Mixed-mode Data Research Articles

Experimental and numerical analysis of fracture parameters of adhesively bonded joints at low temperatures

In this work, the fracture parameters of the adhesive joints were investigated at low temperatures. The results of the tensile experiments showed that by decreasing the temperature, the ultimate strength, yield strength and elastic modulus have increasing trends. The effect of temperature and loading mode on the fracture toughness parameters of adhesive joints were examined by using the special test loading device in which by varying the loading angle, pure mode I, II and mixed mode data were obtained experimentally. The results showed that by decreasing the temperature, critical stress intensity factors and critical strain energy release rates increased. However, the increasing trend in mode I was gentle and in mode II was significant. Non-dimensional stress intensity factors were also determined by using finite element method at low temperatures and different loading modes. In order to understand the failure mechanism of adhesive joints, fracture surfaces of samples were analyzed, which showed that, in mode I, the failure mechanism was of a cohesive type and by decreasing the temperature, the joint became brittle and the fracture surface became smooth. The interfacial failure in mode II revealed that the fracture toughness of the adhesive was greater than that of the interface, which leads to deviation of crack growth path from the cohesive to the interfacial fracture.

Perceptions regarding active management of the Cross-timbers forest resources of Oklahoma, Texas, and Kansas: A SWOT-ANP analysis

The Cross-timbers ecoregion, which stretches from north central Texas, through central Oklahoma, and up into southern Kansas, represents the broad ecotone between the eastern deciduous forest and the grasslands of the southern Great Plains. The region is threatened by both natural and anthropogenic factors including climate variability, the encroachment of eastern redcedar (Juniperus virginiana), and urbanization. In particular, fire exclusion has dramatically changed the structure and composition of the Cross-timbers forests, which historically experienced multiple fires per decade. Active management practices such as prescribed fire, timber thinning, and fuels reduction are largely absent in the Cross-timbers forested ecosystems. This study utilized a mixed-mode data collection method, which involved focus group meetings as well as online survey administration, to determine how stakeholders perceive active management in the Cross-timbers forests. The requisite data were analyzed using the strengths, weaknesses, opportunities, and threats (SWOT)-Analytic Network Process (ANP) framework. The results suggested that presence of healthy and resilient forests and the opportunities associated with increased revenue could be the driving forces in active Cross-timbers management. However, financial burden and uncontrolled fire were recognized as the major obstacles in these efforts. Tailoring appropriate outreach programs can help traditional and non-traditional stakeholders to identify appropriate management solutions in the Cross-timbers.

A Review of GPS Trajectories Classification Based on Transportation Mode.

GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.

Fatigue life prediction under mixed-mode loading using equivalent stress intensity factor models

Damage tolerance principles are widely used to assess the structural integrity and failure of engineering components. The advances in numerical simulation techniques facilitate the prediction of fatigue life of engineering component, which is essential in damage tolerance design. For the components under mixed mode (I/II) loading, the fatigue crack growth and life is predicted by using a modified form of Paris' law along with the equivalent stress intensity factor (ΔKeq). Numerous ΔKeqmodels are available for correlating the equivalent stress intensity factor range and the fatigue crack growth rate. The knowledge of proper ΔKeqmodel is essential for the accurate fatigue life estimation. In this work, the authors numerically assess the performance of ΔKeqmodels in mixed mode fatigue life prediction with the help of published experimental mixed mode data.

The Effect of Online and Mixed-Mode Measurement of Cognitive Ability

A number of studies, particularly longitudinal surveys, are collecting direct measures of cognitive ability, given its importance as a measure in social science research. As longitudinal studies increasingly switch to mixed-mode data collection, frequently including a web component, differences in survey outcomes including cognitive ability may result from mode effects. Differences may arise due to respondent self-selection into mode or due to the mode causing differential measurement. Using a longitudinal survey that measured cognitive ability after introducing a mixed-mode design with a web component, this research explores if and how mode affects cognitive ability outcomes. This survey allows for control of several possible selection mechanisms, including a limited set of direct cognitive ability measures collected in a single mode in an earlier wave. Findings presented here show clearly that web respondents do better on a number of cognitive ability indicators. However, it does not appear that this is wholly explainable by respondents of different ability self-selecting into particular modes. Rather, it appears that measurement of cognitive ability may differ across modes. This result is potentially problematic as comparability is a key component of using cognitive ability in further research.

