Several longitudinal studies show that over time the American public has become more approving of euthanasia and suicide for terminally ill persons. Yet, these previous findings are limited because they derive from biased estimates of disaggregated hierarchical data. Using insights from life course sociological theory and recently developed cross-classified mixed effects logistic regression, I better account for this liberalization process by disentangling the age, period, and cohort effects that contribute to longitudinal changes in these attitudes. Findings indicate that while attitudes toward euthanasia and suicide have liberalized over time, they remained relatively stable over the past 10 years. Furthermore, this study finds significant age effects in which the probability of agreement to euthanasia and suicide steadily decreases throughout the life course. Contrary to previous research, this study finds that when controlling for age and period effects, there are no significant birth-cohort effects that contribute to longitudinal changes in these attitudes.

Recently, researchers have reformulated Item Response Theory (IRT) models into multilevel models to evaluate clustered data appropriately. Using a multilevel model to obtain item difficulty and person ability parameter estimates that correspond directly with IRT models’ parameters is often referred to as multilevel measurement modeling. Unlike conventional IRT models, multilevel measurement models (MMM) can handle, the addition of predictor variables, appropriate modeling of clustered data, and can be estimated using non-specialized computer software, including SAS. For example, a three-level model can model the repeated measures (level one) of individuals (level two) who are clustered within schools (level three). Limitations in terms of the minimum sample size and number of test items that permit reasonable one-parameter logistic (1-PL) IRT model’s parameters have not been examined for either the two- or three-level MMM. Researchers (Wright and Stone, 1979; Lord, 1983; Hambleton and Cook, 1983) have found that sample sizes under 200 and fewer than 20 items per test result in poor model fit and poor parameter recovery for dichotomous 1-PL IRT models with data that meet model assumptions. This simulation study tested the performance of the two-level and three-level MMM under various conditions that included three sample sizes (100, 200, and 400), three test lengths (5, 10, and 20), three level-3 cluster sizes (10, 20, and 50), and two generated intraclass correlations (.05 and .15). The study demonstrated that use of the two- and three-level MMMs lead to somewhat divergent results for item difficulty and person-level ability estimates. The mean relative item difficulty bias was lower for the three-level model than the two-level model. The opposite was true for the person-level ability estimates, with a smaller mean relative parameter bias for the two-level model than the three-level model. There was no difference between the two- and three-level MMMs in the school-level ability estimates. Modeling clustered data appropriately; having a minimum total sample size of 100 to accurately estimate level-2 residuals and a minimum total sample size of 400 to accurately estimate level-3 residuals; and having at least 20 items will help ensure valid statistical test results. / text

The primary goal of the current study is to develop a more advanced model of information-seeking behaviors. For achieving this goal, it paid attention to two social phenomena characterizing contemporary society: informationalization and globalization. First, focusing on these two influential phenomena, this study investigated how individual-level factors—information overload, information ambiguity, and goal orientations—affected information-seeking behaviors among employees in a multinational corporation. Next, in addition to these individual predictors of information-seeking behaviors, this study explored the effects of two team-level factors—team task interdependence and team tenure—on the relationships between the main predictors and information-seeking behaviors. Last, paying more attention to the multicultural context, this study investigated how these employees in a multinational corporation seek task and feedback information from two culturally different sources: American direct advisors and Korean expatriates. In order to more thoroughly investigate the roles of the cultural backgrounds of information sources, this study explored how American employees perceived the cultural backgrounds of the two culturally different sources and how such perceptions influenced those employees’ information-seeking behaviors. / text

Exposure therapy has received a great deal of support as an effective treatment for social anxiety. However, not all those who undergo exposure therapy improve, and some of those who do respond continue to report significant levels of symptoms. A theorized mechanism of change for exposure therapy is extinction learning. Extinction learning is believed to occur across exposure sessions during which new associations are formed and stored in memory. Individuals with social anxiety are prone to engage in post event processing (PEP), or rumination, after social experiences, which may interfere with extinction learning, and thus attenuate response to treatment. The current study examined whether PEP limits treatment response to two different exposure based treatments, a group based cognitive behavioral intervention and an individually based virtual reality exposure therapy among participants (n = 75) diagnosed with social anxiety disorder. The findings suggested that PEP decreased as a result of treatment and that social anxiety symptoms for those with greater amounts of PEP improved at a slower rate of change than those with lower levels of PEP. Implications for the role of PEP on treatment response are discussed.

