Utilising Recent Advancements in Techniques for the Analysis of Incomplete Multivariate Data to Improve the Data Quality Management of Current Academic Research

This paper discusses the importance of managing data quality in academic research in its relation to satisfying the customer. This focus is on the data completeness objectivedimension of data quality in relation to recent advancements which have been made in the development of methods for analysing incomplete multivariate data. An overview and comparison of the traditional techniques with the recent advancements are provided. Multiple imputation is also discussed as a method of analysing incomplete multivariate data, which can potentially reduce some of the biases which can occur from using some of the traditional techniques. Despite these recent advancements in the analysis of incomplete multivariate data, evidence is presented which shows that researchers are not using these techniques to manage the data quality of their current research across a variety of academic disciplines. An analysis is then provided as to why these techniques have not been adopted along with suggestions to improve the frequency of their use in the future. Source-Reference. The ideas for this paper originated from research work on David J. Fogarty's Ph.D. dissertation. The subject area is the use of advanced techniques for the imputation of incomplete multivariate data on corporate data warehouses.     

Combining Data from Surveys and Administrative Record Systems. The Norwegian Experience

At Statistics Norway administrative data have been extensively used in order to improve the quality of survey data. Various techniques have been used to reduce sampling variance and/or to reduce the effects of non-response. In the present article some of the most commonly used methods are being presented, and based on empirical rather than theoretical evaluations, we give our conclusions concerning their potentials and limitations.     

Assessing social support networks in cross-national comparative surveys: Measurement issues

In recent years many cross-national comparative surveys, such as Gender and Generation Programme, International Social Survey Programme, European Quality of Life Survey, or General Social Survey, have included assessment of social support networks. Generally, two approaches were used; the name generator approach and the role relation approach. As more cross-country surveys are conducted every year, it may seem reasonable to use such data sets, since they provide high quality comparisons across countries. However, one should pay close attention to the measurement instruments, as these may cause unintentional but systematic variability in the observed data. While the data from each survey can be of high quality ??per se??, we claim that results from these surveys are not comparable, owing to their different measurement approaches and survey instruments. In this paper we present data from several experiments which show that scientific findings from these surveys depend significantly on questionnaire design.     

Appraisal of lecturers for the assessment of the quality of post-graduate studies

The paper presents the system of evaluation of lecturers lecturing at the post-diploma studies “The Quality Management in an Enterprise” organized by the Faculty of Organization and Management of the Silesian University of Technology. The system is a part of a wider research program on education quality realized on the basis of a survey performed at the end of each semester. A significant role of such evaluation in an overall assessment of the teaching quality process has been highlighted. The paper shows the metrization of rating results and their presentation in a universal unitary scale of relative states. Various methods of elaborating the results have been proposed. The analysis of the obtained results has been done for the selected cases from the period between 2001 and 2004. The results of the above analysis have been used to improve not only the way of managing the studies but also the teaching techniques of individual lecturers.     

Using mixed methods for the analysis of individuals: a review of necessary and sufficient conditions and an application to welfare state attitudes

When studying individuals, when is the combination of qualitative and quantitative methods better than just one method alone? Whereas the debate in macro-level research, such as in political science about comparing nations, has made progress in identifying meaningful logics for a combination of methods, it is yet unclear how these logics can be applied to the study of individuals. Individual-level dynamics are in tendency less inert than those of nations or organisations. Therefore, a combination of methods is more difficult to justify in individual-level analysis since differences in measurement results could be due to changes in the dynamics rather than due to the application of different techniques. In contrast, the assumption of unit homogeneity seems to be more easily met for individuals than for countries or other higher-level aggregates, facilitating a comparison of like and like. First, this article presents a compilation of conditions scattered across the literature for the analysis of individuals, according to which a mixed-method is preferable to a single-method approach. Second, the application of these conditions is illustrated with an analysis of the impact of intergenerational relationships on welfare state attitudes in Germany on the basis of survey and focus group data.     

Collecting Event Histories with TrueTales:Techniques to Improve Autobiographical Recall Problems in Standardized Interviews

This article deals with the challenges autobiographical recall processes present to validity of events and their dates in the collection of life histories in standardized interviews. Effective interviewing techniques should support the idiosyncratic cognitive structure and the reconstructive memory processes of autobiographical memory by allowing to flexibly explore the interconnections between memories. We describe the techniques and strategies of interviewing and data recording used in the German Life History Study, namely modularization, forward recall and single case data edition. We introduce TrueTales, a CATI- Program designed to support memory and communication in the interview, and results of its experimental evaluation. TrueTales personalizes instructions, questions and probes and permits controlled flexibility in dealing with individual biographies and recall strategies. Working with a calendar-based time-line, it yields more complete, consistent and valid event histories. Finally, we discuss implications for standardized interviewing practices and the concept of standardization.     

