A Five-Step Guide to Conducting SEM Analysis in Counseling Research

Structural Equation Modeling (SEM) is a collection of statistical techniques used to determine the degree to which a proposed theoretical model is supported by data. SEM has been growing in various disciplines as well as in rehabilitation research. It is the goal of this introduction to provide a conceptual overview of SEM. This statistical technique can facilitate a better understanding of large data sets involving theoretical models that have become more frequent in this discipline.

The use of structural equation modelling has become increasingly common in the social and behavioural sciences. Based on a stochastic analysis of the multivariate density function of indicator variables, latent moderated structural Equations (LMS) implements iterative maximum likelihood estimation for the model parameters. K. A. Bollen developed a general method that makes use of the two stage least squares for structural equation models with nonlinear functions of latent variables. In structural equation modelling, the latent variables in the equation are usually linearly related. But in some cases, theory suggests that in addition to the linear effects a product of the latent predictor variables may have an additional effect on a latent criterion variable. In view of non-normality problems with latent interaction models, a stochastic analysis of the density functions involved by interaction models could provide a theoretical background for the development of efficient estimation methods and a better understanding of the major problems associated with latent interaction.

Calls for accountability, coupled with a desire to improve teaching and learning, have prompted many colleges and universities to consider ways of assessing the effects of postsecondary education on student growth and development. Despite widespread support for the concept of assessing student change, relatively few institutions have implemented this type of assessment, in part because of a concern about the best method of measuring change. This article describes the use of structural equation models with latent variables to assess the effects of education on change. Advantages of using structural equation models with latent variables include error-free measurement of change, direct tests of the assumptions underlying change research, along with the power and flexibility of maximum likelihood estimation. An analysis of data on freshman-to-senior gains provides evidence of the advantages of latent variable structural equation modeling and also suggests that the group differences identified by traditional analysis of variance and covariance techniques may be an artifact of measurement error.

Utilizing Structural Equation Modeling in the Development of a Standardized Intervention Assessment Tool

The inverse association between socioeconomic status and smoking is well established, yet the mechanisms that drive this relationship are unclear. We developed and tested four theoretical models of the pathways that link socioeconomic status to current smoking prevalence using a structural equation modeling (SEM) approach. Using data from the 2013 National Health Interview Survey, we selected four indicator variables (poverty ratio, personal earnings, educational attainment, and employment status) that we hypothesize underlie a latent variable, socioeconomic status. We measured direct, indirect, and total effects of socioeconomic status on smoking on four pathways through four latent variables representing social cohesion, financial strain, sleep disturbance, and psychological distress. Results of the model indicated that the probability of being a smoker decreased by 26% of a standard deviation for every one standard deviation increase in socioeconomic status. The direct effects of socioeconomic status on smoking accounted for the majority of the total effects, but the overall model also included significant indirect effects. Of the four mediators, sleep disturbance and psychological distress had the largest total effects on current smoking. We explored the use of structural equation modeling in epidemiology to quantify effects of socioeconomic status on smoking through four social and psychological factors to identify potential targets for interventions. A better understanding of the complex relationship between socioeconomic status and smoking is critical as we continue to reduce the burden of tobacco and eliminate health disparities related to smoking.

A great obstacle for wider use of structural equation modeling (SEM) has been the difficulty in handling categorical variables. Two data sets with known structure between 2 related binary outcomes and 4 independent binary variables were generated. Four SEM strategies and resulting apparent validity were tested: robust maximum likelihood (ML), tetrachoric correlation matrix input followed by SEM ML analysis, SEM ML estimation for the sum of squares and cross-products (SSCP) matrix input obtained by the log-linear model that treated all variables as dependent, and asymptotic distribution-free (ADF) SEM estimation. SEM based on the SSCP matrix obtained by the log-linear model and SEM using robust ML estimation correctly identified the structural relation between the variables. SEM using ADF added an extra parameter. SEM based on tetrachoric correlation input did not specify the data generating process correctly. Apparent validity was similar for all models presented. Data transformation used in log-linear modeling can serve as an input for SEM.

The Role of Providers in Mental Health Services Offered to American-Indian Youths

The past decade has seen a tremendous growth in the use of structural equation modeling (SEM) to address research questions in 2 subfields of behavioral science: cancer prevention and control (e.g., determinants of cancer screening adherence) and behavioral oncology (e.g., determinants of psychosocial adjustment among cancer patients or survivors). The application of SEM in these areas can have far-reaching implications for lowering cancer morbidity and mortality and for improving the psychological well-being and quality of life of those afflicted with, and recovering from, cancer. This article reviews the progress made in the application of SEM to cancer prevention and control and behavioral oncology research, highlighting 2 central uses: theory development and evaluation and scale construction. Directions for the future application of SEM to address research questions concerning cancer prevention and control and behavioral oncology are also discussed.

