scholarly journals Associations of Physical Activity, School Safety and Non-Prescription Steroid Use in Adolescents: A Structural Equation Modeling Approach

2021 ◽  
Vol 19 (1) ◽  
pp. 87
Author(s):  
Timothy Brusseau ◽  
Ryan Burns
Keyword(s):  
Physical Activity ◽  
Risk Behavior ◽  
Latent Variables ◽  
School Safety ◽  
Latent Variable ◽  
Structural Equation ◽  
Self Report ◽  
Equation Modeling ◽  
Physical And Mental Health ◽  
Steroid Use

Non-prescription steroid use can negatively impact adolescent physical and mental health and wellbeing. Determining correlates of this risk behavior is needed to help mitigate its prevalence. Two potential correlates are physical activity and school safety. The purpose of this study was to examine the associations of physical activity, school safety, and non-prescription steroid use within a sample of adolescents from the 2015–2019 US National Youth Risk Behavior Survey (YRBS). A multi-stage cluster sampling procedure yielded a representative sample of US adolescents from the 2015–2019 YRBS (n = 44,066; 49.6% female). Two latent variables indicating physical activity and unsafe schools were the independent variables. The dependent variable was a self-report of non-prescription steroid use. A weighted structural equation model examined the associations between physical activity and unsafe schools with non-prescription steroid use, controlling for age, sex, BMI percentile, race/ethnicity, and sexual minority status. The latent physical activity variable did not associate with non-prescription steroid use (β = 0.007, 95%CI: −0.01–0.02, p = 0.436); however, the unsafe schools latent variable did associate with non-prescription steroid use (β = 0.64, 95%CI: 0.59–0.69, p < 0.001). An unsafe school environment may be a determinant of non-prescription steroid use in adolescents. Physical activity behaviors did not associate with steroid use.

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.

New developments in latent variable panel analyses of longitudinal data

2007 ◽  
Vol 31 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Todd D. Little ◽  
Kristopher J. Preacher ◽  
James P. Selig ◽  
Noel A. Card
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Null Model ◽  
Factorial Invariance ◽  
Equation Modeling ◽  
New Developments ◽  
Mediated Effects

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

Adaptation and Validation of the Athletic Identity Questionnaire-Adolescent for Use With Children

2008 ◽  
Vol 5 (4) ◽  
pp. 539-558 ◽  
Author(s):  
Cheryl B. Anderson ◽  
Karen J. Coleman
Keyword(s):  
Physical Activity ◽  
Construct Validity ◽  
Structural Equation ◽  
Factor Model ◽  
Athletic Identity ◽  
Ethnically Diverse ◽  
Self Report ◽  
Equation Modeling ◽  
Confirmatory Factor ◽  
Child Factors

Background:This article describes the adaptation of the Athletic Identity Questionnaire (AIQ) for Adolescents for use with children and evaluates its construct validity. Based on a theoretical model supported in adults and adolescents, the AIQ-Child measures the general attribute of athletic, which encompasses exercise, sport, and physical activity and assesses 4 dimensions: appearance, competence, importance of activity, and encouragement from 3 sources (parents, friends, teachers/other adults).Methods:The hypothesized 4-factor model was tested using structural equation modeling in 2 samples of 9- and 10-year-old children that were ethnically diverse (N = 432) and Hispanic (N = 504).Results:Confirmatory factor analysis using LISREL 8.71 supported the 4-factor structure in a 40- or 38-item version in sample 1 (RMSEA = .039, .041) and sample 2 (RMSEA = .038, .038). As in the adult and adolescent models, there was also support for a higher-order model. The AIQ-Child factors were positively related to physical activity (r = .51 to .68) and fitness (r = .15 to .41) and negatively related to TV/computer use (r = –.28 to –.03) and adiposity (r = –.32 to .04).Conclusions:Findings support the factorial and construct validity of the AIQ-Child and its use as a self-report instrument in younger children.

scholarly journals ANALISIS KEPUASAN DAN LOYALITAS PELANGGAN DALAM PEMESANAN TIKET PESAWAT SECARA ONLINE MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (PLS)

