The purpose of this study was to investigate the effect of guessing in the assessment of dimensionality in multiple-choice tests using procedures implemented in Mplus and TESTFACT. Levels of item discrimination and the size of correlations between dimensions were also manipulated to explore any interaction between these effects. Four indices based on the proportion of variance, parallel analysis, RMSR reduction and a chi-square difference test were used to estimate dimensionality. The research included two parts, a simulation study using a Monte Carlo approach and an application with TIMSS 2003 data. The simulation study confirmed the guessing effect. TESTFACT appeared to outperform Mplus for most conditions with data that assumed guessing. The proportion of variance and the RMSR reduction indices more accurately estimated dimensionality in Mplus, whereas the chi-square test and parallel analysis performed best with TESTFACT. A discrimination effect was observed clearly in data that assumed no guessing using the parallel analysis index and in data that assumed guessing using the RMSR index for both methods. Less accurate estimation of dimensionality was observed when using Mplus for tests with either high or low discriminating items, and with TESTFACT for tests with lower discriminating items. Higher correlations between dimensions led to more serious estimation problems. When guessing was not modeled, greater influence from the levels in correlations between dimensions and item discriminations was found. Further, a more pronounced discrimination effect was observed in the high correlation condition. With regard to the application of TIMSS data, 70% of the items exhibited guessing behaviors and high correlations were observed between scores on the different dimensions (math and science). Based on the simulation study, guessing and correlation effects should thus be considered carefully when choosing a method for assessing dimensionality. Inconsistency in the dimensionality assessment using the four indices with Mplus was observed (1 to 5 dimensions), whereas TESTFACT consistently estimated 2 dimensions. However, further investigation of the internal structure of the TIMSS assessment did not show any connection to content or cognitive domains.

This study explored outcomes of using the Internet version of the DISCOVER career guidance system by college students who are unsure of their career direction. Previous research indicated mixed results concerning DISCOVER's effectiveness. A review of these studies showed that measures of foundational components of career development (i.e., individuals' knowledge of their skills, interests, and values) consistently improved after DISCOVER use, while more advanced areas of career development (e.g., actual occupational choice) showed mixed results. This study proposed that the Cognitive Information Processing (CIP) theory of career development can be used to assess these different levels under a unifying model. Sixty-three undergraduate students participated in a pretest/posttest study where they were assessed on various measures, including need for cognition, vocational identity, and dysfunctional career thinking before and after using DISCOVER. Participants were found to have increased levels of vocational identity and lower levels of dysfunctional career thinking after DISCOVER use. Of particular note, individuals with higher levels of vocational identity prior to using DISCOVER showed greater decreases in dysfunctional career thinking after using DISCOVER than those with lower initial levels of vocational identity. These results offer support to the proposal that an understanding of one's interests, skills, and values must be achieved before an individual can make additional gains on more advanced levels of career decision making. Additionally, patterns of DISCOVER use among college students in this study indicate that individuals did not plan a strategy prior to using the program, suggesting that college students could benefit from additional instruction prior to using DISCOVER. Also, participants did not express an interest in discussing their DISCOVER results with others while they were using the program, implying that college students believe using a computer-assisted career guidance system is a solitary endeavor. This study recommends that a "one size fits all" approach to using DISCOVER should not be taken by college career counselors; rather, counselors should make an initial assessment of a client's stage within the career development process and then offer suggestions to the individual regarding how best to use the program.

This project is a single case study of a private orphanage in Northeastern China. Three questions guided the investigation at the orphanage: 1) What do providers believe is their role in meeting the children's social-emotional needs? 2) How do providers' routines support children's social-emotional development? and 3) How do providers integrate their faith in caring for the children? Observations of and conversations with the orphanage director, co-director, and two direct-care providers were analyzed along the following four categories: Parenting Style, Dependency Paradigms, Daily Routines, and Spiritual Attributions. Data was hand-coded and analyzed for relevant themes within the four categories. Results support previous research suggesting Chinese parenting is best described as a blend of authoritarian and authoritative styles. Results also indicate that adult providers' intentional identification with traditional family roles (father, mother, aunt) provide the intimate environment that fosters children's social-emotional development. Additionally, a blending of both institutional and family care frameworks were evident in the daily routines of providers. Spiritual Attributions of God as a Source of Meaning and God as My Helper were significant contributions to the emotional support of providers and provided the vehicle for passing on spiritual beliefs to children.

