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Using R for consumer psychological research
Research Analytics | Strategy & InsightSeptember 2014
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AgendaWhat this presentation will cover
About me1
Analytics Challenge2
Standard Analytics3
Quantitative Psychology4
Artificial General Intelligence5
Conclusion6
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About me
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About meBackground & experience
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Analytics Challenge
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Analytics ChallengeTransition KM from SPSS to R
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Analytics ChallengeChange management & user aids
rcommander.com
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Standard Analytics
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Standard AnalyticsAn overview of a subset of common techniques
Tools
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Quantitative Psychology
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Quantitative PsychologyQuantitative consumer psychology in a nutshell
• Quantitative consumer psychology specializes in measurement, methodology and research design and analyses relevant data to better understand and (hopefully) predict consumer cognition and behaviour
• psych: Procedures for Psychological, Psychometric, and Personality Research• IAT: Functions to use with data from the Implicit Association Test
• Useful packages in R:
• StatMatch: Integrating two or more data sources
• Initial developments in R
• Computational Cognitive Modelling
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Traditional Psychometrics [Package ‘psych’]
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Package ‘psych’Numerous standard psychometric analyses at your fingertips
• Author and maintainer: Prof. William Revelle | [email protected]• Scale construction using factor analysis, cluster analysis, and reliability analysis• Graphical models (e.g., SEM) for path diagrams to explore and test theories
Perceived Usefulness
External Variables
Perceived Ease of Use
Attitude Toward Using
Behavioural Intention to
UseActual Use
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Implicit Association Test [Package ‘IAT’]
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Overview of IATWhat is this good for?
• The IAT is a computerised method for indirectly measuring the strength of the association via a double-categorisation task
• Psychological assumption: People are able to categorise strongly associated concepts more quickly than concepts that are weakly associated (Greenwald et al., 1998)
• The IAT effect is usually interpreted as a measure of implicit attitude
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IAT: Experimental detailsHow does it work?
ScienceorMale
Liberal Artsor
Female
Engineering
ScienceorMale
Liberal Artsor
Female
Mike
Liberal ArtsorMale
Scienceor
Female
Engineering
Liberal ArtsorMale
Scienceor
Female
Tiffany
• The difference (D-Scoring algorithm) in response latencies between the two concepts for the congruent and incongruent pairing provides the basis for the IAT measure (Greenwald, 2003)
Congruent pairing Incongruent pairing
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Package ‘IAT’Measuring hidden cognitive associations
• Author and maintainer: Dan Martin | [email protected]
• Implements the standard D-Scoring algorithm (Greenwald et al., 2003)
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Data Integration [Package ‘StatMatch’]
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Package ‘StatMatch’Data Integration / Data Fusion• The availability of data is often a serious
problem
• Data fusion provides a way out by combining information from different sources into a single data set
• The algorithm typically consists of three steps.
I. Decide on the direction of the fusion and the purpose of the fusion
II. The matching distance is calculated over some subset of the common variables
III. Loop through datasets and assign donors based on distance match and penalty weight
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Package ‘StatMatch’Data Integration / Data Fusion• Statistical matching integrates different data sources in order to
investigate the relationship among variables not jointly observed in a single data source
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Computational Cognitive Modelling
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Computational Cognitive ModellingWhat is this?
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Computational Cognitive ModellingSchematic overview
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Soar: Artificial General Intelligence (AGI)
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AGI using R?
Package ‘rrules’General rule engine: Lisp-like rule processing
Package 'MDPtoolbox'Reinforcement learning (QL)
Package 'neuralnet'Neural networks
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ConclusionThe future for R in Quantitative Psychology looks promising
• R has the potential to replace SPSS due to it’s flexibility, power, and increasing availability of packages
• There are still several significant challenges to R becoming mainstream (at least in psychology)
• Computational modelling is critical for mechanistically understanding cognition
• The R language is still largely underexplored in computational cognitive modelling
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Thank you!