2018 ◽  
Vol 7 (4) ◽  
pp. 361-372
Author(s):  
Trisnawati Gusnawita Berutu ◽  
Abdul Hoyyi ◽  
Sugito Sugito
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Service Providers ◽  
Web Design ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Internet Service ◽  
Access Security

Technology advances are bring rapid changes, thus bringing the world to the information society. From this technological progress thus e-commerce emerged, as a means to meet the needs of goods and services through internet access (online). This is what the airlines utilized by cooperating with various internet service providers (online), to provide convenience and comfort of airplane passengers in buying tickets without having to come directly to the place and through intermediaries. To provide the best service, need to know what factors that influence customer satisfaction in ordering airline tickets online. Appropriate modeling for this problem using structural equation modeling, with Partial Least Square (PLS) approach. The PLS approach is chosen because it is not based on several assumptions, one of these is the normal multivariate assumption. In this research, the exogenous latent variables used are performance, access, security, sensation, information, and web design, while the endogenous latent variables are satisfaction and loyalty. Based on the results of the analysis it can be concluded that the latent variables of access, security, sensation, information, and web design are able to explain the latent satisfaction variable of 70.32% while the satisfaction latent variable is able to explain the latent variable of loyalty by 36.02%. 

Conscientiousness and Cardiometabolic Risk: A Test of the Health Behavior Model of Personality Using Structural Equation Modeling

2021 ◽  
Author(s):  
Mark C Thomas ◽  
Katherine A Duggan ◽  
Thomas W Kamarck ◽  
Aidan G C Wright ◽  
Matthew F Muldoon ◽  
...  
Keyword(s):  
Physical Activity ◽  
Substance Use ◽  
Health Behavior ◽  
Health Behaviors ◽  
Latent Variable ◽  
Structural Equation ◽  
Cardiometabolic Risk ◽  
Equation Modeling ◽  
Inverse Association ◽  
Subjective Sleep Quality

Abstract Background High trait conscientiousness is associated with lower cardiometabolic risk, and health behaviors are a putative but relatively untested pathway that may explain this association. Purpose To explore the role of key health behaviors (diet, physical activity, substance use, and sleep) as links between conscientiousness and cardiometabolic risk. Methods In a cross-sectional analysis of 494 healthy, middle-aged working adults (mean age = 42.7 years, 52.6% women, 81.0% White), participants provided self-reports of conscientiousness, physical activity, substance use, diet, and sleep, and wore monitors over a 7-day monitoring period to assess sleep (Actiwatch-16) and physical activity (SenseWear Pro3). Cardiometabolic risk was expressed as a second-order latent variable from a confirmatory factor analysis involving insulin resistance, dyslipidemia, obesity, and blood pressure. Direct, indirect, and specific indirect effect pathways linking conscientiousness to health behaviors and cardiometabolic risk were examined. Unstandardized indirect effects for each health behavior class were computed separately using bootstrapped samples. Results After controlling for demographics (sex, age, race, and education), conscientiousness showed the predicted, inverse association with cardiometabolic risk. Among the examined health behaviors, objectively-assessed sleep midpoint variability (b = −0.003, p = .04), subjective sleep quality (b = −0.003, p = .025), and objectively-assessed physical activity (b = −0.11, p = .04) linked conscientiousness to cardiometabolic risk. Conclusions Physical activity and sleep partially accounted for the relationship between conscientiousness and cardiometabolic risk.