The use of structural equation modelling (SEM) for research studies in construction related field has been on the increase over the years. The essence of this study is not to compare the level of usage of SEM with other modelling methods, neither is it to examine its extent of adoption in construction management - as this has been researched in previous works - but to arrive at a common ground for future construction related research works, based on the findings and recommendations from existing studies on the subject of SEM. Research materials within and outside the field of construction management were reviewed and it was discovered that SEM using AMOS (covariance approach) is the most appropriate method for construction research studies. This is not just because it is the most available of the software programs, but because of the numerous benefits and advantages highlighted from previous studies. The study also recommended appropriate sample size as well as cut-off value for various required goodness-of-fit tests of SEM model.

This study was aimed to explore the application of Structural Equation Modeling (SEM) in the researches relevant with the field of athletic conducted from 2004 to 2010. Through the analysis and conclusion based on the latent growth curve model (LGM), it was hoped to better comprehend all these papers from 2004 to 2010 with SEM application. It was shown that a total of 192 SEM-applied athletic papers were produced during this certain period of time. There were only six SEM papers in 2004. Within these six years, it was increased to 50 papers a year in 2010. This was a tremendous growth in the quantities of papers SEM-applied in athletic fields, averagely increasing 0.3 paper year by year. SEM-applied papers had exceeding reliability and validity compared to those with conventional statistical methods. Moreover, the estimation of latent variables was the other strength of SEM. Many reference books related to SEM were continuously published. With a rapid increase in the theories underlying SEM, it was proved that SEM is a sophistically-developed statistical methodology. The findings and suggestions of this research can be referenced by other athletic related researches in research design and the application of SEM.

One of the major challenges for epidemiologists is to understand causal relationships between risk factors and health outcomes by analysing data from observational studies. Epidemiologists know too well that correlations, such as those in regression analysis, rarely mean causation, and it would be very desirable if there is a methodology for observational studies, analogous to randomization in experimental studies, which can discover causes and effects amongst variables or at least confirm or refute the proposed causal relationships. Randomized controlled trials (RCTs) are certainly the ‘gold standard’ of establishing causes and effects, but quite often it is either unethical or unfeasible to conduct RCTs to test causal relations in epidemiological research. Epidemiologists need a methodology, which is sort of a combination of the directed acyclic graphs (DAGs) for conceptual construction of causal models and regression analysis for testing those models. It is therefore surprising that structural equation modelling (SEM) has not been so frequently used in epidemiology as in the social sciences, given that both epidemiologists and social scientists want to delineate causes and effects from observational data. The difference between DAGs and path diagrams in SEM is almost trivial to epidemiologists: the path between two variables can only have one direction in DAGs. 1,2 An individual path in SEM is tested in the same way the regression coefficient is in regression analysis, and model fit indices provided by SEM software packages help the analysts to assess the adequacy of the proposed causal model compared with the observed associations in the sample data. 3,4 Why then is SEM still under-utilized in epidemiology? This is a question posed by a commentary 5 in this journal a few years ago. The answers cited included unfamiliar terminology (SEM theory is formulated in Greek), restriction in the assumptions of variables (outcome variables need to be continuous), difficulties in testing interaction and non-linear relationship (it can be quite tedious to set up SEM models to do these), and equivalent models (two different causal models imply the same correlation structure and consequently, it is impossible to tell which is better). 6 Recent advances in SEM theory and software development has resolved some of these issues: new estimation methods do not require the strict assumption of multivariate normality, 7 and the outcome variables can be binary, ordinal or counts. 7–9 The basic rationale behind SEM is rather simple: multiple linear equations are used to specify causal relationship between variables some of which are manifest variables (i.e. observed and collected by the researchers), whilst others are latent variables (i.e. derived from the observed variables by specifying their relations using equations), such as those in factor analysis. The multiple equations in each causal model entail a certain structure of correlational relationships between observed variables which is usually given as a correlation (or covariance) matrix . The estimation procedure is to minimize the difference between and the observed correlation/covariance matrix S formulated by a likelihood function. The 2 -test is then used to evaluate the difference between these two matrices by taking into account the number of the estimated parameters in the proposed model. When the 2 -value is large (i.e. the difference between the two matrices is large) relative to the model’s degree of freedom, the proposed model is rejected, i.e. something in the causal relationships specified by the proposed model is not quite right and require a second thought. When the 2 -value is small, we fail to reject the model or tentatively accept the model as adequate. Because of the possibility of equivalent models, our model may still be wrong in terms of the causal relations amongst variables but happen to entail the same correlation structure (the same S) of the ‘true’ model. So SEM seems to be an endeavour to search for the truth by approximation, and in this aspect, doing SEM is quite similar to the process of ‘Conjectures and Refutations’ as described by the great philosopher

