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Adults with diabetes or obesity are more likely to be physically inactive than healthy adults. Physical activity is essential in the management of both diseases, necessitating targeted interventions in these groups. This study analysed physical inactivity (defined as not taking part in leisure-time physical activity) in over 100,000 adults in Germany considering their body mass index and the presence of diabetes. Furthermore, the relationship between specific socio-demographic factors with physical inactivity was investigated, particularly focussing diabetic and obese people, to refine the identification of risk-groups for targeted interventions on physical activity promotion. Data from 13 population-based health surveys conducted in Germany from 1997 to 2018 were used. The relevant variables extracted from these datasets were merged and employed in the analyses. We included data from 129,886 individuals in the BMI analyses and 58,311 individuals in the diabetes analyses. Logistic regression analyses were performed to identify the importance of six socio-demographic variables (age, sex/gender, education, income, employment, and migration) for the risk of physical inactivity. Obese and diabetic people reported a higher prevalence of physical inactivity than those who were not affected. Logistic regression analyses revealed advanced age, low education level, and low household income as risk factors for physical inactivity in all groups. A two-sided migration background and unemployment also indicated a higher probability of physical inactivity. Similar socio-demographic barriers appear to be important determinants of physical inactivity, regardless of BMI status or the presence of diabetes. However, physical activity promoting interventions in obese and diabetic adults should consider the specific disease-related characteristics of these groups. A special need for target group specific physical activity programmes in adults from ethnic minorities or of advanced age was further identified.

Using Item Response Theory to analyse survey data from a representative sample of 551 Swedish citizens, a new 16-question measure of fact-based financial literacy is developed and validated. Uni-dimensionality of the measure is verified, and expected correlations are observed with an existing measure of fact-based financial literacy, a measure of subjective financial literacy or confidence, and age, gender, and income. A significant impact of fact-based and subjective financial literacy are found on three time-ordered stages of individuals’ retirement behaviour: planning, saving, and investment management. It is concluded that policies increasing final literacy are important in different phases of the life cycle.

This paper analyses the gender pay gap among full-time and part-time employees, focusing on the labour market in the Republic of Serbia. Using data from the annual household survey on income and living conditions, this paper examines whether the crisis caused by the COVID-19 pandemic has affected the extent of the pay gap in the Republic of Serbia between the two groups of workers who can be defined as full-time and part-time employees in terms of the employment contract. Separate models were estimated for full-time and part-time employees in the wage sector using the 2019-2020 data sample. Applying this approach reveals differences in wages between men and women depending on working hours. The efficiency of full-time and part-time employed women in earning their potential wage is 9.3% and 19.8% lower than that of men, respectively. In Serbia, during the COVID-19 period, there was a significant decline in part-time employment for both men and women, but this trend continued after the crisis, showing a new reality in the labour market.

Background Logistic regression is a useful statistical technique commonly used in many fields like healthcare, marketing, or finance to generate insights from binary outcomes (e.g., sick vs. not sick). However, when applying logistic regression to complex survey data, which includes complex sampling designs, specific methodological issues are often overlooked. Methods The systematic review extensively searched the PubMed and ScienceDirect databases from January 2015 to December 2021, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, focusing primarily on the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). 810 articles met the inclusion criteria and were included in the analysis. When discussing logistic regression, the review considered multiple methodological problems such as the model adequacy assessment, handling dependence of observations, utilization of complex survey design, dealing with missing values, outliers, and more. Results Among the selected articles, the DHS database was used the most (96%), with MICS accounting for only 3%, and both DHS and MICS accounting for 1%. Of these, it was found that only 19.7% of the studies employed multilevel mixed-effects logistic regression to account for data dependencies. Model validation techniques were not reported in 94.8% of the studies with limited uses of the bootstrap, jackknife, and other resampling methods. Moreover, sample weights, PSUs, and strata variables were used together in 40.4% of the articles, and 41.7% of the studies did not use any of these variables, which could have produced biased results. Goodness-of-fit assessments were not mentioned in 75.3% of the articles, and the Hosmer–Lemeshow and likelihood ratio test were the most common among those reported. Furthermore, 95.8% of studies did not mention outliers, and only 41.0% of studies corrected for missing information, while only 2.7% applied imputation techniques. Conclusions This systematic review highlights important gaps in the use of logistic regression with complex survey data, such as overlooking data dependencies, survey design, and proper validation techniques, along with neglecting outliers, missing data, and goodness-of-fit assessments, all of which point to the need for clearer methodological standards and more thorough reporting to improve the reliability of results. Future research should focus on consistently following these standards to ensure stronger and more dependable findings.

Disease-modifying therapies (DMTs) are vital for managing multiple sclerosis (MS), but research using administrative data often excludes patient preferences and factors clinicians consider in treatment decisions. Patient experience data are crucial to understand and improve MS treatment initiation, adherence, and outcomes. A cross-sectional survey of US adults with MS or clinically isolated syndrome was conducted online from December 2022 to January 2023 by the MS Coalition. A mixed methods analysis was conducted: logistic regression for quantitative data and thematic analysis of qualitative data. Among 1,323 participants (median age 55; 78% female), 80% expressed concerns about loss of independence, 65% about financial impacts, 64% about emotional impacts, 57% about relationships, and 42% about careers. Emotional tolls included identity loss, stress from navigating healthcare, and financial strain on families. Concerns varied by age, sex, and disability status. Nearly all participants (97%) reported DMT experience, with 73% having used two or more DMTs. Key factors in initiating DMT included slowing disease progression (92%), preventing relapses (89%), and following medical advice (89%). Financial barriers, such as high out-of-pocket costs, led to treatment delays or discontinuation in 19%. Barriers varied by demographic factors and included stress from medication costs, insurance denials, and fear of losing health coverage. Financial assistance was crucial for many. Half of participants had stopped a DMT due to doctor recommendations, side effects, or insurance issues. The survey highlights the emotional and financial burdens of living with MS, including concerns about independence and relationships. The findings underscore the need for comprehensive care and provide actionable recommendations for managed care, research, and healthcare providers.

This article discusses the theoretical possibilities and practical implications of survey data recycling and survey data harmonization. Using the example of political participation (participation in demonstration) of the Russian-speaking population in former Soviet states, the article presents the procedure of key variable harmonization (minority status), the rules, and the procedures of creating a harmonization control variable, and the possibilities of using harmonized variables in substantive statistical analysis. The harmonization procedures described in this article can be used to study other rare events and other minority groups – studies that often struggle with small and insufficient samples.

The paper identifies the information content at the firm level of qualitative business survey data by examining the consistency between these data and the quantitative data that are provided by the same respondents to the UK's Office for National Statistics in Official surveys. Since the qualitative data are published ahead of the quantitative data the paper then assesses the ability of the qualitative data to predict the firm level quantitative data.

In the past several years a wide range of methods for the construction of regression trees and other estimators based on the recursive partitioning of samples have appeared in the statistics literature. Many applications involve data collected through a complex sample design. At present, however, relatively little is known regarding the properties of these methods under complex designs. This article proposes a method for incorporating information about the complex sample design when building a regression tree using a recursive partitioning algorithm. Sufficient conditions are established for asymptotic design L 2 consistency of these regression trees as estimators for an arbitrary regression function. The proposed method is illustrated with Occupational Employment Statistics establishment survey data linked to Quarterly Census of Employment and Wage payroll data of the Bureau of Labor Statistics. Performance of the nonparametric estimator is investigated through a simulation study based on this example.

Recent surveys led by the author in Karaburun Peninsula discovered multiple prehistoric sites. This article introduces one of the Neolithic sites called Kömür Burnu in this marginal zone of coastal western Anatolia. The site offered various advantages to early farmer-herders including freshwater and basalt sources as well as proximity to agricultural lands, forested areas and marine resources. The plain slipped pottery from the site suggests a date between 6200-6000 cal. BC for the Neolithic occupation. P-XRF characterization of obsidian pieces from Kömür Burnu revealed that these were acquired from two different sources (Melos-Adamas and Göllüdağ). These constitute the first evidence for the participation of Karaburun early farmer-herders in the exchange networks that were active in Neolithic Anatolia and the Aegean. The differential technological features of these pieces concur well with the dual obsidian mobility model suggested by M. Milić for the western Anatolian Neolithic. 

Household surveys, one of the main innovations in social science research of the last century, are threatened by declining accuracy due to reduced cooperation of respondents. While many indicators of survey quality have steadily declined in recent decades, the literature has largely emphasized rising nonresponse rates rather than other potentially more important dimensions to the problem. We divide the problem into rising rates of nonresponse, imputation, and measurement error, documenting the rise in each of these threats to survey quality over the past three decades. A fundamental problem in assessing biases due to these problems in surveys is the lack of a benchmark or measure of truth, leading us to focus on the accuracy of the reporting of government transfers. We provide evidence from aggregate measures of transfer reporting as well as linked microdata. We discuss the relative importance of misreporting of program receipt and conditional amounts of benefits received, as well as some of the conjectured reasons for declining cooperation and for survey errors. We end by discussing ways to reduce the impact of the problem including the increased use of administrative data and the possibilities for combining administrative and survey data.