The reliability, validity, discriminant and predictive properties of the social Phobia Inventory (SoPhI)

This paper reports on the psychometric properties of the Social Phobic Inventory (SoPhI) a 21-item scale that was designed to measure social anxiety according to the criteria of DSM-IV (American Psychiatric Association, APA (1994) Diagnostic and Statistical Manual of Mental Disorder , 4th Edn., Washington). Factor analysis of the SoPhI using data from a clinical sample of respondents with social phobia revealed one factor which explained approximately 59% of variance and which demonstrated strong internal reliability ( agr= 0.93). The SoPhI demonstrated concurrent validity with the SPAI ( r = 0.86) and convergent validity with the Fear of Negative Evaluations-Revised ( r = 0.68). The predictive utility of the scale was demonstrated in a sample of university students classified as extroverted, normal, shy/introverted, and phobic/withdrawn ( -2 57%). Multivariate Analysis of Variance (MANOVA) revealed that the combined university sample differed from the clinical sample on the summated scores on the SoPhI and that 43% ( -2 ) of this difference was attributable to group membership. This figure rose to 58% attributable to group membership when these same groups were compared for differences on the 21 individual items. Scores of the SoPhI that are indicative of concern and of possible diagnostic criteria, as well as suggestions for future research, are discussed.

Brief report : The defense mechanisms of homophobic adolescent males : a descriptive discriminant analysis

The study examined the role of defense mechanisms in homophobic attitudes of older male adolescents aged 17e18 years. A cross-sectional survey collected data from final year high school students (N ¼ 86) attending an all male school in a regional centre in Victoria, Australia. The school was identified by teachers as having a problematic culture of homophobic intolerance. Participants were divided into homophobic and non-homophobic groups based on their scores on the Homophobia Scale Questionnaire. Discriminant analysis was conducted to identify the predictors that would best categorise students into those two groups on the basis of defense styles derived from the Defense Style Questionnaire-40 (DSQ-40). The strongest predictors of homophobia amongst defense styles were idealisation, denial, somatisation and devaluation accounting for 18.31%, 17.64%, 13.10% and 11.35% of the variance, respectively. Results generally supported the larger contribution of more immature defenses to higher levels of homophobia.

