The aim of this study is to combine queer theories with quantitative methods. In social sciences, queer theories are being widely and increasingly used. Quantitative method is also widely used, and as a traditional method it can be regarded as a cornerstone in social sciences, sociology in particular. However, the two perspectives, queer theories and quantitative method, seem at a first glance to be very different in their understanding of social phenomena. Queer theories have, with some exceptions, mainly focused on qualitative research concerning such phenomena as sexuality, while the field of science is quite diverse. In this field, except from qualitative, queer research, there are also quantitative studies that use surveys to gather information.This study combines the two perspectives, thus creating a study inspired by queer theories and focusing on the methodological problems that occur when embracing queer theories’ elements of difference and social constructivism, and using a method which aims to standardize… The study also explores if the descriptions of sex and sexuality proposed by queer theories are reflected in the population. Using surveys, information have been gathered from students of five different institutions at Uppsala University.

The study of social network dynamics has become an increasingly important component of many disciplines in the social sciences. In the past decade, statistical models and methods have been proposed which permit researchers to draw statistical inference on these dynamics. This thesis builds on one such family of models, the stochastic actor oriented model (SAOM) proposed by Snijders [2001]. Goodness of fit for SAOMs is an area that is only just beginning to be filled in with appropriate methods. This thesis proposes a Mahalanobis distance based, Monte Carlo goodness of fit test that can depend on arbitrary features of the observed network data and covariates. As remediating poor fit can be a difficult process, a modified model distance (MMD) estimator is devised that can help researchers to choose among a set of model elaborations. In practice, panel data is typically used to draw SAOM-based inference. This thesis also proposes a score-type test for time heterogeneity between the waves in the panel that is computationally cheap and fits into a convenient, forward model selecting workflow. Next, this thesis proposes a rigorous method for aggregating so-called relational event data (e.g. emails and phone calls) by extending the SAOM family to a family of hidden Markov models that suppose a latent social network is driving the observed relational events. Finally, this thesis proposes a measurement model for SAOMs inspired by error-in-variables (EiV) models employed in an array of disciplines. Like the relational event aggregation model, the measurement model is a hidden Markov model extension to the SAOM family. These models allow the researcher to specify the form of the mesurement error and buffer against potential attenuating biases and other problems that can arise if the errors are ignored.

Researchers interested in measuring people's underlying attitudes towards an object (e.g., abortion) often collect Likert data by administering a survey. Likert data consist of surveyees' responses to statements about the object, where responses fall into ordered categories running from `Strongly agree' to `Strongly disagree' or into a `Don't Know / Can't Choose' category. Two examples of Likert data are used for illustrative purposes. The first dataset was collected by the author from American and British graduate students at Oxford University and contains items measuring underlying abortion attitudes. The second dataset was taken from British and American responses to the 1995 National Identity Survey (NIS) and contains items measuring underlying national pride and immigration attitudes. A model for Likert data and underlying attitudes is introduced. This model is more principled than existing models. It treats people's underlying attitudes as latent variables, and it specifies a relationship between underlying attitudes and responses that is consistent with attitudinal research. Further, the formal probability model for responses allows people's interpretation of the response categories to differ. The model is fitted by maximising an appropriate likelihood. Variants of the model are used to analyse Likert data in three contexts; in each, the method using our model compares favourably to existing methods. First, the model is used to visualise the structure underlying the abortion attitude data. This method of visualization produces more sensible plots than analogous multivariate data visualization methods. Second, the model is used to select the statements whose responses (in the abortion attitude data) best reflect underlying abortion attitudes. Our method of statement selection more closely adheres to attitude researchers' stated aims than popular methods based on sample correlations. Third, the model is used to investigate how underlying national pride varies with nationality in the NIS data and also how underlying abortion attitude varies with gender, religious status, and nationality in the abortion attitude data. Unlike methods currently used by social scientists to model the relationship between attitudes and covariates, our method controls for the effects of differing response category interpretation. As a result, inferences about group differences in underlying attitudes are more robust to group differences in response category interpretation.

This thesis proposes and discusses a statistical model for interpreting forensic DNA mixtures. We develop methods for estimation of model parameters and assessing the uncertainty of the estimated quantities. Further, we discuss how to interpret the mixture in terms of predicting the set of contributors. We emphasise the importance of challenging any interpretation of a particular mixture, and for this purpose we develop a set of diagnostic tools that can be used in assessing the adequacy of the model to the data at hand as well as in a systematic validation of the model on experimental data. An important feature of this work is that all methodology is developed entirely within the framework of the adopted model, ensuring a transparent and consistent analysis. To overcome the challenge that lies in handling the large state space for DNA profiles, we propose a representation of a genotype that exhibits a Markov structure. Further, we develop methods for efficient and exact computation in a Bayesian network. An implementation of the model and methodology is available through the R package DNAmixtures.