The semantic structure of accuracy in eyewitness testimony.

In two studies, we examined if correct and incorrect statements in eyewitness testimony differed in semantic content. Testimony statements were obtained from participants who watched staged crime films and were interviewed as eyewitnesses. We analyzed the latent semantic representations of these statements using LSA and BERT. Study 1 showed that the semantic space of correct statements differed from incorrect statements; correct statements were more closely related to a dominance semantic representation, whereas incorrect statements were more closely related to a communion semantic representation. Study 2 only partially replicated these findings, but a mega-analysis of the two datasets showed different semantic representations for correct and incorrect statements, with incorrect statements more closely related to representations of communion and abstractness. Given the critical role of eyewitness testimony in the legal context, and the generally low ability of fact-finders to estimate the accuracy of witness statements, our results strongly call for further research on semantic content in correct and incorrect testimony statements.

The ABC of happiness: Validation of the tridimensional model of subjective well-being (affect, cognition, and behavior) using Bifactor Polytomous Multidimensional Item Response Theory.

Background: Happiness is often conceptualized as subjective well-being, which comprises people's evaluations of emotional experiences (i.e., the affective dimension: positive and negative feelings and emotions) and judgements of a self-imposed ideal (i.e., the cognitive dimension: life satisfaction). Recent research has established these two dimensions as primary parts of a higher order factor. However, theoretical, conceptual, and empirical work suggest that people's evaluations of harmony in their life (i.e., the sense of balance and capacity to behave and adapt with both acceptance and flexibility to inter- and intrapersonal circumstances) constitutes a third dimension (i.e., the behavioral dimension). This tridemensional conceptualization of subjective well-being has recently been verified using Unidimensional Item Response Theory (UIRT) and Classical Test Theory (CTT). Here, we use a recently developed and more robust approach that combines these two methods (i.e., Multidimensional Item Response Theory, MIRT) to simultaneously address the complex interactions and multidimensionality behind how people feel, think, and behave in relation to happiness in their life. Method: A total of 435 participants (197 males and 238 females) with an age mean of 44.84 (sd = 13.36) responded to the Positive Affect Negative Affect Schedule (10 positive affect items, 10 negative affect items), the Satisfaction with Life Scale (five items), and the Harmony in life Scale (five items). We used Bifactor-Graded Response MIRT for the main analyses. Result: At the general level, each of the 30 items had a strong capacity to discriminate between respondents across all three dimensions of subjective well-being. The investigation of different parameters (e.g., marginal slopes, ECV, IECV) strongly reflected the multidimensionality of subjective well-being at the item, the scale, and the model level. Indeed, subjective well-being could explain 64 % of the common variance in the whole model. Moreover, most of the items measuring positive affect (8/10) and life satisfaction (4/5) and all the items measuring harmony in life (5/5) accounted for a larger amount of variance of subjective well-being compared to that of their respective individual dimensions. The negative affect items, however, measured its own individual concept to a lager extent rather than subjective well-being. Thus, suggesting that the experience of negative affect is a more independent dimension within the whole subjective well-being model. We also found that specific items (e.g., "Alert", "Distressed", "Irritable", "I am satisfied with my life") were the recurrent exceptions in our results. Last but not the least, experiencing high levels in one dimension seems to compensate for low levels in the others and vice versa. Conclusion: As expected, the three subjective well-being dimensions do not work separately. Interestingly, the order and magnitude of the effect by each dimension on subjective well-being mirror how people define happiness in their life: first as harmony, second as satisfaction, third as positive emotions, and fourth, albeit to a much lesser degree, as negative emotions. Ergo, we argue that subjective well-being functions as a complex biopsychosocial adaptive system mirroring our attitude towards life in these three dimensions (A: affective dimension; B: behavioral dimension; C: cognitive dimension). Ergo, researchers and practitioners need to take in to account all three to fully understand, measure, and promote people's experience of the happy life. Moreover, our results also suggest that negative affect, especially regarding high activation unpleasant emotions, need considerable changes and further analyses if it is going to be included as a construct within the affective dimension of a general subjective well-being factor.

Psychometric properties of persian version of escapism scale among Iranian adolescents.

The aim of this study was to assess the psychometric properties of the Persian version of the Escapism Scale among Iranian adolescents aged 14-18. Between January 2021 and August 2021, cross-sectional study was conducted using a convenience sampling method to select 566 participants (340 girls and 226 boys) to investigate the relationship between physical activity and mental health in adolescents. The participants completed several questionnaires, including the Escapism Scale, Erikson Psychosocial Stage Inventory (EPSI), Satisfaction with Life Scale (SWLS), Hope Scale (AHS), Smartphone Addiction Scale-Short Version (SAS-SV), and General Self-efficacy (GSE). Construct validity, reliability using Cronbach's alpha, and concurrent validity were used to evaluate the Escapism Scale's validity and reliability. Results of the Confirmatory Factor Analysis (CFA) indicated that a two-factor model provided a good fit for the data: sbX2 = 179.99 (p < 0.01); SRMR = 0.07; RMR = 0.56, CFI = 0.91; NFI = 0.89; IFI = 0.91; NFI = 0.89; GFI = 0.93; AGFI = 0.90, RMSEA = 0.076). The Cronbach's alpha coefficient for escapism was 0.73. The study found a significant positive relationship between escapism and smartphone addiction (r = 0.19). Additionally, a significant negative relationship was observed between escapism and hope (r=-0.31), satisfaction with life (r=-0.34), and general self-efficacy (r=-0.33). Furthermore, a significant relationship was found between escapism and gender. Lastly, the study found a significant relationship between escapism and identity confusion (r = 0.164, P < 0.01) and identity coherence (P < 0.01, r = 29). In conclusion, the Escapism Scale is a valid and reliable tool for assessing escapism and psychological evaluations in Iranian adolescents. These results may inform future research and suggest re-testing in clinical populations.

Removing Biases in Communication of Severity Assessments of Intimate Partner Violence: Model Development and Evaluation.

BACKGROUND: To support a victim of violence and establish the correct penalty for the perpetrator, it is crucial to correctly evaluate and communicate the severity of the violence. Recent data have shown these communications to be biased. However, computational language models provide opportunities for automated evaluation of the severity to mitigate the biases. OBJECTIVE: We investigated whether these biases can be removed with computational algorithms trained to measure the severity of violence described. METHODS: In phase 1 (P1), participants (N=71) were instructed to write some text and type 5 keywords describing an event where they experienced physical violence and 1 keyword describing an event where they experienced psychological violence in an intimate partner relationship. They were also asked to rate the severity. In phase 2 (P2), another set of participants (N=40) read the texts and rated them for severity of violence on the same scale as in P1. We also quantified the text data to word embeddings. Machine learning was used to train a model to predict the severity ratings. RESULTS: For physical violence, there was a greater accuracy bias for humans (r2=0.22) compared to the computational model (r2=0.31; t38=-2.37, P=.023). For psychological violence, the accuracy bias was greater for humans (r2=0.058) than for the computational model (r2=0.35; t38=-14.58, P<.001). Participants in P1 experienced psychological violence as more severe (mean 6.46, SD 1.69) than participants rating the same events in P2 (mean 5.84, SD 2.80; t86=-2.22, P=.029<.05), whereas no calibration bias was found for the computational model (t134=1.30, P=.195). However, no calibration bias was found for physical violence for humans between P1 (mean 6.59, SD 1.81) and P2 (mean 7.54, SD 2.62; t86=1.32, P=.19) or for the computational model (t134=0.62, P=.534). There was no difference in the severity ratings between psychological and physical violence in P1. However, the bias (ie, the ratings in P2 minus the ratings in P1) was highly negatively correlated with the severity ratings in P1 (r2=0.29) and in P2 (r2=0.37), whereas the ratings in P1 and P2 were somewhat less correlated (r2=0.11) using the psychological and physical data combined. CONCLUSIONS: The results show that the computational model mitigates accuracy bias and removes calibration biases. These results suggest that computational models can be used for debiasing the severity evaluations of violence. These findings may have application in a legal context, prioritizing resources in society and how violent events are presented in the media.

