Using a Fractional Factorial Design to Increase Direct Mail Response at<i>Mother Jones</i>Magazine

Ledolter, Johannes; Swersey, Arthur J.

doi:10.1080/08982110600846622

Cited by 15 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such designs tend to be more resource-intensive than those with two levels per factor, but they can be useful under some circumstances (see Myers & Montgomery 21 ). A more cost-efficient variety of factorial experiment is the fractional factorial (e.g., Ledolter & Swersey; 22 Collins et al; 4 Wu & Hamada 23 ), in which costs are reduced by implementing only a carefully selected subset of conditions.…”

Section: Discussionmentioning

confidence: 99%

Factorial Experiments

Dziak¹,

Kugler²,

Trail

2014

American Journal of Preventive Medicine

226

126

View full text Add to dashboard Cite

Background An understanding of the individual and combined effects of a set of intervention components is important for moving the science of preventive medicine interventions forward. This understanding can often be achieved in an efficient and economical way via a factorial experiment, in which two or more independent variables are manipulated. The factorial experiment is a complement to the randomized controlled trial (RCT); the two designs address different research questions. Purpose This article offers an introduction to factorial experiments aimed at investigators trained primarily in the RCT. Method The factorial experiment is compared and contrasted with other experimental designs used commonly in intervention science to highlight where each is most efficient and appropriate. Results Several points are made: factorial experiments make very efficient use of experimental subjects when the data are properly analyzed; a factorial experiment can have excellent statistical power even if it has relatively few subjects per experimental condition; and when conducting research to select components for inclusion in a multicomponent intervention, interactions should be studied rather than avoided. Conclusions Investigators in preventive medicine and related areas should begin considering factorial experiments alongside other approaches. Experimental designs should be chosen from a resource management perspective, which states that the best experimental design is the one that provides the greatest scientific benefit without exceeding available resources.

show abstract

Section: Discussionmentioning

confidence: 99%

Factorial Experiments

Dziak¹,

Kugler²,

Trail

2014

American Journal of Preventive Medicine

226

126

View full text Add to dashboard Cite

show abstract

“…Holland and Cravens (1973) presented the essential features of fractional factorial a t i o n a l J o u r n a l o f Q u a l i t y & R e l i a b i l i t y M a n a g e m e design and illustrated a very interesting example looking into the effect of advertising and other critical factors on the sales of candy bars. Ledolter and Swersey (2006) described the power of a fractional factorial experiment to increase the subscriptions response rate of Mother Jones magazine. Anderson (2009) compiled a number of excellent examples regarding the applications of experimental design in the service environment, which include: identifying the service design parameters that influence the service quality characteristics or CTQs in the eyes of customers; identifying the key service process or system variables that influence the process or system performance; minimizing the time to respond to customer complaints; minimizing errors on service orders; reducing the service delivery time to customers (e.g.…”

Section: Experimental Design In Servicementioning

confidence: 99%

Understanding and evaluating teaching effectiveness in the UK higher education sector using experimental design

Antony

Karamperidis

Antony

et al. 2019

IJQRM

View full text Add to dashboard Cite

Purpose The purpose of this paper is to demonstrate the power of experimental design as a technique to understand and evaluate the most important factors which influence teaching effectiveness for a postgraduate course in a higher education (HE) context. Design/methodology/approach The methodology involves the execution of a case study in the form of an experiment in a business school setting. The experiment was carried out with the assistance of over 100 postgraduate students from 26 countries. The data were collected over a two year period (2015 and 2016) from a postgraduate course offered by the same tutor for repeatability reasons. Findings The key findings of the experiment have clearly indicated that students’ perceptions of teaching effectiveness based on intuition and guesswork are not identical to the outcomes from a simple designed experiment. Moreover, the results of the experiment provided a greater stimulus for the wider applications of the technique to other processes across the case study HE sector. Research limitations/implications One of the limitations of the study is that the experiment was conducted for a popular postgraduate course. It would be beneficial to understand the results of the experiment for less popular postgraduate courses in the university in order to drive improvements. Moreover, this research was conducted only for postgraduate courses and the results may vary for undergraduate courses. This would be an interesting study to understand the differences in the factors between undergraduate and postgraduate teaching effectiveness. Practical implications The outcome of this experiment would help everyone who is involved in teaching to understand the factors and their influences to improve students’ satisfaction scores during the delivery of teaching. Originality/value This paper shows how experimental design as a pure manufacturing technique can be extended to a HE setting.

show abstract

“…Holland and Cravens [13] presented the essential features of fractional factorial design and illustrated a very interesting example looking into the effect of advertising and other critical factors on the sales of candy bars. Ledolter and Swersey [6] described the power of a fractional As human factors are an important factor, randomization is necessary in the context of service As there is an intangible component with the delivery of service, results may not be as reliable as manufacturing Careful selection of factor levels is required due to involvement of people and the interaction between the customer and service provider Roger Hoerl, General Electric, USA Personally, I think the cultural issues are greater. For one, there is rarely a culture of using the scientific method or applying continuous improvement in most service organizations Persuade people to follow a systematic methodology for process improvement and to convince them to rely on the power of data to drive the decision-making process [8] used a Taguchi robust parameter design methodology in order to improve the response-time performance of an information group operation that was responsible for addressing customer complaints concerning a small software export company.…”

Section: Benefits Of Doe In Non-manufacturing/service Industry: a Review Of The Literature And Some Key Findingsmentioning

confidence: 99%

Design of experiments for non-manufacturing processes: benefits, challenges and some examples

Antony

Coleman

Montgomery

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development.There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in nonmanufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments.

show abstract

Using a Fractional Factorial Design to Increase Direct Mail Response atMother JonesMagazine

Cited by 15 publications

References 12 publications

Factorial Experiments

Factorial Experiments

Understanding and evaluating teaching effectiveness in the UK higher education sector using experimental design

Design of experiments for non-manufacturing processes: benefits, challenges and some examples

Contact Info

Product

Resources

About