Search

We propose a novel cross-sequential approach to accelerating the collection of user feedback while providing a broad perspective by analyzing samples of users at various stages of service and product experience. User experience changes over time as the relationship between user and product evolves. Understanding temporal changes is important for designing positive long-term user experience and supporting customer loyalty. However, companies and researchers face time and cost constraints, which is why longitudinal studies tracking user experience over an extended period of time are extremely rare. The cross-sequential approach we propose reduces these barriers without compromising user-feedback quality. We tested the cross-sequential approach in evaluating the user experience of a commercial Internet security service and identified user experience spanning seven months in only seven weeks with two online surveys. We compared the cross-sequential approach to the cross-sectional approach commonly used in the industry. The cross-sectional approach identified differences between cohorts of 252 users. The cross-sequential approach, on the other hand, allowed us to identify changes in individual users' user experience. These changes could also be tracked to different usage phases. This information could guide the design of better long-term user experience for the service. The positive and negative changes in user enjoyment over time are relevant as they were reflected in users' loyalty to the service, replicating results from longitudinal studies. Thus, the cross-sequential approach can be especially useful when quick, cost-effective user feedback is needed regarding extended periods of using services or products.

Purpose This paper aims to provide a comprehensive review of the state-of–the-art design methods for additive manufacturing (AM) technologies to improve functional performance. Design/methodology/approach In this survey, design methods for AM to improve functional performance are divided into two main groups. They are design methods for a specific objective and general design methods. Design methods in the first group primarily focus on the improvement of functional performance, while the second group also takes other important factors such as manufacturability and cost into consideration with a more general framework. Design methods in each groups are carefully reviewed with discussion and comparison. Findings The advantages and disadvantages of different design methods for AM are discussed in this paper. Some general issues of existing methods are summarized below: most existing design methods only focus on a single design scale with a single function; few product-level design methods are available for both products’ functionality and assembly; and some existing design methods are hard to implement for the lack of suitable computer-aided design software. Practical implications This study is a useful source for designers to select an appropriate design method to take full advantage of AM. Originality/value In this survey, a novel classification method is used to categorize existing design methods for AM. Based on this classification method, a comprehensive review is provided in this paper as an informative source for designers and researchers working in this field.

The purpose of this article is to inform researchers about and encourage the use of longitudinal designs to further understanding of human resource development and organizational theory. This article presents information about a variety of longitudinal research designs, related statistical procedures, and an overview of general data collecting approaches, covering their strengths and weaknesses. The article also serves as a reminder of the importance of alignment between the research purpose, questions and methodology while highlighting the benefits of using multiple methods. Finally, importance of research replicability in the context of longitudinal inquiry is stressed.

The study reported in this paper describes the evaluation of the Social Implication Design (SID) method. This effect-driven design method aims to support designers in designing the influence of design manifestations on behaviour in order to counteract social issues. To study the effectiveness of the method, both a multiple-case study with designers and a narrative-based study with social experts have been executed. Based on our findings we discuss the strengths and weaknesses of the method, and suggest improvements. We conclude this paper by reflecting on our approach as research methodology for assessing effect-driven design methods and argue that qualitative studies, prior to validating design methods quantitatively, will increase the significance of design methodology for design practice.

One of the major features of product-service systems (PSS) design is a variety of design elements. This makes its design process complex and obscure. This study aims to develop a method for analysing how the design process influences the features of design solutions in the conceptual design of PSS. In this study, protocol analysis is applied to plural PSS design sessions. Simultaneously, their design solutions are evaluated relatively based on several aspects. Their design processes are analysed in connection with the above evaluation results. The findings obtained by using the proposed method will contribute to creating practical design guidelines for superior PSS development.