Women Are Perceived to Have More Power Than Men in Domains That Are Viewed As Important in U.S. Tweets.

Previous research of gender differences in power has largely focused on the public domain (e.g., leadership positions), and to a lesser extent power on the private domain (e.g., power in private relationships), where people may perceive these domains to be more or less important in their lives. We studied gender differences in preference weighted power (PWP) in different domains by weighting the perceived power by the perceived importance in life in a large set of Twitter messages from the United States (N = 9,286,471). The results showed that men (tweets including "he") are semantically related to general power, and women (tweets including "she") are related to importance in life, which in previous research have been connected to the public and private domains, respectively. Importantly, women had higher PWP than men. In conclusion, men are perceived to have more general power and women more PWP in U.S. tweets.

Precise language responses versus easy rating scales-Comparing respondents' views with clinicians' belief of the respondent's views.

BACKGROUND: Closed-ended rating scales are the most used response format for researchers and clinicians to quantify mental states, whereas in natural contexts people communicate with natural language. The reason for using such scales is that they are typically argued to be more precise in measuring mental constructs; however, the respondents' views as to what best communicates mental states are frequently ignored, which is important for making them comply with assessment. METHODS: We assessed respondents' (N = 304) degree of depression using rating scales, descriptive words, selected words, and free text responses and probed the respondents for their preferences concerning the response formats across twelve dimensions related to the precision of communicating their mental states and the ease of responding. This was compared with the clinicians' (N = 40) belief of the respondent's view. RESULTS: Respondents found free text to be more precise (e.g., precision d' = .88, elaboration d' = 2.0) than rating scales, whereas rating scales were rated as easier to respond to (e.g., easier d' = -.67, faster d' = -1.13). Respondents preferred the free text responses to a greater degree than rating scales compared to clinicians' belief of the respondents' views. CONCLUSIONS: These findings support previous studies concluding that future assessment of mental health can be aided by computational methods based on text data. Participants prefer an open response format as it allows them to elaborate, be precise, etc., with respect to their mental health issues, although rating scales are viewed as faster and easier.

Assessment of depression and anxiety in young and old with a question-based computational language approach.

Middle aged adults experience depression and anxiety differently than younger adults. Age may affect life circumstances, depending on accessibility of social connections, jobs, physical health, etc, as these factors influence the prevalence and symptomatology. Depression and anxiety are typically measured using rating scales; however, recent research suggests that such symptoms can be assessed by open-ended questions that are analysed by question-based computational language assessments (QCLA). Here, we study middle aged and younger adults' responses about their mental health using open-ended questions and rating scales about their mental health. We then analyse their responses with computational methods based on natural language processing (NLP). The results demonstrate that: (1) middle aged adults describe their mental health differently compared to younger adults; (2) where, for example, middle aged adults emphasise depression and loneliness whereas young adults list anxiety and financial concerns; (3) different semantic models are warranted for younger and middle aged adults; (4) compared to young participants, the middle aged participants described their mental health more accurately with words; (5) middle-aged adults have better mental health than younger adults as measured by semantic measures. In conclusion, NLP combined with machine learning methods may provide new opportunities to identify, model, and describe mental health in middle aged and younger adults and could possibly be applied to the older adults in future research. These semantic measures may provide ecological validity and aid the assessment of mental health.

Natural language analyzed with AI-based transformers predict traditional subjective well-being measures approaching the theoretical upper limits in accuracy.

