Abstract: 1. Introduction 2. Respondents' understanding of survey questions 3. The role of memory in survey responding 4. Answering questions about date and durations 5. Attitude questions 6. Factual judgments and numerical estimates 7. Attitude judgments and context effects 8. Mapping and formatting 9. Survey reporting of sensitive topics 10. Mode of data collection 11. Impact of the application of cognitive models to survey measurement.

Abstract: Quantitative research is designed to test well-specified hypotheses, determine whether an intervention did more harm than good, and find out how much a risk factor predisposes persons to disease. Equally important, qualitative research offers insight into emotional and experiential phenomena in health care to determine what, how, and why. There are 4 essential aspects of qualitative analysis. First, the participant selection must be well reasoned and their inclusion must be relevant to the research question. Second, the data collection methods must be appropriate for the research objectives and setting. Third, the data collection process, which includes field observation, interviews, and document analysis, must be comprehensive enough to support rich and robust descriptions of the observed events. Fourth, the data must be appropriately analyzed and the findings adequately corroborated by using multiple sources of information, more than 1 investigator to collect and analyze the raw data, member checking to establish whether the participants' viewpoints were adequately interpreted, or by comparison with existing social science theories. Qualitative studies offer an alternative when insight into the research is not well established or when conventional theories seem inadequate. JAMA. 2000;284:357-362

Abstract: In two prior papers in our series on qualitative research (Frankel & Devers (2000a, 2000b) Qualitative research: a consumer's guide, Education for Health, 13, 113-123; Frankel & Devers (2000) Study design in qualitative research—1: developing research questions and assessing research needs, Education for Health, 13, 251-261), we examine two critical issues in qualitative research design: sampling, including identifying and negotiating access to research sites and subjects, and data collection and management We describe these two key steps in the qualitative research design process, discuss challenges that often emerge when pursuing these steps, and provide guidelines for addressing them Qualitative research most often uses "purposive," rather than random, sampling strategies A good understanding of these sampling strategies and why they are used is central to designing a credible qualitative study In addition, given the real-world context in which most qualitative research is carried out, identifying and negotiating access to research sites and subjects are critical parts of the process We also provide suggestions for developing and maintaining productive and mutually satisfying research relationships with sites and subjects Finally, data collection and management are often neglected subjects in qualitative research We offer practical advice on how to collect and manage qualitative data, including factors to consider when deciding how struc- tured the data collection process should be, the pros and cons of audio- and/or videotaping compared with note-taking, and tips for writing up eeld notes and document management A forthcoming, enal paper in the series will focus on qualitative data analysis and the publication of qualitative research results

Abstract: Preface. Part 1. Introductory Chapters. 1. Definitions, Purposes, and Dimensions of Research. 2. Planning a Quantitative Research Project. Part 2. Quantitative Research Approaches, Questions, and Designs. 3. Variables, Research Questions, and Hypotheses. 4. Research Approaches. 5. Randomized Experimental and Quasi-Experimental Designs. 6. Single-Subject Designs. 7. Nonexperimental Approaches and Designs. 8. Internal Validity. Part 3. Sampling, Measurement and Data Collection. 9. Sampling and Introduction to External Validity. 10. Measurement and Descriptive Statistics. 11. Measurement Reliability. 12. Measurement Validity. 13. Types of Data Collection Techniques. 14. Ethical Issues in Conducting the Study. 15. Practical Issues in Data Collection and Coding. Part 4. Data Analysis and Interpretation. 16. Making Inferences from Sample Data I: The Null Hypothesis Significance Testing Approach. 17. Making Inferences From Sample Data II: The Evidence-Based Approach. 18. General Design Classifications for Selection of Difference Statistical Methods. 19. Selection of Appropriate Statistical Methods: Integration of Design and Analysis. 20. Data Analysis and Interpretation - Basic Difference Questions. 21. Analysis and Interpretation of Basic Associational Research Questions. 22. Analysis and Interpretation of Complex Research Questions. Part 5. Evaluating and Writing Research Reports. 23. Evaluating Research Validity: Part I. 24. Evaluating Research Validity: Part II. 25. Narrative Evaluations of the Five Sample Articles. 26. Evaluating Research for Evidence-Based Practice. 27. Writing the Research Report. Appendixes A. Suggested Readings. B. Confusing Terms. K. Kidd, C. Glossary. D. Writing Research Problems and Questions. E. Questions for Evaluating Research Validity. D. Quick, F. Making APA Tables and Figures. References. Indexes.

