Vocabulary, Concepts and Usages of Structural Equation Modeling
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle in Structural Equation Modeling for Health and Medicine, 2021
Directly observable phenomena, such as height or weight, are routinely captured by a single measure. Measurable (directly observed) variables are also called manifest variables. Researchers may not have a single observed measure that adequately describes phenomena such as depression, pain behavior, happiness or anxiety. Multiple measures are often used to obtain reliable estimates, particularly for self-report or rater administered survey questionnaires. Medical researchers and practitioners alike need reliable and valid measures in order to understand symptoms, conditions and other clinically relevant measures that cannot be quantified directly. Variables that are not directly observed are considered latent variables and are also regularly labeled latent traits and/or latent constructs. Latent variable analyses can be useful for assessing the measurement properties of clinical screening, assessment and symptom scales.
Advances in social measurement: A Rasch measurement theory
Francis Guillemin, Alain Leplège, Serge Briançon, Elisabeth Spitz, Joël Coste in Perceived Health and Adaptation in Chronic Disease, 2017
Measurement is distinguished by a unit. The idea of a unit can be illustrated readily with the idea of a beam balance for measuring mass. The two objects constructed or found to have the same mass can be demonstrated readily with a beam balance. The identical mass of the two objects can be declared the unit. Then, if the two objects with the unit mass are placed on one side of the balance, and a new object with a mass balances the beam, the new mass is two times the mass of the unit. The process can, in principle, be continued in establishing a measurement of mass of other objects in a relevant range for the balance. A second feature of measurement that the beam balance exemplifies is that to measure the mass of an object it is necessary to somehow manifest the mass. In the above example, the effect of gravity on the masses is manifested on the instrument, the beam balance, in a controlled way. It is common to suggest that social measurement requires manifestation of variables because, instead of being observable, they are latent. The above example indicates that manifesting a variable through some instrument is also necessary in the measurement of mass and is typical in the natural sciences. Therefore, the idea of a latent variable is a feature of all measurement and is not confined to social variables.
Correlational-based methods
Claudio Violato in Assessing Competence in Medicine and Other Health Professions, 2018
Factor analysis is a collection of methods used for exploring the correlations between a number of variables seeking the underlying clusters or subsets called factors or latent variables. According to the principles of factor analysis, variables correlate because they are determined in part by common underlying influences. Patterns of correlations among individual personality variables, for example, are thought to reflect underlying processes that effect students’ behaviors and performance. “Conscientiousness” is thought to be a personality trait characterized by organization, purposeful action, self-discipline, and a drive to achieve. These behaviors, therefore, should be highly correlated.
The Relationship Among Food Safety Knowledge, Attitude, and Behavior of Young Turkish Women
Published in Journal of the American College of Nutrition, 2020
SEM is a comprehensive statistical technique used to test causal relationships between observed (measured) and unobserved (latent) variables. While observed variables can be directly measured in some way, latent variables cannot be measured directly but are implied by the covariances between two or more indicator variables (25). The intensive use of SEM in many different areas is due to the fact that it takes into account the measurement errors of the observed variables, unlike traditional methods (25,26). Another reason for the widespread use of SEM in scientific research is the ability to develop, predict, and test multivariate models that include both direct effects from one variable to another, as well as indirect effects between two variables with the effect of a mediating variable. The complexity of testing multivariable models makes it a necessity to use computer software in SEM applications (27).
An approach for knowledge acquisition from a survey data by conducting Bayesian network modeling, adopting the robust coplot method
Published in Journal of Applied Statistics, 2022
Derya Ersel, Yasemin Kayhan Atılgan
According to the conducted surveys in social sciences, many characteristics, such as support of a political party, economic progress, racial discrimination, alienation, irregularity, domestic violence, or religious loyalty can not be directly observed. However, we can use some observable characteristics which are indicative of them. For example, even though religious loyalty is not directly observable, the frequency of going to worship and prayer can be measured directly. It is usually thought that the observed variables are affected by latent or unobserved variables. So, the latent variables are defined by taking the relations between observed variables into account. Several statistical methods have been proposed to analyze these relations and define latent variables. One of these methods is the Latent Class Analysis (LCA). In a general sense, LCA is used to identify multi-level discrete latent variables using two or more cross classified observed variables [35].
Modeling the relationship between shift work and cardiometabolic risk through circadian disruption, sleep and stress pathways
Published in Chronobiology International, 2022
Haley Golding, Jennifer A. Ritonja, Andrew G. Day, Kristan J. Aronson, Joan Tranmer
The two-step SEM procedure was used for our main statistical analysis (Anderson and Gerbing 1988), with a full information maximum likelihood (FIML) approach to account for missing values (Li and Lomax 2017). SEM employs a combination of factor analysis and path analysis. Factor analysis is a data reduction technique whereby an unmeasurable construct (latent variable) is defined by the covariance of three to five measured variables purportedly caused by the latent variable. Path analysis determines the direct and indirect associations in a complex model where variables may act in both independent and dependent roles. The original conceptual model by Knutsson and Boggild (2000) hypothesized pathways linking shift work to cardiovascular disease through four intermediates: 1) circadian disruption, 2) sleep, 3) unhealthy behaviors and 4) stress. In our study, the category of unhealthy behaviors was not included in the present model for simplicity and due to incomplete data. Additionally, job and life stress was considered as separate variables.
Related Knowledge Centers
- Artificial Intelligence
- Big Five Personality Traits
- Bioinformatics
- Chemometrics
- Factor Analysis
- Psychometrics
- Statistical Inference
- Observation
- Psychology
- G Factor