scholarly journals Discoveries and novel insights in ecology using structural equation modeling

2019 ◽  
Vol 12 ◽  
Author(s):  
Daniel Laughlin ◽  
James Grace
Keyword(s):  
Structural Equation Modeling ◽  
Quantitative Analysis ◽  
Indirect Effects ◽  
Structural Equation ◽  
Data Science ◽  
Positive Response ◽  
Major Elements ◽  
Equation Modeling

As we enter the era of data science (Lortie 2018), quantitative analysis methodologies are proliferating rapidly, leaving ecologists with the task of choosing among many alternatives. The use of structural equation modeling (SEM) by ecologists has increased in recent years, prompting us to ask users questions about their experience with the methodology. Responses indicate an enthusiastic endorsement of SEM. Two major elements of respondent’s experiences seem to contribute to their positive response, (1) a sense that they are obtaining more accurate explanatory understanding through the use of SEM and (2) excitement generated by the discovery of novel insights into their systems. We elaborate here on the detection of indirect effects, offsetting effects, and suppressed effects, and demonstrate how discovering these effects can advance ecology.

scholarly journals The reasoned action approach applied to health behavior: Role of past behavior and test of some key moderators using meta-analytic structural equation modeling

2018 ◽  
Author(s):  
Martin S Hagger ◽  
Juho Polet ◽  
Taru Lintunen
Keyword(s):  
Structural Equation Modeling ◽  
Health Behavior ◽  
Indirect Effects ◽  
Structural Equation ◽  
Social Cognitive ◽  
Reasoned Action ◽  
Equation Modeling ◽  
Reasoned Action Approach ◽  
Data Set ◽  
Past Behavior

Rationale: The reasoned action approach (RAA) is a social cognitive model that outlines the determinants of intentional behavior. Primary and meta-analytic studies support RAA predictions in multiple health behaviors. However, including past behavior as a predictor in the RAA may attenuate model effects. Direct effects of past behavior on behavior may reflect non-conscious processes while indirect effects of past behavior through social cognitive variables may represent reasoned processes. Objective: The present study extended a previous meta-analysis of the RAA by including effects of past behavior. The analysis also tested effects of candidate moderators of model predictions: behavioral frequency, behavior type, and measurement lag.Method: We augmented a previous meta-analytic data set with correlations between model constructs and past behavior. We tested RAA models that included and excluded past behavior using meta-analytic structural equation modeling and compared the effects. Separate models were estimated in studies on high and low frequency behaviors, studies on different types of behavior, and studies with longer and shorter measurement lag.Results: Including past behavior attenuated model effects, particularly the direct effect of intentions on behavior, and indirect effects of experiential attitudes, descriptive norms, and capacity on behavior through intentions. Moderator analyses revealed larger intention-behavior and past behavior-behavior effects in high frequency studies, but the differences were not significant. No other notable moderator effects were observed.Conclusion: Findings indicate a prominent role for habitual processes in determining health behavior and inclusion of past behavior in RAA tests is important to yield precise estimates of model effects.

scholarly journals Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis (Preprint)

2020 ◽  
Author(s):  
Se Young Jung ◽  
Hee Hwang ◽  
Keehyuck Lee ◽  
Ho-Young Lee ◽  
Eunhye Kim ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Decision Support ◽  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Structural Equation ◽  
Adverse Drug Events ◽  
Ease Of Use ◽  
Barriers And Facilitators ◽  
Equation Modeling ◽  
Successful Implementation

BACKGROUND Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts. OBJECTIVE In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into their clinical setting. This study followed the implementation process for an MDSS within a comprehensive EHR system and analyzed the relevant barriers and facilitators. METHODS A mixed research methodology was adopted. Data from a structured survey and 15 in-depth interviews were integrated. Structural equation modeling was conducted for quantitative analysis of factors related to user adoption of MDSS. Qualitative analysis based on semistructured interviews with physicians was conducted to collect various opinions on MDSS implementation. RESULTS Quantitative analysis revealed that physicians’ expectations regarding ease of use and performance improvement are crucial. Qualitative analysis identified four significant barriers to MDSS implementation: alert fatigue, lack of accuracy, poor user interface design, and lack of customizability. CONCLUSIONS This study revealed barriers and facilitators to the implementation of MDSS. The findings can be applied to upgrade MDSS in the future.

