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How does Confirmatory Factor Analysis (CFA) differ from Exploratory Factor Analysis (EFA)?

Confirmatory factor analysis

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Confirmatory Factor Analysis (CFA) differs from Exploratory Factor Analysis (EFA) in their purposes and methodologies. CFA tests pre-determined factor structures, aiming to confirm relationships between observed variables and latent constructs. On the other hand, EFA explores unknown scale dimensions, uncovering underlying constructs. CFA is rigid and may struggle with slightly misspecified models, while EFA often provides accurate parameter estimates, especially when the rotation procedure aligns with the correlation of latent factors. Additionally, CFA validates measurement models, ensuring construct validity, convergent validity, and composite reliability. In contrast, EFA is more suitable when an a priori structure cannot be hypothesized and latent factors are correlated. Overall, CFA confirms existing structures, while EFA explores and uncovers new dimensions.

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Open access•Journal Article•DOI The Study Adapted Instruments Based on Confirmatory Factor Analysis (CFA) to Validate Measurement Models of Latent Constructs Hafidza Baharum, Aniza Ismail, Zainudin Awang, Lisa McKenna, Roszita Ibrahim, Zainah Mohamed, Nor Haty Hassan - Show less +6 more 06 Feb 2023-International Journal of Environmental Research and Public Health 2 Citations	CFA validates existing models, while EFA explores new ones. CFA confirms construct validity, convergent validity, and reliability, whereas EFA aids in model development by identifying underlying factors.
Open access•Journal Article•DOI Confirmatory factor analysis (cfa) of the accounting teacher professional identity scale (atpis) Muhammad Fahmi Johan Syah, Sharul Effendy Janudin, Mahaliza Mansor - Show less +2 more 15 Dec 2022-Jurnal pendidikan Ilmu Sosial	CFA validates pre-defined factor structure, like the ATPIS in the study, while EFA explores data to identify underlying factor structure without preconceived notions.
Journal Article•DOI Is exploratory factor analysis always to be preferred? A systematic comparison of factor analytic techniques throughout the confirmatory–exploratory continuum. 25 May 2023-Psychological Methods	CFA is rigid, struggles with slight misspecifications, and requires a priori structure, while EFA is more flexible, accurate with parameter estimates, and beneficial when latent factors are correlated.
Journal Article•DOI Confirmatory Factor Analysis (CFA) of Managerial Entrepreneurial Competencies Model Required by Technical College Students for Self-employment Abubakar Muhammad 10 Jul 2023	CFA confirms pre-established factor structure, while EFA explores new factor structures. CFA validates existing theories, whereas EFA generates hypotheses for further research.
Book Chapter•DOI Introduction to Confirmatory Factor Analysis (CFA)) using Lavaan in R Statistical Programming Language Huawen Liu 01 Jan 2023	CFA tests pre-determined factor structures, while EFA explores unknown dimensions. CFA confirms relationships between observed variables and latent constructs, whereas EFA uncovers underlying constructs of a scale.

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What is confirmation factor analysis?4 answersConfirmatory Factor Analysis (CFA) is an advanced statistical technique used to explain the covariance structure of high-dimensional data by identifying a small number of factors. It involves computing the correlation matrix implied by the model, crucial for parameter estimation and validity testing. CFA has evolved over the past 30 years, with extensions like multilevel, second-order, bifactor, and Bayesian models addressing measurement issues effectively. Widely applied in organizational research, understanding CFA complexities, proper execution, result reporting, and interpretation is vital for research advancement. For instance, a study on halal tourist attractions utilized CFA to analyze the structural validity of potential levels, revealing six components: Accessibility, Environment, Tourism Management, Facilities, Value, and Response. Overall, CFA serves as a powerful tool for testing construct validity and reliability in various research domains.

What is confirmatory factor analysis?5 answersConfirmatory factor analysis (CFA) is an advanced statistical technique used to explain the covariance structure of high-dimensional data by utilizing a small number of factors. It involves computing the correlation matrix implied by the model, which is critical for estimating the model's parameters and testing its validity. CFA has evolved over the years, with extensions such as multilevel, second-order, bifactor, and Bayesian CFA models being developed. Understanding how to properly conduct CFA analyses, report the results, and interpret their implications is important for advancing organizational research. CFA is also a part of structural equation modeling (SEM), where it examines whether collected data fit a hypothesized model of what the data are meant to measure. In the context of job satisfaction, CFA is used to analyze the factors that influence job satisfaction and identify dimensions and indicators that shape it.

What are the different uses of confirmatory factor analysis reliability and validity in social science?3 answersConfirmatory factor analysis (CFA) is commonly used in social science research for assessing reliability and validity. CFA helps in scale development and validation studies, providing a statistical method to confirm the underlying constructs of a measurement tool. In the context of organizational commitment, CFA is used to evaluate the validity and reliability of measuring instruments, such as the ones based on the theory of Allen and Meyer. CFA is also employed to test construct validity and reliability of translated questionnaires, as seen in the case of the ATTRAKDIFF questionnaire for students' experience in online learning. In social science research, CFA is utilized to assess the reliability and validity of research findings, ensuring that the measurements and constructs used are accurate and consistent. Additionally, CFA is applied to validate the DSEA questionnaire, which measures sleep and dreaming anxiety and emotions, providing evidence of reliability and internal structural validity.

What is confirmatory factor analysis?3 answersConfirmatory factor analysis (CFA) is an advanced statistical technique used to explain the covariance structure of high-dimensional data by utilizing a small number of factors. It involves computing the correlation matrix implied by the model, which is critical for estimating the model's parameters and testing its validity. CFA has evolved over the years, with extensions such as multilevel, second-order, bifactor, and Bayesian CFA models being developed. It is widely used in organizational research, and understanding its complexities is important for conducting and evaluating studies that incorporate CFA. CFA can also be used to establish measurement models for latent constructs and examine their replicability and generalizability in new samples. In the context of the Noetic Signature Inventory (NSI), CFA was used to confirm a 12-factor structure, supporting its validity and reliability as a tool for assessing noetic characteristics.

Should there be an ROI for confirmatory analysis in cognitive neuroscience?5 answersThere should be an ROI for confirmatory analysis in cognitive neuroscience. The use of ROIs allows for focused analysis on specific brain regions of interest. It helps in exploring models that accurately reflect the neurophysiological characteristics of the data. ROI analysis provides powerful insights into neural representations of cognition, emotions, behaviors, and neuropathology of psychiatric disorders. However, it is important to ensure that the selection of ROIs is justified and based on existing literature. The rationale for ROI selection should be clearly stated and supported by quantitative methods such as meta-analysis. Additionally, the sharing of whole-brain unthresholded maps of effect size and binary ROIs in publicly accessible repositories can enhance the validity of ROI-based studies.

What is exploratory factor analysis?5 answersExploratory factor analysis (EFA) is a statistical method used to identify underlying factors in a set of observed variables. It is commonly used in fields such as psychology and social sciences to understand the structure of data and uncover latent dimensions. EFA aims to explain the variation in the data by constructing a few non-observable variables called common factors. These factors are identified through linear combinations of the original variables, with their coefficients known as loadings. The factors are interpreted based on the magnitudes of these loadings. EFA also involves determining the appropriate number of factors and may include procedures such as factor extraction, factor retention, and factor rotation.

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