WOS:000401635900021 The traditional decision-making process during early design stages deals with deterministically evaluating the design candidates in accordance with concrete objectives by assuming optimal or nominal design performance values for the candidates. However, this may increase subjectivity in the decision process since the design knowledge during early design is usually imprecise and incomplete, and mostly needs to be iteratively updated throughout product design development. To diminish the subjectivity, the knowledge of the design requirements can be precisely and accurately represented by means of probabilistic constraints that describe the uncertainties in the design requirements; therefore, in this work, a systematic design framework supported by reliability analysis is developed in such a way that it is able to provide an effective connection among the early design steps especially both at system level and component level. Thus, the probability of failures of the design candidates and their sub-solutions are investigated, based on design constraints with Gaussian distributions, or lower and upper bounds, by utilizing Monte Carlo method. To illustrate the potential applicability and efficacy of the proposed framework, a two-finger gripper design problem is considered. The results clearly demonstrate that the proposed framework is effective to achieve reliable design solutions that have uncertain quantitative characteristics to be used further in probabilistic structural analysis during the next design stages such as embodiment and detail design stages. Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [BIDEP 2219] This research was supported by the Scientific and Technological Research Council of Turkey (TUBITAK), under the BIDEP 2219-International Postdoctoral Research Fellowship Programme.

Single case designs (SCDs) and randomized small group (RSG) designs are two options for researchers who have limited resources and who would like to demonstrate the experimental effect of an interv...

Longitudinal research is well suited for investigating phenomena that change over time. With the growing acceptance of mixed methods, researchers are combining qualitative and quantitative approaches within longitudinal research. However, little attention has been paid to how researchers integrate longitudinal mixed methods databases. The purpose of this methodological review was to describe how researchers combine mixed methods and longitudinal approaches in practice and delineate dimensions and issues inherent within these complex designs. We examined published empirical studies from the health sciences that self-identified as longitudinal and mixed methods. Our results identify major dimensions, variations, and issues for designing longitudinal mixed methods research and suggest recommendations for researchers interested in using this complex approach.

Recently, scholars in various disciplines have called for the use of longitudinal research designs to test and build theory. Their argument is that key phenomena in virtually every theory change over time. As such, cross-sectional research designs used to test and extend theories do not provide insights that help understand the nature of temporal relationships between variables that are central to theory. Evidence is emerging that in some cases the strength and the direction of the relationship between variables found using longitudinal data is quite different relative to that found using cross-sectional data. The view expressed in this paper is that longitudinal research brings with it both new opportunities and challenges for information systems (IS) researchers. Opportunities will come in the form of explicitly incorporating time in testing and applying IS theories to cast new light on prior research that has been predominantly based on cross-sectional designs. At the same time, challenges will come in the form of proposing hypotheses on interrelationships between variables over time, and using newer data analytic techniques that are better suited to analyzing longitudinal data. We provide illustrations that highlight both advantages and challenges associated with longitudinal research in the field of IS.

Previous literature on learners’ epistemological beliefs about physics has almost exclusively focused on analysis of university classroom instruction and its effects on students’ views. However, little is known about other populations or factors other than classroom instruction on learners’ epistemologies. In this study, we used a cross-sequential method, combining both longitudinal and cross-sectional designs, to investigate an epistemological progression trend from preservice to in-service teachers. Six cohorts of participants were studied, who either were then attending or had completed an undergraduate teacher preparation program in physics at a major Chinese university. These cohorts were incoming freshmen, end-of-year freshmen, end-of-year sophomores, end-of-year juniors, end-of-year seniors, and 1st-year high school physics teachers who were about to enter the 2nd year of teaching. We used the Colorado Learning Attitudes about Science Survey (CLASS) as both a pretest and a post-test to gauge the changes in the participants’ epistemological views over an entire academic year. Follow-up interviews were also conducted to explore factors responsible for such changes. Results showed that the epistemological trend as measured by CLASS did not increase monotonically. Instead, there was a decrease in the epistemological trend among the incoming freshmen in their first year undergraduate studies, followed by a long stasis until the end of the senior year. Then, there was a rebound for the end-of-year seniors in their 1st year of teaching, followed by another plateau. Interviews revealed that the competitive learning environment, increased content difficulty, and unfamiliar pedagogies in college were major factors that negatively influenced incoming freshmen’s views about physics. Conversely, a role change from student to teacher and relatively easy content in high school positively impacted end-of-year seniors’ views about physics and learning.