The quality of student-teacher relationships plays an important role in a child's educational experiences. Empirically, students who possess positive relationships with their teachers have an increased likelihood of positive school attitudes as well as positive school "outcomes" such as higher grades. Children with conflicted student-teacher relationships are at increased risk for academic problems such as poor grades and repeating a grade. It is therefore important to consider not only what the child brings into the classroom, but also what kind of relationship evolves in order to minimize factors contributing to lower student-teacher quality. The current investigation uses an attachment theory perspective to look at student-teacher quality. It is longitudinal in design, with four goals relating to change in student-teacher relationships from one school grade to the next: (1) to document stability and change in teacher-reported relationship quality, (2) to test whether children who engage in more externalizing behaviors have poorer student-teacher relationships, as reported by the teacher, (3) to see if teachers rated higher in negativity and lower in social connectedness report poorer quality student-teacher relationships, and (4) to see if teachers rated higher in warmth and sensitivity report positive student-teacher relationships. Participants are 24 first and second grade students from urban, low-income homes, attending a public elementary school. The investigation uses previously collected data from the 2004-2005 school year and follow-up data collected during the 2005-2006 school year. Data were collected via parent reports, teacher ratings, and classroom observations of teacher and student behavior. Cluster analysis is used to describe results from Year 2. Three clusters emerged regarding student-teacher relationship quality: positive, average, and high conflict/low closeness relationships. Results are only descriptive in nature and need to be individualized; factors that may have shaped student-teacher relationship quality (e.g., student disruptive behaviors, teacher emphasis on control in the classroom, and "goodness of fit" between student and teacher interactions) in one case are not of equal importance in another one. Findings are discussed in terms of their implications for the empirical use of teacher-reported STR construct as well as their implications for future research and training.

This study aimed to compare the efficiencies of multi-dimensional CAT versus uni-dimensional CAT based on the multi-dimensional graded response model and provide information about the optimal size of the item pool. Item selection and ability estimation methods based on multi-dimensional graded response models were developed and two studies, one based on simulated data, the other based on real data, were conducted. Five design factors were manipulated: correlation between dimensions, item pool size, test length, ability level, and number of estimated dimensions. A modest effect due to the correlation between dimensions on the outcome measures was observed, although the effect was found primarily for correlations of 0 versus 0.4. Based on a comparison of the correlation condition equal to zero with correlation conditions greater than zero, the multi-dimensional CAT was more efficient than the uni-dimensional CAT. As expected, ability level had an impact on the outcome measures. A multi-dimensional CAT provided more accurate estimates for those examinees with average true ability values than those with true ability values in the extreme range. The multi-dimensional CAT was over-estimated for examinees with negative true ability values and under-estimated for examinees with positive true ability values. This is consistent with Bayesian estimation methods which shrink estimates toward the mean of the prior distribution. As the number of estimated dimensions increased, more accurate estimates were achieved. This supports the idea that the ability of one dimension can be used to augment the information available to estimate ability in another dimension. Finally, larger item pools and longer tests yielded more accurate and reliable ability estimation, although greater difference in efficiency was realized when comparing shorter tests and smaller item pools. Information on the optimal item pool size was provided by plotting the outcome measures versus the item pool size. The plots indicated that, for short tests, the optimal item pool size was 20 items; for longer test, the optimal item pool size was 50 items. However, if item exposure control or content balancing were an issue, a larger item pool would be needed to achieve the same efficiency in ability estimates.

This dissertation investigated the use of various techniques in modeling non-linear change in the context of latent growth modeling. A simulation study was conducted utilizing four between subjects factors: sample size (50, 75, 100, 150, 200, 300 and 500), slope variance (.15, .45 and .75), factor correlation (.15, .45 and .75) and growth curve (exponential, logarithmic and logistic). There was also a single within subjects factor: fit technique (quadratic, unspecified and spline). The outcomes of interest were the ÷2 model fit statistic and the following goodness-of-fit indices: CFI, GFI, AGFI, SRMR and RMSEA. Results indicated the unspecified technique provided the best statistical estimates of model fit while the quadratic technique provided the worst. This result was consistent across all of the between subject factor conditions. The spline technique performed very similarly to the quadratic technique. These results suggest applied researchers should pay very close attention when utilizing polynomial techniques and should also strongly consider the unspecified technique as either the model of choice or as a comparison to results obtained for another model.

The purpose of this study was to describe the interactions between a classroom facilitator and student teachers during whole class case-based discussions. Five questions were explored. 1) What role did the facilitator play in whole class discussions? 2) How did particular cases elicit problem solving? 3) What kinds of engagement did students demonstrate in each discussion? 4) How did individuals differ in their participation during case discussions? 5) How did turn-taking between students and facilitator, change over time? The data sources were transcripts of videotaped whole-class case discussions and a semi-structured interview with the facilitator. This study analyzed, in detail, both the content of the case discussions in the framework of problem solving strategies and the interaction between the facilitator and the students during the seven whole-class discussions. The content of the discussions was analyzed specifically looking at the aspects of problem solving, which regards problem analysis, solution analysis, knowledge resources and individualized perspectives. The interaction analyses were categorized as asking, probing, agreeing, challenging, clarifying, paraphrasing, praising, inviting, referring and elaborating. The results revealed that the facilitator was more often guiding and supporting student participation in the first three discussions. The facilitator orchestrated the discussion by sharing responsibilities for learning and teaching together. The content and the nature of the case influenced the format of the discussion. Students personal and professional experiences affected their engagement and contribution to the discussions. This study gives some insights into how whole-class discussion works and how the facilitator and students engage in these discussions. This study reveals that interaction and content of discussions could be affected not only by students background knowledge and previous experiences, but also by the facilitators intentions and purposes.