We show that using a recent break-through in artificial intelligence -transformers-, psychological assessments from text-responses can approach theoretical upper limits in accuracy, converging with standard psychological rating scales. Text-responses use people's primary form of communication -natural language- and have been suggested as a more ecologically-valid response format than closed-ended rating scales that dominate social science. However, previous language analysis techniques left a gap between how accurately they converged with standard rating scales and how well ratings scales converge with themselves - a theoretical upper-limit in accuracy. Most recently, AI-based language analysis has gone through a transformation as nearly all of its applications, from Web search to personalized assistants (e.g., Alexa and Siri), have shown unprecedented improvement by using transformers. We evaluate transformers for estimating psychological well-being from questionnaire text- and descriptive word-responses, and find accuracies converging with rating scales that approach the theoretical upper limits (Pearson r = 0.85, p < 0.001, N = 608; in line with most metrics of rating scale reliability). These findings suggest an avenue for modernizing the ubiquitous questionnaire and ultimately opening doors to a greater understanding of the human condition.

Language and gender: Computerized text analyses predict gender ratios from organizational descriptions.

Previous research has shown that language in job adverts implicitly communicates gender stereotypes, which, in turn, influence employees' perceived fit with the job. In this way, language both reflects and maintains a gender segregated job market. The aim of this study was to test whether, and how, language in organizational descriptions reflects gender segregation in the organizations by the use of computational text analyses. We analyzed large Swedish companies' organizational descriptions from LinkedIn (N = 409), testing whether the language in the organizational descriptions is associated with the organizations' employee gender ratio, and how organizational descriptions for organizations with a majority of women and men employees differ. The statistical analyses showed that language in the organizational descriptions predicted the employee gender ratio in organizations well. Word clouds depicting words that differentiate between organizations with a majority of women and men employees showed that the language of organizations with a higher percentage of women employees was characterized by a local focus and emphasis on within-organizations relations, whereas the language of organizations with a higher percentage of men employees was characterized by an international focus and emphasis on sales and customer relations. These results imply that the language in organizational descriptions reflects gender segregation and stereotypes that women are associated with local and men with global workplaces. As language communicates subtle signals in regards to what potential candidate is most sought after in recruitment situations, differences in organizational descriptions can hinder underrepresented gender groups to apply to these jobs. As a consequence, such practices may contribute to gender segregation on the job market.

Voters' view of leaders during the Covid-19 crisis: Quantitative analysis of keyword descriptions provides strength and direction of evaluations.

Objectives: Previous research suggests that governments usually gain support during crises such as the Covid-19. However, these findings are based on rating scales that only allow us to measure the strength of this support. This article proposes a new measure of how voters evaluate Prime Ministers (PM) by asking for descriptive keywords that are analyzed by natural language processing. Methods: By collecting a representative sample of citizens' own key words describing their PM in 15 countries in Europe during the outbreak of Covid-19, and analyzing these by latent semantic analysis and a multiple OLS regression, we could quantify the strength and direction of voters' view. Results: The strength analysis supported previous studies that describing the PM with positive words was strongly associated with vote intention. Furthermore, a change in the direction of the attitudes from "good" to "honest" was found. A new finding was that the pandemic was associated with an increase in polarization. Conclusions: The keyword evaluation analysis provides opportunities of evaluating both strength and direction of voters' view of their PM, where we show new results related to increased polarization and shift in the direction of attitudes.

What you say and what I hear-Investigating differences in the perception of the severity of psychological and physical violence in intimate partner relationships.

