Piecewise latent class growth analysis (LCGA) was used to examine growth patterns in reading comprehension and passage reading fluency on easyCBM, a popular formative assessment system. Unlike conventional growth modeling, LCGA takes into account the heterogeneity of growth and may provide reliable predictions for later development. Because current methods for classifying students are still questionable, this modeling technique could be a viable alternative classification method to identifying students at risk for reading difficulty. Results from this study suggested heterogeneity in reading development. The latent classes and growth trajectories from the LCGA models were found to align closely with easyCBM's risk rating system. However, results from one school district did not fully generalize across another. The implications for future research on examining growth in reading are discussed.

Obesity, as defined as BMI at or above the 95th percentile on the Centers for Disease Control and Prevention’s growth charts, has increased almost 3-fold among children in the United States since 1980. Overweight in adolescence has been associated with increased fat retention and high blood pressure in adulthood, among other symptoms of metabolic syndrome. However, normative patterns of weight change in childhood have not been developed. Groups of children may follow different trajectory patterns of BMI change over time. If common trajectory patterns could be identified, and their risk factors and outcomes understood, more nuanced intervention with families and children at risk for obesity could be developed. This study used a national dataset of 1,364 children whose weight and length was measured 12 times from birth through 15 ½ years. Testing both latent class growth analysis and growth mixture modeling identified four distinct subgroups, or classes, of BMI growth trajectory from 24 months – 8th grade. These classes were compared on numerous demographic, biological, and psychosocial risk factors identified in previous research as related to obesity. Classes were differentiated primarily on the child’s BMI at 15 months, the mother’s BMI at 15 months, birth weight for age, and percent increase in birth weight. Being male, Black, and lower SES were also related to membership in the higher-BMI trajectory classes. Of the psychosocial factors, maternal sensitivity, maternal depression, and attachment classification were also related to BMI class. Membership in these trajectories strongly predicted weight-related and blood-pressure outcomes at 15 ½ years over and above individual risk factors, demonstrating that patterns of change themselves are highly influential. The best-fitting models of weight-related outcomes at 15 ½ years included change trajectory in combination with biological, psychosocial, and SES risk factors from 0-24 months, with R² ranging from .31 = .50. Characteristics predicting adolescent overweight can be identified in the first years of life and should trigger the development and implementation of early intervention protocols in obstetrics and pediatrics. / text

This dissertation explored the academic growth in mathematics of a longitudinal cohort of 21,567 Oregon students during middle school on a state accountability test. The student test scores were used to calculate estimates of school performance based on four different accountability models (percent proficient [PP], change in PP, multilevel growth, and growth mixture). On average, 72% of Oregon eighth graders were proficient in mathematics in 2012, 71% in the average school, and 6% more students in this cohort demonstrated mathematics proficiency compared to 2011. The two-level unconditional multilevel growth model estimated the average intercept (Grade 6) to be 228.4 (SE = 0.07) scale score points with an average middle school growth rate of 5.40 scale points per year (SE = 0.02) on the state mathematics test. Student demographic characteristics were a statistically significant improvement on the unconditional model. A major shortcoming of this research, however, was the inability to find successful model convergence for any three-level growth model or any growth mixture model. A latent class growth analysis was used to uncover groups of students who shared common growth trajectories. A five-latent class solution best represented the data with the lowest BIC and a significant LMR p. Two of the latent classes were students who had high achievement in Grade 6 and demonstrated high growth across middle school and a second group with low sixth grade achievement that had below average growth in middle school. Student-level demographic predictors had statistically significant relations with growth characteristics and latent class membership. In comparing school performance based on the four different models, it was found that, although statistically correlated, the models of school performance ranked schools differently. A school’s percentage of proficient students in Grade 8 correlated moderately (r = [.60, .70]) with growth over the middle school years as estimated by the growth and LCGA models. About 70% to 80% of schools ranked more than 10 percentiles differently for every pairwise comparison of models. These results, like previous research call into question whether currently used models of school performance produce consistent and valid descriptions of school performance using state test scores.

Due to difficulties in elucidating neurobiological aspects of psychological disorders, the National Institute of Mental Health (NIMH) created the Research Domain Criteria (RDoC), which encourages novel conceptualizations of the relationship between neurobiological circuitry and clinical difficulties. This approach is markedly different from the Diagnostic and Statistical Manual of Mental Disorders (DSM) based approach that has dominated clinical research to date. Thus, RDoC necessitates exploration of novel experimental and statistical approaches. Fear learning paradigms represent a promising methodology for elucidating connections between acute threat (“fear”) circuitry and fear-related clinical difficulties. However, traditional analytical approaches rely on central tendency statistics, which are tethered to a priori categories and assume homogeneity within groups. Growth Mixture Modeling (GMM) methods such as Latent Class Growth Analysis (LCGA) may be uniquely suited for examining fear learning phenotypes. However, just three extant studies have applied GMM to fear learning and only one did so in a human population. Thus, the degree to which classes identified in known studies represent characteristics of the general population and to which GMM methodology is applicable across populations and paradigms is unclear. This preliminary study applied LCGA to a fear learning lab study in an attempt to identify heterogeneity in fear learning patterns based on a posteriori classification. The findings of this investigation may inform efforts to move toward a trans-diagnostic conceptualization of fear learning. Consistent with the goals laid out in RDoC, explication of fear learning phenotypes may eventually provide critical information needed to spur innovation in psychotherapeutic and psychopharmacological treatment. / Master of Science / To date, most clinical psychology research has been based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is a catalog of mental health disorders that was originally designed to facilitate communication among clinicians. Many experts contend that this approach has hampered progress in the field of biological clinical psychology research. Thus, the National Institute of Mental Health (NIMH) created a new template for biological clinical psychology research called the Research Domain Criteria (RDoC). Since RDoC calls for a complete overhaul in the conceptualization of clinical dysfunction, this approach requires statistical and experimental innovation. One traditional experimental approach that may be helpful in understanding the RDoC topic of acute threat (“fear”) is called Pavlovian Fear Learning (PFL). However, traditional PFL studies have utilized statistical methods that are based on comparing group averages and require researchers to determine groups of interest based on theory before the study begins. This is problematic because RDoC calls for research that begins with evidence rather than theory. Growth Mixture Modeling (GMM) is a statistical methodology that may allow researchers to analyze fear learning data without having to begin with theoretically determined categories such as DSM disorders. However, little research has tested how well this approach would work. This study is just the second to apply a GMM approach to a human PFL study. The findings from this investigation may inform efforts to develop a statistical technique that is well suited for RDoCian research and may also spur innovation in psychotherapeutic and psychopharmacological treatment.

京都大学 / 0048 / 新制・課程博士 / 博士(人間健康科学) / 甲第20058号 / 人健博第39号 / 新制||人健||3(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 桂 敏樹, 教授 任 和子, 教授 川村 孝 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM