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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.

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.

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.

This paper analyses the main components and distribution of household net assets in Croatia on the basis of the data from the Household Finance and Consumption Survey (HFCS) by taking into account different socio-demographic characteristics of households. The main results indicate that real assets are widely distributed among households, whereby 85% of households own the household main residence. Financial assets and liabilities account for larger share among wealthier households. The analysis of the main determinants establishing the position of an individual household in distribution of assets has additionally highlighted the importance of the household main residence (HMR). Households with inherited HMR are less likely to be positioned in the lowest net asset quintile. In addition, households with HMR in the city of Zagreb or on the Adriatic Coast are more likely to be in higher asset quintile groups. The survey has also found that the level of household income, educational attainment, labour market status and age of the household reference person affect the probability of positioning a household in a certain net asset quintile.

Linked social media and survey data have the potential to be a unique source of information for social research. While the potential usefulness of this methodology is widely acknowledged, very few studies have explored methodological aspects of such linkage. Respondents produce planned amounts of survey data, but highly variant amounts of social media data. This study explores this asymmetry by examining the amount of social media data available to link to surveys. The extent of variation in the amount of data collected from social media could affect the ability to derive meaningful linked indicators and could introduce possible biases. Linked Twitter data from respondents to two longitudinal surveys representative of Great Britain, the Innovation Panel and the NatCen Panel, show that there is indeed substantial variation in the number of tweets posted and the number of followers and friends respondents have. Multivariate analyses of both data sources show that only a few respondent characteristics have a statistically significant effect on the number of tweets posted, with the number of followers being the strongest predictor of posting in both panels, women posting less than men, and some evidence that people with higher education post less, but only in the Innovation Panel. We use sentiment analyses of tweets to provide an example of how the amount of Twitter data collected can impact outcomes using these linked data sources. Results show that more negatively coded tweets are related to general happiness, but not the number of positive tweets. Taken together, the findings suggest that the amount of data collected from social media which can be linked to surveys is an important factor to consider and indicate the potential for such linked data sources in social research.