Abstract: Physical therapists are among the most common users of electromyography as a method for understanding function and dysfunction of the neuromuscular system. However, there is no collection of references or a source that provides an overview or synthesis of information that serves to guide either the user or the consumer of electromyography and the data derived. Thus, the purpose of this article is to present a guide, accompanied by an inclusive reference list, for the use and interpretation of kinesiologic electromyographic data. The guide is divided into 4 major sections: collecting, managing, normalizing, and analyzing kinesiologic electromyographic data. In the first of these sections, the issues affecting data collection with both indwelling and surface electrodes are discussed. In the second section, data management through alternative forms of data processing is addressed. In the third section, various reasons and procedures for data normalization are discussed. The last section reviews qualitative descriptors once used as the only means of analyzing data, then focuses on more quantitative procedures that predominate today. The guide is intended as a tool for students, educators, clinicians, and beginning researchers who use and interpret kinesiologic electromyographic data. Modifications will likely be needed as alternative forms of collecting, managing, normalizing, and analyzing electromyographic data are proposed, used in various settings, and reported in the literature.

Abstract: A system and method for data collection, evaluation, information generation and/or presentation is described More particularly the description relates to a system for collecting, evaluating, generating and presenting data and/or information relating to electronic commerce The system and methods include predictor modules that use recent historical data along with an estimated and/or available population function as the basis for a differential equation that defines the growth of the population to a saturation or maximum attainable level

Abstract: We read with great interest the 2 articles on the use of item response theory (IRT) measurement models in the arena of health status assessment. For reasons that are more accidental than logical, classic approaches have dominated health status assessment until very recently. Now, IRT is entering the field. This is accompanied by enthusiasm for the prospect of deriving better definitions of underlying constructs, new hope for the prospect of individual diagnosis, and an opportunity to turn our attention away from static tests and scales to items and the incremental information they provide. The careful use of IRT-based assessment, either by preselecting an item list that applies to the population being studied, or by computerized adaptive testing (CAT) can make assessment briefer, more flexible, more efficient, and if desired, more precise than conventional approaches.

Abstract: A method and system for analysing and measuring multiple sources of data over a communications network (18) so as to ascertain information or usage of one or more resources, such as resource servers (2). A data collection and processing means (20) collects and processes the data sources which are forwarded to a reporting server (34) as a combined data source made available to interested parties.

Abstract: A data management and archive method and apparatus, such as for implementation in an automated system to monitor and manage status, performance and configuration data for a plurality of networked storage components. Analysis and cross-correlation of data related to the plurality of storage components can be done individually, collectively and/or comparatively. A collection manager component of a workload analyzer is implemented to start and stop data collection in the context of a system comprising at least one storage component (or at least two networked storage components). The collection manager includes a command and control module that coordinates requests of data from at least one collection agent configured on at least one host connected to the storage component(s).

Abstract: Electronic commerce is emerging as the killer domain for data mining technology. The following are five desiderata for success. Seldom are they they all present in one data mining application. 1. Data with rich descriptions. For example, wide customer records with many potentially useful fields allow data mining algorithms to search beyond obvious correlations. 2. A large volume of data. The large model spaces corresponding to rich data demand many training instances to build reliable models. 3. Controlled and reliable data collection. Manual data entry and integration from legacy systems both are notoriously problematic; fully automated collection is considerably better. 4. The ability to evaluate results. Substantial, demonstrable return on investment can be very convincing. 5. Ease of integration with existing processes. Even if pilot studies show potential benefit, deploying automated solutions to previously manual processes is rife with pitfalls. Building a system to take advantage of the mined knowledge can be a substantial undertaking. Furthermore, one often must deal with social and political issues involved in the automation of a previously manual business process.