The correct communication of the severity of violence is essential in the context of legal trials, custody cases, support of victims, etc., for providing fair treatment. A narrator that communicates their experiences of interpersonal violence may rate the seriousness of the incident differently than a rater reading the narrator's text, suggesting that there exist perceptual differences (PD) in severity ratings between the narrator and the rater. We propose that these perceptual differences may depend on whether the narrative is based on physical or psychological violence, and on gender differences. Physical violence may be evaluated as more serious by the receiver of the narrative than by the narrator (Calibration PD), whereas the seriousness of psychological violence may be difficult to convey, leading to a discrepancy in the seriousness ratings between the narrator and the rater (Accuracy PD). In addition, gender stereotypes may influence the seriousness rating (Gender PD), resulting in violence against women being perceived as more serious than the same violence against men. These perceptual differences were investigated in 3 phases using a new experimental procedure. In Phase 1, 113 narrators provided descriptions and seriousness ratings of self-experienced physical and psychological violence in relationships. In Phase 2, 340 independent raters rated the seriousness of 10 randomly selected narrations from Phase 1. In Phase 3, the genders in the narrations were changed to the opposite gender, and seriousness ratings were collected from 340 different raters. Our results confirmed the hypothesized perceptual differences. Violence to male victims was considerably more likely to be seen as severe when the raters were misled to believe the victim was a woman. We propose that these data provide practical guidelines for how to deal with misinformation in the communication of violence. The data also show that mean values and the confidence of such severity ratings need to be adjusted for several factors, such as whether it is self-experienced or communicated, the type of violence, and the gender of the victims and raters.

Freely Generated Word Responses Analyzed With Artificial Intelligence Predict Self-Reported Symptoms of Depression, Anxiety, and Worry.

BACKGROUND: Question-based computational language assessments (QCLA) of mental health, based on self-reported and freely generated word responses and analyzed with artificial intelligence, is a potential complement to rating scales for identifying mental health issues. This study aimed to examine to what extent this method captures items related to the primary and secondary symptoms associated with Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). We investigated whether the word responses that participants generated contained information of all, or some, of the criteria that define MDD and GAD using symptom-based rating scales that are commonly used in clinical research and practices. METHOD: Participants (N = 411) described their mental health with freely generated words and rating scales relating to depression and worry/anxiety. Word responses were quantified and analyzed using natural language processing and machine learning. RESULTS: The QCLA correlated significantly with the individual items connected to the DSM-5 diagnostic criteria of MDD (PHQ-9; Pearson's r = 0.30-0.60, p < 0.001) and GAD (GAD-7; Pearson's r = 0.41-0.52, p < 0.001; PSWQ-8; Spearman's r = 0.52-0.63, p < 0.001) for respective rating scales. Items measuring primary criteria (cognitive and emotional aspects) yielded higher predictability than secondary criteria (behavioral aspects). CONCLUSION: Together these results suggest that QCLA may be able to complement rating scales in measuring mental health in clinical settings. The approach carries the potential to personalize assessments and contributes to the ongoing discussion regarding the diagnostic heterogeneity of depression.

Reevaluating the Influence of Leaders Under Proportional Representation: Quantitative Analysis of Text in an Electoral Experiment.

We propose that leaders play a more important role in voters' party sympathy in proportional representation systems (PR) than previous research has suggested. Voters, from the 2018 Swedish General Election, were in an experiment asked to describe leaders and parties with three indicative keywords. Statistical models were conducted on these text data to predict their vote choice. The results show that despite that the voters vote for a party, the descriptions of leaders predicted vote choice to a similar extent as descriptions of parties. However, the order of the questions mattered, so that the first questions were more predictive than the second question. These analyses indicate that voters tend to conflate characteristics of leaders with their parties during election campaigns, and that leaders are a more important aspect of voting under PR than previous literature has suggested. Overall, this suggests that statistical analysis of words sheds new light of underlying sympathies related to voting.

Computational Language Assessments of Harmony in Life - Not Satisfaction With Life or Rating Scales - Correlate With Cooperative Behaviors.

