in an adult lifespan study of over 100,000 online cognitive test completions / by Annalise LaPlume.

Format:

Book

Author/Creator:

LaPlume, Annalise, author.

Series:

SAGE research methods: doing research online.

SAGE research methods: doing research online

Language:

English

Subjects (All):

Cognition--Testing--Case studies.

Cognition.

Cognition--Age factors--Case studies.

Cognition--Research--Methodology--Case studies.

Physical Description:

1 online resource : illustrations.

Place of Publication:

London : SAGE Publications, Ltd., 2022.

Summary:

In this case study, I describe methodological insights from data analysis of an online adult lifespan dataset (over 100,000 completions, ages 15–100). The data were used to study cross-sectional age differences in cognitive performance. I cover the steps of data analysis for large-scale web-based data, namely data cleaning, analysis, and visualization techniques. In each step, I describe the unique challenges that face analysis of data collected online, and potential solutions to address them, by drawing on practical lessons and examples from this study. First, I address how to identify problematic recordings such as technical issues (incomplete data, multiple completions by the same person, and so forth), unreliable self-reported demographic information (age), and cognitive task outliers (accuracy, response times). I propose rigorous data cleaning as an essential first step to ensure that analytical conclusions are reliable and unbiased. Next, I demonstrate data visualization techniques that are better suited to large online datasets than more conventional techniques (e.g., density plots or locally weighted scatterplot smoothing instead of dot-plots or linear regression). Lastly, I cover the limitations of significance testing in large online datasets, and the value of complementary approaches such as data visualization, effect size estimation, and use of parsimony criteria. I also discuss more sophisticated analysis options enabled by large online datasets, such as non-linear regression, model comparison and selection, data resampling, and addition of covariates.

Notes:

Description based on XML content.

ISBN:

9781529600865 :

OCLC:

1301363332