Abstract: OBJECTIVE: To evaluate the validity of racial/ethnic information in California birth certificate data. DATA SOURCES: Computerized birth certificate data and postpartum interviews with California mothers. STUDY DESIGN AND DATA COLLECTION: Birth certificates were matched with face-to-face structured postpartum interviews with 7,428 mothers to compare racial/ethnic information between the two data sources. Interviews were conducted in Spanish or English during delivery stays at 16 California hospitals, 1994-1995. PRINCIPAL FINDINGS: The sensitivity of racial/ethnic classification in birth certificate data was very high (94 percent to 99 percent) for African Americans, Asians/Pacific Islanders, Europeans/Middle Easterners, and Latinas (Hispanics). For Native Americans, however, the sensitivity was only 54 percent. The positive predictive value of birth certificate classification of race/ethnicity was high for all racial/ethnic groups (96 percent to 97 percent). CONCLUSIONS: Despite limited training of birth clerks, the maternal racial/ethnic information in California birth certificate data appears to be a valid measure of self-identified race and Hispanic ethnicity for groups other than Native Americans.

Abstract: A remote data collection system for collecting usage data from an endpoint. The monitoring module (40) has a wireless transmitter to transmit the usage data. The system also includes a receiver (100) to receive usage data from the wireless transmitter of the monitoring modules, the receiver is capable of receiving in parallel data transmitted at arbitrary frequencies within a radio channel.

Abstract: In December 1997,250 children were asked to list their Christmas wishes. These requests were then compared to the commercials that were broadcast at the time of data collection. Sixty-seven percent of the seven- and eight-year-olds, 49% of the 9- and 10-year-olds, and 40% of the 11-and 72-year-olds asked for at least one advertised product. Children's gender and age, as well as their level of exposure to the network that aired the most commercials, were significant predictors of their requests for advertised products.

Abstract: Prior to the past decade, much research examining outcomes of home care programs, including efforts at delaying institutional placement, maintaining function, and supporting independence, was atheoretical in character. Outcomes hoped for were often unobserved. New policy developments require comprehensive assessment of need and aggregation of this assessment information. As more and more patients leave hospitals with complex clinical problems and extensive rehabilitative goals there has been a corresponding explosion of home care services. Social care models, while they still exist, are becoming a smaller component of the overall home care market. In this changing environment, questions are now being asked concerning the appropriateness of the care programs in home care and other post-acute care settings. There are also concerns that need to be addressed about movement of clients between post-acute settings. In this paper, we describe a set of proposed functional, behavioural, and social outcome measures that are germane to evaluating the efficacy of programmatic efforts within the post-acute continuum. Data were collected with a standardized data collection instrument, the Resident Assessment Instrument for Home Care (RAI-HC). We provide data summarizing these proposed outcomes and evidence of known groups validity in a cross-national sample of home care clients. Data highlight the differing characteristics of clients across these agencies and provide evidence that this standardized data collection instrument can capture data that is reliable and valid for describing populations and evaluating program effectiveness.

Abstract: A method of integrating host application software with data collection devices (e.g., bar code scanners) located on remote, wireless terminals. A data collection object executes on the host computer, using a predetermined interface between the host application software and the data collection object. That interface, and the communications between the host application software and the data collection object, are configured so that to the host application software the data collection device appears to be local hardware on the host computer. The data collection object creates and executes threads of execution for controlling operation of the data collection device, with the threads communicating with the remote terminals via a host computer transport layer, the wireless link, and a remote computer transport layer at the remote terminals. A data collection device driver on the remote terminal receives communications from the data collection object, and returns information to the data collection object, over the remote computer transport layer, wireless link, and host computer transport layer.

Abstract: A method of integrating host application software with data collection devices (e.g., bar code scanners) located on remote, wireless terminals. A data collection object executes on the host computer, using a predetermined interface between the host application software and the data collection object. That interface, and the communications between the host application software and the data collection object, are configured so that to the host application software the data collection device appears to be local hardware on the host computer. The data collection object creates and executes threads of execution for controlling operation of the data collection device, with the threads communicating with the remote terminals via a host computer transport layer, the wireless link, and a remote computer transport layer at the remote terminals. A data collection device driver on the remote terminal receives communications from the data collection object, and returns information to the data collection object, over the remote computer transport layer, wireless link, and host computer transport layer.

