Thesis advisor: Scott C. Seider / Engagement in sociopolitical activism, such as protesting, has important implications for youth of color and for the communities in which they live (Ballard & Ozer, 2016; Ginwright, 2010; Hope & Spencer, 2017). Critical Consciousness (CC; Freire, 1970/1998; Watts et al., 2011) and Youth Sociopolitical Development Theory (Youth SPD; Watts & Flanagan, 2007) are two prominent frameworks for investigating sociopolitical activism among youth of color. Although both frameworks position motivation as one of the key factors influencing youth activism, motivation is narrowly defined as a single construct—one’s sense of efficacy to effect change. Using motivation constructs from two established motivation frameworks, Self-Determination Theory (SDT; Deci & Ryan, 2008; Ryan & Deci, 2000) and Regulatory Focus Theory (RFT; Higgins, 1997), this dissertation investigated the multidimensional nature of motivation in relation to Black and Latinx adolescents’ commitment to activism. Drawing from a longitudinal data set examining Black and Latinx adolescents’ civic development over four years of high school (N = 733), I used group differential approaches (latent profile analysis, latent profile transition analysis, and latent profile moderation) to (a) identify distinct combinations of motivations among Black and Latinx high school students in ninth, tenth, and twelfth grade, (b) assess whether and the extent to which adolescents changed profile membership across high school, (c) examine motivation profiles in tenth grade as predictors of commitment to activism in twelfth grade, and (d) examine motivation profiles in tenth grade as moderators of the relation between adolescents’ analysis of social problems in tenth grade and their commitment to activism addressing these problems in twelfth grade (controlling for their initial commitment to activism). I identified two motivation profiles in ninth grade, four motivation profiles in tenth grade, and four motivation profiles in twelfth grade. At both tenth and twelfth grade, I named the motivation profiles: “Low Motivation,” “High Motivation,” “Moderate Motivation, Low Autonomy,” and “Moderate Motivation, High Autonomy.” At both time points, the “Low Motivation” profile comprised the smallest proportion of the sample and the “Moderate Motivation, High Autonomy” profile comprised the largest proportion of the sample. Most youth shifted to a different motivation profile over time. Adolescents in the “High Motivation” profile at the end of tenth grade reported the highest average commitment to activism at the end of twelfth grade; however, this number was only statistically significantly higher than the “Moderate Motivation, Low Autonomy” profile. Contrary to expectations, youths’ social analysis in tenth grade was not predictive of their commitment to activism in twelfth grade; thus, there was no latent profile moderation in relation to social analysis and commitment to activism. Instead, I did find evidence that motivation profile membership moderated the relation between commitment to activism at the end of tenth grade on commitment to activism at the end of twelfth grade. Overall, results suggest that adolescents’ motivation is multidimensional and incredibly dynamic. Future CC/Youth SPD research should consider investigating a more complete set of established motivation constructs in relation to youths’ sociopolitical development. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.

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Idag står världen inför en rad olika miljörelaterade problem. Ett av dessa och det kanske mest omtalade är hur utsläpp av växthusgaser sakta men säkert höjer planetens medeltemperatur. Hållbar utveckling är ett begrepp som driver diskussionen framåt om vad vi behöver göra och hur vi behöver förändras för att lösa problemen. Växthusgaserna och deras hot mot klimatet är starkt relaterat till energi. Förnyelsebara energikällor skulle kunna vara en dellösning på problemet men de kräver energilagring av olika former för att kunna ersätta sina fossila konkurrenter. Termisk energilagring är ett sätt att lagra energi på och kan delas upp i tre olika grupper. Dessa är sensibel, latent och termokemisk värmelagring. Syftet med denna litteraturstudie var att kartlägga olika applikationer av latent värmelagring som kan bidra till ett mer hållbart samhälle i framtiden. Resultatet visar att det finns många olika typer av så kallade fasomvandlingsmaterial (PCMs). Beroende på vid vilka temperaturer värme ska lagras vid används olika PCMs. PCMs kan användas för latent värmelagring inom många olika områden. Byggnader är ett av dessa områden där PCMs kan användas för att kyla och värma utrymmen antingen genom integration i ventilationssystem eller i själva byggnadsmaterialen. Latent värmelagring kan också användas i termiska solkraftverk. Latent värmelagring har på senare tid fått stor uppmärksamhet tack vare PCMs förmåga att lagra värme i små volymer och under konstant temperatur. Dock möter tekniken problem vid värmeöverföringen vilket t.ex. är fallet i lagring av termisk solenergi. Forskning pågår därför för att generellt höja PCMs termiska egenskaper. Ett exempel på detta är Nano-PCM. Resultatet visar även att latent värmelagring idag används av företag som affärsidé för olika tillämpningar. Från resultatet går det att dra slutsatsen att latent värmelagring används idag men att det krävs ytterligare forskning för att tekniken ska kunna konkurrera med andra värmelagringsmetoder. / Today, the world faces a number of environmental-related problems. One of these and perhaps most discussed is how emissions of greenhouse gases slowly but surely raise the planet’s average temperature. Sustainable development is a concept that drives the discussion forward and tells us what we need to do and how we need to change to solve the problems. Greenhouse gases and their threats to the climate are strongly related to energy. Renewable sources of energy could be a partial solution to the problem, but they require energy storage of different forms to replace their fossil competitors. Thermal energy storage is a way of storing energy and can be divided into three different groups. These are sensible, latent and thermochemical heat storage. The purpose of this literature study was to map different applications of latent heat storage that can contribute to a more sustainable society in the future. The result shows that there are many different types of phase changing materials (PCMs). Depending on the temperature at which heat is to be stored, different PCMs are used. PCMs can be used for latent heat storage in many different areas. Buildings are one of these areas where PCMs can be used to cool and heat spaces either through integration into ventilation systems or in the building materials itself. Latent heat storage can also be used in thermal solar power plants. Latent heat storage has recently received great attention thanks to PCMs ability to store heat in small volumes and under constant temperature. However, the technology is experiencing problems in the heat transfer, such is the case in the storage of thermal solar energy. Research is therefore ongoing to generally increase PCMs thermal properties. An example of this is Nano-PCM. The result also shows that latent heat storage today is used by companies as a business concept for various applications. From the result, it can be concluded that latent heat storage is used today, but that further research is required for the technology to compete with other heat storage methods.