Different types of well-being are likely to be associated with different kinds of behaviors. The first objective of this study was, from a subjective well-being perspective, to examine whether harmony in life and satisfaction with life are related differently to cooperative behaviors depending on individuals' social value orientation. The second objective was, from a methodological perspective, to examine whether language-based assessments called computational language assessments (CLA), which enable respondents to answer with words that are analyzed using natural language processing, demonstrate stronger correlations with cooperation than traditional rating scales. Participants reported their harmony in life, satisfaction with life, and social value orientation before taking part in an online cooperative task. The results show that the CLA of overall harmony in life correlated with cooperation (all participants: r = 0.18, p < 0.05, n = 181) and that this was particularly true for prosocial participants (r = 0.35, p < 0.001, n = 96), whereas rating scales were not correlated (p > 0.05). No significant correlations (measured by the CLA or traditional rating scales) were found between satisfaction with life and cooperation. In conclusion, our study reveals an important behavioral difference between different types of subjective well-being. To our knowledge, this is the first study supporting the validity of self-reported CLA over traditional rating scales in relation to actual behaviors.

Weighting power by preference eliminates gender differences.

Power can be applied in different domains (e.g., politics, work, romantic relationships, family etc.), however, we do not always reflect on which domains we have power in and how important power in these domains is. A dominant idea is that men have more power than women. This notion may be biased because the concept of power is associated with public life. We introduce the concept of preference-weighted power (PWP), a measure of power that includes different domains in life, weighted by the domains' subjective importance. Two studies investigated power from this perspective. In Study 1, participants generated words related to power, which were quantified/categorized by latent semantic analysis to develop a semantic measure of the power construct. In Study 2, we computed a PWP index by weighting the participants' self-rated power in different power domains with the importance of having power in that domain. Together the studies suggest that men have more perceived power in the public domain, however, this domain has a lower preference weighting than the private domain where women have more power than men. Finally, when preferences for power in different domains were considered, no gender differences were observed. These results emphasize gender difference in different domains and may change how we perceive men's and women's power in our society.

Validation of a general subjective well-being factor using Classical Test Theory.

BACKGROUND: Subjective Well-Being (SWB) is usually conceptualized in terms of an affective (i.e., judgements of biological emotional reactions and experiences) and a cognitive component (i.e., judgements of life satisfaction in relation to a psychological self-imposed ideal). Recently, researchers have suggested that judgements of harmony in life can replace or at least complement the cognitive component of SWB. Here, however, we go beyond that suggestion and propose that harmony in life should be seen as SWB's social component since it is the sense of balance between the individual and the world around her-a process that comprises acceptance, adaptation, and balance. By adding judgements of one's social interactions (i.e., harmony in life) to judgments of one's life satisfaction (psycho) and judgements of one's emotional reactions (bio), we propose a tentatively biopsychosocial model of SWB. As a first step, we used different factorial models in order to determine if both a general factor and specific sub-factors contribute to the biopsychosocial model of SWB. METHOD: A total of 527 participants responded to the Positive Affect Negative Affect Schedule (PANAS; 20 items), the Satisfaction with Life Scale (SWLS; five items), and the Harmony in life Scale (HILS; five items). We conducted exploratory and confirmatory factor analyses to validate the biopsychosocial model of subjective well-being and a general factor (SWBS). RESULTS: The 20 PANAS items reflected a mixture of general latent structure saturation and specific latent structure saturation, but contributed to their respective specific latent factor (PA: 48%; NA: 49%) more than to the general latent SWBS factor (positive affect: 25%; negative affect: 32%). The five SWLS items contributed to a larger degree to the general SWBS factor (72%) than to life satisfaction itself (22%), while the five HILS items contributed to even a larger degree to the general SWBS factor (98%) than to harmony in life (0%). The bifactor model was the best model compared with all other models we tested (χ2 = 1,660.78, df = 375, p < 0.001); Satorra Bentler χ2 = 1,265.80, df = 375, p < 0.001; CFI = 0.92; Tucker-Lewis Index = 0.91; RMSEA = 0.067. This model of a general SWBS factor explained about 64% of the total variance in the model, while specific SWBS components together explained 15% of the total variance. CONCLUSION: Our study suggests SWB as a general factor in a multidimensional biopsychosocial model. Indeed, as much as 64% of the variance of SWB was explained by this general factor. The SWB components, however, contributed to a different degree to each corresponding factor in the model. For instance, while the affective and cognitive components seem to be their own constructs and also part of the general SWB factor, the social component tested here contributed 0% to its own variance but 98% to the general factor.