Abstract: A data and object monitoring and response system comprising a three tier infrastructure for optimization of interoperability and task specific adaptability. The system gathers information from a plurality of distributed data gathering units and assimilates, processes, analyzes and distributes the gathered data within a common system with rule based data processing for coordinated response to the data. The data gathering units can be locally distributed or widely disbursed. The information gathered can be real-time collection of event data, historical data, systems monitoring, or other data. Regardless of the specific nature of the data, the system taught in the present invention, addresses a number of common problems associated with the collection, assimilation, processing of data. By dividing the system into a three tier interactive structure, the data can be gathered, evaluated and processed independently and efficiently and appropriate response can be effectively implemented. The processing tier, which includes the rules for analysis of the data, exists independent of the operator interface and data gathering tiers. A wide diversity of data collection equipment can be accommodated without modification of the operator interface or the processing tier. Processing rules can be modified without altering the collection and handling of data, and a commonality of data structure eliminates multiple polling of collected data sets.

Abstract: Described are three data quality attributes that are considered relevant to intelligent transportation system (ITS) data archiving: suspect or erroneous data, missing data, and data accuracy. Preliminary analyses of loop detector data from the TransGuide system in San Antonio were performed to identify the nature and extent of these data quality concerns in typical archived ITS data. The findings of the analyses indicated that missing data were inevitable, accounting for about one in five of all possible data records. Error detection rules were developed to screen for suspect or erroneous data, which accounted for only 1 percent of all possible data records. Baseline testing of TransGuide detector accuracy showed mixed results; one location collected traffic volumes within 5 percent of ground truth, whereas traffic volumes at another location ranged from 12 to 38 percent of ground truth. It was concluded that data quality procedures will be essential for realizing the full potential of archived ITS data.

Abstract: An electronic data collection device configured in a substantially two-dimensional arrangement is disclosed. The data collection device uses inexpensive flexible sheet materials to provide a flat framework in which to situate an interconnected combination of electronic components. The components provide an interactive function to supply input-output, control, and power functions. Components can include an information display, switches for responding to questions displayed on the information display, memory for storing responses to the questions, and a controller for controlling the operation of the data collection device. In addition, the device is provided with a data transfer interface that permits stored responses to be gathered by a response data accumulation device, such as a computer, having a corresponding interface.

Abstract: To develop more successful marketing and management strategies, service marketers need to understand how consumers evaluate the quality of their services. However, customer evaluations of service quality are an elusive concept to measure with a single method. The existing literature suggests that triangulation, or the use of multiple methodologies and data sources, would produce more valid and reliable data. This study shows how triangulation can be used in measuring transportation service quality. Data was collected from the general public and transportation officials through surveys, critical incident techniques, and focus group interviews. The results identify the areas of transportation activities with which the respondents are currently satisfied or dissatisfied, and determine the relative importance and funding priorities of various transportation services. The consistencies and inconsistencies of the results from the 2 groups of respondents and from 2 different methodologies are examined and discussed from a managerial point of view.

Abstract: This synthesis will be of interest to transit agency managers, their schedule and operations planning staff, and others who are responsible for information about system operations and ridership. It will also be of interest to others who interact with transit agencies in the reporting of operations data in order to support regular scheduling and operations planning activities for monitoring trends, and for reporting to oversight agencies. This synthesis reviews the state of the practice in how data are analyzed. It addresses methods used to analyze data and what computer systems are used to store and process data. It also covers accuracy issues, including measurement error, and other problems including error in estimates. This document from the Transportation Research Board addresses agency experience with different data collection systems, giving attention to management error, the need for sampling, and methods for screening, editing, and compensating for data imperfection. Sample reports from selected U.S. and Canadian transit agencies are reproduced in this synthesis.

