Abstract Background - Bankruptcy is an issue that not only affects the company that is registered as bankrupt, but also the society since it has an impact on the economy. Previous studies have been focusing on larger listed companies outside of Sweden hence there is a lack of empirical findings about Swedish companies in this research area. Small and medium companies represent most of the Sweden's labor force and therefore the bankruptcy issue is important to investigate for these companies. Purpose - The purpose of the thesis is to find out which financial information distinguishes bankrupt from non-bankrupt companies in Sweden. In other words, which financial ratios have predictive power on bankruptcy. Furthermore, the thesis wants to provide knowledge towards current and future companies so that they can avoid bankruptcy by paying attention to the ratios distinguished in the thesis and keeping the ratios at an acceptable level. Method – The thesis conducts the research with a quantitative strategy by observing financial information from companies’ annual reports. The logistic regression model is used to test for the 11 ratios, by matching two samples; bankrupt and non-bankrupt companies, as well as a classification matrix, Pearson correlation matrix and variance inflation factor. The bankrupt companies selected, are classified as bankrupt for the period 2016-2019. The thesis implements a deductive approach to establish expectation and deduct which financial ratios are predictive. Conclusion - The thesis ends up with 92 companies, where 46 are bankrupt and 46 are nonbankrupt. Out of the 11 ratios, three are statistically significant and have predictive power on bankruptcy. These three are; debt rate, gross profit margin and, cash and cash equivalents. The debt rate has a positive effect on bankruptcy, which means that a higher debt rate increases the risk of bankruptcy. Gross profit margin and cash and cash equivalents have a negative effect on bankruptcy; as they increase, the risk of bankruptcy decreases.

Eligibility requirements, the pressure to remain eligible at all costs, and demanding time schedules are high stakes issues that affect the National Colligate Athletic Association (NCAA) student-athletes. A gap in research existed on whether college student-athletes’ demographics and engagement predicts their academic success. The purpose of this quantitative research was to determine the extent to which engagement and demographic factors predict student-athletes’ academic success, as measured by a self-reported grade of B or higher in NCAA first-year student-athletes. This study was influenced by Astin’s student involvement theory and Kuh’s concept of engagement. The research question guiding this study addressed the extent to which academic and cocurricular engagement, race, sport played, and gender predict NCAA student-athletes’ academic success. Quantitative data were collected from the 2018 National Survey of Student Engagement. The sample analyzed included 1,985 student-athletes. Logistic regression analysis was used to find that males, wrestlers, football players, and Black or African American student-athletes were less likely to achieve academic success, whereas females, tennis players, and both White and Asian student-athletes were more likely to achieve academic success than their peers. Findings were significant at the .05 level, but the variance explained by the models was less than 10%, which implies limited practical significance. Time spent on cocurricular activities and time spent preparing for class did not predict academic success. The findings of this study may be used by the NCAA and higher education institutions to help understand student-athletes’ behaviors and the implications for supporting academic success.

Eligibility requirements, the pressure to remain eligible at all costs, and demanding time schedules are high stakes issues that affect the National Colligate Athletic Association (NCAA) student-athletes. A gap in research existed on whether college student-athletes' demographics and engagement predicts their academic success. The purpose of this quantitative research was to determine the extent to which engagement and demographic factors predict student-athletes' academic success, as measured by a self-reported grade of B or higher in NCAA first-year student-athletes. This study was influenced by Astin's student involvement theory and Kuh's concept of engagement. The research question guiding this study addressed the extent to which academic and cocurricular engagement, race, sport played, and gender predict NCAA student-athletes' academic success. Quantitative data were collected from the 2018 National Survey of Student Engagement. The sample analyzed included 1,985 student-athletes. Logistic regression analysis was used to find that males, wrestlers, football players, and Black or African American student-athletes were less likely to achieve academic success, whereas females, tennis players, and both White and Asian student-athletes were more likely to achieve academic success than their peers. Findings were significant at the .05 level, but the variance explained by the models was less than 10%, which implies limited practical significance. Time spent on cocurricular activities and time spent preparing for class did not predict academic success. The findings of this study may be used by the NCAA and higher education institutions to help understand student-athletes' behaviors and the implications for supporting academic success.

