The ambition of the study Nyanser av beroende is to analyze and problematize the concept of addiction. A broader aim is to investigate if it in the Swedish population exists different patterns of addiction and how these patterns then look like. The empirical material constitutes of Negativa konsekvenser av ANDT-bruk i den svenska vuxna befolkningen 2014. The selection consists of 26 257 individuals with a response rate of 59, 3 percent (N=15 576). The individuals that at some time during the last year used any narcotic substance and fulfilled at least one of the addiction criterias in the diagnose manual DSM-IV are subjects to the analysis (N=560). By the analyze method of Latent klassanalys (LCA), patterns of addiction have been investigated. The theoretical framework consists of medical and social addiction theory. This to see how different patterns of addiction comply with the medical and social perspectives that can be found in DSM-IV. In the latent class analysis, four different groups with different patterns of addiction are identified: Kontrollförlust, Försökt minska intag, Hard core gruppen och Tolerans. The group’s patterns have further on been connected with sociodemographic factors and substance use. The result of this study indicate that addiction is not a homogeneous concept but that there are differences concerning patterns of use and sociodemographic factors.

Doctor of Philosophy / Department of Agricultural Economics / Jason S. Bergtold / Timothy J. Dalton / Adoption of technological improvements are crucial to increase agricultural productiviy to help reduce poverty by obtaining higher farm incomes due to higher productivity and lower production costs. However, the introduction of new agricultural technologies has not always been successful or had diffuse adoption. Factors that determine farmers’ adoption decisions are: 1) farm and farmers' characteristics; 2) technology attributes, and 3) the farming objective. Understanding these factors and how they affect adoption of new technologies on the farm is crucial to assure higher levels of adoption. The over all purpose of this thesis is to explore the adoption process of new technologies and practices by farmers. This is accomplished through three essays to meet the objectives of the thesis. The purpose of the first essay was to evaluate whether or not farmers in the western U.S. are willing to grow specialized oilseed crops that could be used for certified hydrotreated renewable jet (HRJ) fuel production and incorporate them into existing wheat-based production systems under contract. Results indicate that providing oilseeds crops and contracts with desired attributes and features would positively affect farmers' decisions to incorporate oilseed crops into their rotation system. Preferred seed and contract attributes that may affect a farmer’ adoption decision differ across different geographic regions of the U.S. The second essay focused on identifying factors that impact participation and farmers' decision to adopt soil conservation and fertilization management practices for cassava producers in Thailand and Vietnam. Results indicate that asset ownership and cassava yield positively influence participation. Adoption of new practices was positively linked to farmers’ participation in training activities, use of fish ponds (as a measure of alternative agricultural practices), presence of a nearby starch factory, and slope of the land. Finally, the purpose of the third essay was to examine extension educators' characteristics that affect educators' selection decision of outreach methods in the U.S. This essay examines the diffusion process that impacts adoption of best management practices by farmers. The decision extension educators make for selecting a teaching method is affected by the relationship between the objectives of the learning process and the characteristics of the teaching method.

Much current discourse around policing in the UK stresses the need for a partnership between the police and public and, in particular, the need for the police to be responsive to the concerns of local communities. It is argued that appearing responsive to local needs, and showing a willingness to consult the public in the process of decision making, is likely to increase support for the police. Despite this, detailed analysis of the public’s preferences for policing remains relatively sparse. This thesis uses data from the 2003-04 Metropolitan Police’s Public Attitude Survey (PAS) to consider whether survey data can provide a useful indication of a respondent’s preferences, and how these preferences may vary depending on the characteristics of respondents and the boroughs in which they live. This thesis argues that rather than simply considering some overall measure of the level of policing individuals would like to see, or investigating attitudes towards different functions of the police individually, a more interesting and complete view of preferences for policing can be developed by looking at the mix of policing that individuals best believe will meet their needs. Additionally, it will be shown that differences in respondents’ preferences can be related to both the characteristics of individuals and the nature of the boroughs in which they live. It will be suggested that some of these relationships provide evidence that respondents favour a mix of policing they believe will protect them from perceived threats and reflect their perception of the police’s role within society. In addition, this thesis provides an example of how the techniques of Factor Analysis and Latent Class Analysis can provide greater insight into the data collected in large scale surveys. It is suggested that responses provided to different questions are often related and may represent a more general underlying attitude held by the respondent. It is also argued that using techniques which can handle multilevel data will provide greater explanatory depth by suggesting how a respondent’s attitude may be influenced by the context in which they live. The analysis presented offers new insights into the public’s priorities for policing and demonstrates the worth of the statistical methods employed. However it is, to some extent, limited by the form of the questions within the PAS dataset and by the lack of information about the thought process underlying a respondent’s answers. These concerns will be discussed, along with suggestions for future research.