Design is recognized as a key engineering activity, and engineering is a fundamental part of science education at the K-12 level. However, it is difficult to assess student designs when the range of “correct” answers is wide. Feedback in the form of assessment helps students learn from a design activity and can direct students along the pathway of improvement. The purpose of this paper is to develop an assessment protocol to measure solution quality taking into account both objective and subjective design criteria (e.g. measurements of cost/energy used along with aesthetics). Three protocols are developed by analyzing and comparing 109 high school students' design solutions to a zero-energy home design task. Lessons learned from the first two approaches informed the third approach that highlights the importance of balancing trade-offs. Results suggest the Trade-off Value approach provides an intuitive and accurate way to understand how well a designer has balanced both complementary and competing design criteria. These results can be used as feedback to support a systems design process and to evaluate the relationship between design quality and design behavior.

This work seeks to understand how design practitioners discover, select, and adapt design methods and methodologies. Design methods and methodologies are mainly used for educational purposes and are not formally transferred into design practice and industry. This prevents design practitioners from accessing the rich body of research and knowledge posed by academia. Various web platforms and textbooks allow users to discover or search for design methods, but little support is provided to assess whether or not a method is appropriate for the context or the task at hand. In this exploratory study, interviews were conducted with practicing engineers and designers. Interview responses were coded and analyzed in an effort to understand the patterns in searching, selecting, assessing, and exchanging experiences with peers in professional practice. This analysis showed that interviewees would like to search for design methods based on their desired outcomes. Additionally, interviewees considered their personal contacts to be the most valuable source of new methods. These insights show that web-based communities of practice may be a potential link between academia and industry, but existing web repositories and communities require further development in order to better meet the needs of the design practitioner community.

Mediational studies are often of interest in psychology because they explore the underlying relationship between 2 constructs. Previous research has shown that cross-sectional designs are prone to biased estimates of longitudinal mediation parameters. The sequential design has become a popular alternative to the cross-sectional design for assessing mediation. This design is a compromise between the cross-sectional and longitudinal designs because it incorporates time in the model but has only 1 measurement each of X, M, and Y. As such, this design follows the recommendation of the MacArthur group approach, which stresses the importance of multiple waves of data for studying mediation. These 2 designs were compared to see whether the sequential design assesses longitudinal mediation more accurately than the cross-sectional design. Specifically, analytic expressions are derived for the bias of estimated direct and indirect effects as calculated from the sequential design when the actual mediational process follows a longitudinal autoregressive model. It was found that, in general, the sequential design does not assess longitudinal mediation more accurately than the cross-sectional design. As a result, neither design can be depended on to assess longitudinal mediation accurately.

This paper discusses the methods used to conduct high quality small-scale design experiments. It aims to provide a demonstrator promoting the uptake of more rigorous methods in design research and based on this it aims to specify a body of further work for linking study types and contexts. A small-scale experiment was conducted using methods specifically developed to mitigate four core problem areas identified from review: context, system understanding, methods and controls. The techniques were then critiqued in detail and used to draw several insights for design researchers including the value of control techniques and triangulation of metrics. Finally, the critique is used to specify further research aimed at linking design experimentation and design practice more effectively for design research.

This article is intended to give the reader guidance in evaluating different study designs used in medical research for better scientific quality, reliability and validity of their research. This article explains three main types of study designs with understanding examples. Care and caution with skills and experience needed to design suitable studies and appropriate design coupled with reliable sampling techniques and appropriate statistical analysis, supported by clear objectives with decent communication of the findings, are essential for good research.

