The k-chart, based on support vector data description, has received recent attention in the literature. We review four different methods for choosing the bandwidth parameter, s, when the k-chart is designed using the Gaussian kernel. We provide results of extensive Phase I and Phase II simulation studies varying the method of choosing the bandwidth parameter along with the size and distribution of sample data. In very limited cases, the k-chart performed as desired. In general, we are unable to recommend the k-chart for use in a Phase I or Phase II process monitoring study in its current form. Copyright (c) 2017 John Wiley & Sons, Ltd.

As the volume and variety of available data continue to proliferate, organizations increasingly turn to analytics in order to enhance business decision-making and ultimately, performance. However, the decisions made as a result of the analytics process are only as good as the data on which they are based. In this article, we examine the data quality problem and propose the use of control charting methods as viable tools for data quality monitoring and improvement. We motivate our discussion using an integrated case study example of a real aircraft maintenance database. We include discussions of the measures of multiple data quality dimensions in this online process. We highlight the lack of appropriate statistical methods for the analysis of this type of problem and suggest opportunities for research in control chart methods within the data quality environment. This article has supplementary material online.

We provide an overview and perspective on the Phase I collection and analysis of data for use in process improvement and control charting. In Phase I, the focus is on understanding the process variability, assessing the stability of the process, investigating process-improvement ideas, selecting an appropriate in-control model, and providing estimates of the in-control model parameters. In our article, we review and synthesize many of the important developments that pertain to the analysis of process data in Phase I. We give our view of the major issues and developments in Phase I analysis. We identify the current best practices and some opportunities for future research in this area.

Most Phase I control-chart methods are based on the often untested and unreasonable assumption of normally distributed process observations. While there has been some work on distribution-free Phase I procedures for process location, at present, we found no distribution-free Phase I methods for evaluating process scale. We propose a Phase I control chart that is distribution free when the process is in-control. This method can be used to define the in-control state of the process variability and to aid in identifying an in-control reference sample. The proposed scale charting method is compared with the traditional R- and S-charts using Monte Carlo simulation. We show that the in-control performance of the R- and S-charts is poor in Phase I, both when the process data follow a normal distribution and when the distribution deviates from the normal model. Our proposed method achieves desired in-control performance when used with both normal and nonnormal data and is sensitive to subgroup differences in process scale. We offer advice for combining this method with an existing distribution-free Phase I chart for studying process location.

Much of the work in statistical quality control is dependent on the proper completion of a Phase I study. Many Phase I control charts are based on an implicit assumption of normally distributed process observations. In the beginning stages of process control, little information is available about the process and the normality assumption may not be reasonable. Existing robust and distribution-free control charts are concerned with the establishment of Phase II control limits that are robust to nonnormality or outliers from the Phase I sample. Our literature review revealed no purely distribution-free Phase I control-chart methods. We propose a distribution-free method for defining the in-control state of a process and identifying an in-control reference sample. The resultant reference sample can be used to estimate the process parameters for the Phase II procedure of choice. The proposed rank-based method is compared with the traditional 5 chart using Monte Carlo simulation. The rank-based method compares favorably to the 3 chart when the process is normally distributed and performs better than the X chart in many situations when the process distribution is skewed or heavy tailed.

Although SAS PROC CALIS is not designed to perform multigroup comparisons, it is believed that SAS can be "tricked" into doing so for groups of equal size. At present, there are no comprehensive examples of the steps involved in performing a multigroup comparison in SAS. The purpose of this article is to illustrate these steps. We demonstrate procedures using an example to evaluate the measurement invariance, of communication satisfaction and organizational justice across 2 groups. Following the approach outlined in Byrne (2004), we conduct the same analysis in AMOS and compare the results to those obtained in SAS. We show that the sample size must be input correctly and the degrees of freedom must be adjusted in order for the standard errors and goodness-of-fit statistics to be correct. In addition, several of the fit indexes must be modified to obtain the correct values.

In phase I of statistical process control (SPC), control charts are often used as outlier detection methods to assess process stability. Many of these methods require estimation of the covariance matrix, are computationally infeasible, or have not been studied when the dimension of the data, p, is large. We propose the one-class peeling (OCP) method, a flexible framework that combines statistical and machine learning methods to detect multiple outliers in multivariate data. The OCP method can be applied to phase I of SPC, does not require covariance estimation, and is well suited to high-dimensional data sets with a high percentage of outliers. Our empirical evaluation suggests that the OCP method performs well in high dimensions and is computationally more efficient and robust than existing methodologies. We motivate and illustrate the use of the OCP method in a phase I SPC application on a N=3D354, p=3D1917 dimensional data set containing Wikipedia search results for National Football League (NFL) players, teams, coaches, and managers. The example data set and R functions, OCP.R and OCPLimit.R, to compute the respective OCP distances and thresholds are available in the supplementary materials.

This work builds upon an existing stream of research that seeks to empirically elucidate the role of legitimacy in helping entrepreneurs overcome liabilities of newness. More specifically, we examine the relationship between legitimating activities and performance in new ventures. We add to the empirical literature on the legitimacy/performance relation by focusing on top performing firms (on multiple performance measures) during the initial phase of the organizational life cycle. Moreover, we submit that our longitudinal sample, which includes data from nearly 5,000 new ventures, offers an important opportunity to enhance the external validity of this base of literature. Interestingly, we find that the value of engaging in legitimating activities depends upon the outcome measure. With regard to top line performance, and in accordance with theory and extant literature, we find that companies engaging in more legitimating activities at start-up are more likely to be top performers as they move beyond the "birth" phase. However, with regard to profitability, engaging in these activities may actually be detrimental.

Our study investigated applicant characteristics in response to organizations incorporating an affirmative action policy (AAP) statement in recruitment material. Study participants (N = 217; White upper-level management students) randomly received recruitment material containing one of three statements (e.g., affirmative action, equal employment opportunity (EEO), or no statement regarding affirmative action or EEO) and were asked to evaluate the attractiveness of the organization publicizing the designated policy. Results indicated that individuals responded negatively to AAPs in recruitment material because of prejudice attitudes, the perceived unfairness of such programs (which we relate to equity sensitivity), or in an attempt to protect their own self-interest. (which we relate to general self-efficacy). Additionally, individuals' equity sensitivity and general self-efficacy both moderated the relationship between racial prejudice and organizational attractiveness. Specifically, the negative relationships between participants' prejudice attitudes and the attractiveness of organizations publicizing an affirmative action policy were stronger for benevolents (persons tolerant of situations where they are under-rewarded) and for persons low in self-efficacy. Implications of our findings for organizational recruitment practices are discussed. Copyright (c) 2006 John Wiley & Sons, Ltd.

We are in the midst of a "Data Revolution" that is transforming our economy. This revolution is as large and profound as other major economic shifts from the introduction of the printing press to the Industrial Revolution. In this article, we compare the development of the field of Industrial Statistics as an outgrowth of the Machine Age to the development of the field of Data Science as an outgrowth of the Information Age. It is shown how the knowledge, skills, and abilities required for "Data Scientists" have evolved to support companies as they try to differentiate themselves in the Information Age. It is discussed how the unique skills that Industrial Statisticians possess can provide a transformative advantage to Data Scientists working in the Information Age. Advice is given on how to leverage Industrial Statistics in the midst of the Data Revolution so that we ethically and responsibly implement the tools of Data Science, address important problems, and inclusively educate future generations.