The current study used the network meta-analysis to investigate the effects of early reading interventions with different combinations of phonological processing, decoding, fluency, vocabulary, and reading comprehension among students with reading difficulties (RD) in or before Grade 3. We aimed to test two reading intervention hypotheses: The ingredient-interaction model (various skill combinations produce various effects) and the active-ingredient model (there is the most effective skill or skill combination). Based on 100 studies and over 24,000 participants, results showed across word reading, fluency, and reading comprehension outcomes, 1) multi-skill interventions were more effective than single-skill interventions, 2) the word-focused combination (phonics, fluency, and vocabulary) and the comprehensive combination (addressing all skills) were consistently effective, and 3) intervention effects generally held across different situations (moderators). These findings have two key implications for the intervention and theory of RD in young students. First, interventions should move beyond single-skill instruction to a multi-skill approach that simultaneously teaches foundational skills like phonics, fluency, and vocabulary, alongside reading comprehension. Second, unlike strategy-oriented interventions, where a large number of components can lead to cognitive overload, multiple-component knowledge-based reading interventions create synergistic effects.

Based on 52 studies with samples mostly from English-speaking countries, the current study used Bayesian network meta-analysis to investigate the intervention effectiveness of different reading comprehension strategy combinations on reading comprehension among students with reading difficulties in 3rd through 12th grade. We focused on commonly researched strategies: main idea, inference, text structure, retell, prediction, self-monitoring, and graphic organizers. Results showed (1) instruction of more strategies did not necessarily have stronger effects on reading comprehension; (2) there was no single reading comprehension strategy that produced the strongest effect; (3) main idea, text structure, and retell, taught together as the primary strategies, seemed the most effective; and (4) the effects of strategies only held when background knowledge instruction was included. These findings suggest strategy instruction among students with reading difficulties follows an ingredient-interaction model-that is, no single strategy works the best. It is not "the more we teach, the better outcomes to expect." Instead, different strategy combinations may produce different effects on reading comprehension. Main idea, text structure, and retell together may best optimize the cognitive load during reading comprehension. Background knowledge instruction should be combined with strategy instruction to facilitate knowledge retrieval as to reduce the cognitive load of using strategies.

Creativity is one of the essential skills for the 21st century. Although current advancement in the research converges on its dual features (i.e., originality and appropriateness) and the effect of instructional focus, little is known about how culture and work modality (i.e., individual or collaborative) play a role in the effect. This study examined the moderating role of culture on the impact of instructional focus on creative performance at both individual and team levels. We recruited 144 participants (72 from the United States and 72 from China) to form 48 working teams of trios, half of which were instructed to focus on originality while the other half to focus on appropriateness. Our results revealed a main effect of instructional focus on creativity only at the team level but not at the individual level. More importantly, we found that the individualistic culture yielded the best creative performance with individual work modality when instructed to focus on originality, whereas the collectivistic culture yielded the best creative performance with team work modality when instructed to focus on appropriateness. Theoretical and practical implications are discussed.

Introduction Getting enough sleep is one of the essential lifestyle factors influencing health and well-being. However, there are considerable differences between countries in how much people sleep on average. The present study investigated how socio-economic factors, population variables, and cultural value dimensions are related to sleep duration in a sample of 52 countries. Method The study design was ecological, i.e., the aggregate values for each country were obtained, and their correlations to national average sleep duration were analysed. The sleep duration estimates were based on Sleep Cycle Application (Sleep Cycle AB, Gothenburg, Sweden) data. The socio-economic variables included the economic health of a country (GDP per capita), how well a country is governed (governance quality measured with WGI), and the economic inequality (the gap between rich and poor measured with the Gini index) within a nation. The population variables included the urbanisation rate (proportion of people living in urbanised areas), life expectancy at birth, mean years of schooling among the population aged 25 years and older, median age of the population, and the prevalence of obesity (% of adults with BMI ≥ 30). The cultural value dimensions were measured with Hofstede's cultural value dimensions (power distance, individualism, masculinity, uncertainty avoidance, long-term orientation, and indulgence). The data were analysed by using zero-order correlations, partial correlations, and canonical correlation analyses. Results Results showed a relatively strong intercorrelation between the national average of sleep duration and national happiness, i.e., subjective well-being. Among the socio-economic variables, WGI had the strongest relationship to sleep, whereas among population variables, schooling and obesity had the strongest correlations with sleep. Zero-order correlations between sleep and power distance and individualism were statistically significant, whereas in the partial correlations, individualism and masculinity appeared as important factors. Canonical correlation analysis showed strong correlations between the well-being variables (sleep and happiness) and the socio-economic variables, well-being variables and population variables, and cultural values and well-being variables. Discussion The present study is an opening for a new line of research in which sleep is seen as an essential part of societal life and collective well-being.

