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

计量经济分析 = Econometric analysis

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA’s such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.

/pdf/calculating-and-reporting-effect-sizes-to-facilitate-4a429kama3.pdf

Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

Principles and Practice of Structural Equation Modeling (2nd ed.)

The Publication Manual of the American Psychological Association (American Psychological Association, 2001, American Psychological Association, 2010) calls for the reporting of effect sizes and their confidence intervals. Estimates of effect size are useful for determining the practical or theoretical importance of an effect, the relative contributions of factors, and the power of an analysis. We surveyed articles published in 2009 and 2010 in the Journal of Experimental Psychology: General, noting the statistical analyses reported and the associated reporting of effect size estimates. Effect sizes were reported for fewer than half of the analyses; no article reported a confidence interval for an effect size. The most often reported analysis was analysis of variance, and almost half of these reports were not accompanied by effect sizes. Partial η2 was the most commonly reported effect size estimate for analysis of variance. For t tests, 2/3 of the articles did not report an associated effect size estimate; Cohen's d was the most often reported. We provide a straightforward guide to understanding, selecting, calculating, and interpreting effect sizes for many types of data and to methods for calculating effect size confidence intervals and power analysis.

Effect size estimates: Current use, calculations, and interpretation.

The statistical analysis of mediation effects has become an indispensable tool for helping scientists investigate processes thought to be causal. Yet, in spite of many recent advances in the estimation and testing of mediation effects, little attention has been given to methods for communicating effect size and the practical importance of those effect sizes. Our goals in this article are to (a) outline some general desiderata for effect size measures, (b) describe current methods of expressing effect size and practical importance for mediation, (c) use the desiderata to evaluate these methods, and (d) develop new methods to communicate effect size in the context of mediation analysis. The first new effect size index we describe is a residual-based index that quantifies the amount of variance explained in both the mediator and the outcome. The second new effect size index quantifies the indirect effect as the proportion of the maximum possible indirect effect that could have been obtained, given the scales of the variables involved. We supplement our discussion by offering easy-to-use R tools for the numerical and visual communication of effect size for mediation effects.

/pdf/effect-size-measures-for-mediation-models-quantitative-373kkaf6c8.pdf

Effect size measures for mediation models: quantitative strategies for communicating indirect effects.

The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.

On Effect Size

We explore the impact on employee attitudes of their perceptions of how others outside the organization are treated (i.e., corporate social responsibility) above and beyond the impact of how employees are directly treated by the organization. Results of a study of 827 employees in eighteen organizations show that employee perceptions of corporate social responsibility (CSR) are positively related to (a) organizational commitment with the relationship being partially mediated by work meaningfulness and perceived organizational support (POS) and (b) job satisfaction with work meaningfulness partially mediating the relationship but not POS. Moreover, in order to address limited micro-level research in CSR, we develop a measure of employee perceptions of CSR through four pilot studies. Employing a bifactor model, we find that social responsibility has an additional effect on employee attitudes beyond environmental responsibility, which we posit is due to the relational component of social responsibility (e.g., relationships with community).

https://www3.nd.edu/~kkelley/publications/articles/Glavas_Kelley_2014.pdf

The Effects of Perceived Corporate Social Responsibility on Employee Attitudes

This review examines recent advances in sample size planning, not only from the perspective of an individual researcher, but also with regard to the goal of developing cumulative knowledge. Psychologists have traditionally thought of sample size planning in terms of power analysis. Although we review recent advances in power analysis, our main focus is the desirability of achieving accurate parameter estimates, either instead of or in addition to obtaining sufficient power. Accuracy in parameter estimation (AIPE) has taken on increasing importance in light of recent emphasis on effect size estimation and formation of confidence intervals. The review provides an overview of the logic behind sample size planning for AIPE and summarizes recent advances in implementing this approach in designs commonly used in psychological research.

/pdf/sample-size-planning-for-statistical-power-and-accuracy-in-4mfnovi9ao.pdf

Ken Kelley

Papers

Effect size measures for mediation models: quantitative strategies for communicating indirect effects.

On Effect Size

The Effects of Perceived Corporate Social Responsibility on Employee Attitudes

Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation

Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation