Attributional Style as a Predictor of Hopelessness Depression

2006 ◽  
Vol 20 (4) ◽  
pp. 447-458 ◽  
Author(s):  
Edward D. Sturman ◽  
Myriam Mongrain ◽  
Paul M. Kohn
Keyword(s):  
Attributional Style ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Epidemiological Studies ◽  
Psychomotor Retardation ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Negative Events ◽  
Hopelessness Depression

Stable and global attributions for negative events were tested as predictors of hopelessness depression symptoms, obtained from a diagnostic interview for a past depressive episode in a sample of 102 graduate students. All participants were administered the Structured Clinical Interview for DSM–IV, Center for Epidemiological Studies Depression Scale, Personal Style Inventory, and a modified version of the Extended Attributional Style Questionnaire. A stable and global attributional style for negative events was significantly associated with a composite of hopelessness depression symptoms. A regression analysis revealed that attributional style significantly postdicted hopelessness depression symptoms when controlling for both sociotropy and autonomy. Structural equation modeling supported a model in which stable and global attributions predicted a latent variable, which we refer to as a motivational deficit, involving psychomotor retardation and fatigue as indicators. Therefore, this study obtained some support for the hopelessness model and highlights the vulnerability posed by attributional style ( Abramson, Metalsky, & Alloy, 1989 ).

scholarly journals Postpartum depression symptoms in survey-based research: a structural equation analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Che Wan Jasimah Bt Wan Mohamed Radzi ◽  
Hashem Salarzadeh Jenatabadi ◽  
Nadia Samsudin
Keyword(s):  
Postpartum Depression ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Public Health Problem ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Postpartum Women ◽  
Unhealthy Food ◽  
High Level

Abstract Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

scholarly journals Violence and depressive symptoms during pregnancy in BRISA cohort: using structural equation modeling approach

2019 ◽  
Vol 19 (1) ◽  
pp. 173-184
Author(s):  
Sabrina Varão Oliveira Ribeiro ◽  
Rosângela Fernandes Lucena Batista ◽  
Marizélia Rodrigues Costa Ribeiro ◽  
Kivania Carla Pessoa ◽  
Vanda Maria Ferreira Simões ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Depressive Symptoms ◽  
Pregnant Women ◽  
Maternal Depression ◽  
Structural Equation ◽  
Physical Violence ◽  
Depression Scale ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Psychological Violence

Abstract Objectives: to analyze associations among violence against pregnant women, depressive symptoms during pregnancy and maternal depression symptoms. Methods: a sample of 1,139 mothers was conducted on a prenatal cohort study in the municipality of São Luís in Brazil. Psychological and physical violence against pregnant women were measured by the World Health Organization Violence Against Woman. Depressive symptoms during pregnancy were measured by the Escala de Depressão do Centro de Estudos Epidemiológicos (CES-D) (Depression Scale for Epidemiological Studies Center) and maternal depression symptoms were measured by the Edinburgh Postnatal Depression Scale (EPDS). The conceptual model of the structural equation modeling contained socioeconomic situation, social support, psychological and physical violence and depression during pregnancy as determinants of the maternal depression symptoms. Results: maternal depression symptoms were more frequently reported by pregnant women who suffered psychological violence (Standardized Coefficient, SC=0.256; p-value, p<0.001), physical violence (SC=0.221 p<0.001) and those who presented depressive symptoms during pregnancy SC=0.322, p<0.001). Depressive symptoms during pregnancy mediated the effects on physical and psychological violence on maternal depression. Conclusions: pregnant women who were submitted to psychological and physical violence and presented depressive symptoms during pregnancy frequently reported more of having maternal depression symptoms.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

scholarly journals Insights Into the Correspondence Between Land Use Pattern and Spatial Distribution of Rail Transit Services

2021 ◽  
Author(s):  
Zhongqi Wang ◽  
Qi Han ◽  
Bauke de Vries ◽  
Li Dai
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Operations Management ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Rail Transit ◽  
Land Use Patterns ◽  
Transit Services ◽  
The Relationship