scholarly journals Investigating Associated Factors with Glomerular Filtration Rate : Structural Equation Modeling

2019 ◽  
Author(s):  
Parastoo Jamshidi ◽  
Farid Najafi ◽  
Shayan Mostafaee ◽  
Ebrahem Shakiba ◽  
yahya pasdar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Glomerular Filtration Rate ◽  
Structural Equation Modeling ◽  
Indirect Effects ◽  
Structural Equation ◽  
Associated Factors ◽  
Filtration Rate ◽  
Equation Modeling ◽  
Direct And Indirect Effects ◽  
Male And Female

Abstract Background: Glomerular Filtration Rate (GFR) is a valid indicator for kidney function, both in healthy and diseased people. Different factors can affect GFR. The purpose of this study is to assess a causal model to show direct and indirect effects of GFR-related factors using structural equation modeling. Patients and methods : We analyzed data from recruitment phase of Ravansar Non-Communicable Disease cohort study. Data on socio-behavioral, nutritional, cardiovascular, and metabolic risk factors were entered in a conceptual model in order to test direct and indirect effects of the associated factors on GFR, separately in male and female, using the structural equation modeling. Results : Of 8927 individuals participated in this study, 4212 subjects were male (47.2%) and 4715 subjects were female (52.8%). The obtained standard deviation of GFR was 76.05 (±14.3) per 1.73 . Filtration rate for 11.52%, 72.96% and 15.50% of people were <60, and , respectively. Hypertension in both gender and atherogenic factor in male directly, and in female directly and indirectly had a decreasing effect on GFR. Blood Urea Nitrogen (BUN) and smoking in male and female, directly or indirectly through other variables, was associated with a decrease in GFR. In female, diabetes had a decreasing direct and indirect effect on GFR. Obesity in female was directly associated with increasing and indirectly associated with decreasing filtration. Conclusion : According to our results, increasing age, hypertension, diabetes, obesity and high blood lipids, and BUN had a decreasing direct and indirect effects on GFR. Although low GFR might have different reasons and it is not a consistent sign of CKD, our findings, in line with other reports, provide more detailed informations about important risk factors of low GFR. Public awareness about such factors can improve public practice of positive health behaviours.

scholarly journals Structural Model of Students’ Interest and Self-Motivation to Learning Mathematics

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Dominic Otoo ◽  
Wahab A. Iddrisu ◽  
Justice A. Kessie ◽  
Ernest Larbi
Keyword(s):  
Structural Equation Modeling ◽  
Direct Effect ◽  
Indirect Effects ◽  
Mathematics Anxiety ◽  
Structural Equation ◽  
Direct Relationship ◽  
Structural Model ◽  
Equation Modeling ◽  
Learning Mathematics ◽  
Attitude Towards Mathematics

Students pick up the perception that mathematics is abstract and therefore, the learning of mathematics would yield to them no benefit. With their attitude towards mathematics modelled and their interest for mathematics impacted by this automatic generated perception, they may never again appreciate the beauty of mathematics. In this paper, the researchers used structural equation modeling (SEM), to investigate the variables that affect students’ interest, among the variables, students’ confidence and motivation. The foregoing variables were conceptualized to have a direct effect on students’ interest in mathematics, whilst mathematics anxiety and students’ knowledge of the usefulness of mathematics were conceptualized to have indirect effects on their interest in mathematics moderated by students’ confidence and motivation. The result showed that significantly students’ confidence directly affects students’ interest in the learning of mathematics and there is a direct relationship between confidence and motivation. A student’s knowledge about the usefulness of mathematics indirectly increases the student’s interest in mathematics.