Background In health related research, it is critical not only to demonstrate the efficacy of intervention, but to show that this is not due to chance or confounding variables. Content Single case experimental design is a useful quasi-experimental design and method used to achieve these goals when there are limited participants and funds for research. This type of design has various advantages compared to group experimental designs. One such advantage is the capacity to focus on individual performance outcomes compared to group performance outcomes. Conclusions This comprehensive review demonstrates the benefits and limitations of using single case experimental design, its various design methods, and data collection and analysis for research purposes.

Conceptual design is a critical design phase during which initial design solutions, called design concepts, are developed. These design concepts must be evaluated to ensure they satisfy the specified design requirements and the most appropriate design concept must be selected. It is often difficult for the designer, especially for the novice, to make an appropriate design concept evaluation and selection. Existing work on design evaluation lacks an effective tool for evaluating the temporal performance of the design concepts. To address this problem, a Critical Path Method (CPM) from project management is adapted for design evaluation, whereby a CPM network is converted from a causal behavioral process (CBP) and the methodologies relating to CPM are also applied to design improvement. A case study of a lever-clamp assembly system is also presented to illustrate as well as validate the method.

In view of current developments in the theory and technology of digital design, potential novel directions for environments that support performance-based design are beginning to emerge. The field of performance-based design is defined through an analysis of current work in the field. Various models of performance-based architectural design are presented and discussed. On the basis of this analysis, key concepts and issues in the application of performance-based design in architecture are defined and certain research directions for the development of new approaches are presented. Finally we propose a new approach termed: Performative Design. Performative Design suggests that in creating simulation environments for performance-based architectural design both generative and evaluative capabilities can be integrated within performance-based simulations. The potential of performance-based simulation as a model of performance-based design is explored through a case study from an experimental digital design studio. Implication of this work on future research directions in the field is explicated.

Much design theory research seeks to create, evaluate, improve or optimize design methods. Whether that research focuses on design thinking, tools, methods, or education, short design problems are often provided to participants in order to evaluate the effects of the variables being tested. When designing and creating such problems, certain characteristics may influence design outcomes: experience and exposure to the design problems vary between participants, and each problem may be more or less favorable to the controlled variable. In this paper we conjecture a small set of design problem characteristics that may influence experimental outcomes, and we discuss two experiments targeted at uncovering this influence. In our first experiment we examine differences in evaluation metrics between two design problems. In a follow up experiment we correlate the hypothesized characteristics to the variances in experiment outcome. These early results assist to further compare and contrast the empirical differences in common evaluation metrics, as well as show how familiarity and extent of the subjects’ knowledge of a design problem influence these metrics. We also expose the potential for interaction between the design method and the design problem.

Longitudinal designs are common in the field of athletic training. For example, in the Journal of Athletic Training from 2005 through 2010, authors of 52 of the 218 original research articles used longitudinal designs. In 50 of the 52 studies, a repeated-measures analysis of variance was used to analyze the data. A possible alternative to this approach is the hierarchical linear model, which has been readily accepted in other medical fields. In this short report, we demonstrate the use of the hierarchical linear model for analyzing data from a longitudinal study in athletic training. We discuss the relevant hypotheses, model assumptions, analysis procedures, and output from the HLM 7.0 software. We also examine the advantages and disadvantages of using the hierarchical linear model with repeated measures and repeated-measures analysis of variance for longitudinal data.

A general developmental model is described with the components of age, cohort, and time-of-measurement (period). Two of these components are always confounded in the simple developmental designs of cross-sectional, longitudinal, and time lag approaches. More complex sequential designs are then presented which allow estimation of each separate effect and their interaction. The cohort sequential design allows separation of age and cohort effects. The time sequential design allows separation of age and time-of-measurement (period) effects. The cross sequential design allows separation of cohort and time-of-measurement (period) effects. It is also pointed out that use of a mixed model approach to test convergence hypothesis that ignores random effects will result in the underestimation of within-subject effects and overestimation of between-subject effects. Examples of properly analyzed convergence analyses approaches are identified. Keywords: adult developmental psychology; aging and development psychology; cognitive development; data analysis in psychology; experimental design; individual differences; lifespan and developmentalpsychology; measurement in psychology; methodology; modeling

