Searching for Prosociality in Qualitative Data: Comparing Manual, Closed–Vocabulary, and Open–Vocabulary Methods

McAuliffe, William H.B.; Moshontz, Hannah; McCauley, Thomas Granville; McCullough, Michael E.

doi:10.1002/per.2240

Cited by 4 publications

(3 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to that, open-vocabulary approaches operate from "bottom-up" (data-driven), that is, based on language (text) as such. Algorithms identify related clusters of units (lexical units or elements, for example, punctuation) that naturally occur (and co-occur) within a large set of texts and find lexical and semantic patterns that appear (and appear together) in the data (Park et al, 2015;McAuliffe et al, 2020). Both approaches have their pros and cons; as stated by Eichstaedt et al, "Closed-vocabulary approaches can be rigid, while open-vocabulary approaches can be sensitive to idiosyncrasies of the dataset and the modeler's choices about parameters.…”

Section: Approaches and Methods In Questionmentioning

confidence: 99%

Beyond English: Considering Language and Culture in Psychological Text Analysis

Kučera

Mehl

2022

Front. Psychol.

View full text Add to dashboard Cite

The paper discusses the role of language and culture in the context of quantitative text analysis in psychological research. It reviews current automatic text analysis methods and approaches from the perspective of the unique challenges that can arise when going beyond the default English language. Special attention is paid to closed-vocabulary approaches and related methods (and Linguistic Inquiry and Word Count in particular), both from the perspective of cross-cultural research where the analytic process inherently consists of comparing phenomena across cultures and languages and the perspective of generalizability beyond the language and the cultural focus of the original investigation. We highlight the need for a more universal and flexible theoretical and methodological grounding of current research, which includes the linguistic, cultural, and situational specifics of communication, and we provide suggestions for procedures that can be implemented in future studies and facilitate psychological text analysis across languages and cultures.

show abstract

Section: Approaches and Methods In Questionmentioning

confidence: 99%

Beyond English: Considering Language and Culture in Psychological Text Analysis

Kučera

Mehl

2022

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…Prior studies of New Year's resolutions measured resolutions with broad selfreport nomothetic categories rather than open-ended text (e.g., Woolley & Fishbach, 2016). However, prior studies of mid-level goals, and in particular people's personal strivings, have used idiographic approaches combined with nomothetic coding based on existing manuals or by bottom-up categories, derived qualitatively or using automated methods (McAuliffe et al, 2020;Veilleux et al, 2018).…”

Section: Content Of Resolutionsmentioning

confidence: 99%

Understanding Ordinary Goal Pursuit: Describing and Predicting Success in New Year’s Resolutions

Moshontz¹

2020

Preprint

Self Cite

View full text Add to dashboard Cite

People’s success in achieving their goals can have profound consequences for their subjective and objective well-being. Hundreds of research studies identify factors associated with success in goal pursuit, but little is known about the occurrence and influence of these factors in daily life. This dissertation aims to complement and build on extant, mostly laboratory, research by characterizing ordinary goal pursuit and identifying factors that meaningfully affect it in the context of daily life. The first chapter offers background: a review of prior research, a discussion of potential limitations on the replicability and generalizability of prior research, and an argument for more robust, naturalistic, and descriptive work. The chapters that follow present prospective observational studies focused on pursuit of New Year’s resolutions and used to address eight research questions pertaining to the content and framing of goals people pursue, the outcomes of goal pursuit, and the potentially mutable factors associated with goal achievement. The second chapter presents Study 1, a descriptive study focused on understanding what goals people set as resolutions and the typical process and outcome of pursuit. The third chapter presents Study 2, a study focused on assessing the predictive value of goal-varying factors. Goals varied greatly in their content, properties, and outcomes. Contrary to theory, many resolutions were neither successful nor unsuccessful, but instead were still being pursued or were on hold at the end of the year. Across both studies, the three most common resolution outcomes at the end of the year were achievement (estimates ranged from 20% to 40%), continued pursuit (32% to 60%) and pursuit put on hold (15% to 21%). Other outcomes (e.g., deliberate disengagement) were rare (<1% to 3%). Motivation and habit formation were associated with subjective success consistently, over and above trait self-control, but no other goal-varying properties showed robust associations with goal outcomes. Predictive models suggest that relatively little variance in goal outcomes can be meaningfully predicted by goal-varying properties, and that linear regression models are particularly bad at predicting goal outcomes. This dissertation demonstrates the value of naturalistic, descriptive, and prediction-focused work for advancing understanding of self-regulation.

show abstract

“…10Note, however, that LIWC is based on a straightforward counting method. This means that it is unqualified to understand the context and subtle nuances of language (e.g., irony, humor) like human judges do (McAuliffe et al, 2020; Pang & Ring, 2020), it does not analyze text using sophisticated machine learning approaches like the IBM-WPI does (IBM, 2021), it is not based on personality lexical studies like the HTTP (Holtrop et al, 2022), neither was it developed to measure personality in the first place.…”

mentioning

confidence: 99%

The kernel of truth in text-based personality assessment: A meta-analysis of the relations between the Big Five and the Linguistic Inquiry and Word Count (LIWC).

Koutsoumpis¹,

Oostrom²,

Holtrop³

et al. 2022

Psychological Bulletin

View full text Add to dashboard Cite

The Linguistic Inquiry and Word Count (LIWC) is a popular closed-vocabulary text analysis software program that is used to understand whether individuals' use of linguistic categories (i.e., word categories, such as negative affect) depends on their personality traits. Here, we present the first meta-analysis of the relations between the Big Five personality traits and 52 linguistic categories of the English language. Across 31 eligible samples (n = 85,724), the results showed that (a) self-reported personality traits are significantly correlated with linguistic categories, but the effect sizes are relatively small (the strongest effect sizes between the Big Five and linguistic categories ranged from |ρ| = .08 to .14, and the 52 LIWC categories explained on average 5.1% of personality variance); (b) observerreported personality traits are significantly correlated with linguistic categories, with the effect sizes being small-to-medium (|ρ| = .18-.39, explaining 38.5% of personality variance); (c) 20 linguistic categories (out of 260; 5 Personality Traits × 52 LIWC Categories) correlated both with self-and observer-reported personality traits (the "kernel of truth" in linguistic markers of personality); and (d) 10 study, sample, and task characteristics significantly moderated the correlations of the linguistic categories with personality traits, showing that the effect sizes were mainly stronger for longer texts and older LIWC versions, among others. Public Significance StatementThis meta-analysis identifies the linguistic categories (i.e., word categories, such as negative affect) that individuals use depending on their personality traits, as well as the linguistic categories that other people use to draw personality inferences. Individuals indeed use specific linguistic categories depending on their personality traits and others use specific linguistic categories to draw personality inferences, but those relations are dependent on study and tasks characteristics (e.g., text length, Linguistic Inquiry and Word Count version).

show abstract

Searching for Prosociality in Qualitative Data: Comparing Manual, Closed–Vocabulary, and Open–Vocabulary Methods

Cited by 4 publications

References 53 publications

Beyond English: Considering Language and Culture in Psychological Text Analysis

Beyond English: Considering Language and Culture in Psychological Text Analysis

Understanding Ordinary Goal Pursuit: Describing and Predicting Success in New Year’s Resolutions

The kernel of truth in text-based personality assessment: A meta-analysis of the relations between the Big Five and the Linguistic Inquiry and Word Count (LIWC).

Contact Info

Product

Resources

About