Measurement of composite reliability in research using partial least squares: some issues and an alternative approach
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Summary
This research describes the existence of three important issues concerning the accuracy of composite reliability estimation in PLS analysis: the assumption of equal indicator weights, the bias in loading estimates, and the lack of independence between indicator loadings and weights.
- Type
- article
- Published
- 2013-11-14
- Cited by
- 56
- References
- 53
- OpenAlex
- https://openalex.org/W1986735245
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16220807
Keywords
Reliability (semiconductor), Monte Carlo method, Independence (probability theory), Reliability engineering, Partial least squares regression
References
- Estimating the Reliability of a Test Battery Composite or a Test Score Based on Weighted Item Scoring
- A critical look at partial least squares modeling
- Soft modelling: The Basic Design and Some Extensions
- Editor's comments: PLS: a silver bullet?
- Statistical Strategies for Small Sample Research
- Score Reliability: Contemporary Thinking on Reliability Issues
- Evaluating Effect, Composite, and Causal Indicators in Structural Equation Models
- Lisrel 8: User's Reference Guide
- Validating Instruments in MIS Research
- Structural Equation Modeling and Regression: Guidelines for Research Practice
- The partial least squares approach for structural equation modeling.
- A Non-arbitrary Method of Identifying and Scaling Latent Variables in SEM and MACS Models
- A Method for Obtaining Standard Errors and Confidence Intervals of Composite Reliability for Congeneric Items
- Analytic Estimation of Standard Error and Confidence Interval for Scale Reliability
- Conventional wisdom on measurement: A structural equation perspective.
- Psychometric theory (2nd ed.).
- Beyond the Spearman-Brown: a structural approach to maximal reliability.
- Intraclass Reliability Estimates: Testing Structural Assumptions
- Estimation of maximal reliability: a note on a covariance structure modelling approach.
- The Reliability and Validity of Weighted Composite Scores
Cited by
- Sample Size Determination and Statistical Power Analysis in PLS Using R: An Annotated Tutorial
- On the adoption of partial least squares in psychological research: Caveat emptor
- A new criterion for assessing discriminant validity in variance-based structural equation modeling
- Reflections on Partial Least Squares Path Modeling
- Between-Group Equivalence in Comparisons Using PLS: Results from Three Simulation Studies
- A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling
- Improvements to PLSc: Remaining problems and simple solutions
- Partial least squares path modeling: Time for some serious second thoughts
- The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling
- Variation Sharing: A Novel Numeric Solution to the Path Bias Underestimation Problem of PLS-Based SEM
- Comparison Between CB-SEM and PLS-SEM: Testing and Confirming the Maqasid Syariah Quality of Life Measurement Model
- The Effect of Training Self‐Efficacy on Computer‐Based Training Outcomes: Empirical Analysis of the Construct and Creation of Two Scales
- Polynomial Regression and Measurement Error
- A Study on Assessing the Relationship between Green Marketing and Brand Loyalty in Manufacturing Sector of Greece: A Moderating Role of Green Supply Chain Practices
- Adaption and validity of the “Physical Activity Class Satisfaction Questionnaire» (PACSQ) in Greek educational dancing context
- The continuation and recommendation intention of artificial intelligence-based voice assistant systems (AIVAS): the influence of personal traits
- Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic
- The Physical Activity Class Satisfaction Questionnaire (PACSQ) in Greek educational context: Psychometric properties
- Big data analytics capability as a major antecedent of firm innovation performance
- Incorporation of simulation features to improve higher order thinking skills
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