Structural equation model (SEM) is a general approach to analyze multivariate data. It is a relatively comprehensive model and combines useful characteristics from many statistical approaches, thus enjoys a variety of advantages when dealing complex relationships. This report gives a brief introduction to SEM, focusing especially the comparison of SEM and OLS regression. A simple tutorial of how to apply SEM is also included with the introduction and comparison. SEM can be roughly seen as OLS regression added with features such as simultaneous estimation, latent factors and autocorrelation. Therefore, SEM enjoys a variety of advantages over OLS regression. However, it is not always the case that SEM will be the optimal choice. The biggest concern is the complexity of SEM, for simpler model will be preferable for researchers when the fitness is similar. Two simulation cases, one requires special features of SEM and one satisfies assumptions of OLS regression, are applied to illustrate the choice between SEM and OLS regression. A study using data from US life insurers in the year 1994 serves as a further illustration. The conclusion is when special features of SEM is required, SEM fits better and will be the better choice, while when OLS regression assumptions are satisfied, SEM and OLS regression will fit equally well, considering the complexity of SEM, OLS regression will be the better choice. / text

Master of Science / Department of Statistics / Nora Bello / Ordered categorical responses are frequently encountered in many disciplines. Examples of interest in agriculture include quality assessments, such as for soil or food products, and evaluation of lesion severity, such as teat ends status in dairy cattle. Ordered categorical responses are characterized by multiple categories or levels recorded on a ranked scale that, while apprising relative order, are not informative of magnitude of or proportionality between levels. A number of statistically sound models for ordered categorical responses have been proposed, such as logistic regression and probit models, but these are commonly underutilized in practice. Instead, the ordinary least squares linear regression model is often employed with ordered categorical responses despite violation of basic model assumptions. In this study, the inferential implications of this approach are investigated using a simulation study that evaluates robustness based on realized Type I error rate and statistical power. The design of the simulation study is motivated by applied research cases reported in the literature. A variety of plausible scenarios were considered for simulation, including various shapes of the frequency distribution and different number of categories of the ordered categorical response. Using a real dataset on frequency of antimicrobial use in feedlots, I demonstrate the inferential performance of ordinary least squares linear regression on ordered categorical responses relative to a probit model.

When the Fair Housing Act of 1968 was passed 47 years ago, the United States was in the midst of the civil rights movement and fair housing was identified as a pillar of equality. While, progress has been made, there is much work that needs to be done in order to achieve integration. As a country, the United States is a highly segregated country. It is important to understand the factors that contribute to this and it is important to understand the relationships that exists between them in order to attempt to solve the problem. While the legal barriers to integration have been lifted choices continue to be limited to families of color that lack the resources to live in desirable neighborhoods. The ultimate goal of this study is to examine the relationship between the impact of individual indicators and housing patterns in the greater Tucson/Pima county region. An affordability index, the DEYA index, was created to determine where affordability is at its highest. The index includes different weights for foreclosure, Pima County spending on affordable housing, the existence of Pima County general obligations bond affordable housing projects, land value and inclusion in the community land trust. Once this was determined a regression analysis was used to determine the relationship between affordability and individual factors that may be affecting integration. The indicators used were broken down into 3 categories: the categories were education, housing and neighborhoods and employment and economic health.

Tidigare studier visar att ökad handel spelar en tydlig roll för ett lands ekonomiska tillväxt. Genom att träda in på den globala marknaden öppnas många möjligheter för ökad handel och nya arbetstillfällen. Utrikeshandeln är betydelsefull för små öppna ekonomier som till exempel Kenya och bör utgöra en stor del av landets BNP. I och med detta är det viktigt att studera vilka faktorer som påverkar ett lands utrikeshandel. Syftet med uppsatsen är att undersöka vilka faktorer som påverkar Kenyas export. Analysen visar att handelspartnernas BNP har en betydande påverkan på Kenyas export. Det geografiska avståndet har en negativ påverkan på Kenyas utrikeshandel. De regionala handelsavtalen har som förväntat en positiv påverkan på exporten.

Due to the inherently hierarchical nature of many natural phenomena, data collected rests in nested entities. As an example, students are nested in schools, school are nested in districts, districts are nested in counties, and counties are nested within states. Multilevel models provide a statistical framework for investigating and drawing conclusions regarding the influence of factors at differing hierarchical levels of analysis. The work in this paper serves as an introduction to multilevel models and their comparison to Ordinary Least Squares (OLS) regression. We overview three basic model structures: variable intercept model, variable slope model, and hierarchical linear model and illustrate each model with an example of student data. Then, we contrast the three multilevel models with the OLS model and present a method for producing confidence intervals for the regression coefficients. / text

Support for the use of progress monitoring results for high-stakes decisions is emerging in the literature, but few studies support the reliability of the measures for this level of decision-making. What little research exists is limited to oral reading fluency measures, and their reliability for progress monitoring (PM) is not supported. This dissertation explored methods rarely applied in the literature for summarizing and analyzing progress monitoring results for medium- to high-stakes decisions. The study was conducted using extant data from 92 "low performing" third graders who were progress monitored using mathematics concept and application measures. The results for the participants in this study identified 1) the number of weeks needed to reliably assess growth on the measure; 2) if slopes differed when results were analyzed with parametric or nonparametric analyses; 3) the reliability of growth; and 4) the extent to which the group did or did not meet parametric assumptions inherent in the ordinary least square regression model. The results indicate reliable growth from static scores can be obtained in as few as 10 weeks of progress monitoring. It was also found that within this dataset, growth through parametric and nonparametric analyses was similar. These findings are limited to the dataset analyzed in this study but provide promising methods not widely known among practitioners and rarely applied in the PM literature.

