Development and Linguistic Cue Analysis of the State-Trait Cheerfulness Inventory-Short Form

Artificial Neural Networks for Short-Form Development of Psychometric Tests: A Study on Synthetic Populations Using Autoencoders

Short-form development is an important topic in psychometric research, which requires researchers to face methodological choices at different steps. The statistical techniques traditionally used for shortening tests, which belong to the so-called exploratory model, make assumptions not always verified in psychological data. This article proposes a machine learning-based autonomous procedure for short-form development that combines explanatory and predictive techniques in an integrative approach. The study investigates the item-selection performance of two autoencoders: a particular type of artificial neural network that is comparable to principal component analysis. The procedure is tested on artificial data simulated from a factor-based population and is compared with existent computational approaches to develop short forms. Autoencoders require mild assumptions on data characteristics and provide a method to predict long-form items' responses from the short form. Indeed, results show that they can help the researcher to develop a short form by automatically selecting a subset of items that better reconstruct the original item's responses and that preserve the internal structure of the long-form.

Humor appreciation can be predicted with machine learning techniques

Humor research is supposed to predict whether something is funny. According to its theories and observations, amusement should be predictable based on a wide variety of variables. We test the practical value of humor appreciation research in terms of prediction accuracy. We find that machine learning methods (boosted decision trees) can indeed predict humor appreciation with an accuracy close to its theoretical ceiling. However, individual demographic and psychological variables, while replicating previous statistical findings, offer only negligible gains in accuracy. Successful predictions require previous ratings by the same rater, unless highly specific interactions between rater and joke content can be assessed. We discuss implications for humor research, and offer advice for practitioners designing content recommendations engines or entertainment platforms, as well as other research fields aiming to review their practical usefulness.

The State-Trait Cheerfulness Inventory State Version-Short Form (STCI-S18): An Examination of Language Use and Psychometric Properties

The State-Trait Cheerfulness Inventory-State Version (STCI-S) measures three states of cheerfulness, seriousness, and bad mood as the temperamental basis of humor. The present study investigates (1) the development and psychometric validation of a newly developed short version and (2) test-criterion validity with state measures and language use. Study 1 (N = 933) confirmed the three-dimensional structure, and both the short and standard versions demonstrated strong psychometric properties. Study 2 (N = 617) replicated these findings in a separate sample. Study 3 (N = 750) demonstrated expected intercorrelations with self-report state measures (i.e., inspiration, self-esteem, depression, anxiety). Participants were randomized to expressive writing conditions (i.e., writing about a retrospective cheerful, serious, or bad mood scenario). Unacquainted judges' and participants' ratings of three states in their respective scenarios converged (.44 < r < .62). Retrospective states cheerfulness, seriousness, and bad mood were associated with linguistic categories (e.g., emotional tone, clout, achievement, insight) identified in the Linguistic Inquiry and Word Count (LIWC) software. The impact of personality states on word usage may only be evident in specific contexts and situations. Presence of distinctive language use in these writing samples further demonstrated test-criterion validity and practicality of the STCI-S18.