SmartPLS 結構方程建模軟體 | 新永資訊有限公司

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SmartPLS 結構方程建模軟體

SmartPLS 結構方程建模軟體

  • SmartPLS 結構方程建模軟體
  • 編號
  • 類別
    統計分析軟體
  • 介紹
    SmartPLS是一種具有圖形用戶界面的軟件，用於使用偏最小二乘路徑建模方法進行基於變異的結構方程式建模。用戶可以使用基本PLS-SEM，加權PLS-SEM，一致PLS-SEM和sumscores回歸算法來估計模型及其數據。該軟件可計算標準結果評估標準，並支持其他統計分析。
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SmartPLS analyses

SmartPLS is the workhorse for all PLS-SEM analyses - for beginners as well as experts

Here is our (constantly growing) list of all available calculation methods. Relevant innovative algorithms will also be made available in SmartPLS within a short time. We promise.
  • Partial least squares (PLS) path modeling
  • Ordinary least squares (OLS) regression based on sumscores
  • Consistent PLS (PLSc)
  • Weighted PLS (WPLS), weighted OLS (WOLS) and weighted consistent PLS (WPLSc)
  • Bootstrapping and the use of advanced bootstrapping options
  • Blindfolding
  • Importance-performance map analysis (IPMA)
  • PLS multi-group analysis (MGA): Analyses the difference and significance of group-specific PLS path model estimations
  • Higher-order Models
  • Mediation: Estimation of indirect effects and their bootstrap-based significance testing
  • Moderation: Estimation of interaction effects and their bootstrap-based significance testing
  • Nonlinear relationships: Estimation of quadratic effects and their bootstrap-based significance testing
  • Confirmatory tetrad analysis (CTA): A statistical technique which allows for empirical testing the measurement model setup
  • Finite mixture (FIMIX) segmentation: A latent class approach which allows identifying and treating unobserved heterogeneity in path models
  • Prediction-oriented segmentation (POS): An approach to identify groups of data
  • PLS Predict: A technique to determine the predictive quality of the PLS path model
  • Prediction-oriented model selection

系統需求

 SmartPLS 的最小系統需求
  • 大約 200MB 的可用硬碟空間
  • 至少2GB的RAM
  • Windows 或 MacOSX 的作業系統

安裝

如需要安裝 SmartPLS ，請下載適用於您操作系統的安裝檔，這些是常見的安裝檔，您可以信任它們的默認設置

授權方式

SmartPLS 可以在 學生模式 （免費但受限）或 專業模式 （啟用所有功能）下運行，專業模式需要許可證，可以通過註冊我們的 免費試用密鑰 或 購買 許可證來獲得。

更新紀錄

 Version 4.0.9.6, released 2023-09-01
  • Fixed: Resolved PLS Predict issues with binary constructs and standardized PLS-SEM results for accurate predictions.
  • Fixed: Pairwise regression with intercept problems affecting unstandardized PLS-SEM and PLS Predict.
  • Fixed: MIMIC model validation issues in CB-SEM.
  • Improved: Eliminated indicator duplication from "create data file" function, streamlining data management.
  • Improved: Goodness-of-fit evaluation in CB-SEM.
  • Improved: Added simple validations to identify model identification problems in CB-SEM models.
  • Improved: Enhanced validation in data import dialog for covariance data files, ensuring smoother data handling.
  • Improved: Additional sample projects.
  • Improved: Translations

Version 4.0.9.5, released 2022-06-23

  • Fixed: Minor CB-SEM and CBSEM-Bootstrapping calculation issues.
  • Fixed: Minor translation issues.
  • Improved: Updated internal libraries.
  • Improved: Added option to exclude memory consumptive per-sample results from Bootstrapping and Permutation reports.
  • Improved: Validation and calculation of second order constructs in CB-SEM models.
  • Improved: Changed defaults for creating data files from reports to include other columns.
  • Improved: Additional and extended sample projects.

Version 4.0.7.8, released 2022-08-22

  • Improved: Documentation.
  • Improved: Algorithm dialog.
  • Improved: Speed of all algorithms.
  • Improved: Fundamentally renewed and improved results reports.
  • Improved: Charts and figures from model results.
  • Improved: Additional results for descriptive statistics and many algorithms.
  • Improved: Performance of Excel report generation.
  • Improved: Generation of data groups.
  • Improved: Bootstrapping with fixed seed option.
  • Improved: Higher-order models using the two-stage approach, since the first-stage construct scores can now be stored in a data set within SmartPLS.
  • Fixed: License issues.

Version 3.3.9, released 2022-03-28

  • Fixed: Issues with trial.

Version 3.3.8, released 2022-03-27

  • Fixed: Minor stability issues.
  • Improved: Upgraded internal libraries.

Version 3.3.7, released 2022-01-23

  • Improved: Updated translation files.
  • Improved: Upgraded internal libraries.

Version 3.3.6, released 2022-01-19

  • Improved: Improved performance of several algorithms.
  • Improved: Upgraded internal libraries.

Version 3.3.5, released 2021-12-20

  • Fixed: Deadlocks occurred on some computers during save operations.

Version 3.3.4, released 2021-12-16

  • Fixed: Log4J Vulnerability fix. Log4J updated to a patched version (2.16.0)
  • Improved: Upgraded internal libraries.

Version 3.3.3, released 2021-01-11

  • Improved: Upgraded internal libraries.
  • Improved: GUI adjustments, for a better display under Windows 10 and MacOSX Big Sur.
  • Improved: Indicators that contain non-numeric values are marked with an exclamation mark + tooltip in the datafile editor.
  • Fixed: Display errors and refresh problems under MacOSX Big Sure.
  • Fixed: Performance issue with PLSC algorithm.
  • Fixed: Installation problem in the Persian language area.

SmartPLS 結構方程建模軟體

SmartPLS 是所有 PLS-SEM 分析的主要解決方案 - 適合初學者和專家

以下是我們所有可用分析方式的列表（持續增加中），我們承諾相關的創新分析方式也將在短時間內在 SmartPLS 中提供。
  • Partial least squares (PLS) path modeling
  • Ordinary least squares (OLS) regression based on sumscores
  • Consistent PLS (PLSc)
  • Weighted PLS (WPLS), weighted OLS (WOLS) and weighted consistent PLS (WPLSc)
  • Bootstrapping and the use of advanced bootstrapping options
  • Blindfolding
  • Importance-performance map analysis (IPMA)
  • PLS multi-group analysis (MGA): Analyses the difference and significance of group-specific PLS path model estimations
  • Higher-order Models
  • Mediation: Estimation of indirect effects and their bootstrap-based significance testing
  • Moderation: Estimation of interaction effects and their bootstrap-based significance testing
  • Nonlinear relationships: Estimation of quadratic effects and their bootstrap-based significance testing
  • Confirmatory tetrad analysis (CTA): A statistical technique which allows for empirical testing the measurement model setup
  • Finite mixture (FIMIX) segmentation: A latent class approach which allows identifying and treating unobserved heterogeneity in path models
  • Prediction-oriented segmentation (POS): An approach to identify groups of data
  • PLS Predict: A technique to determine the predictive quality of the PLS path model
  • Prediction-oriented model selection

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