scholarly journals Interpreting latent variables in factor models via convex optimization

2017 ◽  
Vol 167 (1) ◽  
pp. 129-154 ◽  
Author(s):  
Armeen Taeb ◽  
Venkat Chandrasekaran
Keyword(s):  
Convex Optimization ◽  
Latent Variables ◽  
Factor Models

The EV Scaling Method for Variances of Latent Variables

Methodology ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 175-184
Author(s):  
Karl Schweizer ◽  
Stefan Troche
Keyword(s):  
Simulation Study ◽  
Empirical Data ◽  
Latent Variables ◽  
Factor Models ◽  
Scaling Method ◽  
Factor Loadings ◽  
Confirmatory Factor ◽  
Variance Parameters ◽  
Second Procedure ◽  
Estimation Of Variance

Abstract. The paper describes EV scaling for variances of latent variables included in confirmatory factor models. EV-scaled variances can be achieved in two ways: the estimation of variance parameters based on adjusted factor loadings and alternatively the summation of squared factor loadings obtained under the condition that the variance parameter is set equal to one. By definition, the second procedure yields values that are always positive. EV-scaled variances of latent variables show sizes similar to eigenvalues. The outcome of applying this scaling method is demonstrated in empirical data. The results of a simulation study reveal that the outcomes of the two ways virtually always correspond if the data are generated to include the contribution of a latent source. If there is no such source, the exclusion of solutions with negative error variances virtually always leads to correspondence.

scholarly journals Lord–Wingersky Algorithm Version 2.5 with Applications

Psychometrika ◽  
2021 ◽  
Author(s):  
Sijia Huang ◽  
Li Cai
Keyword(s):  
Item Response ◽  
Empirical Data ◽  
Latent Variables ◽  
Recursive Algorithm ◽  
Factor Models ◽  
Model Fit ◽  
Psychological Measurement ◽  
Achievement Growth ◽  
Version 2.0 ◽  
Observed Score

AbstractItem response theory scoring based on summed scores is employed frequently in the practice of educational and psychological measurement. Lord and Wingersky (Appl Psychol Meas 8(4):453–461, 1984) proposed a recursive algorithm to compute the summed score likelihood. Cai (Psychometrika 80(2):535–559, 2015) extended the original Lord–Wingersky algorithm to the case of two-tier multidimensional item factor models and called it Lord–Wingersky algorithm Version 2.0. The 2.0 algorithm utilizes dimension reduction to efficiently compute summed score likelihoods associated with the general dimensions in the model. The output of the algorithm is useful for various purposes, for example, scoring, scale alignment, and model fit checking. In the research reported here, a further extension to the Lord–Wingersky algorithm 2.0 is proposed. The new algorithm, which we call Lord–Wingersky algorithm Version 2.5, yields the summed score likelihoods for all latent variables in the model conditional on observed score combinations. The proposed algorithm is illustrated with empirical data for three potential application areas: (a) describing achievement growth using score combinations across adjacent grades, (b) identification of noteworthy subscores for reporting, and (c) detection of aberrant responses.

Convex Optimization

2004 ◽  
Author(s):  
Stephen Boyd ◽  
Lieven Vandenberghe
Keyword(s):  
Convex Optimization

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

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

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