Testing analog circuits is more difficult than digital circuits. The reasons for this difficulty include continuous time and amplitude signals, lack of well-accepted testing techniques and time and cost required for its realization. The traditional method for testing analog circuits involves measuring all the performance parameters and comparing the measured parameters with the limits of the data-sheet specifications. Because of the large number of data-sheet specifications, the test generation and application requires long test times and expensive test equipment. This thesis proposes an implicit functional testing technique for analog circuits that can be easily implemented in BIST circuitry. The proposed technique does not require measuring data-sheet performance parameters. To simplify the testing only time domain digital input is required. For each circuit under test (CUT) a cross-covariance signature is computed from the test input and CUT's output. The proposed method requires a training sample of the CUT to be binned to the data-sheet specifications. The binned CUT sample cross-covariance signatures are mapped with a supervised machine learning classifier. For each bin, the classifiers select unique sub-sets of the cross-covariance signature. The trained classifier is then used to bin newly manufactured copies of the CUT. The proposed technique is evaluated on synthetic data generated from the Monte Carlo simulation of the nominal circuit. Results show the machine learning classifier must be chosen to match the imbalanced bin populations common in analog circuit testing. For sample sizes of 700+ and training for individual bins, classifier test escape rates ranged from 1000 DPM to 10,000 DPM.

Although not universally accepted, much of the field has converged upon the Five Factor Model (FFM) of personality as constituting a comprehensive taxonomy of normal personality functioning. A weakness common to all personality inventories is excessive length, which can result in examinee fatigue, and ultimately, poor data quality. Computer adaptive testing offers a solution to the test fatigue problem by administering only the items that are informative for that examinee on a given scale. A new test based upon the FFM of normal personality administered in a computer adaptive fashion was constructed. Reliability and validity evidence were obtained, with favorable results. New approaches to the detection of intentional response distortion were explored with mixed results including some promising findings in need of cross-validation. Response latencies were able to discriminate between honest and faking subjects, but the findings were unable to clarify the issue of whether faking is an easier or more laborious cognitive process than honest answering. New directions in computer adaptive personality testing research are proposed. / Ph. D.

The purpose of this research was to investigate the effect that mode of test administration could have on computerised assessment results involving proctored and unproctored test conditions. Two South African test instruments, the Learning Potential Computerised Adaptive Test (LPCAT) and the Career Preference Computerised Adaptive Test (CPCAT) were used in the study. A quantitative, quasi-experimental design was used, and a convenience sample for LPCAT (N=82) and CPCAT (N=81) consisted of employees in the hospitality industry. Using a within-participants design, the dependent t-test was used for statistical analysis. For the total group the LPCAT results yielded no statistically significant differences between the mean scores for the two different modes of administration. For the total group the CPCAT results yielded statistically significant differences in the mean scores per mode of administration for five out of 34 dimensions, however, for the majority of the CPCAT sub-dimensions, the mode of administration did not impact on results. It was concluded that mode of administration did not impact on the cognitive test scores and only to a very limited degree on the non-cognitive test scores. Based on the results the null hypotheses for the effect of mode of administration were not rejected. / Industrial & Organisational Psychology / M.A. (Industrial and Organisational Psychology)

The purpose of this research was to investigate the effect that mode of test administration could have on computerised assessment results involving proctored and unproctored test conditions. Two South African test instruments, the Learning Potential Computerised Adaptive Test (LPCAT) and the Career Preference Computerised Adaptive Test (CPCAT) were used in the study. A quantitative, quasi-experimental design was used, and a convenience sample for LPCAT (N=82) and CPCAT (N=81) consisted of employees in the hospitality industry. Using a within-participants design, the dependent t-test was used for statistical analysis. For the total group the LPCAT results yielded no statistically significant differences between the mean scores for the two different modes of administration. For the total group the CPCAT results yielded statistically significant differences in the mean scores per mode of administration for five out of 34 dimensions, however, for the majority of the CPCAT sub-dimensions, the mode of administration did not impact on results. It was concluded that mode of administration did not impact on the cognitive test scores and only to a very limited degree on the non-cognitive test scores. Based on the results the null hypotheses for the effect of mode of administration were not rejected. / Industrial and Organisational Psychology / M.A. (Industrial and Organisational Psychology)

