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This study aims to examine a comparative analysis of electronic- and paper-based data collection methods.

Survey research approach was adopted to conduct the study. A total of 500 library and information science (LIS) researcher/information professionals selected randomly from seven states in Nigeria represent the sample for the study. A self-designed questionnaire was used to gather data from the respondents. Six objectives were developed to guide the study.

The findings reveal that there is preference for paper-based than the electronic-based data collection methods, and similarly, respondents indicated they cannot use any other data collection methods other than paper-based. Electronic data collection method is beneficial in terms of gathering a representative sample within a short period compared to the paper-based method. The researcher has control over access to the survey in the paper-based method compared to the electronic method, and there is opportunity to monitor the progress of the research and clarify ambiguity by the researcher. The electronic method is limited in terms of collecting data from only the respondents who are computer-literate with good Web skills, while the paper-based method is limited by higher attrition rate, money and time consumption.

The study did not consider the time needed to gather data with each method or the amount of time needed for data entry, cleaning and coding. It is possible that a time-motion study would reveal clear superiority of either one method or the other. The total sample of participants in the study is also limited, considering the total number of LIS researcher/information professionals in Nigeria.

The study has underlined the value of electronic/Web-based collection as a cost- and time-saving instrument in modern LIS research. Regardless of method and trial, electronic data collection could fulfill in all examined parameters, its promises, realizing cost savings of up to a 50 per cent in comparison with the paper-based data collection method.

The paper is solely the idea of the author.

Patient satisfaction is paramount to maintaining high clinical quality assurance. This study seeks to compare response rates, response bias, and the completeness of data between paper and electronic collection modes of a chiropractic patient satisfaction survey.

A convenience sample of 206 patients presenting to a chiropractic college clinic were surveyed concerning satisfaction with their chiropractic care. Paper (in‐clinic and postal) and electronic modes of survey administration were compared for response rates and non‐response bias.

The online data collection mode resulted in fewer non‐responses and a higher response rate, and did not evince response bias when compared to paper modes. The postal paper mode predicted non‐response rates over the in‐clinic paper and online modalities and exhibited a gender bias.

This current study was a single clinic study; future studies should consider multi‐clinic data collections. Busy clinic operations and available staff resources restricted the ability to conduct a random sampling of patients or to invite all eligible patients, therefore limiting the generalizability of collected survey data.

Results of this study will provide data to aid development of survey protocols that efficiently, account for available human resources, and are convenient for patients while allowing for the most complete and accurate data collection possible in an educational clinic setting.

Understanding patient responses across survey modes is critical for the cultivation of quality business intelligence within college teaching clinic settings. This study bridges measurement evidence from three popular data collection modalities and offers support for higher levels of quality for web‐based data collection.

This article summarizes a study that identified and described federal information inventory/locator systems. Such locator systems provide an important means of accessing a range of government information not previously available to the public or other government officials. Overall, the study's goal was to improve access to and use of U.S. government information. The study produced a final report describing study efforts, identifying issues and conclusions, and recommending the design of an networked‐based government‐wide information inventory/locator system (GIILS) (Volume I), the Federal Locator Database (FLD) — a machine‐readable database of descriptive information on some 250 federal databases, of which fifty‐three met the study's criteria as a locator, and a user's guide to that database (Volume II includes a machine‐readable version of the database and the user guide and codebook). The study recommends that the U.S. Office of Management and Budget develop a policy framework requiring agencies to design and maintain machine‐readable locators, meeting certain requirements and standards and that these be accessible over the Internet/NREN.

The performance of hand‐held computer terminals was compared with that of paper and pencil data collection methods. The study was carried out on two wards, with a comparative study in the medical audit unit at Rampton Hospital, one of three special hospitals managed by the Special Hospitals Service Authority. These were analysed, focusing on time factors, error rates, ease of use and financial implications. Findings showed that the electronic system saved time, reduced staffing costs and errors and had unit‐wide potential. The electronic data collection contributed to a cost‐effective and flexible data‐processing system for use in auditing.

This study aims to investigate the preferences of 96 educational researchers on the use of digital technologies in scientific research.

The study was designed as a quantitative-dominant sequential explanatory mixed-method research.

Despite the spreading use of advanced technologies of big data and data mining, the most preferred digital technologies were found to be data analysis programs, databases and questionnaires. The primary reasons of using digital technology in scientific research were to collect data easily and quickly, to reduce research costs and to reach a higher number of participants.