Meta-analysis refers to the quantitative synthesis of information across different studies. Since outcomes from different studies are likely to be reported in different units, study-level results are typically transformed to the same scale before quantitative integration. Typically, this leads to the accumulation and combination of effect sizes. To date, most social scientists have synthesized, or meta-analyzed, zero-order statistics like a correlation. Synthesizing partial effect sizes is an alternative which allows a meta-analysis to account for the influence of nuisance variables when estimating the association between two variables. This dissertation proposes that logistic regression coefficients from different studies, which are a type of partial effect size, can be meta-analyzed. Logistic regression models how a set of covariates relates to a binary dependent variable. Given a key independent variable (IV) of interest, which we can call the focal IV or Xf, the slope estimate (βf) in a logistic regression measures the impact of Xf on Y on the logit (log-odds) scale, while controlling for other variables. Four assumptions justify the possibility of comparing and possibly combing logistic slopes across studies: (1) Y must be on the same scale, (2) Xf must be on the same scale, (3) all effect sizes are logistic regression slopes adjusted for the same covariates, and (4) model specifications are identical. In practice, the third assumption is particularly challenging as different studies inevitably include different sets of control variables. Three simulation studies are implemented to understand how synthesizing a logistic regression slope on the logit scale is affected by several factors. Across these three simulation studies, the following meta-analytic variables are tested: (1) the size of the partial effect size (βf), (2) Study-level sample size (k), (3) Within-study sample size (N), (4) the degree of between-study variance, (5) a continuous vs. a binary focal predictor, (6) the level of collinearity between Xf and other covariates included in primary studies, (7) the magnitude of non-focal variable slopes, (8) different covariate sets used in primary-level studies, and (9) meta-analytical method. Simulation performance is based on how the bias and mean-squared error (MSE) are affected by each of these simulation parameters. Overall, results suggest that when the four assumptions introduced above are satisfied, meta-analyzing logistic regression slopes is remarkably accurate as the summary effect resulting from the standard random-effects meta-analytic model leads to small levels of bias and MSE under a variety of conditions. When the assumptions are broken (and particularly the third assumption of identical covariate sets), the pooled slope estimator can have large degrees of bias. The bias is a function of within-study sample size, between-study sample size, distribution of the focal IV (i.e., continuous vs. categorical variable), multicollinearity, the magnitude of non-focal variable slope parameters, diversity in covariate sets, and choice of meta-analytical methods. The MSE is a function of study-level sample size, within-study sample size, distribution of the focal IV (i.e., continuous vs. categorical variable), multicollinearity, the magnitude of non-focal variable slope parameters, diversity in covariate sets, and choice of meta-analytical methods. A complex four-way interaction is discovered between collinearity, the magnitude of non-focal variable slope parameters, diversity in covariate sets, and choice of meta-analytical methods. An applied example focusing on estimating the effects of albumin on mortality is also presented to complement the simulation results.

The analysis of gaining, retaining and maintaining customer trust is a highly topical issue in the e-commerce industry to mitigate the challenges of increased competition and volatile customer relationships as an effect of the increasing use of the internet to purchase goods. This study is conducted at the Swedish online fashion retailer NA-KD with the aim of gaining better insight into customer behavior that determines purchases, returns and churn. Therefore, the objectives for this study are to identify the group of loyal customers as well as construct models to predict customer loyalty, frequent returns and customer churn. Two separate approaches are used for solving the problem where a clustering model is constructed to divide the data into different customer segments that can explain customer behaviour. Then a classification model is constructed to classify the customers into the classes of churners, returners and loyal customers based on the exploratory data analysis and previous insights and knowledge from the company. By using the unsupervised machine learning method K-prototypes clustering for mixed data, six clusters are identified and defined as churned, potential, loyal customers and Brand Champions, indecisive shoppers, and high-risky churners. The supervised classification method of bias reduced binary Logistic Regression is used to classify customers into the classes of loyal customers, customers of frequent returns and churners. The final models had an accuracy of 0.68, 0.75 and 0.98 for the three separate binary classification models classifying Churners, Returners and Loyalists respectively.

Thesis advisor: Zhushan Mandy Li / Differential item functioning (DIF) occurs when examinees of equal ability from different groups have different probabilities of correctly responding to certain items. DIF analysis aims to identify potentially biased items to ensure the fairness and equity of instruments, and has become a routine procedure in developing and improving assessments. This study proposed a DIF detection method using regularization techniques, which allows for simultaneous investigation of all items on a test for both uniform and nonuniform DIF. In order to evaluate the performance of the proposed DIF detection models and understand the factors that influence the performance, comprehensive simulation studies and empirical data analyses were conducted. Under various conditions including test length, sample size, sample size ratio, percentage of DIF items, DIF type, and DIF magnitude, the operating characteristics of three kinds of regularized logistic regression models: lasso, elastic net, and adaptive lasso, each characterized by their penalty functions, were examined and compared. Selection of optimal tuning parameter was investigated using two well-known information criteria AIC and BIC, and cross-validation. The results revealed that BIC outperformed other model selection criteria, which not only flagged high-impact DIF items precisely, but also prevented over-identification of DIF items with few false alarms. Among the regularization models, the adaptive lasso model achieved superior performance than the other two models in most conditions. The performance of the regularized DIF detection model using adaptive lasso was then compared to two commonly used DIF detection approaches including the logistic regression method and the likelihood ratio test. The proposed model was applied to analyzing empirical datasets to demonstrate the applicability of the method in real settings. / Thesis (PhD) — Boston College, 2019. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

