Psychometricians and test developers use DIF analysis to determine if there is possible bias in a given test item. This study examines the conditions under which two predominant methods for determining differential item function compare with each other in item bias detection using an effect size statistic as the basis for comparison. The main focus of the present research was to test whether or not incorporating an effect size for LR DIF will more accurately detect DIF and to compare the utility of an effect size index across MH DIF and LR DIF methods. A simulation study was used to compare the accuracy of MH DIF and LR DIF methods using a p value or supplemented with an effect size. Effect sizes were found to increase the accuracy of DIF and the possibility of the detection of DIF across varying ability distributions, population distributions, and sample size combinations. Varying ability distributions and sample size combinations affected the detection of DIF, while population distributions did not seem to affect the detection of DIF.

Psychometricians and test developers use DIF analysis to determine if there is possible bias in a given test item. This study examines the conditions under which two predominant methods for determining differential item function compare with each other in item bias detection using an effect size statistic as the basis for comparison. The main focus of the present research was to test whether or not incorporating an effect size for LR DIF will more accurately detect DIF and to compare the utility of an effect size index across MH DIF and LR DIF methods. A simulation study was used to compare the accuracy of MH DIF and LR DIF methods using a p value or supplemented with an effect size. Effect sizes were found to increase the accuracy of DIF and the possibility of the detection of DIF across varying ability distributions, population distributions, and sample size combinations. Varying ability distributions and sample size combinations affected the detection of DIF, while population distributions did not seem to affect the detection of DIF.

Velocity-difference induced focusing (V-DIF) with a dynamic pH junction in capillary electrophoresis (CE) using a sample with a pH different from that of the background electrolyte (BGE) was developed in our group, but the mechanism was not well understood. In this work, the mechanism of this focusing technique was first studied using an appropriate dye to monitor the pH of the BGE and the sample during the focusing process. A mechanism was proposed based on the experimental results. This technique was then applied to serotonin to improve the detection limit when CE was used with a UV absorption detector. It was also applied to focus amino acids, peptides, and proteins to improve the concentration sensitivity. It is found that the pKa rather than the pI of the analytes is the key criterion for selecting the pH for the sample and for the BGE to obtain the optimum focusing for these molecules. Since UV detection only provides migration time information, more structure information is obtained by using a photodiode array (PDA) and mass spectrometer (MS) for peak identification. Comparisons were made between the PDA detection and MS detection for aromatic amino acids with V-DIF using a dynamic pH junction. This V-DIF technique was then applied to non-aromatic amino acids with MS detection. It was used at low pH with positive ESI-MS detection and at high pH with negative ESI-MS ionization. The results of the two methods were compared and discussed. Finally, the preparative operation of continuous flow counterbalanced CE (FCCE) was studied. The effects of larger sample volumes and multiple capillary systems on improving the purification yield were investigated.

Multiple-indicators multiple-causes (MIMIC) modeling is often employed for measurement invariance testing under the structural equation modeling framework. This Monte Carlo study explored the behaviors of MIMIC as a measurement invariance testing method in different research situations. First, the performance of MIMIC under the factor loading noninvariance conditions was investigated through model fit evaluations and likelihood ratio tests. This study demonstrated that the violation of factor loading invariance was not detected by any of the typically reported model fit indices. Consistently, the likelihood ratio tests for MIMIC models exhibited poor performance in identifying noninvariance in factor loadings. That is, MIMIC was insensitive to the presence of factor loading noninvariance, which implies that factor loading invariance should be examined through other measurement invariance testing techniques. To control Type I error inflation in detecting the noninvariance of intercepts or thresholds, this simulation study with both continuous and categorical variables employed the likelihood ratio test with two critical value adjustment strategies, Oort adjustment and Bonferroni correction. The simulation results showed that the likelihood ratio test with Oort adjustment not only controlled Type I error rates below the basal Type I error rates but also maintained high power across study conditions. However, it was observed that power to detect the noninvariant variables slightly attenuated with multiple (i.e., two) noninvariant variables in a model. Given that the modification index is the chi-square difference after relaxing one parameter for estimation, this study investigated modification indices under four research scenarios based on a combination of the cutoffs of modification indices and the procedures of model modification: (a) the noniterative method (i.e., modification indices at the initial stage of model modification) using the conventional critical value, (b) the noniterative method using the Oort adjusted critical value, (c) the iterative procedure of model modification using the conventional critical value, and (d) the iterative procedure using the Oort adjustment. The iterative model search procedure using modification indices showed high performance in detecting noninvariant variables even without critical value adjustment, which indicates that iterative model search specification does not require critical value adjustment in identifying the noninvariance correctly. On the other hand, when the noniterative procedure was used, the Oort adjustment yielded adequate results.

