Bayesian Structural Equation Modeling Research Articles

Event-level positive and negative reinforcement risk factors for alcohol use: Moderation by individual-level alcohol consequences and post-traumatic stress disorder symptom severity.

The multistage model of drug addiction posits that risk processes contributing to alcohol use change as individuals develop alcohol use disorders. However, few studies have tested this theory outside of the lab or at the event level. We assessed whether event-level associations between positive reinforcement (e.g., positive affect, sociability expectancies) and negative reinforcement risk factors (e.g., negative affect, tension reduction expectancies) and same-/next-day alcohol consumption varied as a function of an individual's level of alcohol consequences. Given elevated alcohol use consequences among individuals with post-traumatic stress disorder (PTSD) and disruptions in reward processing that affect this population, we also tested whether these processes differed based on the presence and severity of PTSD. We used data from a 30-day ecological momentary assessment study with 174 undergraduate women who regularly engaged in heavy episodic drinking. A majority (78%) of the sample had experienced sexual assault and 44% had current PTSD. Analyses used Bayesian multilevel structural equation modeling with diffuse (non-informative) priors. We used markov chain monte carlo (MCMC) algorithms to generate a series of 10,000 random draws from the multivariate posterior distribution of our sample for each model. Results partially supported the multistage model. Event-level negative reinforcement risk factors only predicted more alcohol consumption among individuals who experienced more alcohol consequences. Findings for positive reinforcement risk factors were partially consistent with hypotheses. Overall, findings appear to operate similarly across PTSD symptom severity. Results suggest that interventions for heavy episodic drinking could benefit from attending to an individual's level of alcohol consequences. For example, preventive interventions for individuals who tend to experience few consequences may benefit more from addressing positive reinforcement risk factors, while treatment interventions for those who experience more consequences may benefit from attending to both positive and negative reinforcement.

Impact of Team Teaching on Student Teachers’ Professional Identity: A Bayesian Approach

Workplace learning in teacher education is essential for creating and recreating the professional identity of student teachers. Innovative interventions, such as team teaching between student teachers and mentors at the workplace, are assumed to facilitate learning to teach. This experimental study provides valuable insight into the impact of team teaching on student teachers’ professional identity by implementing distinct student teaching formats: team teaching (A1intervention), team teaching with support (A2intervention), and traditional teaching (Control intervention). In this study, professional identity is understood as a multidimensional concept that consists of six components: student teachers’ learning and regulation activities, reflective thinking, teacher efficacy, beliefs about learning and teaching, motivation, and collaborative activities. A total of 464 student teachers from a Flemish College of Education were randomly assigned to one of the three student teaching formats. The overall findings of Bayesian structural equation modeling reveal significant impacts of team teaching with support compared to both team teaching and traditional teaching as well as a significant impact of team teaching over traditional teaching on three crucial components of student teachers’ professional identity, i.e., their learning and regulation activities, reflective thinking, and motivation.

Supplemental Material for Examining the Complex Relations Between Childhood Adversity, Mindfulness, Attachment, and Various Personality Outcomes: A Bayesian Structural Equation Modeling Approach

Supplemental Material for Examining the Complex Relations Between Childhood Adversity, Mindfulness, Attachment, and Various Personality Outcomes: A Bayesian Structural Equation Modeling Approach

The Sensitivity of Bayesian Fit Indices to Structural Misspecification in Structural Equation Modeling

This study examined the false positive (FP) rates and sensitivity of Bayesian fit indices to structural misspecification in Bayesian structural equation modeling. The impact of measurement quality, sample size, model size, the magnitude of misspecified path effect, and the choice or prior on the performance of the fit indices was also investigated. The Bayesian fit indices examined in this study included PPP, DIC, BRMSEA, BCFI, BTLI. The results from the simulation study showed that BRMSEA, BCFI, and BTLI failed to detect structural misspecification. The performance of DIC depended majorly on measurement quality, and the sensitivity of PPP depended on sample size, measurement quality, the magnitude of the omitted path effect, and the choice of prior. Informative prior with too narrow precisions resulted in higher FP rates. Empirical implications for applied researchers and future research directions were discussed.

The Association between Mediated Deprivation and Ovarian Cancer Survival among African American Women.