MODELING OF SEA SURFACE TEMPERATURE BASED ON PARTIAL LEAST SQUARE - STRUCTURAL EQUATION

2021 ◽  
Vol 14 (2) ◽  
pp. 170-182
Author(s):  
Miftahuddin Miftahuddin ◽  
Retno Wahyuni Putri ◽  
Ichsan Setiawan ◽  
Rina Suryani Oktari
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Partial Least Square ◽  
Least Square ◽  
Sea Surface ◽  
Equation Modeling

Variability of Sea Surface Temperature (SST) is one of the climatic features that influence global and regional climate dynamics. Missing data (gaps) in the SST dataset are worth investigating since they may statistically alter the value of the SST change. The partial least square-structural equation modeling (PLS-SEM) approach is used in this work to estimate the causality relationships between exogenous and endogenous latent variables. The findings of this study, which are significant indicators that have a loading factor value > 0.7 are as follows: i) sea surface temperature (oC) as a measure of the latent variable changes in SST, ii) wind speed (m/s) and relative humidity (%) as a measure of the latent variable of weather, and iii) air temperature (oC), long-wave solar radiation (w/m2) as a measure of climate latent variables. The size of the Rsquare value is influenced by the number of gaps. The results of the boostrapping show that the latent variables of weather and climate have a significant effect on changes in SST which are indicated by the value of tstatistics > ttabel. The structural model obtained Changes in SST (η) = -0.330 weather + 0.793 climate + ζ. The model shows that the weather has a negative coefficient, which means that the better the weather conditions, the lower the SST changes. Climate has a positive coefficient, which means that the better the climate, the SST changes will also increase. Rising sea surface temperatures caused by an increase in climate can lead to global warming, impacting El-Nino and La-Nina events.

scholarly journals Social and Psychological Factors Associated With Adolescent Physical Activity

2016 ◽  
Vol 13 (9) ◽  
pp. 957-963 ◽  
Author(s):  
Jeanette M. Garcia ◽  
John R. Sirard ◽  
Ross Larsen ◽  
Meg Bruening ◽  
Melanie Wall ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Support ◽  
Psychological Factors ◽  
Structural Equation ◽  
Perceived Social Support ◽  
Cross Sectional Study ◽  
Sem Analysis ◽  
Self Report ◽  
Equation Modeling ◽  
Cross Sectional

Objective:The purpose of this study was to examine, using structural equation modeling, the associations between nominated friend physical activity (PA), friend social support with individual PA-related psychological factors, and adolescent PA.Methods:Data were obtained from EAT 2010 (Eating and Activity Among Teens), a large cross-sectional study conducted in 20 middle and high schools. The sample consisted of 1951 adolescents (mean age: 14.25 ± 1.96, 54% female, 68% ethnic minorities). PA, parent and friend social support (perceived social support for PA from parents and friends), and psychological measures (PA enjoyment, PA self-efficacy, and PA barriers) were assessed by self-report questionnaires. The SEM analysis consisted of 1 observed variable: friend PA, and 2 latent constructs: psychological factors, perceived social support.Results:The model was a good fit, indicating that there were significant direct effects of both friend PA (P < .01) and psychological factors (P < .0001) on adolescent PA. In addition, psychological factors mediated the association between friend PA and adolescent PA.Conclusion:The results of this model suggest that psychological factors and friend PA are associated with adolescent PA, and that psychological factors may play an important role. Future studies should further examine the association of both friend PA and psychological variables with adolescent PA.

scholarly journals Regularized Structural Equation Modeling with Stability Selection

2020 ◽  
Author(s):  
Xiaobei Li ◽  
Ross Jacobucci
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
High Dimensional ◽  
Free Lunch ◽  
Stability Selection ◽  
Selection Operator ◽  
No Free Lunch

Regularization methods such as the least absolute shrinkage and selection operator (LASSO) are commonly used in high dimensional data to achieve sparser solutions. They are also becoming increasingly popular in social and behavioral research. Recently methods such as regularized structural equation modeling (SEM) and penalized likelihood SEM have been proposed, trying to transfer the benefits of regularization to models with latent variables involved. However, some drawbacks of the LASSO such as high false positive rates (FPRs) and inconsistency in selection results persist at the same time. We propose the use of stability selection (Meinshausen &amp; Bu ̈hlmann, 2010) as a mechanism to overcome these limitations, demonstrating simulation conditions in which it improves performance, and simulation conditions in which it does not. In this paper, we point out that there is no free lunch, and researchers should be aware of those problems when applying regularization to latent variable models, concluding with an empirical example and further discussion of the application of regularization to SEM.

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