Velocity-difference induced focusing (V-DIF) with a dynamic pH junction in capillary electrophoresis (CE) using a sample with a pH different from that of the background electrolyte (BGE) was developed in our group, but the mechanism was not well understood. In this work, the mechanism of this focusing technique was first studied using an appropriate dye to monitor the pH of the BGE and the sample during the focusing process. A mechanism was proposed based on the experimental results. This technique was then applied to serotonin to improve the detection limit when CE was used with a UV absorption detector. It was also applied to focus amino acids, peptides, and proteins to improve the concentration sensitivity. It is found that the pKa rather than the pI of the analytes is the key criterion for selecting the pH for the sample and for the BGE to obtain the optimum focusing for these molecules. Since UV detection only provides migration time information, more structure information is obtained by using a photodiode array (PDA) and mass spectrometer (MS) for peak identification. Comparisons were made between the PDA detection and MS detection for aromatic amino acids with V-DIF using a dynamic pH junction. This V-DIF technique was then applied to non-aromatic amino acids with MS detection. It was used at low pH with positive ESI-MS detection and at high pH with negative ESI-MS ionization. The results of the two methods were compared and discussed. Finally, the preparative operation of continuous flow counterbalanced CE (FCCE) was studied. The effects of larger sample volumes and multiple capillary systems on improving the purification yield were investigated.

Velocity-difference induced focusing (V-DIF) with a dynamic pH junction in capillary electrophoresis (CE) using a sample with a pH different from that of the background electrolyte (BGE) was developed in our group, but the mechanism was not well understood. In this work, the mechanism of this focusing technique was first studied using an appropriate dye to monitor the pH of the BGE and the sample during the focusing process. A mechanism was proposed based on the experimental results. This technique was then applied to serotonin to improve the detection limit when CE was used with a UV absorption detector. It was also applied to focus amino acids, peptides, and proteins to improve the concentration sensitivity. It is found that the pKa rather than the pI of the analytes is the key criterion for selecting the pH for the sample and for the BGE to obtain the optimum focusing for these molecules. Since UV detection only provides migration time information, more structure information is obtained by using a photodiode array (PDA) and mass spectrometer (MS) for peak identification. Comparisons were made between the PDA detection and MS detection for aromatic amino acids with V-DIF using a dynamic pH junction. This V-DIF technique was then applied to non-aromatic amino acids with MS detection. It was used at low pH with positive ESI-MS detection and at high pH with negative ESI-MS ionization. The results of the two methods were compared and discussed. Finally, the preparative operation of continuous flow counterbalanced CE (FCCE) was studied. The effects of larger sample volumes and multiple capillary systems on improving the purification yield were investigated. / Science, Faculty of / Chemistry, Department of / Graduate

Differential Item Functioning (DIF) occurs when examinees with the same ability have different probabilities of endorsing an item. Conventional DIF detection methods (e.g., the Mantel-Hansel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable (e.g., Cohen & Bolt, 2005). True source of DIF may be unobserved, including variables such as personality, response patterns, or unmeasured background variables. The Factor Mixture Model (FMM) is designed to detect unobserved sources of heterogeneity in factor structures, and an FMM with binary outcomes has recently been used for assessing DIF (DeMars & Lau, 2011; Jackman, 2010). However, FMMs with binary outcomes for detecting DIF have not been thoroughly explored to investigate both types of between-class latent DIF (LDIF) and class-specific observed DIF (ODIF). The present simulation study was designed to investigate whether models correctly specified in terms of LDIF and/or ODIF influence the performance of model fit indices (AIC, BIC, aBIC, and CAIC) and entropy, as compared to models incorrectly specified in terms of either LDIF or ODIF. In addition, the present study examined the recovery of item difficulty parameters and investigated the proportion of replications in which items were correctly or incorrectly identified as displaying DIF, by manipulating DIF effect size and latent class probability. For each simulation condition, two latent classes of 27 item responses were generated to fit a one parameter logistic model with items’ difficulties generated to exhibit DIF across the classes and/or the observed groups. Results showed that FMMs with binary outcomes performed well in terms of fit indices, entropy, DIF detection, and recovery of large DIF effects. When class probabilities were unequal with small DIF effects, performance decreased for fit indices, power, and the recovery of DIF effects compared to equal class probability conditions. Inflated Type I errors were found for invariant DIF items across simulation conditions. When data were generated to fit a model having ODIF but estimated LDIF, specifying LDIF in the model fully captured ODIF effects when DIF effect sizes were large. / text