Deprivation indices are often used to adjust for socio-economic disparities in health studies. Their role has been partially evaluated for certain population-level cancer outcomes, but examination of their role in ovarian cancer is limited. In this study, we evaluated a range of well-recognized deprivation indices in relation to cancer survival in a cohort of self-identified Black women diagnosed with ovarian cancer. This study aimed to determine if clinical or diagnostic characteristics lie on a mediating pathway between socioeconomic status (SES) and deprivation and ovarian cancer survival in a minority population that experiences worse survival from ovarian cancer. We used mediation analysis to look at the direct and indirect causal effects of deprivation indices with main mediators of the SEER stage at diagnosis and residual disease. The analysis employed Bayesian structural equation models with variable selection. We applied a joint Bayesian structural model for the mediator, including a Weibull mixed model for the vital outcome with deprivation as exposure. We selected modifiers via a Monte Carlo model selection procedure. The results suggest that high SES-related indices, such as Yost, Kolak urbanicity (URB), mobility (MOB) and SES dimensions, and concentrated disadvantage index (CDI), all have a significant impact on improved survival. In contrast, area deprivation index (ADI)/Singh, and area level poverty (POV) did not have a major impact. In some cases, the indirect effects have very wide credible intervals, so the total effect is not well estimated despite the estimation of the direct effect. First, it is clear that commonly used indices such as Yost, or CDI both significantly impact the survival experience of Black women diagnosed with epithelial ovarian cancer. In addition, the Kolak dimension indices (URB, MOB, mixed immigrant: MICA and SES) also demonstrate a significant association, depending on the mediator. Mediation effects differ according to the mediator chosen.

Moderating Effect of eHealth Literacy on the Associations of Coronaphobia With Loneliness, Irritability, Depression, and Stigma in Chinese Young Adults: Bayesian Structural Equation Model Study.

The COVID-19 pandemic has led to an increase in known risk factors for mental health problems. Although medical information available through the internet and smartphones has greatly expanded, people's ability to seek, eschew, and use reliable web-based medical information and services to promote their mental health remains unknown. This study aims to explore the associations between coronaphobia and 4 frequently reported mental health problems, loneliness, irritability, depression, and stigma, during the COVID-19 pandemic and to assess the moderating effects of eHealth literacy (eHL) on the adjustment of these relationships in Chinese young adults. The data used in this study were collected from a web-based survey of the general Chinese population, aged between 18 and 30 years, conducted in China between December 2022 and January 2023. A nonprobability snowball sampling method was used for data collection. A Bayesian structural equation model (BSEM) using parameter expansion was used to estimate the moderating effect of eHL on the relationship between coronaphobia and psychological problems. The posterior mean and 95% highest density intervals (HDIs) were estimated. A total of 4119 participants completed the questionnaire and provided valid responses. Among them, 64.4% (n=2653) were female and 58.7% (n=2417) were rural residents. All measures showed statistically significant but minor-to-moderate associations (correlation coefficients ranged from -0.04 to 0.65). Significant heterogeneity was observed between rural and urban residents at the eHL level, and coronaphobia was observed. The BSEM results demonstrated that eHL was a significant moderator in reducing the negative effects of coronaphobia on loneliness (posterior mean -0.0016, 95% HDI -0.0022 to -0.0011), depression (posterior mean -0.006, 95% HDI -0.0079 to -0.004), stigma (posterior mean -0.0052, 95% HDI -0.0068 to -0.0036), and irritability (posterior mean -0.0037, 95% HDI -0.0052 to -0.0022). The moderating effects of eHL varied across the rural and urban subsamples. Using BSEM, this study demonstrated that improving eHL can significantly mitigate the negative effects of coronaphobia on 4 COVID-19-related mental health problems in Chinese young adults. Future eHL initiatives should target rural communities to ensure equal access to information and resources that can help protect their mental health during the pandemic.

Predictors of the Use of a Mental Health-Focused eHealth System in Patients With Breast and Prostate Cancer: Bayesian Structural Equation Modeling Analysis of a Prospective Study.