Trauma and Maternal Caregivers as Risks for Executive Function Deficits Among Youth Who Have Sexually Harmed

Sexual Abuse ◽  
2021 ◽  
pp. 107906322098828
Author(s):  
Adam Brown ◽  
Jamie Yoder ◽  
Kristina Fushi
Keyword(s):  
Structural Equation Modeling ◽  
Executive Functioning ◽  
Indirect Effects ◽  
Structural Equation ◽  
Maternal Attachment ◽  
Equation Modeling ◽  
Direct And Indirect Effects ◽  
Trauma Symptoms ◽  
Maternal Caregivers ◽  
Executive Functioning Deficits

Despite robust research linking trauma events to the commission of sexual harm by youth, the majority of victims do not become victimizers, imploring us to study potential interceding influences. Some research indicates that intermediary factors like attachment characteristics, trauma symptomatology, and executive functioning may be critical in understanding sexual harm committed by youth. This study explored relationships between trauma events, trauma symptoms, and attachment characteristics, and their relationship to executive functioning in a sample of 196 youth who committed sexual harm. Results revealed bivariate associations between trauma events, symptomatology, and maternal attachment characteristics. Structural equation modeling revealed numerous direct and indirect effects on the path to deficits in executive functioning, and that sexual abuse, above and beyond other forms of traumatic events, contributed to executive functioning deficits via trauma symptoms. The results are contextualized and implications are discussed.

scholarly journals Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

10.2196/18758 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e18758
Author(s):  
Se Young Jung ◽  
Hee Hwang ◽  
Keehyuck Lee ◽  
Ho-Young Lee ◽  
Eunhye Kim ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Decision Support ◽  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Structural Equation ◽  
Adverse Drug Events ◽  
Ease Of Use ◽  
Barriers And Facilitators ◽  
Equation Modeling ◽  
Successful Implementation

Background Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts. Objective In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into their clinical setting. This study followed the implementation process for an MDSS within a comprehensive EHR system and analyzed the relevant barriers and facilitators. Methods A mixed research methodology was adopted. Data from a structured survey and 15 in-depth interviews were integrated. Structural equation modeling was conducted for quantitative analysis of factors related to user adoption of MDSS. Qualitative analysis based on semistructured interviews with physicians was conducted to collect various opinions on MDSS implementation. Results Quantitative analysis revealed that physicians’ expectations regarding ease of use and performance improvement are crucial. Qualitative analysis identified four significant barriers to MDSS implementation: alert fatigue, lack of accuracy, poor user interface design, and lack of customizability. Conclusions This study revealed barriers and facilitators to the implementation of MDSS. The findings can be applied to upgrade MDSS in the future.

scholarly journals Investigating Associated Factors with Glomerular Filtration Rate : Structural Equation Modeling

2019 ◽  
Author(s):  
Parastoo Jamshidi ◽  
Farid Najafi ◽  
Shayan Mostafaee ◽  
Ebrahem Shakiba ◽  
yahya pasdar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Glomerular Filtration Rate ◽  
Structural Equation Modeling ◽  
Indirect Effects ◽  
Structural Equation ◽  
Associated Factors ◽  
Filtration Rate ◽  
Equation Modeling ◽  
Direct And Indirect Effects ◽  
Male And Female

Abstract Background: Glomerular Filtration Rate (GFR) is a valid indicator for kidney function, both in healthy and diseased people. Different factors can affect GFR. The purpose of this study is to assess a causal model to show direct and indirect effects of GFR-related factors using structural equation modeling. Patients and methods : We analyzed data from recruitment phase of Ravansar Non-Communicable Disease cohort study. Data on socio-behavioral, nutritional, cardiovascular, and metabolic risk factors were entered in a conceptual model in order to test direct and indirect effects of the associated factors on GFR, separately in male and female, using the structural equation modeling. Results : Of 8927 individuals participated in this study, 4212 subjects were male (47.2%) and 4715 subjects were female (52.8%). The obtained standard deviation of GFR was 76.05 (±14.3) per 1.73 . Filtration rate for 11.52%, 72.96% and 15.50% of people were <60, and , respectively. Hypertension in both gender and atherogenic factor in male directly, and in female directly and indirectly had a decreasing effect on GFR. Blood Urea Nitrogen (BUN) and smoking in male and female, directly or indirectly through other variables, was associated with a decrease in GFR. In female, diabetes had a decreasing direct and indirect effect on GFR. Obesity in female was directly associated with increasing and indirectly associated with decreasing filtration. Conclusion : According to our results, increasing age, hypertension, diabetes, obesity and high blood lipids, and BUN had a decreasing direct and indirect effects on GFR. Although low GFR might have different reasons and it is not a consistent sign of CKD, our findings, in line with other reports, provide more detailed informations about important risk factors of low GFR. Public awareness about such factors can improve public practice of positive health behaviours.

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