Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges

Alexander, Leo; Mulfinger, Evan; Oswald, Frederick L.

doi:10.1002/per.2305

Cited by 35 publications

(31 citation statements)

References 133 publications

(143 reference statements)

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“…For example, even a simple variable such as “step count”—measured via different pedometers, wearables, and smartphones—varies between devices and deviates from the steps counted by human raters (Case et al., 2015). Knowledge about the reliability and validity of features is important to inform appropriate data collection and interpretation of the resulting model's content validity (Alexander et al., 2020). Much research is still to be done to ensure that ML personality measures appropriately measure the target constructs.…”

Section: Psychometric Issuesmentioning

confidence: 99%

“…Another pressing issue is whether PC produces fair results, that is, whether it performs equally well across different groups (Alexander et al., 2020), which may pertain to race, ethnicity, gender, sexuality, disability, confession, class, or culture. Any kind of algorithm‐based discrimination might result in amplifications of social inequalities due to systematic discrepancies in access to personalized services or targeted manipulation (Kusner & Loftus, 2020).…”

Section: Ethical Legal and Societal Issuesmentioning

confidence: 99%

“…This new area of research may inform personality theory in an unprecedented fashion through the detection of complex patterns that cannot be retrieved by traditional statistical approaches. PC also holds the potential to yield real‐world applications that might improve health and well‐being through personalized interventions (Alexander et al., 2020). There has been an increase in PC studies in recent years (Mehta et al., 2019), originating not only from personality psychology but also from computer science given the centrality of highly complex data‐analytical methods.…”

Section: Introductionmentioning

confidence: 99%

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Personality computing: New frontiers in personality assessment

Phan

Rauthmann

2021

Social & Personality Psych

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Personality Computing (PC) is a burgeoning field at the intersection of personality and computer science that seeks to extract personality‐relevant information (e.g., on Big Five trait levels) from sensor‐assessed information (e.g., written texts, digital footprints, smartphone usage, non‐verbal behavior, speech patterns, game‐play, etc.). Such sensor‐based personality assessment promises novel and often technologically sophisticated ways to unobtrusively measure individual differences in a highly precise, granular, and faking‐resistant manner. We review the different conceptual underpinnings of PC; survey how well different types of sensors can capture different types of personality‐relevant information; discuss the evaluation of PC performance and psychometric issues (reliability and validity) of sensor‐derived scores as well as ethical, legal, and societal implications; and highlight how modern personality and computer science can be married more effectively to provide practically useful personality assessment. Together, this review aims to introduce readers to the opportunities, challenges, pitfalls, and implications of PC.

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Section: Psychometric Issuesmentioning

confidence: 99%

Section: Ethical Legal and Societal Issuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Personality computing: New frontiers in personality assessment

Phan

Rauthmann

2021

Social & Personality Psych

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“…Thankfully, as of yet there is little of evidence of the prevalence of these practices. Nonetheless, as advanced computer technologies are rapidly entering mainstream research, psychologists must-and increasingly do-discuss the ethical challenges and personal responsibilities that arise from using these technologies (Alexander et al, 2020;Kosinski et al, 2015Kosinski et al, , 2016Matz et al, 2020), including powerful language algorithms like the GPT-2 and their applications like the PIG.…”

Section: Outlook and Conclusionmentioning

confidence: 99%

Let the Algorithm Speak: How to Use Neural Networks for Automatic Item Generation in Psychological Scale Development

Götz¹,

Maertens²,

Linden³

2021

Preprint

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Measurement is at the heart of scientific research. As many—perhaps most—psychological constructs cannot be directly observed, there is a steady demand for sound self-report scales to assess such latent constructs. However, scale development is a tedious process that requires researchers to produce good items in large quantities. In the current tutorial, we introduce, explain, and apply the Psychometric Item Generator (PIG), an open-source, free-to-use, self-sufficient natural language processing algorithm that produces large-scale, human-like, customised text output within a few mouse clicks. The PIG is based on the GPT-2, a powerful generative language model, and runs on Google Colaboratory—an interactive virtual notebook environment that executes code on state-of-the-art virtual machines at no cost. We demonstrate that based on an input of three sentences, the PIG produces 65 items that pass initial face validity checks within a single iteration of code and a runtime of less than one minute. The PIG does not require any prior coding skills or access to computational resources and can be easily tailored to any desired context by simply switching out short linguistic prompts in a single line of code. Additionally, the PIG can also be used as a bottom-up tool to expand and diversify the conceptual understanding of a construct or derive hypotheses about its relationships to other, existing constructs. In short, we present an effective, novel machine learning solution to an old psychological challenge. As such, the PIG will not only not require you to learn a new language—but speak yours.

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“…The current paper provides a brief review of the current research on social media text mining and its application to personality assessment. Specifically, we integrate major discussions in recent reviews that emphasize the need for psychometric and theoretical validity evidence of big data personality assessment methods (Alexander et al, 2020;Bleidorn & Hopwood, 2019;Tay et al, 2020) with technical issues that researchers and practitioners need to consider in conducting text mining research (e.g., social media text analysis methods, text preprocessing). Additionally, throughout the paper, we provide readers with references to various user-friendly softwares and guidelines that readers can consult in conducting their own text mining research.…”

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confidence: 99%

Machine Learning Approach to Personality Assessment and Its Application to Personnel Selection

Ock

2021

Korean J. Ind. Organ. Psychol.

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As we enter the digital age, new methods of personality testing-namely, machine learning-based personality assessment scales-are quickly gaining attraction. Because machine learning-based personality assessments are made based on algorithms that analyze digital footprints of people’s online behaviors, they are supposedly less prone to human biases or cognitive fallacies that are often cited as limitations of traditional personality tests. As a result, machine learning-based assessment tools are becoming increasingly popular in operational settings across the globe with the anticipation that they can effectively overcome the limitations of traditional personality testing. However, the provision of scientific evidence regarding the psychometric soundness and the fairness of machine learning-based assessment tools have lagged behind their use in practice. The current paper provides a brief review of empirical studies that have examined the validity of machine learning-based personality assessment, focusing primarily on social media text mining method. Based on this review, we offer some suggestions about future research directions, particularly regarding the important and immediate need to examine the machine learning-based personality assessment tools’ compliance with the practical and legal standards for use in practice (such as inter-algorithm reliability, test-retest reliability, and differential prediction across demographic groups). Additionally, we emphasize that the goal of machine learning-based personality assessment tools should not be to simply maximize the prediction of personality ratings. Rather, we should explore ways to use this new technology to further develop our fundamental understanding of human personality and to contribute to the development of personality theory.

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Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges

Cited by 35 publications

References 133 publications

Personality computing: New frontiers in personality assessment

Personality computing: New frontiers in personality assessment

Let the Algorithm Speak: How to Use Neural Networks for Automatic Item Generation in Psychological Scale Development

Machine Learning Approach to Personality Assessment and Its Application to Personnel Selection

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