French validation of the Twenty-Item Value Inventory

IntroductionPersonal values are important for understanding psychological phenomena by serving as predictors of individuals’ attitudes and behavior. The Twenty-Item Value Inventory (TwIVI, Sandy et al., 2017) was developed to assess values with items from the Portrait Values Questionnaire (PVQ, Schwartz et al., 2001).ObjectiveThe goal of this study was to assess the reliability and validity of the French version of the TwIVI.MethodTwo French-Canadian samples of adults (N1 = 825; N2 = 429) were recruited in order to test the reliability and validity of the TwIVI.ResultsResults revealed that the French version of the TwIVI showed satisfying temporal stability, convergence validity with the PVQ, and concurrent validity with personality traits. Some issues were observed for internal consistency and structural validity, assessed with both multidimensional scaling and exploratory structural equation modeling.ConclusionOverall, these findings partially support the psychometric qualities of the French version of the TwIVI. Implications for theory and research on values are discussed.     

Critical action among Asian and Hispanic/Latinx youth: Identifying a multidimensional measure and exploring within-group differences

Critical action—behaviours aimed at dismantling systems of oppression—must be examined within youths' racialized experiences and should incorporate cultural and sociohistorical factors. We considered an expansive list of items capturing youth behaviours to create a novel four-factor (service, community change, expression, and care) measure of critical action for Asian and Hispanic/Latinx youth. Multiple distinct profiles of critical action were identified within both racial-ethnic groups, and associations between the profiles and sociodemographic and contextual support variables were explored. Gender differences in the type of critical action were found in both racial-ethnic groups, pointing to the potential influence of gender roles on critical action among these populations. Differences in critical action patterns were also found between those born in the U.S. versus those born outside the U.S.; access to critical action may differ within racial-ethnic groups depending on birthplace and associated nuances in familial and cultural contexts. This paper demonstrated a need for attending to variation between and within groups in the study of critical action in order to effectively support racialized youth's coping within and resistance against systems of oppression.     

How infant-directed actions enhance infants’ attention,learning, and exploration: Evidence from EEG and computational modeling

When teaching infants new actions, parents tend to modify their movements. Infants prefer these infant-directed actions (IDAs) over adult-directed actions and learn well from them. Yet, it remains unclear how parents’ action modulations capture infants’ attention. Typically, making movements larger than usual is thought to draw attention. Recent findings, however, suggest that parents might exploit movement variability to highlight actions. We hypothesized that variability in movement amplitude rather than higher amplitude is capturing infants’ attention during IDAs. Using EEG, we measured 15-month-olds’ brain activity while they were observing action demonstrations with normal, high, or variable amplitude movements. Infants’ theta power (4–5 Hz) in fronto-central channels was compared between conditions. Frontal theta was significantly higher, indicating stronger attentional engagement, in the variable compared to the other conditions. Computational modelling showed that infants’ frontal theta power was predicted best by how surprising each movement was. Thus, surprise induced by variability in movements rather than large movements alone engages infants’ attention during IDAs. Infants with higher theta power for variable movements were more likely to perform actions successfully and to explore objects novel in the context of the given goal. This highlights the brain mechanisms by which IDAs enhance infants’ attention, learning, and exploration.     

Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023)

In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR].” In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.     

Stolen elections: How conspiracy beliefs during the 2020 American presidential elections changed over time

Conspiracy beliefs have been studied mostly through cross-sectional designs. We conducted a five-wave longitudinal study (N = 376; two waves before and three waves after the 2020 American presidential elections) to examine if the election results influenced specific conspiracy beliefs and conspiracy mentality, and whether effects differ between election winners (i.e., Biden voters) versus losers (i.e., Trump voters) at the individual level. Results revealed that conspiracy mentality kept unchanged over 2 months, providing first evidence that this indeed is a relatively stable trait. Specific conspiracy beliefs (outgroup and ingroup conspiracy beliefs) did change over time, however. In terms of group-level change, outgroup conspiracy beliefs decreased over time for Biden voters but increased for Trump voters. Ingroup conspiracy beliefs decreased over time across all voters, although those of Trump voters decreased faster. These findings illuminate how specific conspiracy beliefs are, and conspiracy mentality is not, influenced by an election event.     

Distress,wellbeing, and growth amidst COVID-19: Considering the dynamic interplay between positive and negative anticipatory emotions

Although negative anticipatory emotions are typically seen as risk factors for poorer psychological outcomes over COVID-19, emotion theorists suggest that this risk may be attenuated if balanced by the experience of positive emotion. Thus, the current study examined whether interactions between positive and negative anticipatory emotions were concurrently associated with psychological distress and greater personal wellbeing/posttraumatic growth (PTG) at three distinct periods (i.e., pre-lockdown, during lockdown, post-lockdown), and whether associations varied by these three COVID-19 time periods. The study utilizes two large longitudinal Australian samples, surveyed in 2020 prior to, during, and after a strict 4-month lockdown that occurred in Australia. Overall, positive emotions attenuated the adverse psychological outcomes arising from higher levels of negative emotion (i.e., higher psychological distress and lower personal wellbeing). Observed effects varied according to COVID-19 threat exposure. Specifically, the interaction was significantly associated with psychological distress prior to the lockdown for Sample 2, and during the lockdown for both samples. The interaction was significantly associated with wellbeing (Sample 2) prior to, and during, the lockdown but only marginally associated post-lockdown. The interaction, however, was not significantly associated with PTG (Sample 1). The results suggest that it is valuable for future research to consider greater emotional complexity (i.e., mixed emotions) over COVID-19, and other stressors more generally, to encompass a more nuanced understanding of resilience.     

Using self-evaluation to improve difficult-to-read handwriting of secondary students

We used delayed multiple-baseline and multiple-probe techniques to assess the effects of self-evaluation training on the legibility of cursive handwriting. Five secondary special education students with difficult-to-read handwriting served as participants. All participants improved the legibility of their handwriting during self-evaluation training. A systematic withdrawal of the training package demonstrated maintenance of treatment effects. For the most part, the students' handwriting remained at least 90% legible to judges following the systematic withdrawal of the self-evaluation training.     

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.     

Analyzing sequential categorical data: Individual variation in markov chains

Markov chains are probabilistic models for sequences of categorical events, with applications throughout scientific psychology. This paper provides a method for anlayzing data consisting of event sequences and covariate observations. It is assumed that each sequence is a Markov process characterized by a distinct transition probability matrix. The objective is to use the covariate data to explain differences between individuals in the transition probability matrices characterizing their sequential data. The elements of the transition probability matrices are written as functions of a vector of latent variables, with variation in the latent variables explained through a multivariate regression on the covariates. The regression is estimated using the EM algorithm, and requires the numerical calculation of a multivariate integral. An example using simulated cognitive developmental data is presented, which shows that the estimation of individual variation in the parameters of a probability model may have substantial theoretical importance, even when individual differences are not the focus of the investigator's concerns.Research contributing to this article was supported by B.R.S. Subgrant 5-35345 from the University of Virginia. I thank the DADA Group, Bill Fabricius, Don Hartmann, William Griffin, Jack McArdle, Ivo Molenaar, Ronald Schoenberg, Simon Tavaré, and several anonymous reviewers for their discussion of these points.