scholarly journals Clarifying the Implicit Assumptions of Two-Wave Mediation Models via the Latent Change Score Specification: An Evaluation of Model Fit Indices

2021 ◽  
Vol 12 ◽  
Author(s):  
Matthew J. Valente ◽  
A. R. Georgeson ◽  
Oscar Gonzalez
Keyword(s):  
Linear Regression ◽  
Mediation Analysis ◽  
Change Score ◽  
Model Fit ◽  
Fit Indices ◽  
Cross Sectional ◽  
Model Fit Indices ◽  
Latent Change Score ◽  
Linear Regression Modeling ◽  
Mediation Models

Statistical mediation analysis is used to investigate mechanisms through which a randomized intervention causally affects an outcome variable. Mediation analysis is often carried out in a pretest-posttest control group design because it is a common choice for evaluating experimental manipulations in the behavioral and social sciences. There are four different two-wave (i.e., pretest-posttest) mediation models that can be estimated using either linear regression or a Latent Change Score (LCS) specification in Structural Equation Modeling: Analysis of Covariance, difference and residualized change scores, and a cross-sectional model. Linear regression modeling and the LCS specification of the two-wave mediation models provide identical mediated effect estimates but the two modeling approaches differ in their assumptions of model fit. Linear regression modeling assumes each of the four two-wave mediation models fit the data perfectly whereas the LCS specification allows researchers to evaluate the model constraints implied by the difference score, residualized change score, and cross-sectional models via model fit indices. Therefore, the purpose of this paper is to provide a conceptual and statistical comparison of two-wave mediation models. Models were compared on the assumptions they make about time-lags and cross-lagged effects as well as statistically using both standard measures of model fit (χ2, RMSEA, and CFI) and newly proposed T-size measures of model fit for the two-wave mediation models. Overall, the LCS specification makes clear the assumptions that are often implicitly made when fitting two-wave mediation models with regression. In a Monte Carlo simulation, the standard model fit indices and newly proposed T-size measures of model fit generally correctly identified the best fitting two-wave mediation model.

scholarly journals Association between Dietary Inflammatory Index and Periodontitis: A Cross-Sectional and Mediation Analysis

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1194
Author(s):  
Vanessa Machado ◽  
João Botelho ◽  
João Viana ◽  
Paula Pereira ◽  
Luísa Bandeira Lopes ◽  
...  
Keyword(s):  
Linear Regression ◽  
Systemic Inflammation ◽  
Mediation Analysis ◽  
White Blood Cells ◽  
Case Definition ◽  
Mediation Effect ◽  
Dietary Inflammatory Index ◽  
Clinical Attachment Loss ◽  
Cross Sectional ◽  
Inflammatory Index

Inflammation-modulating elements are recognized periodontitis (PD) risk factors, nevertheless, the association between dietary inflammatory index (DII) and PD has never been appraised. We aimed to assess the association between DII and PD and the mediation effect of DII in the association of PD with systemic inflammation. Using the National Health and Nutrition Examination Survey 2009–2010, 2011–2012 and 2013–2014, participants who received periodontal exam and provided dietary recall data were included. The inflammatory potential of diet was calculated via DII. PD was defined according to the 2012 case definition. White blood cells (WBC), segmented neutrophils and C-reactive protein (CRP) were used as proxies for systemic inflammation. The periodontal measures were regressed across DII values using adjusted multivariate linear regression and adjusted mediation analysis. Overall, 10,178 participants were included. DII was significantly correlated with mean periodontal probing depth (PPD), mean clinical attachment loss (CAL), thresholds of PPD and CAL, WBC, segmented neutrophils and DII (p < 0.01). A linear regression logistic adjusted for multiple confounding variables confirmed the association between DII and mean PPD (B = 0.02, Standard Error [SE]: 0.02, p < 0.001) and CAL (B = −0.02, SE: 0.01, p < 0.001). The association of mean PPD and mean CAL with both WBC and segmented neutrophils were mediated by DII (from 2.1 to 3.5%, p < 0.001). In the 2009–2010 subset, the association of mean CAL with serum CRP was mediated by DII (52.0%, p < 0.01). Inflammatory diet and PD may be associated. Also, the inflammatory diet significantly mediated the association of leukocyte counts and systemic inflammation with PD.

scholarly journals Estimating age-based developmental trajectories using latent change score models based on measurement occasion

2019 ◽  
Author(s):  
Eduardo Estrada
Keyword(s):  
Repeated Measures ◽  
Developmental Process ◽  
Developmental Trajectories ◽  
Monte Carlo Study ◽  
Analytical Framework ◽  
Change Score ◽  
Model Fit ◽  
Time Range ◽  
Measurement Occasion ◽  
Latent Change Score

Estrada, Hamagami, &amp; Ferrer, (2019). Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1647822. Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score models (LCS) provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.

scholarly journals A study on development of Perceived Quality-Image Scale in Sports Organizations

2018 ◽  
Vol 15 (4) ◽  
pp. 2407
Author(s):  
Yeşim Bayrakdaroglu ◽  
Dursun Katkat
Keyword(s):  
Reliability Analysis ◽  
Model Fit ◽  
Measurement Tool ◽  
Fit Indices ◽  
Confirmatory Factor Analyses ◽  
Sports Organizations ◽  
Confirmatory Factor ◽  
Quality Image ◽  
Model Fit Indices ◽  
Marketing Activities

