M
Martin C. Seay
Researcher at Kansas State University
Publications - 35
Citations - 462
Martin C. Seay is an academic researcher from Kansas State University. The author has contributed to research in topics: Financial literacy & Population. The author has an hindex of 10, co-authored 31 publications receiving 309 citations.
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Journal ArticleDOI
Bounded Rationality and Use of Alternative Financial Services
TL;DR: In this article, the authors explored the role of actual (objective) and perceived (subjective) financial knowledge in the decision-making process and found that individuals with lower objective financial knowledge and those that are overconfident in their self-assessed knowledge level are significantly more likely to utilize AFS instruments.
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Financial Knowledge and Short-Term and Long-Term Financial Behaviors of Millennials in the United States
TL;DR: In this article, the role of financial knowledge in various short-term and long-term financial behaviors among Millennials in the United States was investigated, and consistent multivariate results find financial knowledge to be positively associated with performing positive short-time and longterm financial behaviours.
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Personality and Saving Behavior Among Older Adults
Sarah D. Asebedo,Melissa J. Wilmarth,Martin C. Seay,Kristy L. Archuleta,Gary L. Brase,Maurice MacDonald +5 more
TL;DR: In this article, the authors investigated how psychological characteristics influence saving behavior within a sample of 1,380 U.S. preretirees aged 50-70 from the Health and Retirement Study (HRS).
Journal Article
Positive Psychological Attributes and Retirement Satisfaction
Sarah D. Asebedo,Martin C. Seay +1 more
TL;DR: In this paper, the authors investigated the association between positive psychological attributes and retirement satisfaction using a sample of 5,146 retired individuals from the 2006 and 2008 waves of the Health and Retirement Study (HRS).
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Complex Samples and Regression-Based Inference: Considerations for Consumer Researchers
Robert B. Nielsen,Martin C. Seay +1 more
TL;DR: In this article, the authors demonstrate that researchers who treat data collected via complex sampling procedures as if they were collected via simple random sample (SRS) may draw improper inferences when estimating regression models.