Adjusting Measurement Bias in Sequential Mixed-Mode Surveys Using Re-Interview Data

In mixed-mode surveys, mode differences in measurement bias, also called measurement effects or mode effects, continue to pose a problem to survey practitioners. In this paper, we discuss statistical adjustment of measurement bias to the level of a measurement benchmark mode in the context of inference from mixed-mode data. Doing so requires auxiliary information, which we suggest collecting in a re-interview administered to a sub-set of respondents to the first stage of a sequential mixed-mode survey. In the re-interview, relevant questions from the main survey are repeated. After introducing the design and presenting relevant statistical theory, this paper evaluates by Monte Carlo simulation the performance of six candidate estimators that exploit re-interview information. In the simulation parameters are systematically varied that define the size and type of measurement and selection effects between modes in the mixed-mode design. Our results indicate that the performance of the estimators strongly depends on the true measurement error model. However, one estimator, called the inverse regression estimator, performs particularly well under all considered scenarios. Our results suggest that the re-interview method is a useful approach to adjust measurement effects in the presence of non-ignorable selectivity between modes in mixed-mode data.

Using New Technologies for Time Diary Data Collection: Instrument Design and Data Quality Findings from a Mixed-Mode Pilot Survey

Recent years have witnessed a steady growth of time-use research, driven by the increased research and policy interest in population activity patterns and their associations with long-term outcomes. There is recent interest in moving beyond traditional paper-administered time diaries to use new technologies for data collection in order to reduce respondent burden and administration costs, and to improve data quality. This paper presents two novel diary instruments that were employed by a large-scale multi-disciplinary cohort study in order to obtain information on the time allocation of adolescents in the United Kingdom. A web-administered diary and a smartphone app were created, and a mixed-mode data collection approach was followed: cohort members were asked to choose between these two modes, and those who were unable or refused to use the web/app modes were offered a paper diary. Using data from a pilot survey of 86 participants, we examine diary data quality indicators across the three modes. Results suggest that the web and app modes yield an overall better time diary data quality than the paper mode, with a higher proportion of diaries with complete activity and contextual information. Results also show that the web and app modes yield a comparable number of activity episodes to the paper mode. These results suggest that the use of new technologies can improve diary data quality. Future research using larger samples should systematically investigate selection and measurement effects in mixed-mode time-use survey designs.

Becoming a PI: agency, persistence and some luck!

Purpose This paper aims to examine the experience of gaining research independence by becoming a principal investigator (PI) – an aspiration for many post-PhD researchers about whom little is known. It provides insight into this experience by using a qualitative narrative approach to document how 60 PIs from a range of disciplines in one European and two UK universities experienced working towards and achieving this significant goal. Design/methodology/approach Within the context of a semi-structured interview, individuals drew and elaborated a map representing the emotional high and low experiences of the journey from PhD graduation to first PI grant, and completed a biographic questionnaire. Findings Regardless of the length of the journey from PhD graduation to first PI grant, more than a third noted the role that luck played in getting the grant. Luck was also perceived to have an influence in other aspects of academic work. This influence made it even more important for these individuals to sustain a belief in themselves and be agentive and persistent in managing the challenges of the journey. Originality/value The study, unusual in its cross-national perspective, and its mixed mode data collection, offers a nuanced perspective on the interaction between agency and an environment where the “randomness factor” plays a role in success. The function of luck as a support for sustained agency and resilience is explored.

Surveying End-of-Life Medical Decisions in France: Evaluation of an Innovative Mixed-Mode Data Collection Strategy

BackgroundMonitoring medical decisions at the end of life has become an important issue in many societies. Built on previous European experiences, the survey and project Fin de Vie en France (“End of Life in France,” or EOLF) was conducted in 2010 to provide an overview of medical end-of-life decisions in France.ObjectiveTo describe the methodology of EOLF and evaluate the effects of design innovations on data quality.MethodsEOLF used a mixed-mode data collection strategy (paper and Internet) along with follow-up campaigns that employed various contact modes (paper and telephone), all of which were gathered from various institutions (research team, hospital, and medical authorities at the regional level). A telephone nonresponse survey was also used. Through descriptive statistics and multivariate logistic regressions, these innovations were assessed in terms of their effects on the response rate, quality of the sample, and differences between Web-based and paper questionnaires.ResultsThe participation rate was 40.0% (n=5217). The respondent sample was very close to the sampling frame. The Web-based questionnaires represented only 26.8% of the questionnaires, and the Web-based secured procedure led to limitations in data management. The follow-up campaigns had a strong effect on participation, especially for paper questionnaires. With higher participation rates (63.21% and 63.74%), the telephone follow-up and nonresponse surveys showed that only a very low proportion of physicians refused to participate because of the topic or the absence of financial incentive. A multivariate analysis showed that physicians who answered on the Internet reported less medication to hasten death, and that they more often took no medical decisions in the end-of-life process.ConclusionsVarying contact modes is a useful strategy. Using a mixed-mode design is interesting, but selection and measurement effects must be studied further in this sensitive field.