Infection levels of eastern oysters by the unicellular pathogen Perkinsus marinus have been associated with anthropogenic influences in laboratory studies. However, these relationships have been difficult to investigate in the field because anthropogenic inputs are often associated with natural influences such as freshwater inflow, which can also affect infection levels. We addressed P. marinus-land use associations using field-collected data from Murrells Inlet, South Carolina, USA, a developed, coastal estuary with relatively minor freshwater inputs. Ten oysters from each of 30 reefs were sampled quarterly in each of 2 years. Distances to nearest urbanized land class and to nearest stormwater outfall were measured via both tidal creeks and an elaboration of Euclidean distance. As the forms of any associations between oyster infection and distance to urbanization were unknown a priori, we used data from the first and second years of the study as exploratory and confirmatory datasets, respectively. With one exception, quarterly land use associations identified using the exploratory dataset were not confirmed using the confirmatory dataset. The exception was an association between the prevalence of moderate to high infection levels in winter and decreasing distance to nearest urban land use. Given that the study design appeared adequate to detect effects inferred from the exploratory dataset, these results suggest that effects of land use gradients were largely insubstantial or were ephemeral with duration less than 3 months.

Among 10,384 rural Colorado female patients who received MDC 14 (obstetric services) from 2000 to 2003, 6,615 (63.7%) were admitted to their local rural hospitals; 1,654 (15.9%) were admitted to other rural hospitals; and 2,115 (20.4%) traveled to urban hospitals for inpatient services. This study is to examine how network participation, service scopes, and market competition influences rural women's choice of hospital for their obstetric care. A conditional logistic regression analysis was used. The network participation (p < 0.01), the number of services offered (p < 0.05), and the hospital market competition had a positive and significant relationship with patients' choice to receive obstetric care. That is, rural patients prefer to receive care from a hospital that participates in a network, that provides more number of services, and that has a greater market share (i.e., a lower level of market competition) in their locality. Rural hospitals could actively increase their competitiveness and market share by increasing the number of health care services provided and seeking to network with other hospitals.

The bypassing of rural hospitals increased in Colorado's rural communities during the 1990s. To understand this phenomenon, this study explores why rural Medicare patients in Colorado bypassed their local rural hospitals when they could have received health care services at their nearest local hospital. To identify both individual factors and institutional variables associated with hospital choice behavior, the conditional logistic regression model analyzes 4,099 rural Medicare patients who received heart failure and shock procedures. This study determines that both institutional variables (ownership type, number of beds, number of services, accreditation, and distance between the hospital and a patient's residence) and patient variables (age, length of stay, race, and total charge) are significant in patients' hospital choice. This study suggests that rural hospitals could build cooperative relationships with other large rural and urban hospitals.

To determine whether behavioral factors differ among metabolic conditions and self-reported health, and to determine whether self-reported health is a valid predictor of metabolic syndrome (MetS). A total of 2997 individuals (≥ 40 years old) were selected from four biennial U.S. National Health and Nutrition Examination Surveys (2007–2014). A set of weighted logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs)Individuals with light physical activity are more likely to have MetS and report poor health than those with vigorous physical activity with OR = 3.22 (95% CI: 2.23, 4.66) and 4.52 (95% CI: 2.78, 7.33), respectively. Individuals eating poor diet have greater odds of developing MetS and reporting poor health with OR = 1.32 (95% CI: 1.05, 1.66) and 3.13 (95% CI: 2.46, 3.98). The aforementioned relationships remained significant after adjustment for demographic and socio-economic status. A potential intervention strategy will be needed to encourage individuals to aggressively improve their lifestyle to reduce MetS and improve quality of life. Despite the significant association between self-reported health with MetS, a low sensitivity indicated that better screening tools for MetS, diabetes and cardiovascular disease are essential.