In a typical design process, the decision making process by which desirable and predictive outcomes are achieved is clearly defined by problem definition, goals and objectives setting, design criteria development, design solution generation and evaluation of the solutions. In contrast, the current literature on Universal Design typically jumps from Universal Design as an ideal and set of principles to Universal Design as an artifact. Without interpreting Universal Design principles into specific design criteria, it is not possible to understand design intent, reliably evaluate design outcomes, replicate design processes or outcomes, or generalize findings to other products and environments. In this paper, an universal design process has been proposed and illustrated in a case study of a universally designed voting system in which Universal Design has been applied throughout the design process in a consistent and explicit way to produce a desirable Universal Design outcome.

This paper focuses on better understanding how design processes affect outcomes in mechanical engineering design. Following a general design research methodology, process data were collected from student design journals and tested for association with design quality as measured by an external evaluation using a virtual design of experiments approach. The results indicate that more time spent on system-level design (including problem generation, idea generation, and analysis activity) and on concept-level problem definition activity associate positively with design quality, while more time spent on concept-level idea generation, detailed design, and refinement associated negatively with design quality. These results imply that student designers should emphasize problem definition, information gathering, and system-level design work more so than high-level idea generation activities.

Domain-independent models of the design process are an important means for facilitating interdisciplinary communication and for supporting multidisciplinary design. Many so-called domain-independent models are, however, not really domain independent. We state that to be domain independent, the models must abstract from domain-specific aspects, be based on the study of several design disciplines, and be useful for many design disciplines and for multidisciplinary design teams. This paper presents a domain-independent descriptive design model that is developed by studying similarities and differences between design processes in three design disciplines and which is based on the general theory of state-transition systems. The main concepts of the descriptive model are a design situation and a design activity. The descriptive model is applied in a domain-independent prescriptive model that supports designers with structured reflection on a design process; reflection provides an important possibility in improving the effectiveness and efficiency of design processes.

This paper describes a multi-disciplinary design optimization approach to the conceptual design of a commercial aircraft with relaxed static stability. Longitudinal flight dynamics analysis and control design are performed concurrently with other disciplinary analysis to augment and improve handling qualities. The developed methodology enables control-configured designs providing higher freedom of change at the conceptual design stage. A design example demonstrates the effectiveness of the proposed integrated approach to improve performance goals.

This article argues that ideal longitudinal research is characterized by the seamless integration of three elements: (a) a well-articulated theoretical model of change observed using (b) a temporal design that affords a clear and detailed view of the process, with the resulting data analyzed by means of (c) a statistical model that is an operationalization of the theoretical model. Two general varieties of theoretical models are considered: models in which the time-related change of primary interest is continuous, and those in which it is characterized by movement between discrete states. In addition, two general types of temporal designs are considered: the longitudinal panel design and the intensive longitudinal design. For each general category of theoretical models, some of the analytic possibilities available for longitudinal panel designs and for intensive longitudinal designs are discussed. The article concludes with brief discussions of two issues particularly relevant to longitudinal research—missing data and measurement—and a few words about exploratory research.

This paper introduces the research design matrix as a method of planning research projects. The research design matrix is a system of rows and columns into which the components of a research project fit, including the goal, objectives, definitions, hypotheses, variables, methods of analysis and anticipated conclusions. Thus, the matrix encapsulates the research design, or what the researcher intends to do in the investigation. Given the arrangement of the various concepts involved, a logic is imposed on the project from the beginning of the planning process. The paper has three objectives. The first is to sketch out the background to the thinking underlying the research design matrix. The second is to develop a schematic framework for the research design matrix, defining terms and highlighting the flexibility that can be achieved in its use. The final objective is to present fully developed research design matrices for two research projects that have been successfully completed to give some idea in practice of the approach's viability and robustness.