AI, or artificial intelligence, is a technology of creating algorithms and computer systems that mimic human cognitive abilities to perform tasks. Many industries are undergoing revolutions due to the advances and applications of AI technology. The current study explored a burgeoning field—Psychometric AI, which integrates AI methodologies and psychological measurement to not only improve measurement accuracy, efficiency, and effectiveness but also help reduce human bias and increase objectivity in measurement. Specifically, by leveraging unobtrusive eye-tracking sensing techniques and performing 1470 runs with seven different machine-learning classifiers, the current study systematically examined the efficacy of various (ML) models in measuring different facets and measures of the emotional intelligence (EI) construct. Our results revealed an average accuracy ranging from 50–90%, largely depending on the percentile to dichotomize the EI scores. More importantly, our study found that AI algorithms were powerful enough to achieve high accuracy with as little as 5 or 2 s of eye-tracking data. The research also explored the effects of EI facets/measures on ML measurement accuracy and identified many eye-tracking features most predictive of EI scores. Both theoretical and practical implications are discussed.

During the COVID-19 pandemic, Swedish universities had to shift from face-to-face teaching to internet-based distant learning (DL). DL differs from classroom teaching and may have a negative impact on students’ emotions while studying. Students’ experiences related to DL may reflect their personality, resilience, that is, Sense of Coherence (SOC), and preference for the education method. In this study, students’ emotions related to DL and the relationship between personality factors, SOC and positive and negative emotions related to DL were studied. One hundred ninety-seven university students filled in an online survey about positive and negative emotions related to DL, personality factors (Big-5), SOC, frequency of applying COVID-19 distancing measures, and frequency and freedom to choose DL. The survey was completed in March-April 2021 (Sample 1), when all lectures were delivered from a distance and in November 2021 (Sample 2), when lectures were on the campus. There were no differences between the frequency of negative and positive emotions. Agreeableness (Sample 1) and Neuroticism (Sample 2) correlated positively with negative emotions. SOC correlated negatively with negative emotions in Sample 2. In regression analyses of the combined data, Agreeableness was positively and Openness to Experience was negatively related to negative emotions. Agreeableness was negatively and Openness to Experience positively related to positive emotions related to forced DL. DL—even forced one—has both positive and negative effects on students’ emotions. These effects depend on students’ personality characteristics to some degree. SOC might reduce the negative effects of forced distance learning.

Within-person research has become increasingly popular over recent years in the field of organizational studies for its unique theoretical and methodological advantages for studying dynamic intrapersonal processes (e.g., Dalal et al., Journal of Management 40:1396–1436, 2014; McCormick et al., Journal of Management 46:321–350, 2020). Despite the advancements, there remain serious challenges for many organizational researchers to fully appreciate and appropriately implement within-person research—more specifically, to correctly conceptualize and compute the within-person measurement reliability, as well as navigate key within-person research design factors (e.g., number of measurement occasions, T; number of participants, N; and scale length, I) to optimize within-person reliability. By conducting a comprehensive Monte Carlo simulation with 3240 data conditions, we offer a practical guideline table showing the expected within-person reliability as a function of key design factors. In addition, we provide three easy-to-use, free R Shiny web applications for within-person researchers to conveniently (a) compute expected within-person reliability based on their customized research design, (b) compute observed validity based on the expected reliability and hypothesized within-person validity, and (c) compute observed within-person (as well as between-person) reliability from collected within-person research datasets. We hope these much-needed evidence-based guidelines and practical tools will help enhance within-person research in organizational studies.