Despite the widespread availability and importance of nonnumeric data, marketers do not have the tools to extract information from large amounts of nonnumeric data. This dissertation attempts to fill this void: I developed a scalable methodology that is capable of extracting information from extremely large volumes of nonnumeric data. The proposed methodology integrates concepts from information retrieval and content analysis to analyze textual information. This approach avoids a pervasive difficulty of traditional content analysis, namely the classification of terms into predetermined categories, by creating a linear composite of all terms in the document and, then, weighting the terms according to their inferred meaning. In the proposed approach, meaning is inferred by the collocation of the term across all the texts in the corpus. It is assumed that there is a lower dimensional space of concepts that underlies word usage. The semantics of each word are inferred by identifying its various contexts in a document and across documents (i.e., in the corpus). After the semantic similarity space is inferred from the corpus, the words in each document are weighted to obtain their representation on the lower dimensional semantic similarity space, effectively mapping the terms to the concept space and ultimately creating a score that measures the concept of interest. I propose an empirical application of the outlined methodology. For this empirical illustration, I revisit an important marketing problem, the effect of movie critics on the performance of the movies. In the extant literature, researchers have used an overall numerical rating of the review to capture the content of the movie reviews. I contend that valuable information present in the textual materials remains uncovered. I use the proposed methodology to extract this information from the nonnumeric text contained in a movie review. The proposed setting is particularly attractive to validate the methodology because the setting allows for a simple test of the text-derived metrics by comparing them to the numeric ratings provided by the reviewers. I empirically show the application of this methodology and traditional computer-aided content analytic methods to study an important marketing topic, the effect of movie critics on movie performance. In the empirical application of the proposed methodology, I use two datasets that combined contain more than 9,000 movie reviews nested in more than 250 movies. I am restudying this marketing problem in the light of directly obtaining information from the reviews instead of following the usual practice of using an overall rating or a classification of the review as either positive or negative. I find that the addition of direct content and structure of the review adds a significant amount of exploratory power as a determinant of movie performance, even in the presence of actual reviewer overall ratings (stars) and other controls. This effect is robust across distinct opertaionalizations of both the review content and the movie performance metrics. In fact, my findings suggest that as we move from sales to profitability to financial return measures, the role of the content of the review, and therefore the critic's role, becomes increasingly important.