In the field of education, decisions are influenced by the results of various high stakes measures. Investigating the presence of differential item functioning (DIF) in a set of items ensures that results from these measures are valid. For example, if an item measuring math self-efficacy is identified as having DIF then this indicates that some other characteristic (e.g. gender) other than the latent trait of interest may be affecting an examinee’s score on that particular item. The use of hierarchical generalized linear modeling (HGLM) enables the modeling of items nested within examinees, with person-level predictors added at level-2 for DIF detection. Unlike traditional DIF detection methods that require a reference and focal group, HGLM allows the modeling of a continuous person-level predictor. This means that instead of dichotomizing a continuous variable associated with DIF into a focal and reference group, the continuous variable can be added at level-2. Further benefits of HGLM are discussed in this study. This study is an extension of work done by Williams and Beretvas (2006) where the use of HGLM with polytomous items (PHGLM) for detection of DIF was illustrated. In the Williams and Beretvas study, the PHGLM was compared with the generalized Mantel-Haenszel (GMH), for DIF detection and it was found that the two performed similarly. A Monte Carlo simulation study was conducted to evaluate HGLM’s power to detect DIF and its associated Type 1 error rates using the constrained form of Muraki’s Rating Scale Model (Muraki, 1990) as the generating model. The two methods were compared when DIF was associated with a continuous variable which was dichotomized for the GMH and used as a continuous person-level predictor with PHGLM. Of additional interest in this study was the comparison of HGLM’s performance with that of the GMH under a variety of DIF and sample size conditions. Results showed that sample size, sample size ratio and DIF magnitude substantially influenced the power performance for both GMH and HGLM. Furthermore, the power performance associated with the GMH was comparable to HGLM for conditions with large sample sizes. The mean performance for both DIF detection methods showed good Type I error control.

Most quantitative research is conducted by randomly selecting members of a population on which to conduct a study. When statistics are run on a sample, and not the entire population of interest, they are subject to a certain amount of error. Many factors can impact the amount of error, or bias, in statistical estimates. One important factor is sample size; larger samples are more likely to minimize bias than smaller samples. Therefore, determining the necessary sample size to obtain accurate statistical estimates is a critical component of designing a quantitative study. Much research has been conducted on the impact of sample size on simple statistical techniques such as group mean comparisons and ordinary least squares regression. Less sample size research, however, has been conducted on complex techniques such as hierarchical linear modeling (HLM). HLM, also known as multilevel modeling, is used to explain and predict an outcome based on knowledge of other variables in nested populations. Ordinal logistic HLM (OLHLM) is used when the outcome variable has three or more ordered categories. While there is a growing body of research on sample size for two-level HLM utilizing a continuous outcome, there is no existing research exploring sample size for OLHLM. The purpose of this study was to determine the impact of sample size on statistical estimates for ordinal logistic hierarchical linear modeling. A Monte Carlo simulation study was used to investigate this research query. Four variables were manipulated: level-one sample size, level-two sample size, sample outcome category allocation, and predictor-criterion correlation. Statistical estimates explored include bias in level-one and level-two parameters, power, and prediction accuracy. Results indicate that, in general, holding other conditions constant, bias decreases as level-one sample size increases. However, bias increases or remains unchanged as level-two sample size increases, holding other conditions constant. Power to detect the independent variable coefficients increased as both level-one and level-two sample size increased, holding other conditions constant. Overall, prediction accuracy is extremely poor. The overall prediction accuracy rate across conditions was 47.7%, with little variance across conditions. Furthermore, there is a strong tendency to over-predict the middle outcome category.