Based on 378 studies, 541 independent samples, and over 34,000 participants, the current meta-analysis aimed to explore the associations between cognition and reading difficulties (RD). Results showed that compared with age-matched typically developing (TD) peers, RD showed deficits across processing speed, short-term memory, attention, working memory, inhibition, switching, visuospatial skills, and updating (g = -0.72 to -0.48), with more individual differences in inhibition, processing speed, switching, attention, visuospatial skills, and short-term memory (The natural logarithm of ratio of standard deviations [SDR] = .03-.17). The cognitive deficits among those with RD were more severe with age, with lower reading and intelligence (IQ) scores, and with verbal cognitive tasks. Individual differences in cognitive skills among those with RD were larger with age, with comprehensive/nonverbal IQ identification, and with reading comprehension identification. Comorbidity did not affect the cognitive profile of RD strongly. Meta-analytic structural equation modeling showed phonological processing and language comprehension explained over 70% of the variance between RD and TD across cognitive skills, yet executive function and visuospatial skills contributed uniquely to RD. These findings highlight a domain-specific cognitive path to RD. That is, cognitive deficits may lead to insufficient language development, causing RD indirectly. However, the association between cognitive deficits and RD is not unidirectional or static. Across development, insufficient accumulation of language skills among those with RD also affects cognitive development, especially in the verbal domain. Without high-quality and sustained instruction, such reciprocal associations between cognitive deficits and RD, forming a vicious circle, may be one major reason for persistent reading struggles among individuals with RD. Public Significance Statement Reading difficulties (RD) are related to a comprehensive set of cognitive deficits, yet there are large individual differences among individuals with RD on most cognitive skills. Executive function and visuospatial skills are uniquely related to RD even after controlling for language skills. These findings suggest that a comprehensive cognitive evaluation may provide more accurate information for individualized instruction for individuals with RD: Either addresses and compensates for cognitive deficits or utilizes cognitive strengths within the framework of explicit instruction on phonological processing, language comprehension, and reading.

In response to increased public consciousness around racism after George Floyd’s killing, many organizations released public statements to condemn racism and affirm their stance on diversity, equity, and inclusion (DEI). However, little is known about the specific thematic contents of various diversity statements and their implications on important organizational outcomes. Taking both inductive and deductive approaches, the current research conducted two studies to advance our understanding in this area. We first employed novel unsupervised machine learning techniques and comprehensively analyzed the texts of diversity statements publicly released by Fortune 1000 companies in early June 2020. Our structural topic modeling uncovered six underlying latent semantic topics: 1) general DEI terms, 2) supporting Black community, 3) acknowledging Black community, 4) committing to diversifying the workforce, 5) miscellaneous words, and 6) titles and companies. Furthermore, drawing from the identity-blind and consciousness diversity ideologies framework and leveraging tens of millions of employee rating data points on Glassdoor.com, we further found evidence that companies that released (vs. did not release) diversity statements and companies whose diversity statements emphasized identity-conscious (vs. identity-blind) topics were more positively evaluated by their employees online. Our findings shed light on important theoretical implications for current diversity research and offer practical recommendations for organizational scientists and practitioners in diversity management.

This meta-analysis aimed to systematically investigate the cognitive and linguistic correlates of both word decoding and reading comprehension among children with Autism Spectrum Disorder (ASD) across orthographies. Based on data from 26 studies of 1.92- to 18.92-year-old children with ASD, we found that (1) intelligence, theory of mind, and executive function exhibited modest associations (rs = .41, .46, and .39, respectively) with reading; (2) phonological awareness, semantic skills, and syntax skills also showed modest associations (rs = .53, .50, and .53, respectively) with reading; (3) cognitive and linguistic skills showed comparable contributions to word decoding and reading comprehension, when both skills were analyzed in the same model with meta-analytic structural equation modeling; (4) age, language type, publication type, sample type, and reading measures did not moderate the relations. Taken together, these findings suggested that although children with ASD exhibit language weaknesses, their linguistic skills still made important contributions to reading development beyond cognitive skills. Further, cognitive skills may compensate for language deficits in children with ASD’s reading development. Implications for reading interventions in children with ASD were also discussed.

When one sibling has autism spectrum disorder, the sibling relationship is often characterized by poorer quality with fewer interactions. Because sibling relationships provide a vital social framework for development, they have the capacity to be a risk or protective factor, depending on the quality of the relationship. One way to improve the quality of the sibling relationship is through typically developing sibling participation in a support group. In this study, researchers randomly assigned typically developing siblings to a 10-week support group or attention-only control group. Typically developing siblings in the support group showed significant improvements in the quality of their sibling relationship and interactions with their sibling with autism spectrum disorder compared to the control group. Autism spectrum disorder severity and externalizing behavior moderated the effects of the support group on positive affect. Findings suggest the positive impact of a support group on sibling relationships, a relationship that has the potential to be protective. Lay abstract The sibling relationship can be negatively impacted when one child has autism spectrum disorder. One way to improve the quality of that relationship is through typically developing sibling participation in a support group in which they learn about autism spectrum disorder and coping skills, develop a peer network, and discuss their feelings. Compared to participating in a similar group without a focus on autism spectrum disorder, siblings in the support group showed improvements in the quality of the sibling relationship. Findings suggest that sibling support groups can be a valuable resource to improve sibling relationship quality when one sibling has autism spectrum disorder.

