Abstract This study investigated the discriminant validation of the Test of Reading Performance (TORP), a new scale designed to evaluate the reading performance of elementary-school students. The sample consisted of 181 elementary-school students drawn from public elementary schools in northern Greece using stratified random procedures. The TORP was hypothesized to measure six constructs, namely: “letter knowledge,” “phoneme blending,” “word identification,” “syntax,” “morphology,” and “passage comprehension.” Using standard deviations (SD) from the mean, three groups of students were formed as follows: A group of low achievers in reading (N = 9) including students who scored between -1 and -1.5 SD from the mean of the group. A group of students at risk of reading difficulties (N = 6) including students who scored between -1.5 and -2 SDs below the mean of the group. A group of students at risk of serious reading difficulties (N = 6) including students who scored -2 or more SDs below the mean of the group. The rest of the students (no risk, N = 122) comprised the fourth group. Using discriminant analyses it was evaluated how well the linear combination of the 15 variables that comprised the TORP could discriminate students of different reading ability. Results indicated that correct classification rates for low achievers, those at risk for reading problems, those at risk of serious reading problems, and the no-risk group were 89%, 100%, 83%, and 97%, respectively. Evidence for partial validation of the TORP was provided through the use of confirmatory factor analysis and indices of sensitivity and specificity. It is concluded that the TORP can be ut ilized for the identification of children at risk for low achievement in reading. Analysis of the misclassified cases indicated that increased variability might have been responsible for the existing misclassification. More research is needed to determine the discriminant validation of TORP with samples of children with specific reading disabilities.
Identifying Students at Risk of Failing a Subject by Using Learning Analytics for Subsequent Customised Tutoring