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.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The wholesale trading companies are facing new challenges in terms of the level of service, which is an important factor which determines the level of sales and profitability of companies. Thus, currently, companies are aimed to deliver orders to their respective customers on time, in good condition and complete. This file focuses on the importance of warehouse management for mass consumption distributors, since it's the warehouses that determine the operation and productivity costs of these. Thus, some innovative techniques such as the lean warehouse and allocation tools improve working conditions with the aim of reducing downtime and distances. A pilot programme of implementation of the tools mentioned in the warehouse of a wholesale distributor was carried out. As a result of the research, the distance traveled in the warehouse was reduced by 22%, the reduction of times in the picking process, achieving the optimal delivery time and generating an increase in the sales made of 215,720.22 soles. These results prove to be decisive in the performance of the supply chain (inventory, cost, delay and risk), since the key to success for this industry is to satisfy the needs of customers in the shortest possible time.

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.

Objective. To determine if the new 2016 version of the North American Pharmacy Licensure Examination (NAPLEX) affected scores when controlling for student performance on other measures using data from one institution. Methods. There were 201 records from the classes of 2014-2016. Doubly robust estimation using weighted propensity scores was used to compare NAPLEX scaled scores and pass rates while considering student performance on other measures. Of the potential controllers of student performance: Pharmacy Curricular Outcomes Assessment (PCOA), scaled composite scores from the Pharmacy College Admission Test (PCAT), and P3 Grade Point Average (GPA). Only PCOA and P3 GPA were found to be appropriate for propensity scoring. Results. The weighted NAPLEX scaled scores did not significantly drop from the old (2014-2015) to the new (2016) version of NAPLEX. The change in pass rates between the new and old versions of NAPLEX were also non-significant. Conclusion. Using data from one institution, the new version itself of the NAPLEX did not have a significant effect on NAPLEX scores or first-time pass rates when controlling for student performance on other measures. Colleges are encouraged to repeat this analysis with pooled data and larger sample sizes.

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.

>Magister Scientiae - MSc / Wetlands have been recognised as one of the most intrinsically valuable and threatened ecosystems in the world. Global estimates indicate that wetlands are being lost or transformed at a rapid rate, exacerbated by projected climate change impacts. This has prompted the need to improve wetland mapping to address the conservation and management of these ecosystems effectively. However, this remains a challenge. Current mapping approaches estimates of wetland extent vastly underestimate the true extent. Ancillary data has been acknowledged to improve the accuracy of mapping the distribution of wetlands.

This paper empirically explores how well the established resource theory can explainwhy individuals in European countries participate or not participate through e-participation.Focusing on key resources, the essay also examines the difference in degree of explanationbetween resources on an individual level and country contextual resources. Through logisticregression analysis using variables and nearly 40 000 cases from ESS and the UN E-governmentSurvey, the study finds the resource theory explaining a majority of the results, while at the sametime it fails to contribute satisfying explanations in some areas. Furthermore, our analysisconclude that individual resources has greater impact on individual participation than countrycontextual resources. The best model for understanding individual e-participation from aresource theory perspective still needs to include country contextual resources.