Introduction: in the Brazilian population it is noted that obesity is increasing in all ages, particularly in the elderly, due to changes in habits and the consumption of foods with high energy density. The objective of this study was to reanalyze the data from a Food Frequency Questionnaire (FFQ) in order to obtain new food patterns from the elderly population. Methods: sociodemographic data, morbidities, and Food Frequency Questionnaire (FFQ) results were collected from a representative sample of 355 elderly in the city of Botucatu, São Paulo, Brazil, and stratified by Basic Health Unit in the municipality. The data from the FFQ were transformed into daily consumption, and only foods with an intake of at least 40 % were included in the analysis. Eating patterns were obtained by means of an exploratory and confirmatory factor analysis using the principal component method, associating them with obesity and demographic variables obtained via structural equation models (SEMs). Results: with the data from the FFQ, four eating patterns were obtained using the exploratory and confirmatory factor analysis: healthy, traditional, snacks and weekend meals, and mild diet. Using SEMs and considering general obesity as measured by the body mass index (BMI), being female, younger, hypertensive, diabetic, and having lower adherence to the traditional pattern increases BMI. Additionally, using SEMs and considering central obesity as measured by waist circumference (WC), being hypertensive or diabetic, and having a lower adherence to the traditional pattern increases WC. Conclusion: removing excess zeroes from FFQ data it was possible to obtain well-defined eating patterns using the exploratory and confirmatory analysis, and to associate them with obesity through SEMs.

Celem artykułu jest opisanie założeń formalnych dotyczących wykorzystywania metody równań strukturalnych (Structural Equation Model – SEM). Układy równań strukturalnych służą do weryfikowania teorii i jej założeń.W artykule zostało opisanych pięć założeń budowy każdego modelu pomiarowego, którego stworzenie poprzedza weryfikację założeń teoretycznych za pomocą układu równań strukturalnych. Głównym celem budowy modelu pomiarowego jest sprawdzenie budowy zmiennych latentnych, w tym: a) sprawdzenie rzetelności zmiennych latentnych, b) obliczenie wariacji zmiennych latentnych, c) sprawdzenie trafności różnicowej zmiennych latentnych, d) sprawdzenie wysokości ładunków czynnikowych (lambd λ) oraz e) sprawdzenie statystyk dopasowania modelu.Kolejno omówione zostały założenia modelu weryfikowanego za pomocą jednopoziomowego układu równań strukturalnych SEM: a) sposoby wyłączania ścieżek w modelu, b) sprawdzenie dopasowania modelu teoretycznego do danych empirycznych.W celu przybliżenia omawianych treści zostały przedstawione grafy prezentujące model pomiarowy, a także model SEM.

SummaryDespite the recent debate on theories, logistics and SCM research still lacks a focus on theory development. Research will undoubtedly advance through rigorous empirical approaches, such as the use of structural equation modeling (SEM) within theory construction. SEM is well established in many fields of economic research, as it allows for validity of the structures and constructs in proposed theoretical models to be tested. This paper discusses the contribution of SEM to theory development and presents guidelines for the application of SEM to analyze both measurement and structural models. It exemplifies the use of SEM to capture and analyze the impact of logistics on the performance of companies. Based on a sample of 245 German companies, it emerges that logistics service levels have a greater impact on the performance of companies than logistics costs.

هدف البحث الحالي إلى الكشف عن مطابقة النموذج البنائي المقترح للعلاقات بين اضطراب الشخصية العدوانية– السلبية وسلوك إيذاء الذات كمتغيرات وسيطة، واستراتيجيات التنظيم الانفعالي كمتغير تابع، لدى الأحداث ذوي الإعاقة العقلية البسيطة، وتكونت العينة من(110) من الأحداث ذوي الإعاقة العقلية البسيطة، منهم (73) ذكور، و(37) إناث، وترواحت أعمارهم بين(12-17) عامًا، بمتوسط عمري (15,69)، وانحراف معياري (1,95)، وتمثلث الأدوات في ثلاثة مقاييس هي: اضطراب الشخصية العدوانية - السلبية، وسلوك إيذاء الذات، واستراتيجيات التنظيم الانفعالي، من إعداد/الباحثان، طبقت هذه الأدوات على عينة البحث الأساسية خلال الفصل الدراسي الثاني (2023/2024)، واعتمد البحث علي المنهج الوصفي الارتباطي، وباستخدام حزمة البرامج الإحصائية (SPSS.V23)، وبرنامج (AMOS.26)، لحساب الأساليب الإحصائية اختبار ت T-test، ومعامل الارتباط لبيرسون، وتحليل الانحدار الخطي البسيط وأسلوب تحليل المسار، واستخدام نمذجة المعادلة البنائية للتحقق من مدى مطابقة النموذج المقترح مع بيانات عينة البحث، وأسفرت النتائج عن أنه توجد علاقة طردية دالة إحصائيًا بين اضطراب الشخصية العدوانية- السلبية وسلوك إيذاء الذات لدى الأحداث ذوي الإعاقة العقلية البسيطة، ووجود اختلاف بين نسب إسهام متغيرات البحث في التنبؤ لدى عينة البحث من لأحداث ذوي الإعاقة العقلية البسيطة ، كما تم التوصل إلى نموذج بنائي مقترح يفسر العلاقات أو التأثيرات المباشرة وغير المباشرة بين متغرات البحث