Recent findings suggest that there is considerable inter-individual variability in how mood affects sexual arousal. The current research proposes that the Dual Control Model may be important to explaining this variation. Thirty-three participants (18 male and 15 female) aged 18 to 45, attended three laboratory sessions where they completed questionnaires assessing pre-existing mood and propensity for sexual excitation and inhibition, then watched a series of neutral and erotic films. Participants continuously indicated their subjective sexual arousal during each film, while genital temperature was measured using thermographic imaging. The results indicated that mood interacted with the elements of the Dual Control Model to significantly predict both genital and subjective sexual arousal. The interactions between mood and sexual excitation and inhibition tended to better predict genital arousal for female participants and subjective sexual arousal for male participants. The results suggest that Dual Control Model is an important factor in understanding how mood affects sexual arousal. / Canadian Institutes of Health Research, Canadian Male Sexual Health Council, Fonds Recherche Santé du Québec, Pfizer and the Ontario Ministry of Training, Colleges and Universities.

Emotionally Focused Couple Therapy (EFT; Johnson, 2004) is an empirically validated approach to couple therapy that uses attachment theory to understand the needs and emotions of romantic partners. In EFT, relationship distress is conceptualized as resulting from negative affect, emotional disconnection, and unmet attachment needs. Although EFT is recognized as one of the most researched and effective approaches to couple therapy, little research has examined theoretically related characteristics of couples to changes in marital satisfaction throughout EFT. The present doctoral thesis examined this area of literature. Thirty-two couples were provided approximately 21 sessions of EFT. The goal of the first study was to identify intake characteristics related to change in marital satisfaction over the course of EFT. Couples completed self-report measures of marital satisfaction, attachment security, relationship trust, and emotional control at pre- and post-therapy and after each therapy session. Individuals higher on self-report attachment anxiety and higher levels of emotional control had greater change in marital satisfaction over the course of EFT. The goal of the second study was to examine intake levels of attachment security and its relationship to the occurrence of the blamer-softening event, a key change event in EFT, and changes in marital satisfaction. Results indicated that the occurrence of a blamer-softening event significantly predicted positive changes in marital satisfaction. Results also suggested that the occurrence of a softening event significantly moderated the relationship between attachment avoidance at intake and change in marital satisfaction from pre- to post-therapy. For couples who completed a blamer-softening event, partners with lower levels of attachment avoidance were more likely to have positive changes in marital satisfaction. However, this relationship was not evident for attachment anxiety. Overall, results from this thesis suggest that attachment security is a key characteristic of couple partners for therapists to consider when implementing EFT. Therapists may benefit from assessing attachment security at the start of therapy to help inform them of the emotion regulating strategies used by couple partners. This information may help therapists to tailor specific interventions such that couples may begin to develop more secure attachment bonds.

Parent child communications about sex play an important role in influencing adolescent’s sexual behaviors and attitudes. The present study was conducted to examine how sexual communications between African American mothers and their children change over a period of three years in the areas of sex education, communication about risk reduction, and child and parent report of responsiveness. Hierarchical linear modeling (HLM) analyses found significant linear or curvilinear trajectory in communication with sons and daughters in all areas. Gender differences were found such that daughters received more communication than sons. Furthermore, daugthers’ sexual maturation was found to be associated with a decrease in the rate of decline of communication about general sex information. For sons, mothers decreased in rates of responsiveness as sons got older; however, as sons’ sexual maturation increased, rates of declining responsiveness slowed down.

A microwave module supplying up-to 1 Watt output power at 2.4-2.5 GHz frequency band was investigated. First the module was operated at low power levels and the output power was predicted using the small signal S-parameters of the module. A method was developed to obtain its large signal model using Advanced Design System (ADS) simulator&rsquo / s nonlinear analyses facilities. Later using the large signal model of the module simulations carried out to obtain larger powers up-to 1 Watt. The implementation of the module was performed using the SMD components on a microstrip substrate and the characteristics of the module were compared to the ones obtained using simulation results.