Validation of Two Short Personality Inventories Using Self-Descriptions in Natural Language and Quantitative Semantics Test Theory.

BACKGROUND: If individual differences are relevant and prominent features of personality, then they are expected to be encoded in natural language, thus manifesting themselves in single words. Recently, the quantification of text data using advanced natural language processing techniques offers innovative opportunities to map people's own words and narratives to their responses to self-reports. Here, we demonstrate the usefulness of self-descriptions in natural language and what we tentatively call Quantitative Semantic Test Theory (QuSTT) to validate two short inventories that measure character traits. METHOD: In Study 1, participants (N 1 = 997) responded to the Short Character Inventory, which measures self-directedness, cooperativeness, and self-transcendence. In Study 2, participants (N 2 = 2373) responded to Short Dark Triad, which measures Machiavellianism, narcissism, and psychopathy. In both studies, respondents were asked to generate 10 self-descriptive words. We used the Latent Semantic Algorithm to quantify the meaning of each trait using the participants' self-descriptive words. We then used these semantic representations to predict the self-reported scores. In a second set of analyses, we used word-frequency analyses to map the self-descriptive words to each of the participants' trait scores (i.e., one-dimensional analysis) and character profiles (i.e., three-dimensional analysis). RESULTS: The semantic representation of each character trait was related to each corresponding self-reported score. However, participants' self-transcendence and Machiavellianism scores demonstrated similar relationships to all three semantic representations of the character traits in their respective personality model. The one-dimensional analyses showed that, for example, "loving" was indicative of both high cooperativeness and self-transcendence, while "compassionate," "kind," and "caring" was unique for individuals high in cooperativeness. The words "kind" and "caring" indicated low levels of Machiavellianism and psychopathy, whereas "shy" or "introvert" indicated low narcissism. We also found specific keywords that unify or that make the individuals in some profiles unique. CONCLUSION: Despite being short, both inventories capture individuals' identity as expected. Nevertheless, our method also points out some shortcomings and overlaps between traits measured with these inventories. We suggest that self-descriptive words can be quantified to validate measures of psychological constructs (e.g., prevalence in self-descriptions or QuSTT) and that this method may complement traditional methods for testing the validity of psychological measures.

Creative utterances about person-centered care among future health care professionals are related to reward dependence rather than to a creative personality profile.

BACKGROUND: Creativity can be defined as the creation of something that is novel, useful, and valuable for society (i.e., high-level creativity) and/or everyday life. In this context, people have implicit theories of creativity as being either non-malleable (i.e., a fixed creative mindset) or malleable (i.e., a growth creative mindset). Our aim was twofold: (1) to test an improved creative mindset priming paradigm (i.e., adding high-level/everyday creativity perspectives and using an organizational important task) by assessing if participants used different ways to answer to the prime and (2) to analyse the relationship between personality and creative utterances regarding an important topic in participants' future professions. METHOD: Students (N = 73) from different health care professions were randomly assigned to the non-malleable or malleable creative mindset priming paradigm (i.e., fixed vs. growth) and then asked to write about (a) their own creativity, (b) person-centered care in their professions (i.e., unusual use test), and to (c) self-rate their personality (Temperament and Character Inventory). We used natural language processing methods (i.e., Latent Semantic Algorithm) to analyse participants' responses in the different conditions and also responses in relation to self-reported personality. RESULTS: The fixed versus growth condition was predicted (r = .55, p < 0.0001), following Bonferroni correction for multiple comparisons by participants' descriptions about creativity. Although the condition was not predicted (r = .07, p < 0.2755) by participants' utterances about person-centered care, a t-test suggested that participants used words that were semantically different depending on the condition they were randomly assigned to (t(2371) = 5.82, p = .0000). For instance, participants in the growth condition used verbs more frequently, while those in the fixed condition used the personal pronoun I more often. Finally, only the temperament trait of reward dependence (r = .32, p < 0.01) predicted the person-centered care utterances. CONCLUSION: We argue that the paradigm successfully primed participants to write about creativity and person-centered care using narratives with different semantic content. However, individuals' ambition to be socially accepted, rather than creative personality traits, elicited the utterances about person-centered care. The creative mindset priming paradigm presented here along language processing methods might be useful for measuring creative potential at work. We suggest that if health care personnel's notions of the activities related to care are generated from their drive to be socially accepted and not from a truly creative profile, the activities might be self-serving and not person-centered.