The use of digital technologies in scientific research is considered a revolutionary action, which creates innovative opportunities. Through digitalized life, probably for the first time in history, the educational researchers have analytical information, which we can benefit from more than the individual's own statements in research involving human factor. However, there are a few studies that investigated the preferences of educational researchers who use digital technologies in their scientific research.

Within the last 15 years, law enforcement agencies have increased their collection of data on vehicle stops. A variety of resource guides, research reports, and peer‐reviewed articles have outlined the methods used to collect these data and conduct analyses. This literature is spread across numerous publications and can be cumbersome to summarize for practical use by practitioners and academics. This article seeks to fill this gap by detailing the current best practices in vehicle stop data collection and analysis in state police agencies.

The article summarizes the data collection techniques used to assist in identifying racial/ethnic disparities in vehicle stops. Specifically, questions concerning why, when, how, and what data should be collected are addressed. The most common data analysis techniques for vehicle stops are offered, including an evaluation of common benchmarking techniques and their ability to measure at‐risk drivers. Vehicle stop outcome analyses are also discussed, including multivariate analyses and the outcome test. Within this summary, strengths and weaknesses of these techniques are explored.

In summarizing these approaches, a body of best practices in vehicle stop data collection and analysis is developed.

Racial profiling continues to be a contentious issue for law enforcement and the community. A considerable body of research has developed to assess the prevalence of racial profiling. This article offers social scientists and practitioners a comprehensive, succinct, peer‐reviewed summary of the best practices in vehicle stop data collection and analysis.

In this chapter, we address the question if and how modern technology can be used to design questionnaires, diaries, web sites, and experiments to improve the validity of reliability of active data collection instruments. In particular, it discusses the history of computer-assisted activity diary data, reenactment sessions, stated preference methods, and interactive computer experiments with a special focus on the design of these instruments in terms of respondent support and user interfaces. Empirical evidence and experience suggests that although fascinating instruments may increase respondent motivation and involvement and therefore improve the reliability of the measurements, there is also the danger that respondents' answers are influenced by features of the electronic instrument that are not essential, reducing validity and reliability.

From the moment the ASU Libraries migrated from dumb terminals to PC workstations for access to electronic resources, there was great concern that these workstations would be used “inappropriately”. Whether students should be allowed to check their e‐mail from the workstations was debated, as was the need to restrict access to the Internet. The Information Technology division at the University was concerned with how the Library would provide accountability for anything that happened at a public workstation. After much discussion, it was decided that the Library PC workstations would provide unrestricted access to the Internet. Telnet access was limited to library‐related resources requiring Telnet, in an effort to keep students from doing computing assignments and personal e‐mail on these workstations. The systems department developed an approach to data collection that included both an observational system and an electronic data collection system, using sampling techniques, to collect data on the use of the PC workstations at the main library (Hayden) at Arizona State University. This presentation will describe the data collection techniques, the results of data analysis, and how the results of data analysis are being used to better manage the PC workstations in the Library.

The Minnesota State University System's Project for Automated Library Systems (MSUS/PALS) is a fully integrated library system that serves over 150,000 patrons on a network of 53 state university, community college, private college, and state agency libraries throughout Minnesota.

The balanced scorecard is gaining momentum as an assessment framework for academic libraries in North America. The purpose of this paper is to examine locally designed assessments available on Association of Research Libraries (ARL) members’ web sites and maps the data to the standard ISO 11620 – Library Performance Indicators (PIs), which is a version of the balanced scorecard. The questions are: first, does data from ARL member web sites cluster round certain ISO 11620 PIs? Second, what implications does data clustering have on internal planning and cross-institutional collaborations? Third, will future standards such as ISO 16439 complement the framework and methods in ISO 11620?

Using the quantitative content inventory and qualitative content audit method, this research analyzes ARL members’ locally designed assessment data. The data grouped within a library category, such as collections, is mapped to PIs found in the ISO 11620.

The locally designed assessment data covers a variety of library processes and maps to many ISO 11620 PIs. From this research libraries can develop methods for tying assessment activities into a comprehensive framework like the balanced scorecard. Using ISO 11620 can advance assessment planning. Implementing this standard can lay the foundation for activities that might arise from standards under development such as ISO 16439 – Library Impact Standard.

This is the first research to gather, exam, and map ARL members’ assessment data to the framework of the ISO 11620 standard.