AbstractThe identification of the relationship between land use and transport lays the foundation for integrated land use and transport planning and management. This work aims to investigate how rail transit is linked to land use. The research on the relationship between land use and rail-based transport is dominated by the impacts of rail projects on land use, without an in-depth understanding of the reverse. However, it is important to note that issues of operation management rather than new constructions deserve greater attention for regions with established rail networks. Given that there is a correspondence between land use patterns and spatial distribution of heavy railway transit (HRT) services at such regions, the study area (i.e., the Netherlands) is partitioned by the Voronoi diagram of HRT stations and the causal relationship between land use and HRT services is examined by structural equation modeling (SEM). The case study of Helmond (a Dutch city) shows the potential of the SEM model for discussing the rail station selection problem in a multiple transit station region (MTSR). Furthermore, in this study, the node place model is adapted with the derivatives of the SEM model (i.e., the latent variable scores for rail service levels and land use characteristics), which are assigned as node and place indexes respectively, to analyze and differentiate the integration of land use and HRT services at the regional level. The answer to whether and how land use affects rail transit services from this study strengthens the scientific basis for rail transit operations management. The SEM model and the modified node place model are complementary to be used as analytical and decision-making tools for rail transit-oriented regional development.

scholarly journals When Goal Pursuit Gets Hairy: A Longitudinal Goal Study Examining the Role of Controlled Motivation and Action Crises in Predicting Changes in Hair Cortisol, Perceived Stress, Health, and Depression Symptoms

2021 ◽  
pp. 216770262199521
Author(s):  
Anne Catherine Holding ◽  
Emily Moore ◽  
Amanda Moore ◽  
Jérémie Verner-Filion ◽  
Isabelle Ouellet-Morin ◽  
...  
Keyword(s):  
Perceived Stress ◽  
Structural Equation ◽  
Goal Pursuit ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Hair Cortisol ◽  
Controlled Motivation ◽  
Goal Motivation ◽  
Action Crises ◽  
Action Crisis

The action crisis is a critical phase in goal striving during which the goal pursuer feels conflicted about persevering with the goal or initiating disengagement. Recent research suggests that goal motivation, specifically controlled motivation (i.e., pursuing a goal out of obligation and pressure), increases the likelihood of experiencing action crises. In turn, action crises in goal pursuit have been linked to increases in depression symptoms and cortisol. In the present 8-month longitudinal study, we tracked university students’ personal goals to examine whether the pursuit of controlled goals and the experience of action crises was associated with increasing levels of hair cortisol, perceived stress, poor health, and depression symptoms ( N = 156). Structural equation modeling suggested that experiencing action crises in goal pursuit was associated with increases in markers of stress, depression, and ill-being. This effect was partially explained by controlled goal motivation. The clinical and theoretical implications of these findings are discussed.

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

scholarly journals A Behavioral Model of Drivers’ Mean Speed Influenced by Weather Conditions, Road Geometry, and Driver Characteristics Using a Driving Simulator Study

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mehdi Zolali ◽  
Babak Mirbaha ◽  
Maziyar Layegh ◽  
Hamid Reza Behnood
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Driving Simulator ◽  
Weather Conditions ◽  
Geometric Design ◽  
Equation Modeling ◽  
Novice Drivers ◽  
Sight Distance ◽  
The Mean

Driving above the speed limit is one of the factors that significantly affect safety. Many studies examined the factors affecting the speed of vehicles in the simulated environment. The present study aimed to analyze drivers’ characteristics, time and weather conditions, and geometric features’ effect on mean speed in simulated conditions simultaneously. In this regard, the simulator experiment data of 70 drivers were collected in a two-lane rural highway at six different times, and weather scenarios and their socioeconomic characteristics were collected by a questionnaire. Structural equation modeling (SEM) was used to capture the complex relationships among related variables. Eleven variables were grouped into four latent variables in the structural model. Latent variables including “Novice Drivers,” “Experienced Drivers,” “Sight Distance,” and “Geometric Design” were defined and found significant on their mean speed. The results showed that “Novice Drivers” have a positive correlation with the mean speed. Meanwhile, “Experienced Drivers,” who drive 12% slower than the novice group, negatively affect the mean speed with a standard regression weight of −0.08. This relation means that young and novice drivers are more inclined to choose higher speeds. Among variables, the latent variable “Sight Distance” has the most significant effect on the mean speed. This model shows that foggy weather conditions strongly affect the speed selection behavior and reduce the mean speed by 40%. Nighttime also reduces mean speed due to poor visibility conditions. Furthermore, “Geometric design” as the latent variable indicates the presence of curves on the simulated road, and it can be concluded that the existence of a curve on the road encourages drivers to slow down, even young drivers. It is noteworthy that the parts of the simulated road with a horizontal curve act as a speed reduction tool for drivers.

