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Background: In the dynamic landscape of modern business, understanding customer satisfaction is crucial for success and competitiveness. This research intends to analyze the various elements that affect customer contentment, concentrating on the impact of demographic characteristics, economic inclinations, and involvement in loyalty programs. Methods: Employing logistic regression analysis, this research analyzes data collected from a diverse customer base across various industries. The study explores the relationship between customer satisfaction (binary dependent variable) and key independent variables, including age, income level, and loyalty program participation. Model validation is conducted through the Hosmer-Lemeshow test, along with the assessment of model fit using Cox & Snell and Nagelkerke R² metrics. Multicollinearity is checked using the Variance Inflation Factor (VIF). Results: The logistic regression model reveals that age and income level significantly influence customer satisfaction, with younger customers and individuals with greater income levels being more probable to report satisfaction. Additionally, participation in loyalty programs emerges as a strong predictor of customer satisfaction. The model demonstrates good fit and predictive ability, as indicated by statistical tests and graphical analyses, including an ROC curve and a Predicted Probability Plot. Conclusions: The research offers significant understanding into the determinants of customer satisfaction, highlighting the importance of demographic factors, economic status, and loyalty programs. These findings offer both theoretical contributions to the field of customer satisfaction research and practical implications for business strategies focused on customer engagement and loyalty.

Received: 17 November 2023 / Accepted: 17 February 2024 / Published: 5 March 2024

Poverty being multi-dimensional in nature is the product of various interactive socioeconomic factors. Some of the factors shaping economic status of the household may be cited as widowhood, disability, illiteracy, ageing, household size, household status, dependency, low wages of the female workers, household responsibilities etc. Theory suggests that the ability of a household to earn a given level of income is to a great extent determined by the characteristics internal to the household. The main purpose of this paper is to identify the factors that explain their relative effect on poverty of the household. Poverty thus captured at micro level is expected to provide insights for polices to alleviate poverty at national level. The standard econometric method of logistic regression technique has been used to determine the extent to which the factors influence the probability of a household being poor. The paper is based on data obtained from a sample survey conducted in Bangladesh during 2008–09.

The case study examines what factors drive countries to adopt new counterterrorism legislation. The proliferation of new counterterrorism laws after September 11 made many experts question how countries decide to legislate. According to a common assumption, states produce new counterterrorism legislation in response to the direct terrorist threat. However, after September 11, alternative explanations surfaced, but very little empirical research followed to probe the issue. When I conducted the study in 2013, virtually no research had focused on the empirical testing of factors related to state decisions to legislate. Partially, this was due to the lack of data. I collected an original dataset of country-level counterterrorism legislation. I recorded counterterrorism laws for 193 countries and analyzed the data with the help of logistic regression analysis. This case study examines the use of logistic regression models. I discuss the process of data collection and the challenges associated with it. I review the processes of constructing regression models and overcoming issues related to the lack of data. The case study includes lessons learnt relevant for logistic regression analysis and quantitative analysis more broadly.

SummaryFamily planning knowledge, attitude and practice surveys typically assess respondents' reproductive attitudes and intentions to use contraception. Longitudinal observation of individual respondents nevertheless shows that such questions are not strongly predictive of subsequent behaviour. This study examines 3 years' data which show that a set of such responses to questions are nevertheless substantially superior in predicting behaviour than any single indicator. Thus statistical techniques which bring into account the apparent multidimensionality of contraceptive motivation can greatly improve upon the estimation of future practice of family planning in a population.

It is a common assumption that children with disabilities are more likely to experience victimization than their peers without disabilities. However, there is a paucity of robust research supporting this assumption in the current literature. In response to this need, we conducted a logistic regression analysis using a national dataset of responses from 26,572 parents/caregivers to children with and without disabilities across all 50 states, plus the District of Columbia. The purpose of our study was to acquire a greater understanding of the odds of victimization among children with and without intellectual disability (ID), while controlling for several child and parent/adult demographic correlates. Most notably, our study revealed that children with ID have 2.84 times greater odds of experiencing victimization than children without disabilities, after adjusting for the other predictors in the model. Implications for future research and practice are discussed.

