Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

# Evaluating Structural Equation Models with Unobservable Variables and Measurement Error > OpenAlex Metadata Hub · https://openalex.org/W4235678817 ## Bibliographic - **DOI:** 10.1177/002224378101800104 - **Year:** 1981 - **Citations:** 68442 - **Open Access:** No (closed) - **License:** — - **Source:** https://doi.org/10.1177/002224378101800104 ## Authors - Claes Fornell - David F. Larcker ## Abstract The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model. ## Keywords Unobservable, Structural equation modeling, Econometrics, Variance (accounting), Explanatory power, Observational error, Statistics, Type I and type II errors, Sample size determination, Errors-in-variables models, LISREL, Mathematics, Assertion, Sample (material), Bivariate analysis, Goodness of fit, Computer science ## Concepts - Unobservable - Structural equation modeling - Econometrics - Variance (accounting) - Explanatory power - Observational error - Statistics - Type I and type II errors - Sample size determination - Errors-in-variables models - LISREL - Mathematics - Assertion - Sample (material) - Bivariate analysis - Goodness of fit - Computer science - Programming language - Chromatography - Epistemology - Chemistry - Philosophy - Business - Accounting --- *Metadata only — full text not imported unless Open Access license permits.*

Bài “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error” được TradingBase chuyển thành Knowledge Product cho trader — không phải trang đọc abstract OpenAlex. Tóm lược học thuật (đã diễn giải): The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model. Phần Trading Insights bên dưới nối nghiên cứu với Forex, vàng, USD, lãi suất và risk regime — để bạn đưa vào journal và playbook. Metadata DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường do Content Factory sinh.