Life-cycle modeling for design (LCMD) is a methodology for assessing the life-cycle impacts for a complex product with many individual components starting from initial design phases when few design specifications have been made. The methodology combines life-cycle assessment (LCA) with probabilistic design methods in a way that forecasts attributes of possible final designs yet reduces information needs. Specifically, LCMD is a methodology for generating arrays of design scenarios that communicate the range of designs being considered by a design team, and estimating missing data for those design scenarios. The main contribution to enhancing standard LCA is the incorporation of methods to estimate physical attributes of individual components for various design options and in four analyses for evaluating the arrays of design scenarios. An automotive case study presented in part 2 of this work demonstrates one application of LCMD.

The inherent dynamic relationships among design tasks performed concurrently at different organizations characterize the complexities of a design chain where designers with diverse expertise need to collaborate across organizational boundaries. To ensure timely completion of inter-related design tasks, metrics to facilitate the early evaluation of design concepts are crucial. The ability to evaluate and select suitable design concepts at an early stage will ensure better solutions and greater savings in time and effort further downstream. This paper proposes a new approach based on the rough set theory to design concept analysis. The approach aims at early detection of design inadequacy. A so-called information system is constructed using the information gleaned from design concepts and design capabilities, and analyzed using the rough set theory to derive a set of design rules for design concept analysis. The approach embodies a technique for handling attributes with unavailable information, which is a frequent occurrence in design. This paper presents details of the proposed approach, the novel technique, and a case study.

A discussion of knowledge and Knowledge-Based design as related to the design of aircraft is presented. The paper discusses the perceived problem with existing design studies and introduces the concepts of design and knowledge for a Knowledge-Based design system. A review of several Knowledge-Based design activities is provided. A Virtual Reality, Knowledge-Based system is proposed and reviewed. The feasibility of Virtual Reality to improve the efficiency and effectiveness of aerodynamic and multidisciplinary design, evaluation, and analysis of aircraft through the coupling of virtual reality technology and a Knowledge-Based design system is also reviewed. The final section of the paper discusses future directions for design and the role of Knowledge-Based design.

Understanding how leaders develop, adapt, and perform over time is central to many theories of leadership. However, for a variety of conceptual and methodological reasons, such longitudinal research remains uncommon in the leadership domain. The purpose of this paper is to introduce the leadership scholar to conceptual issues involved in longitudinal design and analysis, and then demonstrate the application of random coefficient modeling (RCM) as a framework capable of modeling longitudinal leadership data. The RCM framework is an extension of the traditional regression model, so many readers will already have the fundamental knowledge required to use RCM. However, RCM has the additional capability of analyzing the kinds of data commonly found in longitudinal studies, including correlated observations, missing data, and heterogeneity over time. Further, the RCM allows for testing predictors of change over time. Thus, we introduce conceptual issues related to longitudinal research, discuss RCM within the context of regression, and conclude with an application of the RCM approach. We use a common substantive example throughout the paper to facilitate our discussion of the RCM.

Designs for second-order response surface models play an important role in response surface methodologies. The central composite design and Box-Behnken designs have served as popular choices for designs for second-order models when design economy and precise prediction variance are desired. The Box-Behnken design has been widely used in completely randomized experiments, split-plot experiments, and within the robust parameter design setting. This article provides a historical background for the Box-Behnken design, describes its construction, and reviews various contributions in the literature regarding the use of these designs. Keywords: response surface methodology; second-order model; design of experiments

The study of developmental trajectories involves examining how and why a measure or index of behavior changes over time. Within the huge body of work devoted to different forms of work commitment, very little has directly and explicitly addressed developmental issues. Theory suggests that some forms of work commitment develop prior to actual experience of the focus of the commitment (e.g., normative commitment and work ethic). For affective and continuance commitment, however, it has been hypothesized that experience is essential. In most instances, this experience is gained after entry into the work force and, specifically, into an organization. Therefore, understanding how these types of commitment develop and highlighting the factors related to tenure that cause changes in commitment will not only fill a gap in the empirical work and strengthen commitment theory, but will help managers to optimize the commitment of their employees. This paper outlines the current understanding of changes in commitment with time and describes a research method, taken from the developmental psychology literature, that overcomes some of the problems inherent to the approaches most frequently used to imply developmental change in organizational psychology. It concludes with suggestions for future research incorporating this method.