Both theory and empirical evidence support the existence of “aggressive-victims,” a subgroup of youth who have been found to experience the negative outcomes associated with being an aggressor and being a victim. It remains unclear, however, if aggressive-victims possess risk factors that are unique from youth who are either aggressive or victimized. The present study sought to: (a) identify subgroups of seventh grade adolescents who differ in their patterns of aggression and victimization, (b) determine the number and structure of subgroups differ by school or sex, and (c) investigate whether aggressive-victims differ from all other subgroups in their social and emotional functioning. Secondary analyses were conducted on baseline data from 984 seventh grade adolescents participating in a randomized controlled trial evaluating an expressive writing intervention. Latent class analysis identified four subgroups of adolescents representing predominant-aggressors, predominant-victims, aggressive-victims, and youth with limited involvement. This pattern was consistent across sex and across schools that differed in the demographics of the adolescents. The findings indicate that aggressive victims are highly similar to predominant-aggressors and do not possess any unique characteristics beyond their pattern of involvement in both aggression and victimization. Further evidence of unique differences in risk factors is needed to support prevention and intervention efforts that are tailored to meet the specific needs of aggressive-victims. Future research should consider addressing methodological limitations of the present study, such as by examining continuous indicators, including additional indices of social and emotional functioning, or investigating differential item functioning.

Up to date, it is unclear how Choice-Based Conjoint (CBC) models perform in terms of forecasting (external) real world aggregate shop data. In this contribution, we measure the performance of a Latent Class CBC model - not with an experimental holdout sample - but with aggregate real world scanning data. We find that the CBC model does not accurately predict real world market shares. In order to improve the forecasting performance, we propose a correction scheme based on external scanner data. Our analysis based on 8 brands shows that the use of the proposed correction vector improves the performance measure considerably. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Latent class analysis explains dependency structures in multivariate categorical data by assuming the presence of latent classes. We investigate the specification of suitable priors for the Bayesian latent class model to determine the number of classes and perform variable selection. Estimation is possible using standard tools implementing general purpose Markov chain Monte Carlo sampling techniques such as the software JAGS. However, class specific inference requires suitable post-processing in order to eliminate label switching. The proposed Bayesian specification and analysis method is applied to the Hungarian heart disease data set to determine the number of classes and identify relevant variables and results are compared to those obtained with the standard prior for the component specific parameters.