This thesis treats the modelling of a high-dimensional data set of longitudinal binary responses. The data consists of default indicators from different nations around the world as well as some explanatory variables such as exposure to underlying assets. The data used for the modelling is an aggregated term which combines several of the default indicators in the data set into one.  The modelling sets out from a portfolio perspective and seeks to find underlying correlations between the nations in the data set as well as see the extreme values produced by a portfolio with assets in the nations in the data set. The modelling takes a copula approach which uses Gaussian copulas to first formulate several different models mathematically and then optimize the parameters in the models to best fit the data set. Models A and B are optimized using standard stochastic gradient ascent on the likelihood function while model C uses variational inference and stochastic gradient ascent on the evidence lower bound for optimization. Using the different Gaussian copulas obtained from the optimization process a portfolio simulation is then done to examine the extreme values. The results show low correlations in models A and B while model C with it's additional regional correlations show slightly higher correlations in three of the subgroups. The portfolio simulations show similar tail behaviour in all three models, however model C produces more extreme risk measure outcomes in the form of higher VaR and ES. / Denna uppsats behandlar modellering av en datauppsättning bestående av högdimensionell longitudinell binärrespons. Datan består av konkursindikatorer för ett flertal suveräna stater runtom världen samt förklarande variabler så som exponering mot underliggande tillgångar. Datan som används i modelleringen är en aggregerad term som slår samman flera av konkursindikatorerna till en term. Modellerandet tar ett portföljperspektiv och försöker att finna underliggande korrelationer mellan nationerna i datamängden så väl som extremförluster som kan komma från en portfölj med tillgångar i de olika länderna som innefattas av datamängden. Utgångspunkten för modellerandet är ett copula-perspektiv som använder Gaussiska copulas där man först försöker matematiskt formulera flertalet modeller för att sedan optimera parametrarna i dessa modeller för att bäst passa datamängden till hands. För modell A och modell B optimeras log-likelihoodfunktionen med hjälp av stochastic gradient ascent medan i modell C används variational inference och sedan optimeras evidence lower bound med hjälp av stochastic gradient ascent. Med hjälp av de anpassade copula-modellerna simuleras sedan olika portföljer för att se vilka extremvärden som kan antas. Resultaten visar små korrelationer i modell A och B medan i modell C, med dess ytterligare regionala korrelationer, visas något större korrelation i tre av undergrupperna. Portföljsimuleringarna visar liknande svansbeteende i alla tre modeller, men modell C ger upphov till större riskmåttvärden i portföljerna i form av högre VaR och ES.

Although there has been a recent surge in research examining comorbidity between externalizing and internalizing disorders in childhood, relatively less work has examined relations between specific externalizing conditions (i.e., oppositional defiant disorder (ODD) symptoms) and their co-occurrence with specific internalizing conditions (i.e., depressive symptoms). Furthermore, little empirical work has evaluated potential underlying processes, such as emotion dysregulation, which may explain relations between co-occurring ODD and depressive symptoms. There is also a paucity of research examining developmental patterns of co-occurring ODD and depressive symptoms. In the present study, I used latent class and latent transition analyses to (a) identify groups of children based on ODD and depressive symptom levels, (b) determine whether emotion dysregulation predicted co-occurring ODD and depressive symptoms, and (c) examine developmental patterns of change and continuity in groups across middle childhood within a community-based sample. Children were characterized by three latent classes based on ODD and depressive symptom severity: a group with very low levels of ODD or depressive symptoms, an ODD-only group with low levels of symptoms, and a co-occurring ODD and depressive symptom group with moderate levels of ODD and low levels of depressive symptoms. Furthermore, emotion dysregulation predicted to the class with moderate levels of ODD and low levels of depressive symptoms, although prediction from emotion dysregulation to class membership depended on the methodology used to index emotion dysregulation. Results of the LTA analyses suggested that symptom severity was relatively stable across middle childhood, with little evidence of changes in developmental patterns of ODD and depressive symptoms. Overall, the results of this study provide an important foundation for more sophisticated empirical inquiry regarding co-occurring ODD and depressive symptoms in childhood and potential processes that may explain their onset and development. / Psychology

A multitude of research demonstrates that ADHD is associated with negative psychological correlates and outcomes among children, such as academic difficulties and peer relationship problems. Youth with ADHD also experience high rates of comorbidity or co-occurring conditions, including mood, anxiety, oppositional defiant, and conduct disorders. However, few studies have investigated the development of co-occurring psychological symptoms among youth with ADHD over time and across different developmental periods. Shared risk factors likely contribute to the development of ODD, CD, anxiety, and depression among youth with ADHD. Emotion regulation and parenting style may confer risk or resilience for the development of co-occurring symptoms, but research is wanting. The current study examined an existing sample of youth who were recruited at age 10-12 and were followed at age 12-14 and 16. Analyses aimed to (a) identify subgroups of youth varying in type and levels of ADHD and co-occurring symptoms at three different time points using latent class analyses, (b) examine stability of membership and transitions to classes that differ in levels of ADHD and co-occurring symptoms using latent transition analyses, and (c) investigate emotion regulation and parenting style as predictors of stability and transitions among classes. Results revealed different patterns of ADHD and co-occurring symptoms, including a Low Symptoms class at each time point. Classes of youth with ADHD+Externalizing problems and ADHD+Internalizing problems emerged at ages 10-12 and 12-14. At age 16, two classes with qualitatively and quantitatively different externalizing and internalizing symptoms were identified. Latent transition analyses revealed transitions into the Low Symptoms class from each time point, but also stability and transitions to other symptomatic classes. Predictor analyses indicated that emotion regulation and parenting style were associated with transitions among and stability within classes, but findings were dependent on whether classes were defined primarily by co-occurring externalizing or internalizing symptoms. Results of the present study indicate that children with ADHD are likely to exhibit a range of psychological symptoms, but the frequency and quality of co-occurring symptoms may change over time. Emotion regulation and parenting may be potential targets for enhanced interventions among youth with ADHD with and without co-occurring symptoms. / Psychology