Abstract: Recent policy changes and methodological advances have led to new modeling approaches of increasing complexity in transportation research. Some of these approaches require new kinds of data. Moreover, the increasing complexity of these models often also implies that more detailed data are required, leading to increased demands on respondents. This workshop resource paper focuses on activity-based models. It identifies the data required to estimate these models, discusses possible approaches to collect the data and formulates quality requirements for these data. Observations in this paper are largely based on two projects conducted for the Dutch Ministry of Transport, Public Works and Water Management. One project involved an examination of the validity, reliability and data quality of alternate ways of collecting diary data. The other, still ongoing, involves an activity-based model, called ALBATROSS.

Abstract: The need for reliable simulation systems has been discussed and recognized by many researchers. At the same time researchers have recognized that the quality of a simulation model's results are strictly related to the quality of the input probability distribution functions. The data used in this research were acquired from the Atkinson-Washington-Zachry joint venture on the Eastside Reservoir Project in California. The data were analyzed using BestFit software to obtain the parameters of the theoretical distribution functions that best described the field data set. The research validated previous warnings about the influence of the class interval decision on the selection of a distribution function when the chi-square fitting test is utilized. A second issue of importance that was encountered was the reliability of goodness-of-fit tests when dealing with large data sets.

Abstract: The means for data collection have never been as advanced as they are today. Moreover, the numerical models we use today have never been so advanced. Feeding and calibrating models against collected measurements, however, represents only a one-way flow: from measurements to the model. The observations of the system can be analyzed further in the search for the information they encode. Such automated search for models accurately describing data constitutes a new direction that can be identified as that of data mining. It can be expected that in the years to come we shall concentrate our efforts more and more on the analysis of the data we acquire from natural or artificial sources and that we shall mine for knowledge from the data so acquired. Data mining and knowledge discovery aim at providing tools to facilitate the conversion of data into a number of forms, such as equations, that provide a better understanding of the process generating or producing these data. These new models combined with the already available understanding of the physical processes—the theory—result in an improved understanding and novel formulations of physical laws and improved predictive capability. This article describes the data mining process in general, as well as an application of a data mining technique in the domain of sediment transport. Data related to the concentration of suspended sediment near a bed are analyzed by the means of genetic programming. Machine-induced relationships are compared against formulations proposed by human experts and are discussed in terms of accuracy and physical interpretability.

Abstract: This project combined Global Positioning Satellite (GPS) and Geographic Information Systems (GIS) technology with small hand-held computers (Personal Digital Assistants - PDAs). In addition, respondents were mailed a training video to assist with installation and use of the equipment. A small, user-friendly, mail-able unit was designed to capture vehicle-based, daily travel information. Nearly 90% of person trips in the U.S. are made in a private vehicle. The unit was developed to capture variables that would be entered by the vehicle driver using a touch-sensitive menu, with items such as trip purpose and vehicle occupancy, and to capture automatically recorded variables such as date, start time, end time, and latitude and longitude at frequent intervals. Finally, after mail-back return of the units, the data are processed to include variables such as travel speed by road classification, trip distance, and trip time. The unit allows for collection of travel data over several days to avoid potential short-term, survey-induced travel behavior changes. This method of data collection has two potential benefits: improving the quality of travel behavior data, and reducing respondent burden, e.g., time on the telephone for reporting travel information. Using GPS technology, while increasing privacy concerns, may improve overall survey data quality in travel behavior studies. The field test was conducted in Lexington, Kentucky, in Fall 1996, with 100 households. The sample of drivers was stratified by age, gender, and presence of children under age 16 in the household. Respondents were asked to use the machine for six days, with the expectation that data from Day 1 and Day 6 many not be useable. Respondents were also asked to recall all their travel for one 24-hour period (Day 5). This process resulted in a complete 24-hour report of trips made by the selected driver by all modes and a 4-day report of trips made in the selected vehicle by all drivers and passengers. Geographic coding of destinations should be much improved using the GPS technology. Also, route choice, functional class usage, and travel speed information is available. This paper focuses on respondents' perceptions on the installation and use of the equipment. Some comparisons between self-reported and machine-recorded travel are discussed, but are limited.