This thesis examines the interactions between road users and the factors that contribute to the occurrence of traffic accidents, and discusses the implications of these interactions with regards to driver behaviour and accident prevention measures. Traffic accident data is collected on a macroscopic level by local police authorities throughout the UK. This data provides a description of accident related factors on a macroscopic level which does not allow for a complete understanding of the interaction between the various road users or the influence of errors made by active road users. Traffic accident data collected on a microscopic level analysis of real world accident data, explaining why and how an accident occurred, can further contribute to a data driven approach to provide safety measures. This data allows for a better understanding of the interaction of factors for all road users within an accident that is not possible with other data collection methods. In the first part of the thesis, a literature review presents relevant research in traffic accident analysis and accident causation research, afterwards three accident causation models used to understand behaviour and factors leading to traffic accidents are introduced. A comparison study of these accident causation coding models that classify road user error was carried out to determine a model that would be best suited to code the accident data according to the thesis aims. Latent class cluster analyses were made of two separate datasets, the UK On the Spot (OTS) in-depth accident investigation study and the STATS19 national accident database. A comparison between microscopic (in-depth) accident data and macroscopic (national) accident data was carried out. This analysis allowed for the interactions between all relevant factors for the road users involved in the accident to be grouped into specific accident segmentations based on the cluster analysis results. First, all of the cases that were collected by the OTS team between the years 2000 to 2003 were analysed. Results suggested that for single vehicle accidents males and females typically made failures related to detection and execution issues, whereas male road users made diagnosis failures with speed as a particularly important factor. In terms of the multiple vehicle accidents the interactions between the first two road users and the subsequent accident sequence were demonstrated. A cluster analysis of all two vehicle accidents in Great Britain in the year 2005 and recorded within the STATS19 accident database was carried out as a comparison to the multiple vehicle accident OTS data. This analysis demonstrated the necessity of in-depth accident causation data in interpreting accident scenarios, as the resulting accident clusters did not provide significant differences between the groups to usefully segment the crash population. Relevant human factors were not coded for these cases and the level of detail in the accident cases did not allow for a discussion of countermeasure implications. An analysis of 428 Powered Two Wheeler accidents that were collected by the OTS team between the years 2000 to 2010 was carried out. Results identified 7 specific scenarios, the main types of which identified two particular looked but did not see accidents and two types of single vehicle PTW accidents. In cases where the PTW lost control, diagnosis failures were more common, for road users other than the PTW rider, detection issues were of particular relevance. In these cases the interaction between all relevant road users was interpreted in relation to one another. The subsequent study analysed 248 Pedestrian accidents that were collected by the OTS team between the years 2000 to 2010. Results identified scenarios related to pedestrians as being in a hurry and making detection errors, impairment due to alcohol, and young children playing in the roadside. For accidents that were initiated by the other road user s behaviour pedestrians were either struck after an accident had already occurred or due to the manoeuvre that a road user was making, older pedestrians were over-represented in this accident type. This thesis concludes by discussing how (1) microscopic in-depth accident data is needed to understand accident mechanisms, (2) a data mining approach using latent class clustering can benefit the understanding of failure mechanisms, (3) accident causation analysis is necessary to understand the types of failures that road users make and (4) accident scenario development helps quantify accidents and allows for accident countermeasure implication discussion. The original contribution to knowledge is the demonstration that when relevant data is available there is a possibility to understand the interactions that are occurring between road users before the crash, that is not possible otherwise. This contribution has been demonstrated by highlighting how latent class cluster analysis combined with accident causation data allows for relevant interactions between road users to be observed. Finally implications for this work and future considerations are outlined.

