scholarly journals Pooling methods for likelihood ratio tests in multiply imputed data sets

2021 ◽  
Author(s):  
Simon Grund ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Likelihood Ratio ◽  
Statistical Models ◽  
Structural Equation ◽  
Linear Models ◽  
R Package ◽  
Likelihood Ratio Tests ◽  
Equation Modeling ◽  
Data Sets ◽  
New Methods ◽  
Multiply Imputed

Likelihood ratio tests (LRTs) are a popular tool for comparing statistical models. However, missing data are also common in empirical research, and multiple imputation (MI) is often used to deal with them. In multiply imputed data, there are multiple options for conducting LRTs, and new methods are still being proposed. In this article, we compare all available methods in multiple simulations covering applications in linear regression, generalized linear models, and structural equation modeling (SEM). In addition, we implemented these methods in an R package, and we illustrate its application in an example analysis concerned with the investigation of measurement invariance.

Performing likelihood ratio tests with multiply-imputed data sets

Biometrika ◽  
1992 ◽  
Vol 79 (1) ◽  
pp. 103-111 ◽  
Author(s):  
XIAO-LI MENG ◽  
DONALD B. RUBIN
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Tests ◽  
Data Sets ◽  
Multiply Imputed

Iranian Coaches Forgiveness and Relationship Quality to Their Athletes

2021 ◽  
pp. 1-8
Author(s):  
Hassan Gharayagh Zandi ◽  
Sahar Zarei ◽  
Mohammad Ali Besharat ◽  
Davoud Houminiyan sharif abadi ◽  
Ahmad Bagher Zadeh
Keyword(s):  
Relationship Quality ◽  
Structural Equation ◽  
Equation Modeling ◽  
Data Sets ◽  
Mediating Role ◽  
Interpersonal Behaviors ◽  
Key Factor ◽  
The Relationship

Coaching has often been viewed as a context within which coaches operate to largely bring about changes in athlete’s performance and flourishing. One key factor to successful outcomes in coaching is the quality of the relationship between coaches and athletes. The coach–athlete relationship is at the heart of coaching; however, limited studies have been conducted on its antecedents. The aim of this study was to investigate the relationship between coaches’ forgiveness and perceived relationship quality toward their athletes through verifying the mediating role of interpersonal behaviors of coaches. A total of 270 Iranian coaches participated in the survey, and the data sets were analyzed using structural equation modeling. Results revealed that forgiveness positively predicted the coaches’ perceived relationship quality with their athletes, and this pathway was mediated by the coaches’ interpersonal behaviors.

scholarly journals Trends in the investigation of social determinants of health: selected themes and methods

2011 ◽  
Vol 27 (1) ◽  
pp. 183-189 ◽  
Author(s):  
Roger Keller Celeste ◽  
João Luiz Bastos ◽  
Eduardo Faerstein
Keyword(s):  
Public Health ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Structural Equation ◽  
Determinants Of Health ◽  
Equation Modeling ◽  
New Methods ◽  
Causal Diagrams ◽  
Pubmed Database ◽  
Race Ethnicity

We analyze bibliometric trends of topics relevant to the epidemiologic research of social determinants of health. A search of the PubMed database, covering the period 1985-2007, was performed for the topics: socioeconomic factors, sex, race/ethnicity, discrimination/prejudice, social capital/support, lifecourse, income inequality, stress, behavioral research, contextual effects, residential segregation, multilevel modeling, regression based indices to measure inequalities, and structural equation modeling/causal diagrams/path analysis. The absolute, but not the relative, frequency of publications increased for all themes. Total publications in PubMed increased 2.3 times, while the subsets of epidemiology/public health and social epidemiologic themes/methods increased by factors of 5.3 and 5.2, respectively. Only multilevel and contextual analyses had a growth over and above that observed for epidemiology/public health. We conclude that there is clearly room for wider use of established techniques, and for new methods to emerge when they satisfy theoretical needs.

On the likelihood ratio test in structural equation modeling when parameters are subject to boundary constraints.

