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.

Out of the Crisis

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.

Thinking fast and slow.

Statistical Analysis with Missing Data

Economic Control of Quality of Manufactured Product.

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

/pdf/an-introduction-to-latent-class-growth-analysis-and-growth-3yadg8aqzn.pdf

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

Data used in nationwide face-to-face surveys are always collected in multistage cluster samples. The relative homogeneity of the clusters selected in this way can lead to design effects at the sampling stage. Interviewers can further homogenize answers within the sampling points. The study presented here was designed to separate between interviewer effects and sampling-point effects using interpenetrated samples for conducting a nationwide survey on fear of crime. Even though one might, given the homogeneity of neighborhoods, assume that sampling-point effects would be especially strong for questions related to fear of crime in one’s neighborhood, we found that, for most items, the interviewer was responsible for a greater share of the homogenizing effect than was spatial clustering. This result can be understood if we recognize that these questions are part of a larger class of survey questions whose subject matter is either unfamiliar to the respondent or otherwise not well anchored in the mind of the respondent. These questions permit differing interpretations to be elicited by the interviewer.

Separating Interviewer and Sampling-Point Effects

Recent years have seen an increase in the amount of statistics describing different phenomena based on “Big Data.” This term includes data characterized not only by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, and the way in which they are collected and disseminated is fundamental. This change constitutes a paradigm shift for survey research. There is great potential in Big Data, but there are some fundamental challenges that have to be resolved before its full potential can be realized. This report provides examples of different types of Big Data and their potential for survey research; it also describes the Big Data process, discusses its main challenges, and considers solutions and research needs.

Big Data in Survey Research AAPOR Task Force Report

Journal of the Royal Statistical Society Series A (Statistics in Society). Special Issue on Paradata

Linking paradata from a survey on labor market participation to administrative data, this article has a unique opportunity to examine nonresponse and measurement error jointly and provide estimates for bias, variance, and mean square error. We find that increased contact attempts resulted in significant reductions in nonresponse bias. Measure- ment error increased somewhat with increased effort, though total bias was nevertheless reduced. One of the key survey indicators revealed an interesting case of counteracting effects of nonresponse and measure- ment error whereby increased effort to recruit nonrespondents led to an increase in mean square error with increased level of effort despite a reduction in nonresponse bias and constant measurement error.

Nonresponse and Measurement Error in Employment Research: Making Use of Administrative Data

Objectives. To assess the impact of the COVID-19 pandemic on mental distress in US adults.Methods. Participants were 5065 adults from the Understanding America Study, a probability-based Internet panel representative of the US adult population. The main exposure was survey completion date (March 10-16, 2020). The outcome was mental distress measured via the 4-item version of the Patient Health Questionnaire.Results. Among states with 50 or more COVID-19 cases as of March 10, each additional day was significantly associated with an 11% increase in the odds of moving up a category of distress (odds ratio = 1.11; 95% confidence interval = 1.01, 1.21; P = .02). Perceptions about the likelihood of getting infected, death from the virus, and steps taken to avoid infecting others were associated with increased mental distress in the model that included all states. Individuals with higher consumption of alcohol or cannabis or with history of depressive symptoms were at significantly higher risk for mental distress.Conclusions. These data suggest that as the COVID-19 pandemic continues, mental distress may continue to increase and should be regularly monitored. Specific populations are at high risk for mental distress, particularly those with preexisting depressive symptoms.

Frauke Kreuter

Papers

Separating Interviewer and Sampling-Point Effects

Big Data in Survey Research AAPOR Task Force Report

Journal of the Royal Statistical Society Series A (Statistics in Society). Special Issue on Paradata

Nonresponse and Measurement Error in Employment Research: Making Use of Administrative Data

Mental Distress in the United States at the Beginning of the COVID-19 Pandemic.