Latent Semantic Analysis Discriminates Children with Developmental Language Disorder (DLD) from Children with Typical Language Development.

Computer based analyses offer a possibility for objective methods to assess semantic-linguistic quality of narratives at the text level. The aim of the present study is to investigate whether a semantic language impairment index (SELIMI) based on latent semantic analysis (LSA) can discriminate between children with developmental language disorder (DLD) and children with typical language development. Spoken narratives from 54 children with DLD and 54 age matched controls with typical language development were summarized in a semantic representation generated using LSA. A statistical model was trained to discriminate between children with DLD and children with typical language development, given the semantic vector representing each individual child's narrative. The results show that SELIMI could distinguish between children with DLD and children with typical language development significantly better than chance and thus has a potential to complement traditional analyses focussed on form or on the word level.

Validation of Subjective Well-Being Measures Using Item Response Theory.

Background: Subjective well-being refers to the extent to which a person believes or feels that her life is going well. It is considered as one of the best available proxies for a broader, more canonical form of well-being. For over 30 years, one important distinction in the conceptualization of subjective well-being is the contrast between more affective evaluations of biological emotional reactions and more cognitive evaluations of one's life in relation to a psychologically self-imposed ideal. More recently, researchers have suggested the addition of harmony in life, comprising behavioral evaluations of how one is doing in a social context. Since measures used to assess subjective well-being are self-reports, often validated only using Classical Test Theory, our aim was to focus on the psychometric properties of the measures using Item Response Theory. Method: A total of 1000 participants responded to the Positive Affect Negative Affect Schedule. At random, half of the participants answered to the Satisfaction with Life Scale or to the Harmony in life Scale. First, we evaluate and provide enough evidence of unidimensionality for each scale. Next, we conducted graded response models to validate the psychometric properties of the subjective well-being scales. Results: All scales showed varied frequency item distribution, high discrimination values (Alphas), and had different difficulty parameters (Beta) on each response options. For example, we identified items that respondents found difficult to endorse at the highest/lowest point of the scales (e.g., "Proud" for positive affect; item 5, "If I could live my life over, I would change almost nothing," for life satisfaction; and item 3, "I am in harmony," for harmony in life). In addition, all scales could cover a good portion of the range of subjective well-being (Theta): -2.50 to 2.30 for positive affect, -1.00 to 3.50 for negative affect, -2.40 to 2.50 for life satisfaction, and -2.40 to 2.50 for harmony in life. Importantly, for all scales, there were weak reliability for respondents with extreme latent scores of subjective well-being. Conclusion: The affective component, especially low levels of negative affect, were less accurately measured, while both the cognitive and social component were covered to an equal degree. There was less reliability for respondents with extreme latent scores of subjective well-being. Thus, to improve reliability at the level of the scale, at the item level and at the level of the response scale for each item, we point out specific items that need to be modified or added. Moreover, the data presented here can be used as normative data for each of the subjective well-being constructs.