2006 ◽  
Vol 11 (4) ◽  
pp. 439-455 ◽  
Author(s):  
Reinoud D. Stoel ◽  
Francisca Galindo Garre ◽  
Conor Dolan ◽  
Godfried van den Wittenboer
Keyword(s):  
Structural Equation Modeling ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Structural Equation ◽  
Equation Modeling ◽  
Ratio Test ◽  
Boundary Constraints

scholarly journals Effect of Robo-Taxi User Experience on User Acceptance: Field Test Data Analysis

2021 ◽  
pp. 036119812110415
Author(s):  
Sunghee Lee ◽  
Soyoung Yoo ◽  
Seongsin Kim ◽  
Eunji Kim ◽  
Namwoo Kang
Keyword(s):  
User Experience ◽  
Statistical Models ◽  
Structural Equation ◽  
User Acceptance ◽  
Equation Modeling ◽  
Perceived Safety ◽  
Taxi Service ◽  
Meaningful Relationships ◽  
Service Experiences ◽  
Depth Interviews

With the advancement of self-driving technology, the commercialization of robot taxi (Robo-taxi) services is expected. However, there is some skepticism as to whether such taxi services will be successfully accepted by real customers because of perceived safety-related concerns; therefore, studies focused on user experience have become more crucial. Although many studies statistically analyze user experience data obtained by surveying individuals’ perceptions of Robo-taxis or indirectly through simulators, there is a lack of research that statistically analyzes data obtained directly from actual Robo-taxi service experiences. Accordingly, based on the user experience data obtained by implementing a Robo-taxi service in the downtown of Seoul and Daejeon in South Korea, this study quantitatively analyzes the effect of user experience on user acceptance through structural equation modeling and path analysis. Balanced and highly valid insights were also obtained by re-analyzing meaningful relationships obtained through statistical models based on the results of in-depth interviews. The results revealed that the experience of the traveling stage had the greatest effect on user acceptance, and the cutting-edge nature of the service and apprehension of technology were emotions that had a significant effect on user acceptance. Based on these findings, guidelines are suggested for the design and marketing of future Robo-taxi services.

Dyadic Designs in Lifespan Developmental Methodology

2019 ◽  
Author(s):  
Jeremy B. Yorgason ◽  
Melanie S. Hill ◽  
Mallory Millett
Keyword(s):  
Repeated Measures ◽  
Structural Equation ◽  
Equation Modeling ◽  
Daily Diaries ◽  
Intensive Longitudinal Data ◽  
New Methods ◽  
Actor Partner Interdependence Model ◽  
Parents And Children ◽  
Research Questions ◽  
The Individual

The study of development across the lifespan has traditionally focused on the individual. However, dyadic designs within lifespan developmental methodology allow researchers to better understand individuals in a larger context that includes various familial relationships (husbands and wives, parents and children, and caregivers and patients). Dyadic designs involve data that are not independent, and thus outcome measures from dyad members need to be modeled as correlated. Typically, non-independent outcomes are appropriately modeled using multilevel or structural equation modeling approaches. Many dyadic researchers use the actor-partner interdependence model as a basic analysis framework, while new and exciting approaches are coming forth in the literature. Dyadic designs can be extended and applied in various ways, including with intensive longitudinal data (e.g., daily diaries), grid sequence analysis, repeated measures actor/partner interdependence models, and vector field diagrams. As researchers continue to use and expand upon dyadic designs, new methods for addressing dyadic research questions will be developed.

scholarly journals From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)

2019 ◽  
Vol 23 (4) ◽  
pp. 651-687 ◽  
Author(s):  
Michael J. Zyphur ◽  
Paul D. Allison ◽  
Louis Tay ◽  
Manuel C. Voelkle ◽  
Kristopher J. Preacher ◽  
...  
Keyword(s):  
Causal Inference ◽  
Structural Equation ◽  
National Income ◽  
Well Being ◽  
Equation Modeling ◽  
Data Sets ◽  
Subjective Well Being ◽  
Panel Model ◽  
Long Run ◽  
Short Run

This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs . We conclude with a discussion of issues surrounding causal inference.

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