eHealth systems have been increasingly used to manage depressive symptoms in patients with somatic illnesses. However, understanding the factors that drive their use, particularly among patients with breast and prostate cancer, remains a critical area of research. This study aimed to determine the factors influencing use of the NEVERMIND eHealth system among patients with breast and prostate cancer over 12 weeks, with a focus on the Technology Acceptance Model. Data from the NEVERMIND trial, which included 129 patients with breast and prostate cancer, were retrieved. At baseline, participants completed questionnaires detailing demographic data and measuring depressive and stress symptoms using the Beck Depression Inventory-II and the Depression, Anxiety, and Stress Scale-21, respectively. Over a 12-week period, patients engaged with the NEVERMIND system, with follow-up questionnaires administered at 4 weeks and after 12 weeks assessing the system's perceived ease of use and usefulness. Use log data were collected at the 2- and 12-week marks. The relationships among sex, education, baseline depressive and stress symptoms, perceived ease of use, perceived usefulness (PU), and system use at various stages were examined using Bayesian structural equation modeling in a path analysis, a technique that differs from traditional frequentist methods. The path analysis was conducted among 100 patients with breast and prostate cancer, with 66% (n=66) being female and 81% (n=81) having a college education. Patients reported good mental health scores, with low levels of depression and stress at baseline. System use was approximately 6 days in the initial 2 weeks and 45 days over the 12-week study period. The results revealed that PU was the strongest predictor of system use at 12 weeks (βuse at 12 weeks is predicted by PU at 12 weeks=.384), whereas system use at 2 weeks moderately predicted system use at 12 weeks (βuse at 12 weeks is predicted by use at 2 weeks=.239). Notably, there were uncertain associations between baseline variables (education, sex, and mental health symptoms) and system use at 2 weeks, indicating a need for better predictors for early system use. This study underscores the importance of PU and early engagement in patient engagement with eHealth systems such as NEVERMIND. This suggests that, in general eHealth implementations, caregivers should educate patients about the benefits and functionalities of such systems, thus enhancing their understanding of potential health impacts. Concentrating resources on promoting early engagement is also essential given its influence on sustained use. Further research is necessary to clarify the remaining uncertainties, enabling us to refine our strategies and maximize the benefits of eHealth systems in health care settings.

The Comparison of Classical and Bayesian Structural Equation Models Through Ordered Categorical Data: A Case Study of Banking Service Quality

This study aims to compare classical Structural Equation Modeling (SEM) and Bayesian Structural Equation Modeling (BSEM) in terms of ordered categorical data. In order to show the relationship between service dimensions and banks’ customers’ satisfactions, a data were analyzed with classical SEM and BSEM parameter estimation methods. In the Banking Service Quality Scale (SERVQUAL), which consists of sequential categorical data, classical SEM and BSEM were compared to evaluate customer satisfaction. In classical SEM, parameter estimations were made according to the Maximum Likelihood (ML) estimation method. In most of the studies using SERVQUAL in the literature, the results found in previous studies could not be used as prior informative because the service dimensions consisted of different number of factors. For this reason, considering that the results could yield similar results with the ML estimation method due to the high sample size, the use of conjugate prior was preferred instead of the non-informative prior due to the ordinal categorical nature of the data in the BSEM analysis. Since the questionnaire used in the study had a Likert type scale structure, the threshold values were calculated for ordered categorical data and used as prior informative. Thus, by using the threshold values obtained from the data set, a faster convergence of the parameters was achieved. As a result, service dimensions affecting satisfaction according to the ML parameter estimation method were found, Assurance, Physical Appearance, and Accessibility. In addition to these, Reliability as a service dimension was found to be also statistically significant in BSEM.

Bayesian modelling of effects of prenatal alcohol exposure on child cognition based on data from multiple cohorts

SummaryHigh levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, but the exact nature of the dose‐response relationship is less well understood. To investigate this relationship, data were assembled from six longitudinal birth cohort studies examining the effects of PAE on cognitive outcomes from early school age through adolescence. Structural equation models (SEMs) are a natural approach to consider, because of the way they conceptualise multiple observed outcomes as relating to an underlying latent variable of interest, which can then be modelled as a function of exposure and other predictors of interest. However, conventional SEMs could not be fitted in this context because slightly different outcome measures were used in the six studies. In this paper we propose a multi‐group Bayesian SEM that maps the unobserved cognition variable to a broad range of observed outcomes. The relation between these variables and PAE is then examined while controlling for potential confounders via propensity score adjustment. By examining different possible dose‐response functions, the proposed framework is used to investigate whether there is a threshold PAE level that results in minimal cognitive deficit.

Estimation of a Structural Equation Modeling of Quality of Life Mediated by Difficulty in Daily Life in Survivors of Breast Cancer.

