目前偏最小平方法路徑模型（PLS-PM）結合預測分析運用於傳統推論方法之領域研究已有許多前景性發展，本論文提供評估PLS-PM模型的預測概括性並使用欲預測資訊去引導和告知模型選擇任務之概念性與實作性的工具，為現有的PLS-PM領域做出貢獻。
對於如何以預測概括性的評估補足現有的推論方法或如何分析模型之過度擬合以促進理論發展目前尚無清楚的定論，本文提出複合過度擬合分析（COA）框架（複合多種方法、解釋性預測之框架）直接解決以上缺點：（1）測量焦點結構的預測性能（2）辨別加劇過度擬合的個別案例（3）辨別無法概括好的樣本外之構念間的結構關係（4）引導質性分析以挖掘此類衝突更深之原因。 我們在一代表性的技術採用模型上展現了此分析框架的實用性，並發現一些使用此框架後的新洞察。
比較行為現象的不同替代性解釋是探索科學過程的核心要素， 為了充分利用PLS-PM的預測功能，研究者必須了解其所使用之預測指標的效能。 在本文中，我們藉由比較標準PLS-PM之準則和以訊息理論得出的模型選擇之準則的性能，從一群備選模型中選擇出最佳的預測模型。 然而因標準值間之差異通常很小，依據模型選擇之準則選擇出的模型可能導致錯誤的信心，因此以PLS-PM之模型比較任務為基礎，我們進一步分析了Akaike權重的有效性。
進行多次蒙特卡羅模擬以分析在不同樣本大小、效應值、項目負荷量和模型建置下模型選擇之標準和Akaike權重的性能，我們評估了指標的有限樣本性能並提供研究者實際操作的建議，發現在無足夠數據作為保留樣本的小樣本且目標是選擇低預測誤差的正確指定模型情況下，樣本內模型選擇標準（尤其是貝葉斯信息量準則（ BIC）和Geweke-Meese標準（GM））可有效替代樣本外標準；當保留樣本條件成立時，表現最佳之樣本外標準包括均方根誤差（RMSE）和平均絕對偏差（MAD）。此外，我們發現從BIC和GM導出的Akaike權重非常適合用於從正確指定的模型中分離出錯誤指定的值，且在不確定模型選擇的情況下，基於AIC的Akaike權重對於建立模型平均的預測十分有用。
本文直接探討了預測分析在科學研究中的作用，以下三個要素對於解釋預測性結構模型特別重要：（1）評估相關性（2）評估可預測性（3）比較競爭性理論。目前，預測在解釋性模型中的第一與第二要素可使用COA框架評估模型的實際效用和預測性能來解決，根據模型的過度擬合來對模型進行基準測試和評估；當遇到過度擬合時，該框架則提供了實用且可操作的補救措施。此外，我們提供了模型選擇的指標與明確的方法來評估在預測環境中模型的選擇並杜絕預測環境中的不確定性–直接解決了第三個要素。這些技術比較了正在分析之多個模型的預測性能，減少了與此類預測模型選擇時的不確定性，並生成了模型平均預測的實際應用。因此我們認為本文以強大的預測分析技術，為欲補足其模型解釋性之研究者提供了必要的工具。

Partial Least Squares Path Modeling (PLS-PM) research has seen several promising developments integrating predictive analytics into what has traditionally been an inferential approach. This thesis contributes to extant PLS-PM methodology by providing both conceptual and practical tools for conducting evaluations of the predictive generalizability of PLS-PM models and using predictive information to guide and inform model selection tasks.
It is not yet clear how evaluations of predictive generalizability can complement existing inferential methods, nor how analysis of model overfit can enhance theory development. We directly address these shortcomings in this thesis by proposing the Composite Overfit Analysis (COA) Framework: a mixed-methods, explanatory-predictive framework that: (1) gauges predictive performance of focal constructs, (2) identifies individual cases that exacerbate overfit, (3) identifies structural relationships between constructs that may not generalize well out-of-sample, and (4) guides qualitative analysis to explore the deeper reasons for such conflicts. We demonstrate the practical utility of our analytical framework on a typical technology adoption model and find that new insights might be discovered and reported using the framework.
Comparing alternative explanations for behavioral phenomena is central to the process of scientific inquiry. To fully benefit from the predictive capabilities of PLS-PM, researchers must understand the efficacy of predictive metrics used. In this thesis, we compare the performance of standard PLS-PM criteria and model selection criteria derived from Information Theory, in terms of selecting the best predictive model among a cohort of competing models. However, selecting one model over others based on model selection criteria may lead to a false sense of confidence as differences in the criteria values are often small. We thus further analyze the efficacy of Akaike weights in PLS-PM-based model comparison tasks.
We conduct several Monte Carlo simulations to analyze the performance of model selection criteria and Akaike weights under various sample sizes, effect sizes, item loadings, and model setups. We thus evaluate the finite-sample performance of the metrics and generate prescriptions of practical use for researchers. We find that under the constraints of small sample size, where there might be insufficient data for a holdout sample, and the goal is selecting correctly specified models with low prediction error, the in-sample model selection criteria, in particular the Bayesian Information Criterion (BIC) and Geweke-Meese Criterion (GM), are useful substitutes for out-of-sample criteria. When a holdout sample is available, the best performing out-of-sample criteria include the root mean squared error (RMSE) and mean absolute deviation (MAD). Further, we find that Akaike weights derived from BIC and GM are well suited for separating incorrectly specified from correctly specified models, and that Akaike weights based on AIC are useful for creating model-averaged predictions under conditions of model selection uncertainty.
This thesis directly addresses the roles for predictive analytics in scientific research. that are of particular importance for explanatory-predictive construct-based models: (1) assessing relevance, (2) assessing predictability, and (3) comparing competing theories. The first and second roles of prediction in explanatory modeling are now addressable by using the COA Framework to evaluate the practical utility and predictive performance of the model. Models can now be benchmarked and evaluated in terms of their overfit, and the framework provides practical, actionable remedies when overfit is encountered. Further, we provide model selection metrics and clear methods for evaluating model selection and rejection uncertainty in the predictive context – directly addressing the third role. These techniques compare predictive performance of multiple models under analysis, reduce the uncertainty associated with such predictive model selection, and generate model-averaged predictions for practical applications. We thus believe this thesis arms researchers with the tools necessary to supplement their explanatory research with strong predictive analytical techniques.

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