浅谈光学仪器的防霉、防雾、防锈

生霉、生雾和生锈是光学仪器的三害,这"三害"直接影响到光学仪器的质量和使用寿命,影响作业任务的完成,因此,我们必须重视这个工作。本文对光学仪器生霉、生雾和生锈的原因进行了分析,指出了防霉、防雾、防锈的方法:利用化学药剂;坚持文明生产;辅料脱脂以及注意存放保管。     

Topics of statistical theory for register‐based statistics and data integration

Official statistics production based on a combination of data sources, including sample survey, census and administrative registers, is becoming more and more common. Reduction of response burden, gains of production cost efficiency as well as potentials for detailed spatial‐demographic and longitudinal statistics are some of the major advantages associated with the use of integrated statistical data. Data integration has always been an essential feature associated with the use of administrative register data. But survey and census data should also be integrated, so as to widen their scope and improve the quality. There are many new and difficult challenges here that are beyond the traditional topics of survey sampling and data integration. In this article we consider statistical theory for data integration on a conceptual level. In particular, we present a two‐phase life‐cycle model for integrated statistical microdata, which provides a framework for the various potential error sources, and outline some concepts and topics for quality assessment beyond the ideal of error‐free data. A shared understanding of these issues will hopefully help us to collocate and coordinate efforts in future research and development.     

Improving retrospective life course data by combining modularized self-reports and event history calendars: experiences from a large scale survey

Event history calendars (EHC) have proven to be a powerful tool for collecting retrospective autobiographical life course data. One problem is that they are only standardized to a limited extent. This restricts their applicability in large-scale surveys. However, in such surveys, a modularized retrospective CATI design can be combined with an EHC. This data revision module is directly integrated into the interview and used as a data revision module, allowing insights from cognitive psychology to be applied. The data revision module stimulates the respondent’s memory retrieval by detecting both temporal inconsistencies, such as gaps, and overlapping or parallel events. This approach was implemented in the IAB-ALWA study (Working and Learning in a Changing World), a large-scale representative telephone survey involving 10,000 respondents. By comparing the uncorrected data with the final data after revision, we can investigate to what extent the application of this data revision module improves data quality or, more precisely, time consistency and dating accuracy of individual reports.     

Would you mind completing this survey: Assessing the state of survey research in supply chain management

In supply chain management, survey research is one of the primary methodologies used by researchers to generate empirical data. Critical to its effectiveness is an acceptable response rate. Recently, concerns have been raised regarding perceived falling response rates. Using data taken from 464 survey-based studies, as drawn from five representative journals that publish survey-based supply chain studies, this study assesses whether these concerns are valid. Furthermore, it assesses whether the various tactics recommended for improving response rates have been successful. The findings indicate that (1) responses rates have been falling (with a significant decrease beginning in 2001); (2) it is difficult to evaluate the effectiveness of the various techniques because of unevenness in reporting of techniques used; and, (3) response rates are significantly influenced by factors such as the number of questions, source of survey population, method of survey delivery, specific respondents targeted, and use of prequalification/pre-notification.     

Evaluating,Comparing, Monitoring,and Improving Representativeness of Survey Response Through R‐Indicators and Partial R‐Indicators

Non‐response is a common source of error in many surveys. Because surveys often are costly instruments, quality‐cost trade‐offs play a continuing role in the design and analysis of surveys. The advances of telephone, computers, and Internet all had and still have considerable impact on the design of surveys. Recently, a strong focus on methods for survey data collection monitoring and tailoring has emerged as a new paradigm to efficiently reduce non‐response error. Paradata and adaptive survey designs are key words in these new developments. Prerequisites to evaluating, comparing, monitoring, and improving quality of survey response are a conceptual framework for representative survey response, indicators to measure deviations thereof, and indicators to identify subpopulations that need increased effort. In this paper, we present an overview of representativeness indicators or R‐indicators that are fit for these purposes. We give several examples and provide guidelines for their use in practice.     

Probing: A dangerous practice in social surveys?

It is widely accepted among survey researchers that the use of probes by their interviewers can lead to more intelligible, more complete, and more easily coded answers from respondents. Yet it is not hard to find examples of the use of probes in the survey research literature that have failed to produce demonstrably better data. Since the poor codability of respondents' answers is the product of a number of factors, merely training interviewers to be more skilful in their use of probes is unlikely to significantly improve the quality of survey data. If probes are to be used, they should be both standardized and used systematically. It would be better, however, to try to eliminate the need for interviewers to have to use probes. Two positive steps the researcher can take in this direction are: to define key terms properly, and to specify the response frameworks that respondents should adopt when formulating replies to particular questions.     

A classification of response scale characteristics that affect data quality: a literature review

Quite a lot of research is available on the relationships between survey response scales’ characteristics and the quality of responses. However, it is often difficult to extract practical rules for questionnaire design from the wide and often mixed amount of empirical evidence. The aim of this study is to provide first a classification of the characteristics of response scales, mentioned in the literature, that should be considered when developing a scale, and second a summary of the main conclusions extracted from the literature regarding the impact these characteristics have on data quality. Thus, this paper provides an updated and detailed classification of the design decisions that matter in questionnaire development, and a summary of what is said in the literature about their impact on data quality. It distinguishes between characteristics that have been demonstrated to have an impact, characteristics for which the impact has not been found, and characteristics for which research is still needed to make a conclusion.     