The purpose of this study was to clarify the structural relationship of quality of life (QOL) in survivors of breast cancer, including difficulty in daily life and negative experiences in daily activities, as health-related indicators. Participants were survivors of breast cancer for more than 2 years after primary breast cancer surgery and belonged to self-help groups. The assessment used FACT-B (QOL), HADS (anxiety and depression), SOC (sense of coherence), WHODAS 2.0 (difficulties in daily life), and CAOD (negative experiences in daily activities). Bayesian structural equation modeling (BSEM) was performed to analyze the hypothesized model. If the causal model was significant, multiplication of the path coefficient from emotional distress (anxiety and depression) to QOL, and from SOC to emotional distress, was considered a direct effect on QOL, and from SOC to difficulty in daily life, from difficulty in daily life to negative experiences in daily activities, and from negative experiences in daily activities to anxiety and depression were considered indirect effects on QOL. The participants comprised 73 survivors of breast cancer. The goodness of fit of the model in the BSEM was satisfactory. The direct effect was 0.274, and the indirect effect was 0.164. An additional finding of this study is that coping with difficulty in daily life and negative experiences in daily activities related to QOL may improve QOL.

Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits

BackgroundAttention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control – the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering – adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies. MethodsThis cross-sectional and correlational study recruited 650 adults (330 males) aged 18–69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online. ResultsLatent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor. ConclusionsAttentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.

Analysis of Ethnic Elements in Xinjiang Music Works Based on PPM Model under the Perspective of “One Belt, One Road

Abstract Under the background of digitalization of big data and information, the national “One Belt and One Road” initiative has produced a new huge effect on the ancient Silk Road between Xinjiang and Eurasia, how to revitalize the traditional music culture of ethnic minorities and how to make the traditional music culture resources of ethnic minorities be effectively protected and developed in the process of protection and inheritance of intangible cultural heritage in the world? Based on the analysis of ethnic elements in Xinjiang music works based on the PPM model under the perspective of “One Belt, One Road”, this paper first introduces the concept of PPM, the Bayesian structure algorithm, constructs the PPM model, and analyzes the data using by the structural equation modeling method, and explores the evaluation of PPM model under different sample sizes and different indirect The performance of four methods, Bayesian structural equation modeling method, structural equation modeling method, classical regression method and Bayesian regression method, was evaluated for the analysis of ethnic elements in Xinjiang music works under different sample sizes and different indirect effect truth values. The results show that the standard errors under the four models become larger as the regression coefficient b between Y and X increases, but in the case of (a=0, b=0.59), the standard error of the structural equation modeling method for the analysis of ethnic elements in Xinjiang music works is 0.048, which is the smallest compared with the standard errors of other algorithms. The structural equation algorithm of the PPM model is very applicable when dealing with practical problems. It can not only be used in theoretical research but also has a great influence on realistic guidance, which has an important theoretical significance and practical value for the survival and development of the traditional music culture of ethnic minorities in China.

Fluid hunter motivation in Central Africa: Effects on behaviour, bushmeat and income

Abstract Individual motivation for the rural use of common‐pool resources (CPRs) can be fluid, with the line between subsistence and commercial often unclear and in flux. Implications of fluid motivation are understudied yet important for social–ecological systems (SESs), such as bushmeat hunting throughout Central Africa that is essential to local protein/nutrition, income and culture. Making locally informative predictions of multiple SESs nested within a landscape‐scale SES has been historically difficult, but community‐driven participatory approaches provide new kinds and quantities of data, opening previously inaccessible doors for research and governance. We apply hierarchical Bayesian structural equation modelling to a novel dataset of 910 hunts from 111 gun and trap hunters across nine villages in Gabon, generated in a participatory process whereby hunters conducted GPS self‐follows in conjunction with paraecologist surveys of their motivation, behaviour and offtake. We (i) establish the human behaviour driving gun‐hunting and trapping success and predict its effect on offtake across villages and (ii) link fluid motivation of gun hunters to their behaviour, number of animals hunted, biomass yielded and income earned. Gun hunts across villages yielded more animals during the night than the day, and when hunters brought high amounts of ammunition and walked far distances from villages. Gun hunts were less successful when coupled with trapping while per‐hunt success of trapping itself was generally low and difficult to predict. Fluid gun hunters hunted fewer animals when motivated strictly by subsistence, despite no reduction in ammunition brought or distance walked, while offtake from strictly commercial versus mixed motivation was the same. Numbers of animals hunted, biomass and income were tightly linked. We discuss the implications of these results for the ecological sustainability of hunting and participatory forecasting in bushmeat research and policy. Read the free Plain Language Summary for this article on the Journal blog.

