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ADANCO

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ADANCO
Original author(s)Jörg Henseler, Theo K. Dijkstra
Developer(s)Composite Modeling GmbH & Co. KG
Initial release2014
Stable release
2.0 / 2015
Engine
    Operating systemWindows & MacOS
    Available inEnglish
    TypeStatistical analysis, Structural equation modeling, Multivariate analysis
    LicenseProprietary software
    Websitecomposite-modeling.com

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    ADANCO (“advanced analysis of composites”) is a software with graphical user interface for variance-based structural equation modeling (SEM)[1] using among others the partial least squares (PLS) method[2][3] including consistent PLS.[4][5] The software can be used in empirical research to analyze primary or secondary data and test theories that consist of relationships between scientific constructs. ADANCO runs on Windows and macOS operating systems.

    Data[edit]

    ADANCO uses raw data saved as .xls or .xlsx files. The columns of the data file represent the variables; the rows represent observations and responses. The first row of data should contain the variable names. Missing values should be coded as “NA”.[3]

    Specifying models[edit]

    ADANCO allows users to specify four different types of measurement models: reflective measurement models (common factor models), causal-formative measurement models (MIMIC models), single-indicator measurement models, and composite-formative measurement models. ADANCO resolves the sign-indeterminacy problem of multi-indicator constructs by means of the dominant-indicator approach.[3]

    Testing models[edit]

    ADANCO allows users to empirically test models using bootstrap-based tests of overall model fit.[4] Moreover, it provides measures of approximate fit.[6]

    References[edit]

    1. Reinartz, Werner; Haenlein, Michael; Henseler, Jörg (2009-12-01). "An empirical comparison of the efficacy of covariance-based and variance-based SEM". International Journal of Research in Marketing. 26 (4): 332–344. doi:10.1016/j.ijresmar.2009.08.001.
    2. Henseler, Jörg (2009-08-01). "On the convergence of the partial least squares path modeling algorithm". Computational Statistics. 25 (1): 107–120. doi:10.1007/s00180-009-0164-x. ISSN 0943-4062.
    3. 3.0 3.1 3.2 Jörg Henseler; Geoffrey Hubona; Pauline Ash Ray (2016-01-07). "Using PLS path modeling in new technology research: updated guidelines". Industrial Management & Data Systems. 116 (1): 2–20. doi:10.1108/IMDS-09-2015-0382. ISSN 0263-5577.
    4. 4.0 4.1 Dijkstra, Theo K.; Henseler, Jörg (2015-01-01). "Consistent and asymptotically normal PLS estimators for linear structural equations". Computational Statistics & Data Analysis. 81: 10–23. doi:10.1016/j.csda.2014.07.008.
    5. Dijkstra, Theo K.; Henseler, Jörg. "Consistent partial least squares path modeling" (PDF). MIS Quarterly. 39 (2): 297–316.
    6. Henseler, Jörg; Dijkstra, Theo K.; Sarstedt, Marko; Ringle, Christian M.; Diamantopoulos, Adamantios; Straub, Detmar W.; Ketchen, David J.; Hair, Joseph F.; Hult, G. Tomas M. (2014-04-01). "Common Beliefs and Reality About PLS Comments on Rönkkö and Evermann (2013)". Organizational Research Methods. 17 (2): 182–209. doi:10.1177/1094428114526928. ISSN 1094-4281.

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