Funding special education provision by means of discriminant function analysis

This thesis is concerned with the development of a funding mechanism, the Student Resource Index, which has been designed to resolve a number of difficulties which emerged following the introduction of integration or inclusion as an alternative means of providing educational support to students with disabilities in the Australian State of Victoria. Prior to 1984, the year in which the major integration or inclusion initiatives were introduced, the great majority of students with disabilities were educated in segregated special schools, however, by 1992 the integration initiatives had been successful in including within regular classes approximately half of the students in receipt of additional educational assistance on the basis of disability. The success of the integration program brought with it a number of administrative and financial problems which were the subject of three government enquiries. Central to these difficulties was the development of a dual system of special education provision. On one hand, additional resources were provided for the students attending segregated special schools by means of weighted student ratios, with one teacher being provided for each six students attending a special school. On the other hand, the requirements of individual students integrated into regular schools were assessed by school-based committees on the basis of their perceived extra educational needs. The major criticism of this dual system of special education funding was that it created inequities in the distribution of resources both between the systems and also within the systems. For example, three students with equivalent needs, one of whom attended a special school and two of whom attended different regular schools could each be funded at substantially differing levels. The solution to these inequities of funding was seen to be in the development of a needs based funding device which encompassed all students in receipt of additional disability related educational support. The Student Resource Index developed in this thesis is a set of behavioural descriptors designed to assess degree of additional educational need across a number of disability domains. These domains include hearing, vision, communication, health, co-ordination (manual and mobility), intellectual capacity and behaviour. The completed Student Resource Index provides a profile of the students’ needs across all of these domains and as such addresses the multiple nature of many disabling conditions. The Student Resource Index was validated in terms of its capacity to predict the ‘known’ membership or the type of special school which some 1200 students in the sample currently attended. The decision to use the existing special school populations as the criterion against which the Student Resource Index was validated was based on the premise that the differing resource levels of these schools had been historically determined by expert opinion, industrial negotiation and reference to other special education systems as the most reliable estimate of the enrolled students’ needs. When discriminant function analysis was applied to some 178 students attending one school for students with mild intellectual disability and one facility for students with moderate to severe intellectual disability the Student Resource Index was successful in predicting the student's known school in 92 percent of cases. An analysis of those students (8 percent) which the Student Resource Index had failed to predict their known school enrolment revealed that 13 students had, for a variety of reasons, been inappropriately placed in these settings. When these students were removed from the sample the predictive accuracy of the Student Resource Index was raised to 96 percent of the sample. By comparison the domains of the Vineland Adaptive Behaviour Scale accurately predicted known enrolments of 76 percent of the sample. By way of replication discriminant function analysis was then applied to the Student Resource Index profiles of 518 students attending Day Special Schools (Mild Intellectual Disability) and 287 students attending Special Developmental Schools (Moderate to Severe Intellectual Disability). In this case, the Student Resource Index profiles were successful in predicting the known enrolments of 85 percent of students. When a third group was added, 147 students attending Day Special Schools for students with physical disabilities, the Student Resource Index predicted known enrolments in 80 percent of cases. The addition of a fourth group of 116 students attending Day Special Schools (Hearing Impaired) to the discriminant analysis led to a small reduction in predictive accuracy from 80 percent to 78 percent of the sample. A final analysis which included students attending a School for the Deaf-Blind, a Hospital School and a Social and Behavioural Unit was successful in predicting known enrolments in 71 percent of the 1114 students in the sample. For reasons which are expanded upon within the thesis it was concluded that the Student Resource Index when used in conjunction with discriminant function analysis was capable of isolating four distinct groups on the basis of their additional educational needs. If the historically determined and varied funding levels provided to these groups, inherent in the cash equivalent of the staffing ratios of Day Special Schools (Mild Intellectual Disability), Special Development Schools (Moderate to Severe Intellectual Disability), Day Special Schools (Physical Disability) and Day Special Schools (Hearing Impairment) are accepted as reasonable reflections of these students’ needs these funding levels can be translated into funding bands. These funding bands can then be applied to students in segregated or inclusive placements. The thesis demonstrates that a new applicant for funding can be introduced into the existing data base and by the use of discriminant function analysis be allocated to one of the four groups. The analysis is in effect saying that this new student’s profile of educational needs has more in common with Group A than with the members of Groups B, C, or D. The student would then be funded at Group A level. It is immaterial from a funding point of view whether the student decides to attend a segregated or inclusive setting. The thesis then examines the impact of the introduction of Student Resource Index based funding upon the current funding of the special schools in one of the major metropolitan regions. Overall, such an initiative would lead to a reduction of 1.54 percent of the total funding accruing to the region’s special schools.

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

Fast cross-validation of kernel fisher discriminant classifiers

Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (^4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.

DDoS discrimination by linear discriminant analysis (LDA)

In this paper, we propose an effective approach with a supervised learning system based on Linear Discriminant Analysis (LDA) to discriminate legitimate traffic from DDoS attack traffic. Currently there is a wide outbreak of DDoS attacks that remain risky for the entire Internet. Different attack methods and strategies are trying to challenge defence systems. Among the behaviours of attack sources, repeatable and predictable features differ from source of legitimate traffic. In addition, the DDoS defence systems lack the learning ability to fine-tune their accuracy. This paper analyses real trace traffic from publicly available datasets. Pearson's correlation coefficient and Shannon's entropy are deployed for extracting dependency and predictability of traffic data respectively. Then, LDA is used to train and classify legitimate and attack traffic flows. From the results of our experiment, we can confirm that the proposed discrimination system can differentiate DDoS attacks from legitimate traffic with a high rate of accuracy.

Discriminant validity of the sensory profile in Australian children with autism spectrum disorder

Objective. The discriminant validity of the Sensory Profile was evaluated by comparing the sensory processing scores of Australian children, 5 to 8 years of age, diagnosed with autism spectrum disorder (ASD) to a control group of children with typical development matched for age and gender. Method. Twenty-six parents of children with ASD and 26 parents of typically developing children without ASD completed the Sensory Profile. Sensory Profile category, factor, and quadrant scores were compared using multivariate analysis to investigate if there were differences between the two groups. Results. The results indicated that the children with ASD had significantly lower sensory processing scores on all fourteen categories, eight out of nine factors, and all four quadrants of the Sensory Profile. Conclusion. The results also provide evidence of discriminant validity of Sensory Profile scores between children with ASD and children with typical development. In addition, the study findings indicate that the Sensory Profile can be used with confidence in cross-cultural contexts, such as Australia.