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.

PracTools
is an R package with functions that compute sample sizes for various types of
finite population sampling designs when totals or means are estimated. One-, two-, and three-stage
designs are covered as well as allocations for stratified sampling and probability proportional to size
sampling. Sample allocations can be computed that minimize the variance of an estimator subject to a
budget constraint or that minimize cost subject to a precision constraint. The package also contains
some specialized functions for estimating variance components and design effects. Several finite
populations are included that are useful for classroom instruction.

/pdf/practools-computations-for-design-of-finite-population-4duoi7u9mq.pdf

PracTools: Computations for Design of Finite Population Samples

Measuring the strength of attitudes in social media data

Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation.

The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations.

Privacy and confidentiality

Survey research aims to collect robust and reliable data from respondents. However, despite researchers’ efforts in designing questionnaires, survey instruments may be imperfect, and question struc...

https://journals.sagepub.com/doi/pdf/10.1177/08944393211032950

Predicting Question Difficulty in Web Surveys: A Machine Learning Approach Based on Mouse Movement Features:

We invited a number of prominent statisticians and statistics educators to glimpse into the future to discuss what they see as the significant challenges to the established statistics curriculum that enculturate students into statistical practices that underpin the activity of statisticians. Peng, Kreuter, and Gould discuss various developments, which are already gaining traction in current society and will support the notion of immersion in a data-rich curriculum. The influence of MOOCs, “big data,” and Bayesian approaches is primarily discussed by these writers in relation to an undergraduate curriculum. Pruim raises some key questions about teaching computation in statistics with a particular emphasis on undergraduates and programming. In the final piece of writing, Witmer and Cobb discuss the increasing influence of Bayesian inference with an emphasis on a curriculum that fosters statistical reasoning and the evaluation of arguments.

Frauke Kreuter

Papers

PracTools: Computations for Design of Finite Population Samples

Measuring the strength of attitudes in social media data

Privacy and confidentiality

Predicting Question Difficulty in Web Surveys: A Machine Learning Approach Based on Mouse Movement Features:

Challenge to the established curriculum: a collection of reflections