Structural Equation Modeling with JMP® Pro

被引:4
作者
Gonzales, Joseph E. [1 ]
机构
[1] Univ Massachusetts, Dept Psychol, Lowell, MA 01854 USA
关键词
JMP (R) Pro; SEM; Structural Equation Modeling; confirmatory factor analysis; longitudinal measurement invariance; MAXIMUM-LIKELIHOOD-ESTIMATION; MISSING-DATA; FACTORIAL INVARIANCE; PERFORMANCE; GROWTH; VARIABLES;
D O I
10.1080/15366367.2020.1809231
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
JMP (R) Pro has introduced a new structural equation modeling (SEM) platform to its suite of multivariate methods of analysis. Utilizing their graphical user interface, JMP Pro has created a SEM platform that is easily navigable for both experienced and novice SEM users. As a new platform, JMP Pro does not have the capacity to implement certain modeling approaches (e.g., multiple-group, multilevel, and mixture models), but it supports many others (e.g., CFA, cross-lagged panel models, growth and difference models, and one-sample time-series). In the present review, strengths and limitations of the platform are detailed, and model features are demonstrated through two worked examples.
引用
收藏
页码:80 / 92
页数:13
相关论文
共 50 条
  • [21] Education: a comparative structural equation modeling study
    Camgoz-Akdag, Hatice
    Zaim, Selim
    CYPRUS INTERNATIONAL CONFERENCE ON EDUCATIONAL RESEARCH (CY-ICER-2012), 2012, 47 : 874 - 880
  • [22] Structural equation modeling of sustainable manufacturing practices
    Vinodh, S.
    Joy, Dino
    CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY, 2012, 14 (01) : 79 - 84
  • [23] Meta-Analytic Structural Equation Modeling With Moderating Effects on SEM Parameters
    Jak, Suzanne
    Cheung, Mike W-L
    PSYCHOLOGICAL METHODS, 2020, 25 (04) : 430 - 455
  • [24] Effects of Missing Data Methods in Structural Equation Modeling With Nonnormal Longitudinal Data
    Shin, Tacksoo
    Davison, Mark L.
    Long, Jeffrey D.
    STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2009, 16 (01) : 70 - 98
  • [25] Analysis of Factors Influencing Technology Transfer: A Structural Equation Modeling Based Approach
    Singhai, Sandeep
    Singh, Ritika
    Sardana, Harish Kumar
    Madhukar, Anuradha
    SUSTAINABILITY, 2021, 13 (10)
  • [26] Critical delay factors in power transmission projects: a structural equation modeling approach
    Pall, Goutom K.
    Bridge, Adrian J.
    Washington, Simon
    Gray, Jason
    Skitmore, Martin
    INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT, 2022, 22 (06) : 1158 - 1170
  • [27] RETRACTED: A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes
    Depaoli, Sarah
    Clifton, James P.
    STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2015, 22 (03) : 327 - 351
  • [28] The Internal Structure of Responses to the Trait Emotional Intelligence Questionnaire-Short Form: An Exploratory Structural Equation Modeling Approach
    Perera, Harsha N.
    JOURNAL OF PERSONALITY ASSESSMENT, 2015, 97 (04) : 411 - 423
  • [29] lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
    Huang, Po-Hsien
    JOURNAL OF STATISTICAL SOFTWARE, 2020, 93 (07): : 1 - 37
  • [30] Modeling motor connectivity using TMS/PET and structural equation modeling
    Laird, Angela R.
    Robbins, Jacob M.
    Li, Karl
    Price, Larry R.
    Cykowski, Matthew D.
    Narayana, Shalini
    Laird, Robert W.
    Franklin, Crystal
    Fox, Peter T.
    NEUROIMAGE, 2008, 41 (02) : 424 - 436