Made available in DSpace on 2016-08-29T15:33:15Z (GMT). No. of bitstreams: 1 tese_4176_.pdf: 6013163 bytes, checksum: 2e413f36a79ecc7f2ea7e35d0744e463 (MD5) Previous issue date: 2011-08-25 / As unidades de processamento gráfico (Graphics Processing Unit -- GPU) surgiram como um poderoso dispositivo computacional e a plataforma Compute Unified Device Architecture (CUDA) como um ambiente adequado para a implementação de um código na GPU. Especializada inicialmente em processamento gráfico, a GPU vem sendo designada à otimização de cálculos lógicos e aritméticos beneficiando diversas áreas de pesquisa com a redução do tempo de computação. O objetivo deste trabalho é mostrar como aplicações em mecânica dos fluidos, discretizadas pelo método das diferenças finitas, podem lucrar bastante com esta tecnologia. Implementações paralelas na GPU em C+CUDA das equações de Navier-Stokes e de transporte são comparadas com uma versão sequencial implementada na CPU em C. É utilizada uma formulação em diferenças finitas implícita-explícita, sendo o algoritmo caracterizado como sendo explícito nas velocidades e temperatura e implícito na pressão. A resolução dos sistemas lineares resultantes é feita utilizando um esquema de coloração Red-Black para as células internas da malha e o método iterativo successive-over-relaxation (SOR), denominado Red-Black-SOR. É discutido neste trabalho os impactos do uso de tipos de dados double e float e também a utilização de memórias shared e global existentes na GPU. O algoritmo C+CUDA é verificado para o seguinte conjunto de problemas conhecidos da literatura: cavidade com cobertura deslizante, escoamento sobre um degrau, escoamento laminar com um obstáculo cilíndrico, convecção natural e convecção de Rayleigh-Bénard, considerando casos bidimensionais e tridimensionais. O tempo de processamento é comparado com o mesmo algoritmo implementado em C. Os resultados numéricos mostraram que é possível alcançar speedups da ordem de 85 vezes para dados float e 61 vezes para dados double utlizando C+CUDA.

Made available in DSpace on 2016-04-04T18:28:05Z (GMT). No. of bitstreams: 1 Izabella Brito Silva.pdf: 2978268 bytes, checksum: 42a14f689272301bde47bd494dc6b321 (MD5) Previous issue date: 2012-02-23 / Studies relating personality traits and intelligence (from the psychometric point of view) are quite new and have been expanding due to the fact that the intelligence may interfere on the way people respond to personality tests (and vice-versa). This research wanted to verify, based on DIF estimates, the influence of intelligence levels, sex, and education on the responses to a personality self-portrait instrument. The sample was composed of 161 high-school and college students from both genders, who answered a BFP and two subtests of BPR-5 (abstract and verbal). For the DIF estimates, it is required to work with 2 groups; therefore the division of groups was according to gender and education. And for the intelligence level, the participants were separated into two groups: low and high average. The estimates of each group s difficulties in the 126 items that form the personality instrument were calculated and compared in order to check whether DIF was present or not. Two DIF measurement criteria were applied. The results with Mantel-Hanzel?s (contrast higher than 0.42) showed that none of the items presented DIF. However, taking into consideration Probability Difference Indices (lower than 0.05), we found that out of 126 items, DIF was present in the groups as follows: 11 for the intelligence level; 17 for education; and 22 for gender. Thus, it is possible to verify that a small amount of items were influenced by the intelligence level of the participants. Gender and the Aperture factor influenced DIF the most. / Estudos que relacionam tra?os de personalidade e intelig?ncia (do ponto de vista psicom?trico) s?o bastante recentes e v?m se ampliando devido ? constata??o de que a intelig?ncia pode exercer influ?ncia no modo como as pessoas respondem a testes de personalidade (e vice-versa). Assim, a presente pesquisa buscou verificar, com o uso da estimativa do funcionamento diferencial do item (DIF), a influ?ncia do n?vel de intelig?ncia, sexo e escolaridade nas respostas a um instrumento de auto-relato da personalidade. A amostra foi composta por 161 estudantes, de Ensino M?dio e Superior, de ambos os sexos, os quais responderam ? Bateria Fatorial de Personalidade e dois subtestes da Bateria de Provas de Racioc?nio (racioc?nio abstrato e verbal). Para a estimativa do DIF existe a exig?ncia de se trabalhar com 2 grupos, de forma que o procedimento de divis?o dos grupos foi a divis?o natural para sexo e escolaridade e para o n?vel de intelig?ncia, os participantes foram divididos em dois grupos: m?dia baixa e m?dia alta. A estimativa da dificuldade de cada grupo em cada um dos 126 itens que comp?em o instrumento de personalidade foi calculada, sendo posteriormente comparadas com o objetivo de identificar a presen?a ou n?o de DIF. Os resultados demonstraram que, usando dois crit?rios de medidas de DIF, vemos que, de acordo com o crit?rio de Mantel-Haenzel (contraste maior que 0,42) nenhum dos itens apresentou DIF, entretanto, considerando-se os ?ndices de diferen?a de probabilidade (menor que 0,05) vemos que para n?vel de intelig?ncia 11 dos 126 itens apresentaram funcionamento diferencial para os grupos, para escolaridade 17 dos 126 itens apresentaram DIF, e por fim, para sexo 22 dos 126 itens apresentaram funcionamento diferencial para os grupos. Dessa forma pode-se verificar que uma propor??o pequena de itens mostraram-se influenciados pelo n?vel de intelig?ncia do respondente, sendo que a maior parte dos itens que apresentaram DIF pertencem a vari?vel sexo e ao fator Abertura.

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