Joint analysis of mixed types of outcomes with latent variables

2020 ◽  
Author(s):  
Deng Pan ◽  
Yingying Wei ◽  
Xinyuan Song
Keyword(s):  
Latent Variables ◽  
Joint Analysis ◽  
Mixed Types

Joint analysis of semicontinuous data with latent variables

2020 ◽  
Vol 151 ◽  
pp. 107005
Author(s):  
Xiaoqing Wang ◽  
Xiangnan Feng ◽  
Xinyuan Song
Keyword(s):  
Latent Variables ◽  
Joint Analysis ◽  
Semicontinuous Data

scholarly journals Joint modelling of mixed outcome types using latent variables

2008 ◽  
Vol 17 (1) ◽  
pp. 53-73 ◽  
Author(s):  
Charles McCulloch
Keyword(s):  
Numerical Calculation ◽  
Simulation Study ◽  
Latent Variables ◽  
Multiple Outcomes ◽  
Joint Models ◽  
Joint Modelling ◽  
Mixed Types ◽  
Marginal Inference

After a brief review of the use of latent variables to accommodate the correlation among multiple outcomes of mixed types, through theoretical and numerical calculation, the consequences of such a construction are quantified. The effects of including latent variables on marginal inference in these models are contrasted with the situation for jointly normal outcomes. A simulation study illustrates the efficiency and reduction in bias gains possible in using joint models, and analysis of an example from the field of osteoarthritis illustrates potential practical differences.

scholarly journals Analysis of multistage in vitro fertilization data with mixed multilevel outcomes using joint modelling approaches

2017 ◽  
Author(s):  
Jack Wilkinson ◽  
Andy Vail ◽  
Stephen A Roberts
Keyword(s):  
In Vitro Fertilization ◽  
Latent Variables ◽  
Joint Analysis ◽  
Joint Modelling ◽  
Vitro Fertilization ◽  
Additional Information ◽  
Routinely Collected Data ◽  
The Creation ◽  
Modelling Approaches

SummaryIn vitro fertilization comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patient’s uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information about the reasons for success or failure. Joint analysis of the sequential responses is complicated by mixed outcome types defined at two levels (patient and embryo). We develop three methods for multistage analysis based on joining submodels for the different responses using latent variables and entering outcome variables as covariates for downstream responses. An application to routinely collected data is presented, and the strengths and limitations of each method are discussed.

Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Processing ◽  
Latent Variables ◽  
Repeated Measures ◽  
Latent Variable ◽  
Model Fit ◽  
Repeated Measures Data ◽  
Confirmatory Factor ◽  
And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

On the measurement of 'latent variables'

2002 ◽  
Author(s):  
Gustavo Mazcorro-Tellez ◽  
Servio T. Guillen Burguete
Keyword(s):  
Latent Variables

Lean diabetes: A joint analysis of the German DIVE and DPV registries

2017 ◽  
Vol 12 (S 01) ◽  
pp. S1-S84
Author(s):  
B Hartmann ◽  
F Groß ◽  
P Bramlage ◽  
S Lanzinger ◽  
T Danne ◽  
...  
Keyword(s):  
Joint Analysis

ANALYSIS THE IMPACT OF THE IMPLEMENTATION OF QUALITY MANAGEMENT SYSTEM (QMS) AND SAFETY AND HEALTHY AT WORK MANAGEMENT SYSTEM (SHWMS / SMK3) ON THE JOB PERFORMANCE OF EMPLOYEES

2018 ◽  
Vol 3 (01) ◽  
pp. 45
Author(s):  
Nur Hidayat ◽  
Indah Kusuma Hayati
Keyword(s):  
Quality Management ◽  
Job Performance ◽  
Latent Variables ◽  
Management System ◽  
Quality Management System ◽  
Partial Least Square ◽  
Least Square ◽  
Global Challenges ◽  
The Impact ◽  
Work Management

Recently, the evolvement of globalization era has been the global challenges that cannot be avoided either by private or government sectors, and they are requested to be survived encountering such the condition. The implementation of Quality Management System (QMS) in the operational company is the way how to guarantee the quality of products or services offered to the people. One of the purposes of QMS implementation is to provide a prime satisfaction to the customers. The impact of QMS implementation is expected to increase job performance of the employees. Besides the implementation of Quality Management System (QMS), the impact of global challenges has been increasing the competitive efforts to execute more effective production process. However, it has required manpower protection accordingly. This research aims to find out whether the implementation of quality management system and safety and healthy at work management system have impacted on the job performance of employees. Objects of this research are the employees in the production department at PT Guna Senaputra Sejahtera Plant 1 Bogor. Data analysis technique of this research has applied software Smart PLS (Partial Least Square). PLS has estimated a model of correlation among the latent variables and correlation between latent variables and its indicators. Result of data processing has indicated that the implementation of Quality Management System (QMS) and system of safety and healthy at work have positively and significantly impacted job performance of employees.Keywords : Quality Management System (QMS), Safety and Healthy at Work System ( SHWS / SMK3), and Job Performance of Employees

ICT SMEs, Performance, Absorptive Capacity and Transformative Capacity

2019 ◽  
Vol 118 (3) ◽  
pp. 178-188
Author(s):  
Yeon-Sung Cho ◽  
Kyung-Il Khoe
Keyword(s):  
Firm Performance ◽  
Market Orientation ◽  
Empirical Analysis ◽  
Absorptive Capacity ◽  
Latent Variables ◽  
Equation Modeling ◽  
Transformative Capacity ◽  
The Impact ◽  
The Empirical Analysis ◽  
Innovative Capacity

This study intends to integrate the relationship of market orientation, innovative capacity and firm performance to Information and Communication Technology(ICT) SMEs. The purpose of this study is to identify the role of absorptive capacity and transformative capacity that affect the performance of ICT SMEs. Hypotheses were established between five latent variables. A total of six hypotheses were established including the moderated effects of absorptive capacity and transformative capacity. Of the data collected after the survey, 112 valid surveys were selected as the final sample, except for 17 questionnaires with high non - response and insincere response. The empirical analysis of this study used smartpls3.0, Partial Least Squares (PLS), a variance-based structural equation modeling. The empirical analysis of this study revealed that the impact of market orientation on innovative capacity was significant. Moreover, the innovative capacity had a positive effect on the performance of ICT SMEs. In addition, the absorptive activity had a positive moderated effect between the market orientation and the innovative capacity. On the other hand, the transformative capacity showed a positive moderated effect in relation to innovative capacity and firm performance. Our empirical results have demonstrated the importance of knowledge based capacity in the ICT SMEs.

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