A Bifactor Approach to Model Multifaceted Constructs in Statistical Mediation Analysis

Dados Bibliográficos

ID20282977
AutoresOscar González (0000-0002-8686-3119, Arizona State University, Tempe, AZ, USA, autor correspondente), David P Mackinnon (0000-0003-0866-6010, Arizona State University, Tempe, AZ, USA)
Ano2018
Volume78
Fascículo1
Páginas5-31
Data de publicação2018-02-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEducational and Psychological Measurement (JOURNAL)
Identificadores do periódicoISSN: 0013-1644 • E-ISSN: 1552-3888
EditoraSAGE Publications (PUBLISHER • US)
DOI10.1177/0013164416673689
PMID29335655
OpenAlexW2530146624
IdiomaEN
Citações recebidas12
Referências citadas61

Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to an outcome. However, current methods do not allow researchers to study the relationships between general and specific aspects of a construct to an outcome simultaneously. This study proposes a bifactor measurement model for the mediating construct as a way to parse variance and represent the general aspect and specific facets of a construct simultaneously. Monte Carlo simulation results are presented to help determine the properties of mediated effect estimation when the mediator has a bifactor structure and a specific facet of a construct is the true mediator. This study also investigates the conditions when researchers can detect the mediated effect when the multidimensionality of the mediator is ignored and treated as unidimensional. Simulation results indicated that the mediation model with a bifactor mediator measurement model had unbiased and adequate power to detect the mediated effect with a sample size greater than 500 and medium a- and b-paths. Also, results indicate that parameter bias and detection of the mediated effect in both the data-generating model and the misspecified model varies as a function of the amount of facet variance represented in the mediation model. This study contributes to the largely unexplored area of measurement issues in statistical mediation analysis

Big Five personality traits · Construct (python library) · Econometrics · Facet (psychology) · Mediation · Outcome (game theory) · Sample (material) · Statistical model · Statistical power · Statistics · Variance (accounting) · Advanced Causal Inference Techniques · Artificial Intelligence · Computer Science · Mathematics · Psychology · Psychometric Methodologies and Testing · Qualitative Comparative Analysis Research · Social Psychology