The purpose of this study is to research how marketing activities of international sports organizations are performed and to develop a scale determining the effects of image management on public. The audiences of interuniversity World Winter Olympic sheld in Erzurum in 2011 participated in the research. Explanatory and Confirmatory Factor Analysis, reliability analysis were performed over the data obtained. All model fit indices of 25-item and four-factor structure of quality-image scale perceived in sports organizations applied were found to be at good level. In line with the findings obtained from the explanatory and confirmatory factor analyses and reliability analysis, it can be uttered that the scale is a valid and reliable measurement tool that can be used in field researches.

scholarly journals Best Practices in the Use of Bifactor Models: Conceptual Grounds, Fit Indices and Complementary Indicators

2018 ◽  
Vol 18 (3) ◽  
Author(s):  
Pablo Ezequiel Flores-Kanter ◽  
Sergio Dominguez-Lara ◽  
Mario Alberto Trógolo ◽  
Leonardo Adrián Medrano
Keyword(s):  
Best Practices ◽  
Empirical Studies ◽  
Model Fit ◽  
Fit Indices ◽  
Research Article ◽  
Bifactor Models ◽  
Model Fit Indices ◽  
Personality Psychopathology ◽  
Using Data ◽  
Published Research

<p>Bifactor models have gained increasing popularity in the literature concerned with personality, psychopathology and assessment. Empirical studies using bifactor analysis generally judge the estimated model using SEM model fit indices, which may lead to erroneous interpretations and conclusions. To address this problem, several researchers have proposed multiple criteria to assess bifactor models, such as a) conceptual grounds, b) overall model fit indices, and c) specific bifactor model indicators. In this article, we provide a brief summary of these criteria. An example using data gathered from a recently published research article is also provided to show how taking into account all criteria, rather than solely SEM model fit indices, may prevent researchers from drawing wrong conclusions.</p>

Is It Worthy to Take Account of the “Guessing” in the Performance of the Raven Test? Calling for the Principle of Parsimony for Test Validation

2020 ◽  
pp. 073428292093092 ◽  
Author(s):  
Patrícia Silva Lúcio ◽  
Joachim Vandekerckhove ◽  
Guilherme V. Polanczyk ◽  
Hugo Cogo-Moreira
Keyword(s):  
Preschool Children ◽  
Item Response Theory ◽  
Item Response ◽  
Real Data ◽  
Model Fit ◽  
Test Validation ◽  
Fit Indices ◽  
Test Information ◽  
Model Fit Indices ◽  
Principle Of Parsimony

The present study compares the fit of two- and three-parameter logistic (2PL and 3PL) models of item response theory in the performance of preschool children on the Raven’s Colored Progressive Matrices. The test of Raven is widely used for evaluating nonverbal intelligence of factor g. Studies comparing models with real data are scarce on the literature and this is the first to compare models of two and three parameters for the test of Raven, evaluating the informational gain of considering guessing probability. Participants were 582 Brazilian’s preschool children ( Mage = 57 months; SD = 7 months; 46% female) who responded individually to the instrument. The model fit indices suggested that the 2PL fit better to the data. The difficulty and ability parameters were similar between the models, with almost perfect correlations. Differences were observed in terms of discrimination and test information. The principle of parsimony must be called for comparing models.

scholarly journals Hospital Nurses’ Perception of Death and Self-Reported Performance of End-of-Life Care: Mediating Role of Attitude towards End-of-Life Care

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 142
Author(s):  
Hyo-Jin Park ◽  
Yun-Mi Lee ◽  
Mi Hwa Won ◽  
Sung-Jun Lim ◽  
Youn-Jung Son
Keyword(s):  
Linear Regression ◽  
End Of Life ◽  
Mediation Analysis ◽  
End Of Life Care ◽  
Cross Sectional Study ◽  
Hospital Nurses ◽  
Life Care ◽  
Cross Sectional ◽  
Mediating Role

Few studies have explored how nurses in acute care hospitals perceive and perform end-of-life care in Korea. Therefore, this study aimed to evaluate the influence of nurses’ perceptions of death on end-of-life care performance and analyze the mediating role of attitude towards end-of-life care among hospital nurses. This cross-sectional study included a total of 250 nurses who have had experience with end-of-life care from four general hospitals in Korea. We used the Korean validated tools with the View of Life and Death Scale, the Frommelt Attitudes Toward Care of the Dying (FATCOD) scale, and the performance of end-of-life care. Hierarchical linear regression and mediation analysis, applying the bootstrapping method. The results of hierarchical linear regression showed that nurses’ positive perceptions of death and attitude towards end-of-life care were significantly associated with their performance of end-of-life care. A mediation analysis further revealed that nurses’ attitude towards end-of-life care mediates the relationship between the perceptions of death and performance of end-of-life care. Our findings suggest that supportive and practical death educational programs should be designed, based on nurses’ professional experience and work environment, which will enable them to provide better end-of-life care.

scholarly journals The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation

2020 ◽  
pp. 001316442094289
Author(s):  
Amanda K. Montoya ◽  
Michael C. Edwards
Keyword(s):  
Factor Analysis ◽  
Root Mean Square ◽  
Exploratory Factor Analysis ◽  
Model Fit ◽  
Mean Square ◽  
Fit Indices ◽  
Scale Evaluation ◽  
Number Of Factors ◽  
Model Fit Indices ◽  
The Impact

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule ( N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.

The poor fit of model fit for selecting number of factors in exploratory factor analysis for scale evaluation

2020 ◽  
Author(s):  
Amanda Kay Montoya ◽  
Michael C. Edwards
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Evaluation Framework ◽  
Model Fit ◽  
Model Specification ◽  
Fit Indices ◽  
Scale Evaluation ◽  
Number Of Factors ◽  
Model Fit Indices ◽  
The Impact

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean squared error of approximate (RMSEA), standardized root mean squared residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor EFA. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions which are over-factored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.

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