Objective of the design process are cost effective designs that meet certain expectations with respect to functionality and appearance. Designs are created in an iterative process where analyses of the structural behavior lead to changes in the design. The use of optimization technology makes design changes to be driven directly by analysis results. The application of optimization allows an efficient search for the right combination of design variables for a certain design. Additional use of stochastic methods in order to analyze the design from a statistical standpoint adds robustness to the design and prevents unpleasant surprises in later physical testing.This paper discusses methodology to optimize structures that undergo impact loading. Objective and constraints are transient dynamic responses. The optimization problem is solved using a sequential response surface method. An explicit finite element code is used to solve the transient dynamic problem. The optimization is not performed on results from single simulations but on statistical results from a stochastic analysis. The stochastic analysis is driven using a Monte Carlo method. Commercial software is used for the implementation of the methodology.The results from the study indicate that a combination of optimization and stochastic analysis can add safety margins to a design with respect to robustness against physical errors in the design itself and against changes in load levels and load cases. However, this initial study must be followed up by more in-depth investigations to fully understand the benefits of combined optimization-stochastic analysis.

The axiomatic design approach has contributed to the advancement of design practice by creating good design evaluation criteria based on design axioms. Complexity measure is important in design evaluation in order to simplify the engineering design. In this paper, we use Boltzmann entropy as the complexity measure and derive mathematical relationships between design complexity and various components in engineering design in the context of the axiomatic design approach. Three components of the design complexity are identified, they are variability, vulnerability and correlation, in which the vulnerability is related to the size of the design problem, the interdependency of design parameters and the sensitivity of functional requirements of the design towards the change in design parameters.

In this paper I aim to present some of the advantages and problems associated with longitudinal quantitative research designs. The main feature of longitudinal designs is that the effect of time on the subjects of the research can be studied better than in cross-sectional designs where several groups (called cohorts), or one cohort divided up on the basis of a feature such as age, may be required. If you compare two groups on the basis of age you cannot assume that any differences you observe are due to age - this is known as the cohort effect. However, longitudinal designs bring some problems in terms of keeping subjects in the study and the longer the time between points at which stages of the study are carried out, the greater the difficulty in retaining subjects. In addition to these general features of longitudinal quantitative research designs, some specific designs and methods of statistical analysis will be considered.

Little research has been done on how designers actually design. Much of design research is concerned with computer-based models or is based on anecdotal evidence of the design process. This paper describes the development and application of a methodology that uses protocol studies of designers engaged in design to investigate the process of designing. A coding scheme is developed and applied to design protocols. The scheme brings structure to the unstructured data of the protocols without detracting from the richness of the data. Results are shown that illustrate the utility of this approach in gaining some insight into how designers design.

Behavioral observations and sociometric ratings were assessed in separate 3- and 4-year-old child care classes across a two year period. A cross sequential design included three cohorts. Cohort A was observed at age 4, Cohort B at ages 3 and 4 and Cohort C was observed at age 3 yielding two separate cross sectional comparisons and a longitudinal comparison across ages 3 and 4. Social network analyses were used to determine the relation between behavioral measures of social interactions and sociometric ratings, developmental changes in the degree of organization of the social networks in each class, and the degree to which gender was an organizing principle in the social structure of both age groups. In general, correspondence between the behavioral observations and sociometric ratings increased with age and the social structure showed greater cohesion in the 4-year-old class. Gender was an organizing principle in the social structure of the 4-year-old class and became more apparent across time in the 3-ye...

SUMMARY One of the most tangible contributions to quality engineering over the last 15 years is the concept of robust design and its concrete realization by means of designed experiments. However, robust design as promoted by the Taguchi method clashes with design theory as it puts emphasis on parameter design as the most important means of robustifying technical systems. It is argued in this article that the design phases that precede parameter design ∥i.e. system design) actually determine the attainable level of product robustness in the parameter design stage. It is shown how qualitative methods in the form of design principles can be utilized in early design phases to support the process of robust design.

