Integrating Moderation and Mediation

2016 ◽  
Vol 20 (4) ◽  
pp. 721-745 ◽  
Author(s):  
Shruti R. Sardeshmukh ◽  
Robert J. Vandenberg
Keyword(s):  
Structural Equation Modeling ◽  
Research Methods ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Latent Variable Interactions ◽  
Variable Interactions ◽  
Do So

It is increasingly common to test hypotheses combining moderation and mediation. Structural equation modeling (SEM) has been the favored approach to testing mediation hypotheses. However, the biggest challenge to testing moderation hypotheses in SEM was the complexity underlying the modeling of latent variable interactions. We discuss the latent moderated structural equation procedure (LMS) approach to specifying latent variable interactions, which is implemented in Mplus, and offer a simple and accessible way of testing combined moderation and mediation hypotheses using SEM. To do so, we provide sample code for six commonly encountered moderation and mediation cases and relevant equations that can be easily adapted to researchers’ data. By articulating the similarities in the two different approaches, discussing the combination of moderation and mediation, we also contribute to the research methods literature.

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

Using WarpPLS in E-collaboration Studies

2010 ◽  
Vol 6 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Research Methods ◽  
Latent Variables ◽  
Structural Equation ◽  
Software Tools ◽  
Equation Modeling ◽  
Modeling Analysis ◽  
Modeling Software ◽  
S Curve ◽  
Complex Phenomena

Most relationships between variables describing natural and behavioral phenomena are nonlinear, with U-curve and S-curve relationships being particularly common. Yet, structural equation modeling software tools do not estimate coefficients of association taking nonlinear relationships between latent variables into consideration. This can lead to misleading results, particularly in multivariate and complex phenomena like those related to e-collaboration. One notable exception is WarpPLS (available from: warppls.com), a new structural equation modeling software currently available in its first release. The discussion presented in this paper contributes to the literature on e-collaboration research methods by providing a description of the main features of WarpPLS in the context of an e-collaboration study. The focus of this discussion is on the software’s features and their use and not on e-collaboration study itself. Particular emphasis is placed on the five steps through which a structural equation modeling analysis is conducted through WarpPLS.

scholarly journals Investigation of influence of growing pigs’ positive affective state on behavioral and physiological parameters using structural equation modeling

2020 ◽  
Vol 98 (2) ◽  
Author(s):  
Katja L Krugmann ◽  
Farina J Mieloch ◽  
Joachim Krieter ◽  
Irena Czycholl
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variable ◽  
Structural Equation ◽  
Affective State ◽  
Body Language ◽  
Physiological Parameters ◽  
Equation Modeling ◽  
Behavioral Tests ◽  
Positive Affective State ◽  
Fattening Pigs

Abstract The aim of the present study was to investigate whether the primarily positive affective state of fattening pigs influences various behavioral and physiological parameters such as the pigs’ playing behavior, way of behaving in behavioral tests, body language signals, or diameter, and astroglia cell numbers of hippocampi, salivary immunoglobulin A (IgA) content, or salivary protein composition. Additionally, the suitability of the variables mentioned was examined to assess the pigs’ positive affective state in practice, which still constitutes a latent variable not itself measurable. For this, a dataset including behavioral and physiological data of 60 fattening pigs from 3 different farms with different housing systems was analyzed by the partial least squares structural equation modeling (PLS-SEM) method. A hierarchical component model (HCM) was used including the pigs’ positive affective state as a higher-order component (HOC) and the behavioral and physiological parameters as lower-order components (LOC). Playing behavior, body language signals, and behavioral tests were revealed, in this order, to be most influenced by the pigs’ positive affective state since these resulted in the corresponding path coefficients (PC) of PC = 0.83, PC = 0.79, and PC = 0.62, respectively. Additionally moderate and weak R2-values occurred for the endogenous latent variables playing behavior (R2 = 69.8%), body language signals (R2 = 62.7%), and behavioral tests (R2 = 39.5%). Furthermore, the indicator of the “locomotor play” showed the highest indicator reliability (IR) (IR = 0.85) to estimate the latent variable of pigs’ positive affective state. The results of the present study supplement the comprehension and assessment of the pigs’ positive affective state in general.

New Conjoint Approaches to Scaling Brand Equity and Optimising share of Preference Prediction

2015 ◽  
Vol 57 (5) ◽  
pp. 701-725 ◽  
Author(s):  
Hervé Guyon ◽  
Jean-François Petiot
Keyword(s):  
Structural Equation Modeling ◽  
Conjoint Analysis ◽  
Brand Equity ◽  
Repeated Measures ◽  
Latent Variable ◽  
Structural Equation ◽  
Probabilistic Models ◽  
Equation Modeling ◽  
New Approach ◽  
First Choice

Ratings-based conjoint analysis suffers two problems: the distortion raised by consumer perceptions of brand equity, and the lack of efficiency of probabilistic models for estimating preference shares. This article proposes two new approaches to scale customer-based brand equity using repeated measures and structural equation modeling and to estimate the share of preferences on the basis of a randomized first choice. The outcome is a new tool to predict accurate preference shares, taking into account product utilities (estimated by rating-based conjoint analysis) and the brand equity related to product attributes (estimated as a latent variable with structural equation modeling). An example with three products illustrates this new approach.

New developments in latent variable panel analyses of longitudinal data

2007 ◽  
Vol 31 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Todd D. Little ◽  
Kristopher J. Preacher ◽  
James P. Selig ◽  
Noel A. Card
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Null Model ◽  
Factorial Invariance ◽  
Equation Modeling ◽  
New Developments ◽  
Mediated Effects

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

A New Dimension of Organizational Justice: Procedural Voice

2009 ◽  
Vol 105 (2) ◽  
pp. 411-426 ◽  
Author(s):  
Denise Jepsen ◽  
John Rodwell
Keyword(s):  
Structural Equation Modeling ◽  
Organizational Justice ◽  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Equation Modeling ◽  
Wide Range

Dimensionality of the Colquitt justice measures was investigated across a wide range of service occupations. Structural equation modeling of data from 410 survey respondents found support for the 4-factor model of justice (procedural, distributive, interpersonal, and informational), although significant improvement of model fit was obtained by including a new latent variable, “procedural voice,” which taps employees' desire to express their views and feelings and influence results. The model was confirmed in a second sample ( N = 505) in the same organization six months later.

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