AIMS: To investigate the potential associations between daily bedtime routines, evening fluctuations in parent mood, and sleep patterns for a sample of children with fragile X syndrome (FXS). This study also investigated whether the association among these factors varies dependent upon the severity of the genetic mutation. SAMPLE: Children ages 3 years, 0 months to 17 years, 11 months with a full or partial mutation of FXS (N = 95). METHOD: In-home assessments were completed including a demographic form, bedtime routine information sheet, and abbreviated Child Sleep Habits Questionnaire (CSHQ), followed by a 14-day sleep diary. Actigraphy was added to the protocol for the local sample of participants (n = 7) from the Fragile X Center of Childrens Hospital of Pittsburgh. RESULTS: Parents reported that 48% of their children with FXS have sleep problems at a level worthy of a sleep clinic referral; 20% of participants received medication to induce sleep, but 58% continued to score in the clinical range on the CSHQ despite receiving medications. Actigraphy showed children with FXS had significantly different sleep parameters than a control group (n =14). Caregiver mood was a significant correlate of sleep disturbances; children with FXS whose caregivers reported feeling more overwhelmed or poorer mood had more sleep disturbances. Inconsistent bedtime routine was also associated with sleep disturbances. CONCLUSIONS: These data strongly suggest that routine clinical care of children with FXS should include careful screening of sleep. Interventions to assist parents in establishing and/or maintaining bedtime routines and managing their mood may be warranted. Effective use of medication to enhance sleep requires further investigation. Additional research is needed to identify specific problems with sleep so that clinical trials focusing on key parameters can be initiated.

One of the sources of inaccuracy in parameter estimates of multilevel models is omitted variable bias, caused by the omission of an important predictor. The purpose of this study was to examine the performance of six estimation procedures in estimating the fixed effects when a level-2 interaction term was omitted from a two-level hierarchical linear model. Four alternative estimators (FE, WLS1, WLS2, WLS3) based on the work of Frees (2001) and the Maximum Likelihood (FML, ReML) estimation methods were examined. Findings of the Monte Carlo study revealed that the FML and ReML methods were the least biased methods when a level-2 interaction was omitted from the multilevel model. FML and ReML produced the lowest RMSD values of all six estimation methods regardless of level-2 sample size, ICC, or effect sizes of the level-2 variables. The difference in the performance of the alternative and Maximum Likelihood (ML) procedures diminished as level-2 sample size and ICC increased. The bias in all six estimation methods did not differ much when the effect sizes of the level-2 predictors varied. When the methods were examined using the ECLS data, the results of the Monte Carlo study were confirmed. The ML methods were the least biased of all the methods when a level-2 interaction term was omitted from the model.

The purpose of this study was to investigate the performance of Beaton~{!/~}s MR and MSR fit indices for assessing goodness-of-fit of IRT models. These statistics are based on a standardized residual calculated from an expected and observed response. The investigation was conducted using a Monte Carlo simulation study that varied conditions relevant to testing applications. This research had three objectives: 1) To identify the sampling distribution of the fit statistics; 2) To assess the Type I error rates under different combinations of manipulated factors; and 3) To investigate the empirical power under different combinations of manipulated factors by introducing different types of model misfit. The sampling distribution of Beaton~{!/~}s MR and MSR statistics belonged to the family of normal distribution. However, there was no basis for a theoretical normal distribution to test the hypothesis of model-data-fit. Therefore, Monte Carlo resampling methods were required to test the hypothesis of model-data-fit for Beaton~{!/~}s fit statistics. Using Monte Carlo resampling methods for hypothesis testing, nominal Type I error rates were observed in this study regardless of test length, sample size, Monte Carlo resample size and number of replications. With regard to empirical power, higher power was observed for Beaton~{!/~}s MR statistic than MSR statistic under the condition that H0 was false for the entire test. Under the condition that H0 was false for a subset of test items, higher power for the misfitting item and more false rejections than expected for all the other items were obtained for Beaton~{!/~}s MR statistic. In contrast, reasonable empirical power for the misfitting item and nominal Type I error rates for all the other items were observed for Beaton~{!/~}s MSR statistic. Based on the results of this study, Beaton~{!/~}s MSR fit statistics can be used to assess goodness-of-fit for both shorter (12 items) and longer test (36 items). The recommended sample size is 500 or more, and a Monte Carlo resample size of 100 should be adequate for hypothesis testing.