摘要：Normal 0 21 false false false DE X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This study addresses the issue of the optimal number of gaps in C-Test passages. An English C-Test battery containing four passages each having 40 blanks was given to 104 undergraduate students of English. The data were entered into SPSS spreadsheet. Out of the complete data with 160 blanks seven additional datasets were constructed. In the first dataset the scores on the first five gaps in each passage were aggregated and the rest of the gaps were ignored, as if each passage had only five gaps. In the second dataset the scores on the first ten gaps were aggregated. In each subsequent dataset five more gas were added. The eight datasets were analyzed and their psychometric properties were compared. The results showed that as the number of gaps in each passage increases item discrimination, reliability and factorial validity of the test increase accordingly. The implications for C-Test application and use are discussed.