Although previous studies on structural equation modeling (SEM) have indicated that the second-order latent growth model (SOLGM) is a more appropriate approach to longitudinal intervention effects, its application still requires researchers to collect at least three-wave data (e.g. randomized pretest, posttest, and follow-up design). However, in some circumstances, researchers can only collect two-wave data for resource limitations. With only two-wave data, the SOLGM can not be identified and researchers often choose alternative SEM models to fit two-wave data. Recent studies show that the two-wave longitudinal common factor model (2W-LCFM) and latent change score model (2W-LCSM) can perform well for comparing latent change between groups. However, there still lacks empirical evidence about how accurately these two-wave models can estimate the group effects of latent change obtained by three-wave SOLGM (3W-SOLGM). The main purpose of this dissertation, therefore, is trying to examine to what extent the fixed effects of the tree-wave SOLGM can be recovered from the parameter estimates of the two-wave LCFM and LCSM given different simulation conditions. Fundamentally, the supplementary study (study 2) using three-wave LCFM was established to help justify the logistics of different model comparisons in our main study (study 1). The data generating model in both studies is 3W-SOLGM and there are in total 5 simulation factors (sample size, group differences in intercept and slope, the covariance between the slope and intercept, size of time-specific residual, change the pattern of time-specific residual). Three main types of evaluation indices were used to assess the quality of estimation (bias/relative bias, standard error, and power/type I error rate). The results in the supplementary study show that the performance of 3W-LCFM and 3W-LCSM are equivalent, which further justifies the different models' comparison in the main study. The point estimates for the fixed effect parameters obtained from the two-wave models are unbiased or identical to the ones from the three-wave model. However, using two-wave models could reduce the estimation precision and statistical power when the time-specific residual variance is large and changing pattern is heteroscedastic (non-constant). Finally, two real datasets were used to illustrate the simulation results. / Doctor of Philosophy / To collect and analyze the longitudinal data is a very important approach to understand the phenomenon of development in the real world. Ideally, researchers who are interested in using a longitudinal framework would prefer collecting data at more than two points in time because it can provide a deeper understanding of the developmental processes. However, in real scenarios, data may only be collected at two-time points. With only two-wave data, the second-order latent growth model (SOLGM) could not be used. The current dissertation compared the performance of two-wave models (longitudinal common factor model and latent change score model) with the three-wave SOLGM in order to better understand how the estimation quality of two-wave models could be comparable to the tree-wave model. The results show that on average, the estimation from two-wave models is identical to the ones from the three-wave model. So in real data analysis with only one sample, the point estimate by two-wave models should be very closed to that of the three-wave model. But this estimation may not be as accurate as it is obtained by the three-wave model when the latent variable has large variability in the first or last time point. This latent variable is more likely to exist as a statelike construct in the real world. Therefore, the current study could provide a reference framework for substantial researchers who could only have access to two-wave data but are still interested in estimating the growth effect that supposed to obtain by three-wave SOLGM.

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.

Extracting meaningful information from large collections of text data is problematic because of the sheer size of the database. However, automated analytic methods capable of processing such data have emerged. These methods, collectively called text mining first began to appear in 1988. A number of additional text mining methods quickly developed in independent research silos with each based on unique mathematical algorithms. How good each of these methods are at analyzing text is unclear. Method development typically evolves from some research silo centric requirement with the success of the method measured by a custom requirement-based metric. Results of the new method are then compared to another method that was similarly developed. The proposed research introduces an experimentally designed testing method to text mining that eliminates research silo bias and simultaneously evaluates methods from all of the major context-region text mining method families. The proposed research method follows a random block factorial design with two treatments consisting of three and five levels (RBF-35) with repeated measures. Contribution of the research is threefold. First, the users perceived a difference in the effectiveness of the various methods. Second, while still not clear, there are characteristics with in the text collection that affect the algorithms ability to extract meaningful results. Third, this research develops an experimental design process for testing the algorithms that is adaptable into other areas of software development and algorithm testing. This design eliminates the bias based practices historically employed by algorithm developers.