描述
SmartPLS 結構方程模式軟體
功能介紹
運行 SmartPLS 分析既有趣又輕鬆，簡單的深入了解您的數據！
- 強大的建模環境可讓您在幾分鐘內創建路徑模型。
- 項目經理幫助您跟踪所有分析和文件。
- 使用顏色、邊框和字體自定義您的模型，以單獨強調您的想法！
- 算法和有意義的默認值的內置解釋讓您輕鬆開始進入 PLS-SEM 世界。
- 組織良好的報告可讓您全面了解您的結果。
- 將您的結果永久保存為 HTML 報告或 Excel 文件。
- 創建數據組以輕鬆運行多組分析。
- 創建交互術語並毫無問題地運行主持人分析。
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
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