Deprivation and segregation in ovarian cancer survival among African American women: a mediation analysis

PurposeDeprivation and segregation indices are often examined as possible explanations for observed health disparities in population-based studies. In this study, we assessed the role of recognized deprivation and segregation indices specifically as they affect survival in a cohort of self-identified Black women diagnosed with ovarian cancer who enrolled in the African American Cancer Epidemiology Study. MethodsMediation analysis was used to examine the direct and indirect effects between deprivation or segregation and overall survival via a Bayesian structural equation model with Gibbs variable selection. ResultsThe results suggest that high socioeconomic status-related indices have an association with increased survival, ranging from 25% to 56%. In contrast, index of concentration at the extremes-race does not have a significant impact on overall survival. In many cases, the indirect effects have very wide credible intervals; consequently, the total effect is not well estimated despite the estimation of the direct effect. ConclusionsOur results show that Black women living in higher socioeconomic status neighborhoods are associated with increased survival with ovarian cancer using area-level economic indices such as Yost or index of concentration at the extremes-income. In addition, the Kolak urbanization index has a similar impact and highlights the importance of area-level deprivation and segregation as potentially modifiable social factors in ovarian cancer survival.

Hierarchical drivers of cryptic biodiversity on coral reefs

AbstractDeclines in habitat structural complexity have marked ecological outcomes, as currently observed in many of the world's ecosystems. Coral reefs have provided a model for such changes in marine ecosystems; still our understanding has been centered on corals and fishes at broad spatial scales when metazoan diversity on coral reefs is dominated by small cryptic taxa (herein: “cryptofauna”). Given the paucity of studies and high taxonomic complexity of the cryptofauna, both of which limit a priori hypotheses, we asked whether hierarchical structuring theory provides a compelling framework to impose order and quantify patterns. In general terms, we explored whether cryptic communities are sufficiently described by broad seascape parameters or limited by a set of processes operating at their distinctly nested microhabitat scale. To address this theory and gaps in knowledge for the cryptofauna, we characterized community structure in coral rubble, an eroded coral condition where biodiversity proliferates. Rubble was sampled along a depth and exposure gradient at Heron Island on the Great Barrier Reef, Australia, to parameterize environmental and morphological indicators of sessile taxa and motile cryptofauna communities. We used a hierarchical study framework from microhabitat to seascape scales, which were evaluated using nonstructured multivariate analyses and Bayesian structural equation modeling. While the nonstructured analyses showed the effects of seascape on the cryptobenthos and its community, this approach overlooked the finer hierarchical patterns in rubble ecology revealed only in the structured model. Seascape parameters (exposure and depth) influenced microhabitat complexity (i.e., rubble branchiness), which determined the cover of sessile organisms on rubble pieces, which shaped the motile cryptofauna community. Rubble is likely to be increasingly prevalent on coral reefs in the Anthropocene and is typically associated with low seascape‐level complexity and reduced macrofaunal richness. Parallel with hierarchical structuring theory, we showed a similar response operating at the microhabitat scale whereby low rubble complexity (i.e., branchiness) reduced cryptobenthic structure, diversity and size spectra. In a future ocean, we expect there may be an initial increase in biodiversity and trophodynamic processes derived from branching rubble, but a delay in ecosystem‐scale outcomes if coral, and thus rubble, generation and complexity is not sustained.

Assessment of generalised Bayesian structural equation models for continuous and binary data.

The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive -values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17, 2012, 313), which involves formulating certain parameters (such as factor loadings) to be approximately zero through the use of informative priors, instead of explicitly setting them to zero. The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for BSEM. The proposed tools can be applied to models for both continuous and binary data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect. We study the performance of the proposed methodology via simulation experiments as well as real data on the 'Big-5' personality scale and the Fagerstrom test for nicotine dependence.

Maternal Mental Health and Children’s Problem Behaviours: A Bi-directional Relationship?

Transactional theory and the coercive family process model have illustrated how the parent-child relationship is reciprocal. Emerging research using advanced statistical methods has examined these theories, but further investigations are necessary. In this study, we utilised linked health data on maternal mental health disorders and explored their relationship with child problem behaviours via the Strengths and Difficulties Questionnaire for over 13 years. We accessed data from the Millennium Cohort Study, linked to anonymised individual-level population-scale health and administrative data within the Secure Anonymised Information Linkage (SAIL) Databank. We used Bayesian Structural Equation Modelling, specifically Random-Intercept Cross-Lagged Panel Models, to analyse the relationships between mothers and their children. We then explored these models with the addition of time-invariant covariates. We found that a mother’s mental health was strongly associated over time, as were children’s problem behaviours. We found mixed evidence for bi-directional relationships, with only emotional problems showing bi-directional associations in mid to late childhood. Only child-to-mother pathways were identified for the overall problem behaviour score and peer problems; no associations were found for conduct problems or hyperactivity. All models had strong between-effects and clear socioeconomic and sex differences. We encourage the use of whole family-based support for mental health and problem behaviours, and recommend that socioeconomic, sex and wider differences should be considered as factors in tailoring family-based interventions and support.