In economy, banking sector has been considered as the main issue for development. Using panel data analyzing to test the relationship between banks performance and economic freedom indexes for 9 emerging market countries in Asia, the paper¡¦s result shows that there is the positive effect between monetary freedom, business freedom, financial freedom and banks performance while investment freedom has a negative impact. It suggests that emerging market countries¡¦ government and the policy maker need to focus deeply on the operation, the efficiency of regulation and improving the economic freedom.

Food inflation in South Africa has been viewed as an important source of underlying inflationary pressures in the economy due to its persistence beyond that of other commodities. Although several studies found food to be one of the factors that influence purchase decisions, there still appears to be an absence of research that directly links food inflation to consumers’ decisions, especially when financing the purchase of new passenger vehicles in South Africa. In this regard, this study investigated whether the increase in the prices of food products has a significant effect on passenger vehicle purchases in South Africa. Leaning on the literature that argues that economic factors do not play much of a role in passenger vehicle purchase decisions in South Africa, it was hypothesised that there is no supported relationship between food inflation and passenger vehicle purchases in South Africa. Using secondary time series data, the Pearson correlation test revealed a negative but insignificant relationship between food inflation and vehicle purchases in South Africa. The ordinary least squares estimate of the purchase function, taking into account several economic factors that influence passenger vehicle purchase decisions in the literature, showed that disposable income of households along with vehicle purchases of the previous period are to be considered as main determinants of vehicle purchases in South Africa. In addition, it was also revealed that new vehicle prices are also a significant determinant of vehicle purchases. The Johansen cointegration test revealed that the variables in the vehicle purchase function were cointegrated in the long run. The vector error correction model showed a long-run relationship, albeit insignificant, between food inflation and vehicle purchases and no relationship between the two variables in the short run. The Granger causality test revealed that food inflation and vehicle purchases are independent from each other, meaning that no causal effect was found between the variables, regardless of the direction of the test. This study concluded that economic factors such as interest rate and fuel price have an insignificant influence on passenger vehicle purchases in South Africa. In the same line, the impact of food inflation on passenger vehicle purchases in South Africa was found to be insignificant, therefore, the conclusion was drawn that the increase in the prices of food products will not play a considerable role in consumers’ decisions regarding passenger vehicle purchase in South Africa. / Business Management / M. Com. (Business Management)

Using paired samples t-tests, this study examines attitudes toward government intervention to decrease the consumption of tobacco, alcohol, and sugar to improve public health in Sweden. The effects of the four sociodemographic variables gender, age, education, and income on attitudes toward health policies are tested using Ordinary Least Squares and ordered probit regressions. The research is performed using cross-sectional data which is supplied by a national survey. The results show that tobacco should be regulated the most, followed by alcohol and lastly sugar. According to the respondents, tobacco and alcohol consumption need clear societal restrictions while individuals should be responsible for their sugar consumption. This implies that tobacco and alcohol restrictions introduced by the government should be effective and should, therefore, reduce the consumption and subsequently decrease a country’s economic costs. The opposite is true for sugar policy. Women, younger people, highly educated people, and people with higher incomes are positively related to support toward tobacco restrictions. Women, younger people, and highly educated people show more support for alcohol restrictions. Lastly, respondents with higher levels of education are more supportive of sugar restrictions.

Background – This study is an attempt to explore the relationship between microcredit and the socio-economic empowerment of women in rural Bangladesh. Microcredit is simply the extension of a small amount of collateral-free institutional loans to jointly liable poor group members to generate employment and income enhancing activities. As it is too difficult for poor members to get loan from the formal credit institutions, Grameen Bank (GB) or other Non-Government Organizations (NGOs) provide small loans to vulnerable groups of the society by which they are expected to empower over his counterparts. Research questions – RQ1: How does micro-credit affect different indicators of women empowerment in the rural areas of Bangladesh? RQ2– Is the impact different from the male counterparts in the sample households? Purpose – This study is an effort to find the impact of microcredit on a number of indicators of women’s empowerment in the rural areas in Bangladesh. Methodology – Quantitative Regression Techniques such as Ordinary Least Square (OLS) and Instrumental Variable (IV) method have been applied to get the relationship between microcredit and women empowerment. Conclusion – Applying nationally representative cross-section survey data, Bangladesh Integrated Household Survey (BIHS) 2015, this thesis is intended to find the causal linkage between microcredit and women empowerment’s with different dimensions of women’s decisions are taken as empowerment indicators: production, resources, income, leadership, savings and time. The analysis has been conducted at the household level. The study assumes that women empowerment is endogenous. After controlling for endogeneity in the estimation by using an instrumental variable (IV) ‘distance to the market’ this study finds a significant relationship between microcredit and different dimensions of women’s empowerment. Participation in the microcredit program is found to be significant in explaining some of the outcome indicators of empowerment for the sampled households.