This article describes and discusses some commonly used single-case experimental designs, including reversal and non-reversal designs. The A-B-A-B design is recommended for use when reversal designs can be used and when the patient's treatment phase needs to be maintained. The B-A-B design is used in situations when studying an intervention or treatment which is already in progress. The A-B1-A-B2-A-Bn design is useful when the aim of the researcher is to study the effects of different levels of a specific independent variable. Two types of non-reversal multiple baseline designs are discussed here.

This paper addresses the problem of representing design history. In particular we describe a design information system that captures three different aspects of design history: exploration of design alternatives, reasons for design decisions and design constraints. These three aspects of design history are basically different and it is appropriate to represent them in different ways. An integrated design information system (IDIS) which supports the representation of these different aspects of design history is presented. Three case studies which have been used to test the system at various stages during its development are presented. One of the components of the system, which uses the issue based representation, was tested using an example from process engineering. The example helped us to identify some of the strengths and weaknesses of that component. This evaluation also enabled us to formulate the principle of preserving temporal integrity. A case study is also presented which tested the ability of the system to retrospectively represent design information that is currently recorded in a design project. This case study also highlighted some of the problems with existing design documentation procedures. In the final case study the system was used off-line to record design information as it was generated. These case studies found that the system could represent the required information and provided a methodology and semi-formal representation that helped the designers. Due to the success of these trials the system is now being used by a team of researchers in an industrial setting to provide the next level of testing.

The paper describes the application to longitudinal research of a method of data collection which combines quantitative and qualitative material. It is claimed that Group Feedback Analysis (GFA) helps to validate data collected by various methods and produces additional ethnographic material and insight into the process under investigation. When the people who supply the data are experienced in the subject matter, as is the case with managers and other employees in organizational research, GFA makes it possible for them to help with the interpretation of the data instead of leaving it entirely to the researcher. The method, which has previously been used in cross-sectional research, is shown to have utility in a longitudinal design where it can facilitate organizational learning. It can also contribute to the refinement of a theoretical model.

Longitudinal research designs with many waves of data have the potential to provide a fine-grained description of program impact, so they should be of special value for evaluation research. This potential has been illusive because our principal analysis methods are poorly suited to the task. We present strategies for analyzing these designs using hierarchical linear modeling (HLM). The basic growth curve models found in most longitudinal applications of HLM are not well suited to program evaluation, so we develop more appropriate alternatives. Our approach defines well-focused parameters that yield meaningful effect-size estimates and significance tests, efficiently combining all waves of data available for each subject. These methods do not require a uniform set of observations from all respondents. The Boys Town Follow-Up Study, an exceptionally rich but complex data set, is used to illustrate our approach. The typical pretest-posttest evaluation research design captures program effectiveness at a single time point, relative to a baseline. Although this simple design provides valuable information, it has obvious limitations. Only by gathering additional waves of data can one begin to answer questions about such matters as the process of change during treatment, the longevity of program effects, and the impact of variation in the length of treatment. Collecting data more frequently and over a longer period should enable

This paper presents a backwards design method for mechanical conceptual design. The method involves starting with a very idealistic semiworking solution and then systematically solving the unworkable parts of the solution until a complete solution is found. The method can work in conjunction with other methods such as Theory of Inventive Problem Solving (TRIZ) or brainstorming. The method tries to exploit the principle that it is easier to critique and modify a design than it is to create a fully working solution in one go. The visual nature of the method means that it is suited to design problems where geometry is important such as mechanism design and machine design. Three case studies of conceptual design are presented. The case studies involve a novel clutch, a novel rotary damping mechanism and a novel worm gearbox. Each of these designs won at least one national design competition in the UK. A simple design experiment has been carried out which indicated that the backwards design method was at least as effective as the morphological chart method for a simple machine design exercise.