## 3 Research Results
### Questionnaire Survey Results
#### 3.2.4 Hierarchical Regression Analyses of Factors Influencing Puppetry Learning - An Introduction  
In this study, it is desirable to apply the hierarchical regression method using the questionnaire survey result (i.e., the data of the users' answers to the questions in the questionnaire survey) to analyze the effectiveness of the proposed system for the users' cognition, learning, promotion, and inheritance of the traditional puppetry after they have experienced the role-play operations on the proposed system constructed by use of TUIs (the puppets) and digital technology (RFID and related software and hardware, audio-visual equipment, etc.). Note that the four aspects of the users' cognition, learning, promotion, and inheritance of the traditional puppetry are described by the contents of the 10 th to 15 th questions in Table 7, which includes more details about the four aspects, such as learning of puppetry skills, the practice of narrations, knowledge of role characteristics, engaging in script writing, self-performance of puppet shows, etc. To carry out the aforementioned analysis of the effectiveness of the proposed system, the answers to the 10 th to 15 th questions were taken as _dependent variables_ labeled as Y1 through Y6; those to the fourth to ninth questions as _independent variables_ labeled as X1 through X6; and those to the first to third questions as _control variables_ labeled as C1 through C3, to conduct a series of hierarchical regression analyses with the aim of finding out the factors (represented by the control and independent variables C1 through C3 and X1 through X6) that influence the results of the above-mentioned four aspects of the users' activities about the puppetry (represented by the dependent variables Y1 through Y6). An illustration of the relation between the three types of variables is shown in Figure 10.  
Figure 10: Relationships between independent variables X1 through X6, dependent variables Y1 through Y6, and control variables C1 through C3. \begin{table}
\begin{tabular}{c c} \hline Kaiser–Meyer–Olkin Measure sampling appropriateness & 0.851 \\ \hline \multirow{3}{*}{Bartlett’s sphericity test} & approximately chi square & 531.839 \\ \cline{2-3}  & Degree of freedom & 105 \\ \cline{2-3}  & Significance & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 8: KMO and Bartlett Verification. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Cronbach’s Alpha**} & **Items** \\ \hline \multicolumn{1}{c}{0.909} & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Reliability Statistics. Furthermore, as illustrated in Figure 4, the questions taken as independent variables X1 through X6 were designed to belong to three indicators, namely, \"easy\", \"effective\", and \"enjoyable\", according to [PERSON] [44] which are called _3E indicators_; and are listed respectively in Table 10, where (X1 and X2) belongs to the \"easy group\", (X3 and X4) to the \"effective group\", and (X5 and X6) to the \"enjoyable group\". In the above-mentioned hierarchical regression analyses, for each dependent variable of Y1 through Y6, denoted as Y\(i\), the following two steps were conducted in this study. 1. Step 1: with C1 through C3 as input variables, a regression was conducted to check the prediction capability of the three variables for the target variable Y\(i\);
2. Step 2: with C1 through C3 as the input control variables and _one_ of the three dependent variable pairs (X1 and X2), (X3 and X4), and (X5 and X6), say denoted as (X\(j\) and X\(k\)), as the independent input variables, a two-level regression is conducted to check the prediction capability of (X\(j\) and X\(k\)) for the target variable Y\(i\).