ECONOMIC EXPERIMENTS,HYPOTHETICAL SURVEYS AND MARKET DATA STUDIES OF INSURANCE DEMAND AGAINST LOW‐PROBABILITY/HIGH‐IMPACT RISKS: A SYSTEMATIC REVIEW OF DESIGNS,THEORETICAL INSIGHTS AND DETERMINANTS OF DEMAND

This paper provides a systematic review of the literature on 80 experimental, hypothetical survey and market data studies of insurance demand against low‐probability/high‐impact risks. The objective of the review is to extract lessons from these studies and to outline an agenda for future research. We contrast the results of experimental and survey studies to findings from market data. We focus on experimental design methods, insurance characteristics, as well as results about theories, heuristics, behavioural biases and explanatory variables. Lessons for policymakers are drawn which can facilitate disaster preparedness.     

Identifying Optimal Items in Quality of Life Assessment

Quality of life has drawn increasing attention in health science and more efforts have been devoted to develop instruments that are valid and reliable to assess quality of life. The World Health Organization (WHO) has devised an instrument World Health Organization Quality of Life Survey- Abbreviated Version (WHOQOL-BREF) to assess quality of life, but the psychometric property of each individual item has not been studied yet. Using item response theory, we examine the properties of the WHOQOL-BRIEF Taiwan version. Samejima’s graded response model was fitted, and item parameters were calculated. The items were ranked base on their discrimination power, and the best items are identified. Several data with subset of items (22, 20, 18, 16 and 14 items) were created by omitting items with lower discrimination power. The test information function of the full questionnaire and the subsets were compared. The results showed there were significant positive correlations between the full questionnaire and the subsets of items and the distributions are similar. The test information function showed the maximum amount of test information spaced over two ends of the theta continuum, and this suggested that the WHOQOL-BREF provided more information for groups with either lower or higher satisfaction of quality of life, while it is less discriminating for individuals in the middle range.     

DATA GAPS,DATA INCOMPARABILITY,AND DATA IMPUTATION: A REVIEW OF POVERTY MEASUREMENT METHODS FOR DATA‐SCARCE ENVIRONMENTS

Questions that often come up in contexts where household consumption data are unavailable or missing include: what are the best existing methods to obtain poverty estimates at a single snapshot in time? and over time? and what are the best available methods to study poverty dynamics? A variety of different techniques have been developed to tackle these questions, but unfortunately, they are presented in different forms and lack unified terminology. We offer a review of poverty imputation methods that address contexts ranging from completely missing and partially missing consumption data in cross‐sectional household surveys, to missing panel household data. We present the various existing methods under a common framework, with pedagogical discussion on their intuition. Empirical illustrations are provided using several rounds of household survey data from Vietnam. Furthermore, we also offer a practical guide with detailed instructions on computer programs that can be used to implement the reviewed techniques.     

The quality of measurement of personal support subnetworks

Data about personal networks and their characteristics are increasingly used in social science research, especially in research about the quality of life, social support and similar topics (Fischer, 1982; Marsden, 1987; van der Poel, 1993b). Since all data about a persons social network are usually obtained from the respondent himself, the quality of such measurements is a very important issue. Among other factors, the type of social support can affect the quality of social network measurement (Ferligoj and Hlebec, 1998, 1999). Differences in the stability of measurement between the core and extended personal network have also been found (Marsden, 1990; Morgan et al., 1997). The closer and the more important an alter is, the more likely it is that (s)he will be named in any measurement (Hoffmeyer-Zlotnik, 1990; Van Groenou et al., 1990; Morgan et al., 1997). In this paper the results of a recent study on the quality of measurement of tie characteristics in different personal subnetworks are presented. The Multitrait-multimethod (MTMM) approach was used for estimating reliability and validity. A meta analysis of reliability and validity estimates was done by hierarchical clustering. The data were collected in the year 2000 by computer assisted face-to-face and telephone interviews from a random sample of 1033 residents of Ljubljana.     

Are We Really Measuring the Quality of Life?Well-being Has Subjective Dimensions,As Well As Objective Ones

A bstract . In order to compare the quality of life (QOL) in U.S. metropolitan areas economic, sociological, political and cultural data are often gathered and equally weighted without regard for the way QOL is perceived by those who live there. Basing our comparisons of U.S. metropolitan areas solely on objective data or official statistics biases the results so that there is little relationship between one set of measures and another. These results contradict conclusions reached by survey researchers who siwdy the perceptual aspects of the quality of life. Therefore, we must rethink the methods used in future of life studies, Psychographic and demographic data must be integrated.