Matrices reasoning tests, which contain missing pieces in matrices that participants attempt to figure out, are one of the most popular types of tests to measure general intelligence. This thesis introduces several methods to develop matrices items, and presents them in different test forms to assess general intelligence. Part 1 introduces the development of a matrices test with reference to Carpenter’s five rules of Raven’s Progressive Matrices. The test items developed were administered together with the Standard Ravens’ Progressive Matrices (SPM). Results based on confirmatory factor analysis and inter-item correlation demonstrate good construct validity and reliability. Item characteristics are explored with Item-Response Theory (IRT) analyses. Part 2 introduces the development of a large item bank with multiple alternatives for each SPM item, with reference to the item components of the original SPM. Results showed satisfactory test validity and reliability when using the alternative items in a test. Findings also support the hypothesis that the combination of item components accounts for item difficulty. The work lays the foundation for the future development of computer adaptive versions of Raven’s Progressive Matrices. Part 3 introduces the development of an automatic matrix item generator and illustrates the results of the analyses of the items generated using the distribution-of-three rule. Psychometric properties of the items generated are explored to support the validity of the generator. Figural complexity, features, and the frequency at which certain rules were used are discussed to account for the difficulty of the items. Results of further analyses to explore the underlying factors of the difficulty of the generated items are presented and discussed. Results showed that the suggested factors explain a substantial amount of the variance of item difficulty, but are insufficient to predict the item difficulty. Adaptive on-the-fly item generation is yet to be possible for the test at this stage. Overall, the methods for creating matrices reasoning tests introduced in the dissertation provide a useful reference for research on abstract reasoning and fluid intelligence measurements. Implications for other areas of psychometric research are also discussed.

Not available / text

This quantitative study examined seventh-grade reading scores to determine the extent to which certain demographic variables (race/ethnicity, gender, socioeconomic status) explain and MAP reading scores predict reading scores on the State of Texas Assessment of Academic Readiness (STAAR) in a selected northeast Texas public school. Standardized assessments only compare the relative performance of an individual student to other groups of students using scaled scores, which can vary from year to year and from state to state. With the advent of computer adaptive testing, this study provides information on the predictive validity of benchmark assessments. Specifically, this study looked for predictive evidence that indicates how accurately test data can predict criterion scores. Findings revealed, through a multiple regression analysis, that the fall MAP Rasch Unit (RIT) scores predicted the STAAR scale scores. Using SPSS version 22, the data were entered and analyzed in a multiple regression model to determine the presence of a statistical trend or lack thereof. Demographic data and MAP scores were entered into the regression model to examine the predictive validity of the MAP assessment in determining student performance on the STAAR seventh-grade state-mandated reading assessment. The statistical analysis revealed that MAP RIT scores explain a significant variance related to seventh-grade STAAR reading scale scores. There is a vital need for tools that improve a student's academic development and MAP assessments have been found to predict performance on state-mandated assessments.

The paper deals with the current perspective of computer aided assessment of students, particularly with computer adaptive testing based on Item Response Theory. Although this testing method has become a world standard, in the Czech Republic it is still, unfortunately, rarely used. The aim of this paper is to describe the current state of the testing techniques used and to create a functional dll library, which will provide basic functionality needed for successful launching of a Web interface for computer adaptive testing of students. In addition, the library should serve as a support to authors in developing tests based on Item Response Theory. The first part provides a basic description of educational assessment methods currently used, and gives an insight into the matters of creating professional testing tools. Other chapters depict the operation of Classical Test Theory, which is now used as a cornerstone of the test development, and further it presents Item Response Theory, as a possible successor to the Classical theory. Both theories described are compared at the end. The second part of the paper is then divided into a theoretical part, which describes the possibility of computer adaptive testing, gives and overview of its origins and development, presents essential forms, and highlights the advantages and disadvantages of this testing method. The practical part contains illustrative examples and descriptions of the methods enabled by the presented dll library.