Logistic regression analysis may well be used to develop a predictive model for a dichotomous medical outcome, such as short‐term mortality. When the data set is small compared to the number of covariables studied, shrinkage techniques may improve predictions. We compared the performance of three variants of shrinkage techniques: 1) a linear shrinkage factor, which shrinks all coefficients with the same factor; 2) penalized maximum likelihood (or ridge regression), where a penalty factor is added to the likelihood function such that coefficients are shrunk individually according to the variance of each covariable; 3) the Lasso, which shrinks some coefficients to zero by setting a constraint on the sum of the absolute values of the coefficients of standardized covariables.Logistic regression models were constructed to predict 30‐day mortality after acute myocardial infarction. Small data sets were created from a large randomized controlled trial, half of which provided independent validation data. We found that all three shrinkage techniques improved the calibration of predictions compared to the standard maximum likelihood estimates. This study illustrates that shrinkage is a valuable tool to overcome some of the problems of overfitting in medical data.

The phenomenon of poverty is a serious problem faced by almost every country in the world. This is because poverty can affect various aspects of people's lives. One of the causes of poverty is due to lack of income and assets to meet basic needs such as food, clothing, housing, health level and acceptable education. In addition, poverty occurs because of the powerlessness of society to get out of the problems it faces. The Central Java regional government incorporated poverty issues into the Regional Medium-Term Development Plan (RPJMD) because Central Java has a high number of poor people. This was done as an effort by the Central Java government to reduce poverty. Therefore, research is needed to find out the variables that most influence poverty in order to assist the government in developing the RPJMD. To find out what factors influence poverty in Central Java with the dichotomous categorical response variable, binary logistic regression analysis was used. The results showed that based on the analysis conducted did not obtain a logistic regression equation model because there were no significant parameters because there were no variables that had a sig value <0.05. Existing variables are Number of Population, Female Head of Household, Number of Children not in School, Number of Disabled Individuals, Number of Chronic Disease Individuals, Unemployment, Non-Electricity Lighting Sources, Unprotected Drinking Water Sources, Kerosene and Wood Cooking Fuels, Location Facilities Defecation (BAB) Not Available, so there are no variables that affect the level of poverty in Central Java Province.

International audience ; The phenomenon of poverty is a serious problem faced by almost every country in the world. This is because poverty can affect various aspects of people's lives. One of the causes of poverty is due to lack of income and assets to meet basic needs such as food, clothing, housing, health level and acceptable education. In addition, poverty occurs because of the powerlessness of society to get out of the problems it faces. The Central Java regional government incorporated poverty issues into the Regional Medium-Term Development Plan (RPJMD) because Central Java has a high number of poor people. This was done as an effort by the Central Java government to reduce poverty. Therefore, research is needed to find out the variables that most influence poverty in order to assist the government in developing the RPJMD. To find out what factors influence poverty in Central Java with the dichotomous categorical response variable, binary logistic regression analysis was used. The results showed that based on the analysis conducted did not obtain a logistic regression equation model because there were no significant parameters because there were no variables that had a sig value <0.05. Existing variables are Number of Population, Female Head of Household, Number of Children not in School, Number of Disabled Individuals, Number of Chronic Disease Individuals, Unemployment, Non-Electricity Lighting Sources, Unprotected Drinking Water Sources, Kerosene and Wood Cooking Fuels, Location Facilities Defecation (BAB) Not Available, so there are no variables that affect the level of poverty in Central Java Province.

This paper deals with the primary causes of informal housing in Greece as well as the observed differentiations in informal housing patterns across space. The spatial level of analysis is the prefectural administrative level. The results of the multinomial logistic regression analysis indicate that Greek prefectures differ in the way they experience the informal housing phenomenon. An explanation for the observed differences may be the separate development paths followed and the diverse range of economic activities in each prefecture. The Greek state has not made provisions for creating the necessary 'urban land stock' in each prefecture, so that everyone interested can find land parcels at an affordable price. On the contrary, the state encourages the informal housing activity by legalizing large areas of such activity sporadically and by introducing legislative initiatives of limited success in dealing with the problem.