Survey research plays a significant role in the way psychologists investigate key relationships which impact human behaviours—and which inform us about undercurrents of a population. Samples are generally taken with the primary function of being able to make inferences which can be generalised to the target population—however, historically the field has consistently relied on small and niche datasets which are not truly representative of the general population. As a consequence, there is an abundance of published research which lacks ecological validity. The alternative approach is to collect larger amounts of data—this approach is extremely costly and in most instances impractical for the researcher. I have termed this conundrum, the cost-insight trade-off, which has traditionally exasperated psychologists. To address this dilemma, I conducted three studies using two alternative methods. Study 1 investigated the relationship between social status and international friendships at a micro and macro level. The building social status hypothesis states that higher social status individuals would reach out more to people and have more international friendships than their poorer counterparts. In contrast, the restrictive social status hypothesis states the higher social status individuals would be reclusive and have fewer international friendships than their poorer counterparts. Findings at both the micro (N = 857; U.S. participants) and macro levels (approximately 50 billion friendships across 187 countries) were in alignment with the restrictive social status hypothesis. Investigating this relationship at this large a scope would not have been possible without utilising Facebook Data—furthermore, for most research projects collecting data at this scale is both too costly and impractical. Study 2 aims to address the limitation of study 1. In this light, a new alternative method, the Survey Forecasting Method, is introduced and used to demonstrate creative capability of combining the latest technology, machine learning techniques and big data (i.e. Twitter). The findings were proof positive that a data collection of only 1,000 participants (at minimum) can be transformed into the power of having a dataset of several hundred thousand participants. In other words, the findings suggest that it is possible to efficiently and effectively forecast scores for potentially millions of people, without them having to complete a single survey. This is a significant step towards developing an alternative survey method; however, the method has only been applied to the Big Five & NEO-IPIP personality traits. Study 3 provides further evidence for the Survey Forecasting Method as a viable alternative to traditional sampling methods. The study examined the relationship between entrepreneurs’ self-efficacy, fear of failure, and well-being at two levels: (a) self-report and forecasted individual level, and (b) forecasted state level (across all 50 U.S. states). Findings show there are differences between each level which provides insights into effects and potential mechanisms which would not potentially be found using traditional “silo’d” methods. The primary aim of this thesis is to provide a viable alternative method to conducting survey research—which allows the researcher to gain deeper insights into the population at less cost and time. Furthermore, this alternative method addresses poor data representativeness. Limitations are addressed and future directions to improve its capability and robustness as a viable survey research methodology are provided.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:744722 |
| Date | January 2018 |
| Creators | Yearwood, Maurice |
| Contributors | Kogan, Aleksandr |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/274944 |
Page generated in 0.0022 seconds