Active or Passive? Examination of Dependency Behavior in Marketing Channels in China

ABSTRACT Purpose Inter-firm dependence is a fundamental construct in the literature of marketing channels. It substantially affects the relationships and behaviors between marketing channel partners. This study aims to examine the differences between active and passive inter-firm dependence regarding the antecedents and effects in the context of marketing channels in China. Methodology/Approach We designed a questionnaire and collected data from the manufacturers in manufacturer and dealer dyads. We obtained 202 valid data in the survey and analyzed the data with Bayesian structural equation modeling (BSEM). Findings First, in a manufacturer and dealer dyad, dealer’s important resources significantly induce active dependence from the manufacturer, and manufacturer’s active dependence, in turn, has a positive impact on manufacturer’s specific asset investments. Second, manufacturer’s specific asset investments positively affect manufacturer’s passive dependence, and this impact on manufacturer’s passive dependence is stronger than the impact of dealer’s important resources on manufacturer’s passive dependence. Third, the manufacturer’s active dependence plays a more important role in increasing dealer’s power than manufacturer’s passive dependence. Contribution Primarily, we clearly define active and passive dependence, and test whether differences exist between these two types of dependence regarding the antecedents and consequences. Besides, we develop a scale of active dependence which may be referred to in the future studies of inter-firm dependence. Implications Marketing managers should be aware of differences between active and passive inter-firm dependence when dealing with partners. They should also recognize that although both important resources and specific asset investments could change the structure of inter-firm dependence, their effects may be different.

Remotely sensed habitat quality index reliably predicts an umbrella species presence but not demographic performance: A case study with open pine forests and red-cockaded woodpeckers

Remote sensing data can be a powerful and cost-effective method for determining the extent, composition, and structure of ecosystems across large areas. To use this tool for effective conservation of individual species, we need to test the assumption that remotely sensed habitat indices correspond to both patch suitability (i.e., presence) and value (i.e., demographic rates) for the species reliant on those habitats. We built an open pine habitat quality index (HQI) from remotely sensed spectral data to identify the condition of pine stands. We correlated the HQI with presence, group size, and fledgling production of an avian species (red-cockaded woodpecker, Dryobates (=Picoides) borealis, RCW) associated with longleaf pine (Pinus palustris) using Bayesian logistic regressions and structural equation models. The HQI was correlated with RCW cavity tree and foraging area presence, with 10.8 and 16.0 increased odds of RCW presence with each unit increase in the HQI, respectively. However, the HQI was not correlated with RCW demographic performance. Given that RCWs are an umbrella species and are currently limited by habitat, that the HQI successfully identifies areas of higher quality habitat for RCW is useful for conservation planning. Improving the specificity of the remotely sensed index could improve the link between the HQI and RCW demographic performance, but might limit its application to RCW, instead of the entire open pine endemic community. Our study suggests that linking generalized habitat indices to species habitat suitability is possible and strengthens the justification for their use in cost-effective, large-scale conservation of imperiled ecosystems.

The Impact of Adults' Used Skills on Their Self-Evaluated Skills and Social Lives Over Time

<p style="text-align: justify;">Previous research focused on individuals’ background, contexts and cognitive performance in education, work, and life. Given the increasing number of people living alone temporarily, the question arises whether the frequent use of skills, including social skills, relates to individuals’ later positively self-evaluated skills and social lives. Based on an integrated framework, the current analysis aimed to disentangle these relationships with longitudinal data from Germany over three years. The target sample consisted of <em>n</em> = 3263 working adults. A Bayesian structural equation model included adults’ frequent use of skills, self-evaluated skills, household size, close friends, and seven covariates (e.g., numeracy and literacy test scores, weekly working hours. The results suggested positive relationships between adults’ frequent use of numeracy, literacy, and social skills and later self-evaluations (except literacy used on self-evaluated numeracy). Those who less frequently used social skills three years earlier were also less likely to have a larger household size than those who reporting frequently using their social skills. Adults who frequently used literacy skills three years earlier reported higher numbers of close friends than those who less frequently used literacy. The findings highlight the importance of adults’ social skills and frequently used skills for self-evaluated numeracy and literacy.</p>