Although text data are ubiquitous in organizations, the advancement of text analysis methods has created an unfortunate bottleneck for many organizational researchers. This paper introduced the Structure Text Model (STM; Roberts et al., 2014), a cutting-edge text modeling method that uses an unsupervised machine learning model to statistically derive latent semantic topics underlying a collection of text documents. More importantly, the STM method has the advantage of modeling document-level variables (i.e., metadata) as covariates, which is critical for organizational research. We also demonstrated the application of STM in diversity research: comprehensively analyzing a large number of diversity statements publicly released by Fortune 1000 companies. Our structural topic modeling uncovered six underlying latent semantic topics: 1) general DEI terms, 2) supporting Black community, 3) acknowledging Black community, 4) committing to diversifying workforce, 5) miscellaneous words, and 6) titles and companies. We further explored and found that the prevalence of these topics varied as a function of company characteristics, including industry sector, CEO race, corporate political orientation, etc. Our paper not only demonstrates the promising application of Structural Text Models in organizational research, but also provides important theoretical implications for current diversity research through the meaningful findings.

Following the deaths of many Black Americans in spring 2020, public consciousness rose around the societal mega-threat of racism. In response, many organizations released public statements to condemn racism and affirm their stance on diversity, equity, and inclusion (DEI). However, little is known about the specific thematic contents covered in such diversity statements and their implications on important organizational outcomes. Taking both inductive and deductive approaches, we conducted two studies to advance our understanding in this area. Study 1 employed structural topic modeling (STM)—an advanced unsupervised machine-learning text-mining technique—and comprehensively analyzed the latent semantic topics underlying the diversity statements publicly released by Fortune 1000 companies in late May and early June 2020. The results uncovered six underlying latent semantic topics: (1) general DEI terms, (2) supporting Black community, (3) acknowledging Black community, (4) committing to diversifying the workforce, (5) miscellaneous words, and (6) titles and companies. Furthermore, drawing from the identity-blindness and identity-consciousness theoretical frameworks and leveraging millions of data points of employees’ DEI ratings retrieved from Glassdoor.com, Study 2 further tested and supported hypotheses that companies were more positively rated by their employees on organizational diversity and inclusion if they (1) released (vs. did not release) diversity statements and (2) emphasized identity-conscious (vs. identity-blind) topics in their diversity statements. Our findings shed light on important theoretical implications for the current research and offer practical recommendations for organizational scientists and practitioners in diversity management.

Structural equation modeling (SEM) and meta-analysis (MA) are both powerful techniques employed frequently throughout the social and behavioral sciences, including applied linguistics. Although meta-analytic data are typically analyzed by calculating weighted means or correlation coefficients, other statistical models such as SEM can also be applied (Schoemann, 2016). SEM models gauge conceptualized models vis-à-vis empirical data across a given domain. Despite a considerable expansion of the analytical repertoire in applied linguistics in recent years (Gass, Loewen, & Plonsky, 2021), this particular technique has yet to be formally introduced or applied. The present methods tutorial, therefore, aims to introduce MASEM to applied linguistics. In doing so, we provide a conceptual rationale for MASEM, an outline of major stages involved, and a worked example of how MASEM might be utilized in the field, along with the data and code necessary for re-running all analyses.

The study investigates whether learners' demographics (e.g., age, education, and intelligence-IQ), language learning experience, and cognitive control predict Chinese (L2) reading comprehension in young adults. Thirty-four international students who studied mandarin Chinese in mainland China (10 females, 24 males) from Bangladesh, Burundi, Congo, Madagascar, Nigeria, Rwanda, South Africa, and Zimbabwe were tested on a series of measures including demographic questionnaires, IQ test, two cognitive control tasks [Flanker Task measuring inhibition and Wisconsin Card Sorting Test (WCST) measuring mental set shifting], and a Chinese reading comprehension test (HSK level 4). The results of correlation analyses showed that education, L2 learning history, L2 proficiency, and previous category errors of the WCST were significantly correlated with Chinese reading comprehension. Further multiple regression analyses indicated that Chinese learning history, IQ, and previous category errors of the WCST significantly predicted Chinese reading comprehension. These findings reveal that aside from IQ and the time spent on L2 learning, the component mental set shifting of cognitive control also predicts reading outcomes, which suggests that cognitive control has a place in reading comprehension models over and above traditional predictors of language learning experience.