測量選擇和偏好的派典決斷： 李克特量表、比較判斷、或綜合模型？以學業歸因為例

Abstract

在社會科學領域中，準確測量個體在不同選項間的偏好至關重要，李克特量表（Likert Scale，LS）與比較判斷（Comparative Judgment，CJ）是兩種常用的測量方法。李克特量表因為廣為人知且易於使用而被廣泛採用；比較判斷則提供了更直接的偏好測量。然而，比較判斷因其強迫選擇的特性，其估計分數的可解釋性受到限制。Böckenholt (2004) 及後續研究者提出了結合李克特量表與比較判斷資料的模型，以發揮兩種測量方法的優勢。儘管有這些重要進展，先前研究中使用的這類聯合Thurstonian模型（Joint Thurstonian models）仍無法適當地處理序數尺度資料、無法考慮題目參數和閾值的異質性，亦無法偵測李克特量表與比較判斷資料間的潛在差異。為了解決這些限制，我們開發了一系列聯合 Thurstonian 模型，包括聯合 Thurstonian 試題反應理論模型（JT-IRT）、聯合 Thurstonian 非加成性概推度理論模型（JT-NAGT）和聯合 Thurstonian 可加成性概推度理論模型（JT-AGT），並檢驗這些新模型在學業歸因測量上的適用性和效度。為達成此目標，我們招募大學生針對真實的重要學業成功或失敗經驗（期中考考好或考壞）提供多重學業歸因（成功組 n_成功 = 396 人；失敗組 n_失敗 = 330 人）。除了常見的努力和能力歸因外，本研究還納入了其他歸因類型，包括考試難度、運氣和情緒。我們使用不同的心理計量模型：從LS資料、CJ資料，或結合兩者的資料中產生不同指標，隨後使用前進選擇法進行「與理論相關構念的聚合與區辨效度分析」（包含智力內隱理論與成就目標取向），以及「與學習適應的效標關聯效度分析」（包含憂鬱情緒、拖延閒混及自我設限），以檢視不同指標之效度。結果顯示：在成功和失敗歸因情境中，基於概推度理論之聯合 Thurstonian 模型（JT-AGT 和 JT-NAGT）顯著優於傳統的 Böckenholt 模型，JT-NAGT在成功情境中表現最佳，而JT-AGT在失敗情境中展現優勢，但這兩者的模型適配度依然不若LS與CJ分離建模之結果。效度分析顯示：能力和努力歸因主要透過LS與效標相關，而運氣歸因則多透過CJ與效標相關；情緒和運氣歸因展現出比傳統強調的能力歸因更強的解釋力。並且，聯合 Thurstonian 指標展現出超越個別LS或CJ的顯著增益效度，顯示整合測量方法能捕捉到單一測量工具無法表徵的獨特變異。這些發現挑戰了當今聚焦於努力與能力歸因之歸因理論，並指出心情和運氣等歸因在歸因研究中具有無法被忽略的解釋力，這代表研究者需採用多種測量方法與多重歸因來掌握學業歸因與適應之間的複雜關係。

In the field of social science, accurately measuring individuals' preferences among alternatives is essential, with two prominent measurement formats—Likert Scale (LS) and Comparative Judgment (CJ)—often employed for this purpose. While LS is widely used because it is well-known and easy to apply, CJ offers a more direct measurement of preferences. However, CJ poses challenges due to its ipsative nature, which can hinder the interpretability and comparability of its estimated scores. Böckenholt (2004) and subsequent researchers proposed models to combine data acquired via LS and CJ, leveraging the strengths of both measurement methods. Despite these significant advances, the joint Thurstonian models used in prior research were unable to adequately model data obtained from ordinal scales, account for heterogeneity in item parameters and thresholds, or detect latent differences between LS and CJ data. To address these limitations, we developed a family of Joint Thurstonian models, including the Joint Thurstonian Item Response Theory (JT-IRT), Joint Thurstonian Nonadditive Generalizability Theory (JT-NAGT) models, and Joint Thurstonian Additive Generalizability Theory (JT-AGT) models, and examined these new models in terms of their applicability and validity in academic attribution. To achieve this objective, college students reported on their multiple causal ascriptions after a real-life experience of a major academic success or failure (i.e., grades received from a midterm: n_success = 396$; n_failure = 330). In addition to the commonly assessed academic attributions of effort and ability, this study also included other attributions, namely, task difficulty, luck, and mood. We then used different mathematical models to generate indicators from LS data alone, CJ data alone, or combined LS and CJ data. Subsequently, we used forward selection methodology to assess validity evidence through convergent and discriminant validity analyses with theoretically related constructs and test-criterion relationships with academic adjustment outcomes.

Across both success and failure attribution scenarios, Joint Thurstonian models with generalized tau structures (JT-AGT and JT-NAGT) substantially outperformed the traditional Böckenholt model, though separate modeling approaches achieved the best overall model fit. The results reveal complex patterns of criterion-related validity. JT-NAGT performed best in success scenarios, while JT-AGT showed advantages in failure contexts. Validity analyses revealed systematic measurement format preferences: ability and effort attributions emerged primarily through LS indicators, while luck attributions predominated through CJ measures. Notably, mood and luck attributions showed stronger explanatory power than traditionally emphasized ability attributions. Joint Thurstonian indicators demonstrated significant incremental validity beyond individual LS or CJ measures, indicating that integrated measurement approaches capture unique attribution variance not fully represented by single-format instruments. These findings challenge current attribution theories that only focus on effort and ability attributions. Moreover, these findings suggest that mood and external attributions (especially luck) cannot be overlooked in attribution research, and researchers need to adopt multiple measurements to grasp the complex relations among academic attributions and adjustment.