scholarly journals FAKTOR-FAKTOR PENENTU PERILAKU GURU SMP MENGGUNAKAN TEKNOLOGI DALAM PEMBELAJARAN ONLINE: STUDI KASUS DI PROVINSI RIAU

2021 ◽  
Vol 22 (2) ◽  
pp. 123-133
Author(s):  
Defrizal Hamka ◽  
Neng Sholihat
Keyword(s):  
Online Learning ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Partial Least Square ◽  
Least Square ◽  
Influential Factor ◽  
Equation Modeling ◽  
Purposive Sampling ◽  
Facility Conditions

The purpose of this research is to investigate factors that influence the intent of behavior using technology in online learning. The study uses structural equation modeling using a partial least square approach to test the hypotheses. Respondents selected using purposive sampling, and the questionnaires were distributed through online surveys and received a response of 96 respondents. Results show that latent variables, performance expectations, business expectations, and facility conditions have a positive and significant relationship with the intent of individual behaviour in the use of technology in online learning. The latent variable "condition facility" is the most influential factor. This research provides an important overview and understanding for policymakers in designing frameworks to pay attention to facility conditions. Further research is suggested in the future covering samples from various provinces in Indonesia. This study adds to the literature primarily on factors affecting behavioral intent to use technology in online learning. Tujuan dari penelitian ini adalah untuk menganalisis faktor-faktor yang mempengaruhi niat perilaku guru menggunakan teknologi dalam pembelajaran online. Penelitian ini menggunakan pemodelan persamaan struktural dengan menggunakan pendekatan partial least square untuk menguji hipotesis. Berdasarkan purposive sampling, kuesioner disebarkan melalui survei online dan mendapat tanggapan dari 96 responden. Hasil penelitian menunjukkan bahwa variabel laten, ekspektasi kinerja, ekspektasi usaha, dan kondisi fasilitas memiliki hubungan positif dan signifikan dengan niat perilaku individu dalam penggunaan teknologi dalam pembelajaran online. Variabel laten “fasilitas kondisi” merupakan faktor yang paling berpengaruh. Penelitian ini memberikan gambaran dan pemahaman penting bagi pembuat kebijakan dalam merancang kerangka kerja untuk memperhatikan kondisi fasilitas. Penelitian lebih lanjut disarankan di masa depan mencakup sampel dari berbagai provinsi di Indonesia. Studi ini menambah literatur terutama pada faktor-faktor yang mempengaruhi niat perilaku untuk menggunakan teknologi dalam pembelajaran online.

Linking employee perceptions and employee attitudes through their levels of organizational identification: a field investigation in India

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Soumendu Biswas
Keyword(s):  
Social Exchange ◽  
Organizational Support ◽  
Organizational Identification ◽  
Latent Variable ◽  
Structural Equation ◽  
Employee Attitudes ◽  
Equation Modeling ◽  
Full Time ◽  
Variable Model ◽  
Content Type

PurposeDespite organizational socialization and support, contemporary managers often perceive employees to be less engaged and attached to their workplace, multiplying their workload with unsolicited vexations and worries. In this connection, the purpose of this paper is to explore and possibly confirm the ameliorative role of organizational identification as a mediator between employees' perceptions of organizational support and justice and their favorable association to their levels of engagement and attenuation of their intentions to quit.Design/methodology/approachSuitable theories such as the social exchange and fairness heuristics theories were examined to select and support the study constructs. Accordingly, the literature was reviewed to formulate the study hypotheses and connect them through a conceptual latent variable model (LVM). Data were collected from 402 full-time managerial executives all over India. The data thus collected were subjected to structural equation modeling (SEM) procedures.FindingsAll the measures used in this study had acceptable reliabilities as indicated by their Cronbach's Alpha values. Based on the SEM procedures all the study hypotheses and one of the competing LVMs labeled as LVM5 was finally accepted.Originality/valueThe distinctive feature of this study is the theoretical compilation of all the study constructs in one LVM and subsequent empirical verification of the same. This study is, perhaps, the first of its kind to examine the implications of such justice-based perceptions of social exchange relations between employees and their organizations in India more so, since it considers support and justice to complement each other as an interactive whole.

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