Research on the dialects of English spoken within the United States shows variation regarding lexical, morphological, syntactic, and phonological features. Previous research has tended to focus on one linguistic variable at a time with variation. To incorporate multiple variables in the same analysis, this thesis uses a latent class analysis to perform a cluster analysis on results from the Harvard Dialect Survey (2003) in order to investigate what phonetic variables from the Harvard Dialect Survey are most closely associated with each dialect. This thesis also looks at how closely the latent class analysis results correspond to the Atlas of North America (Labov, Ash & Boberg, 2005b) and how well the results correspond to Joshua Katz's heat maps (Business Insider, 2013; Byrne, 2013; Huffington Post, 2013; The Atlantic, 2013). The results from the Harvard Dialect Survey generally parallel the findings of the Linguistic Atlas of North American English, providing support for six basic dialects of American English. The variables with the highest probability of occurring in the North dialect are ‘pajamas: /æ/’, ‘coupon: /ju:/’, ‘Monday, Friday: /e:/’ ‘Florida: /ɔ/’, and ‘caramel: 2 syllables’. For the South dialect, the top variables are ‘handkerchief: /ɪ/’, ‘lawyer: /ɒ/’, ‘pajamas: /ɑ/’, and ‘poem’ as 2 syllables. The top variables in the West dialect include ‘pajamas: /ɑ/’, ‘Florida: /ɔ/’, ‘Monday, Friday: /e:/’, ‘handkerchief: /ɪ/’, and ‘lawyer: /ɔj/’. For the New England dialect, they are ‘Monday, Friday: /e:/’, ‘route: /ru:t/’, ‘caramel: 3 syllables’, ‘mayonnaise: /ejɑ/’, and ‘lawyer: /ɔj/’. The top variables for the Midland dialect are ‘pajamas: /æ/’, ‘coupon: /u:/’, ‘Monday, Friday: /e:/’, ‘Florida: /ɔ/’, and ‘lawyer: /ɔj/’ and for New York City and the Mid-Atlantic States, they are ‘handkerchief: /ɪ/’, ‘Monday, Friday: /e:/’, ‘pajamas: /ɑ/’, ‘been: /ɪ/’, ‘route: /ru:t/’, ‘lawyer: /ɔj/’, and ‘coupon: /u:/’. One major discrepancy between the results from the latent class analysis and the linguistic atlas is the region of the low back merger. In the latent class analysis, the North dialect has a low probability of the ‘cot/caught’ low back vowel distinction, whereas the linguistic atlas found this to be a salent variable of the North dialect. In conclusion, these results show that the latent class analysis corresponds with current research, as well as adding additional information with multiple variables.

Obsessive-compulsive disorder (OCD) is relatively common (Ruscio, Stein, Chiu, & Kessler, 2010) and leads to significant functional impairment (World Health Organization, 2001). Research suggests that exposure and response prevention (EX/RP) is efficacious for reducing OCD symptoms (NICE, 2006); however, standard outpatient EX/RP does not effectively alleviate symptom severity among a substantial proportion of OCD patients (Abramowitz, 2006). Intensive EX/RP programs have been developed to address the needs of treatment-refractory OCD patients (Veale et al., 2016). While evidence from effectiveness studies suggests that intensive EX/RP programs lead to significant reductions in OCD symptom severity, a portion of patients do not demonstrate improvement in response to intensive treatment (e.g., Björgvinsson, Hart, et al., 2013; Boschen, Drummond, & Pillay, 2008). These findings underscore the need to identify reliable predictors of OCD patient response to intensive EX/RP to help target clinical and research efforts toward improving treatment outcomes for those least likely to respond to current treatment modalities. Therefore, the proposed study evaluated distinct trajectories of OCD symptom change across six-weeks of intensive treatment and examined factors that predict membership in those trajectory groups. Results suggested that three latent subgroups of OCD patients emerged with one demonstrating symptom relapse during intensive treatment. Further, OCD symptom severity was the only baseline factor that predicted latent-class membership. Implications and future directions of research are discussed.

Half of the students who begin college do not complete a degree or certificate. The odds of completing a degree are decreased if a student has a low socio-economic status (SES), is the first in a family to attend college (first-generation), attends multiple institutions, stops out multiple times, reduces credit loads over time, performs poorly in major-specific coursework, has competing family obligations, and experiences financial difficulties. Stopping out of college does not always indicate that a student is no longer interested in pursuing an education; it can be an indication of a barrier or several barriers faced. Institutions can benefit themselves and students by utilizing person-centered statistical methods to re-engage students they have lost, particularly those near the end of their degree plan. Using demographic, academic, and financial variables, this study applied latent class analysis (LCA) to explore subgroups of seniors who have stopped out of a public four-year Tier One research intuition before graduating with a four-year degree. The findings indicated a six-class model was the best fitting model. Similar to previous research, academic and financial variables were key determinants of the latent classes. This paper demonstrates how the results of an LCA can assist institutions in the decisions around intervention strategies and resource allocations.