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.

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.

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.

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.

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.

Emerging adulthood is often-overlooked in current gastrointestinal (GI) health research; however, epidemiological evidence suggests that GI disorders are increasing in this population. The purpose of this study was to first define common GI symptom subgroups within emerging adults and then to characterize these group differences with key biopsychosocial factors encompassing diet, depression and anxiety symptoms, as well as physical and social functioning related to quality of life. A total of 956 emerging adults from a southeastern US university were surveyed on GI symptoms, psychosocial factors, and demographics. Latent class analysis uncovered three statistically significant GI symptom patterns within the sample identified by the degree of severity: Normal (n=649), Mild (n=257), and Moderate (n=50). This study demonstrated that significant impairment in GI functioning emerges at much earlier ages that are commonly assumed. In addition, these GI symptom levels were associated with important biopsychosocial factors. Assessing GI functioning in emerging adults may provide important insights into understanding the development of FGIDs.

Latent class modelling is one method used in the evaluation of diagnostic tests when there is no gold standard test that is perfectly accurate. The technique demonstrates maximum likelihood estimates of the prevalence of a disease or a condition and the error rates of diagnostic tests or observers. This study reports the effect of departures from the latent class model assumption of independent misclassifications between observers or tests conditional on the true state of the individual being tested. It is found that estimates become biased in the presence of dependence. Most commonly the prevalence of the disease is overestimated when the true prevalence is at less than 50% and the error rates of dependent observers are underestimated. If there are also independent observers in the group, their error rates are overestimated. The most dangerous scenario in which to use latent class methods int he evaluation of tests is when the true prevalence is low and the false positive rate is high. This is common to many screening situations. / Thesis / Master of Science (MS)

Intimate partner violence (IPV) is a prevalent potentially traumatic experience that increases risk for posttraumatic stress symptoms (PTSS). However, there is still considerable heterogeneity in PTSS among women exposed to IPV. Research on IPV has examined the ways in which different characteristics of IPV exposure have separately related to risk for PTSS, specifically the type (physical, psychological, economic, sexual), frequency (number of incidents), severity (minor, severe), and mode of violence (in-person, online). However, it may be important to examine how the integration of these characteristics of IPV differ across ���������������������� ���� ������ ���� ������������ �������������������� �������������� ���������� The current study integrated these characteristics to assess classes of IPV and the relevant associations between concurrent and future PTSS. 264 women between the ages of 18-24 (Mage=20.41, SD=2.99) were recruited as part of a greater longitudinal study examining the relationship between PTSS and co-occurring psychopathology following exposure to IPV and/or sexual assault in the past year. Four classes of IPV across four characteristics of IPV (type, severity, frequency, and mode) were identified with latent class analysis (LCA). (1) history of both mild and severe psychological, physical, and sexual IPV in person and online, (2) history of mild and severe psychological IPV and mild sexual IPV occurring in person and online, (3) history of mild psychological IPV occurring in person and online, (4) past history of one type of IPV occurring in person. Class membership and concurrent and future PTSS were found to be associated with class membership.

no / Background Almost all studies in health research control or investigate socioeconomic position (SEP) as exposure or confounder. Different measures of SEP capture different aspects of the underlying construct, so efficient methodologies to combine them are needed. SEP and ethnicity are strongly associated, however not all measures of SEP may be appropriate for all ethnic groups. Methods We used latent class analysis (LCA) to define subgroups of women with similar SEP profiles using 19 measures of SEP. Data from 11,326 women were used, from eight different ethnic groups but with the majority from White British (40%) or Pakistani (45%) backgrounds, who were recruited during pregnancy to the Born in Bradford birth cohort study. Results Five distinct SEP subclasses were identified in the LCA: (i) "Least socioeconomically deprived and most educated" (20%); (ii) "Employed and not materially deprived" (19%); (iii) "Employed and no access to money" (16%); (iv) "Benefits and not materially deprived" (29%) and (v) "Most economically deprived" (16%). Based on the magnitude of the point estimates, the strongest associations were that compared to White British women, Pakistani and Bangladeshi women were more likely to belong to groups: (iv) "benefits and not materially deprived" (relative risk ratio (95% CI): 5.24 (4.44, 6.19) and 3.44 (2.37, 5.00), respectively) or (v) most deprived group (2.36 (1.96, 2.84) and 3.35 (2.21, 5.06) respectively) compared to the least deprived class. White Other women were more than twice as likely to be in the (iv) "benefits and not materially deprived group" compared to White British women and all ethnic groups, other than the Mixed group, were less likely to be in the (iii) "employed and not materially deprived" group than White British women. Conclusions LCA allows different aspects of an individual’s SEP to be considered in one multidimensional indicator, which can then be integrated in epidemiological analyses. Ethnicity is strongly associated with these identified subgroups. Findings from this study suggest a careful use of SEP measures in health research, especially when looking at different ethnic groups. Further replication of these findings is needed in other populations.