Frauke Kreuter

Bio: Frauke Kreuter is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Paradata & Survey data collection. The author has an hindex of 38, co-authored 185 publications receiving 5738 citations. Previous affiliations of Frauke Kreuter include University of Mannheim & Institut für Arbeitsmarkt- und Berufsforschung.

Social Desirability Bias in CATI, IVR, and Web Surveys The Effects of Mode and Question Sensitivity

Abstract: Although it is well established that self-administered ques- tionnaires tend to yield fewer reports in the socially desirable direction than do interviewer-administered questionnaires, less is known about whether different modes of self-administration vary in their effects on socially desirable responding In addition, most mode comparison stud- ies lack validation data and thus cannot separate the effects of differ- ential nonresponse bias from the effects of differences in measurement error This paper uses survey and record data to examine mode effects on the reporting of potentially sensitive information by a sample of re- cent university graduates Respondents were randomly assigned to one of three modes of data collection—conventional computer-assisted tele- phone interviewing (CATI), interactive voice recognition (IVR), and the Web—and were asked about both desirable and undesirable attributes of their academic experiences University records were used to evaluate the accuracy of the answers and to examine differences in nonresponse bias by mode Web administration increased the level of reporting of sensi- tive information and reporting accuracy relative to conventional CATI, with IVR intermediate between the other two modes Both mode of data collection and the actual status of the respondent influenced whether respondents found an item sensitive

Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study.

Abstract: Background: The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. Objective: The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. Methods: We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. Results: We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. Conclusions: Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.

Data Analysis Using Stata

Abstract: The First Time Starting Stata Setting up your screen Your first analysis Do-files Exiting Stata Working with Do-Files From interactive work to working with a do-file Designing do-files Organizing your work The Grammar of Stata The elements of Stata commands Repeating similar commands Weights General Comments on the Statistical Commands Regular statistical commands Estimation commands Creating and Changing Variables The commands generate and replace Specialized recoding commands Recoding string variables Recoding date and time Setting missing values Labels Storage types, or the ghost in the machine Creating and Changing Graphs A primer on graph syntax Graph types Graph elements Multiple graphs Saving and printing graphs Describing and Comparing Distributions Categories: Few or many? Variables with few categories Variables with many categories Statistical Inference Random samples and sampling distributions Descriptive inference Causal inference Introduction to Linear Regression Simple linear regression Multiple regression Regression diagnostics Model extensions Reporting regression results Advanced techniques Regression Models for Categorical Dependent Variables The linear probability model Basic concepts Logistic regression with Stata Logistic regression diagnostics Likelihood-ratio test Refined models Advanced techniques Reading and Writing Data The goal: The data matrix Importing machine-readable data Inputting data Combining data Saving and exporting data Handling big datasets Do-Files for Advanced Users and User-Written Programs Two examples of usage Four programming tools User-written Stata commands Around Stata Resources and information Taking care of Stata Additional procedures References Author Index Subject Index Exercises appear at the end of each chapter.

Practical Tools for Designing and Weighting Survey Samples

Abstract: Designing Single-stage Sample Surveys.- Multistage Designs.- Survey Weights and Analyses.- Other Topics.- Appendices.

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

An introduction to latent class growth analysis and growth mixture modeling.

Abstract: In recent years, there has been a growing interest among researchers in the use of latent class and growth mixture modeling techniques for applications in the social and psychological sciences, in part due to advances in and availability of computer software designed for this purpose (e.g., Mplus and SAS Proc Traj). Latent growth modeling approaches, such as latent class growth analysis (LCGA) and growth mixture modeling (GMM), have been increasingly recognized for their usefulness for identifying homogeneous subpopulations within the larger heterogeneous population and for the identification of meaningful groups or classes of individuals. The purpose of this paper is to provide an overview of LCGA and GMM, compare the different techniques of latent growth modeling, discuss current debates and issues, and provide readers with a practical guide for conducting LCGA and GMM using the Mplus software. Researchers in the fields of social and psychological sciences are often interested in modeling the longitudinal developmental trajectories of individuals, whether for the study of personality development or for better understanding how social behaviors unfold over time (whether it be days, months, or years). This usually requires an extensive dataset consisting of longitudinal, repeated measures of variables, sometimes including multiple cohorts, and analyzing this data using various longitudinal latent variable modeling techniques such as latent growth curve models (cf. MacCallum & Austin, 2000). The objective of these approaches is to capture information about interindividual differences in intraindividual change over time (Nesselroade, 1991). However, conventional growth modeling approaches assume that individuals come from a single population and that a single growth trajectory can adequately approximate an entire population. Also, it is assumed that covariates that affect the growth factors influence each individual in the same way. Yet, theoretical frameworks and existing studies often categorize individuals into distinct subpopulations (e.g., socioeconomic classes, age groups, at-risk populations). For example, in the field of alcohol research, theoretical literature suggests different classes