Mode Effects on Variances, Covariances, Standard Deviations, and Correlations

The European Social Survey (ESS) program currently includes an investigation of mixed-mode survey designs, in which different respondents complete the survey by different survey modes. Such mixed-mode designs may lower systematic and random selection error if selection effects occur between the modes. Nonetheless, the advantage of selection effects may be counteracted by measurement effects between the modes. In the existing literature, selection and measurement effects in mixed-mode designs have already been extensively researched for mean and proportion parameters. Surprisingly, both effects have hardly been researched for covariance parameters, even though covariances are sufficient statistics for widely used parameters such as correlations, regression coefficients, or and factor loadings. This paper analyzes measurement and selection effects on variances, covariances, standard deviations, and correlations for different items from MTMM experiments within the Estonian European Social Survey Round 6 data by using the instrumental variable method. This analysis yields small to moderate selection and measurement effects, which means that mixed-mode surveys may perform slightly better than single-mode surveys, but also that analysis of mixed-mode data may require small error correction for measurement effects.

A Comparative Study on MMDBM Classifier Incorporating Various Sorting Procedure

Classification is one of the most important methods in data mining. These methods are used to extract meaningful information from large database which can be effectively used for predicting unknown class. The classifier based on decision tree is called Mixed Mode Data Base Miner (MMDBM) which is tested with different sorting techniques (merge sort, quick sort, radix sort) to compare the processing time to SLIQ classifier. In this paper, we carried out a comparative study on MMDBM classifier incorporating various sorting procedure by using Blood Pressure (BP) database, and finally the proposed method MMDBM classifier is one of the best classifiers among SLIQ supervised learning method. This proposed method achieved less processing time and higher rate of accuracy. Keywords: Classification, Data Mining, Java Sorting

Measurement equivalence in mixed mode surveys.

Surveys increasingly use mixed mode data collection (e.g., combining face-to-face and web) because this controls costs and helps to maintain good response rates. However, a combination of different survey modes in one study, be it cross-sectional or longitudinal, can lead to different kinds of measurement errors. For example, respondents in a face-to-face survey or a web survey may interpret the same question differently, and might give a different answer, just because of the way the question is presented. This effect of survey mode on the question-answer process is called measurement mode effect. This study develops methodological and statistical tools to identify the existence and size of mode effects in a mixed mode survey. In addition, it assesses the size and importance of mode effects in measurement instruments using a specific mixed mode panel survey (Netherlands Kinship Panel Study). Most measurement instruments in the NKPS are multi-item scales, therefore confirmatory factor analysis (CFA) will be used as the main analysis tool, using propensity score methods to correct for selection effects. The results show that the NKPS scales by and large have measurement equivalence, but in most cases only partial measurement equivalence. Controlling for respondent differences on demographic variables, and on scale scores from the previous uni-mode measurement occasion, tends to improve measurement equivalence, but not for all scales. The discussion ends with a review of the implications of our results for analyses employing these scales.

Mixed-Mode Fracture Toughness Characterization of Dentistry Cements Based on Numerical and Experimental Analysis

In this paper, the fracture toughness parameters of two types of dentistry cement have been investigated based on numerical and experimental analysis. Dentistry cements are largely applicable and useful when it comes to mending teeth and are used as resin and filler. Composite cement and adhesive resin cement are among the helpful cements in dentistry. As a result of production and the loading conditions in the mouth, the generation of micro-cracks is inevitable. In this research, by producing butterfly Arcan samples including crack and by loading those in different angles, pure mode-I, pure mode-II, and mixed-mode fracture data were obtained. The experiments were conducted by an Arcan fixture and loading device, which had the ability of investigating the fracture parameters of materials in different loading angles. By calculating the geometrical correction factors by using ABAQUS finite element software, fracture toughness and critical energy release rate have been obtained. Also, the effects of crack length, elasticity modulus, and Poisson's coefficient on the fracture parameters and energy release rate in different loading angles have been studied using numerical analysis.

Variable assessment in latent class models

The latent class model provides an important platform for jointly modeling mixed-mode data—i.e., discrete and continuous data with various parametric distributions. Multiple mixed-mode variables are used to cluster subjects into latent classes. While the mixed-mode latent class analysis is a powerful tool for statisticians, few studies are focused on assessing the contribution of mixed-mode variables in discriminating latent classes. Novel measures are derived for assessing both absolute and relative impacts of mixed-mode variables in latent class analysis. Specifically, the expected posterior gradient and the Kolmogorov variation of the posterior distribution, as well as related properties are studied. Numerical results are presented to illustrate the measures.

Two better than one: The use of a mixed-mode data collection to improve the electoral forecast

Two better than one: The use of a mixed-mode data collection to improve the electoral forecast

Relative Mode Effects on Data Quality in Mixed-Mode Surveys by an Instrumental Variable

In order to compare data-quality of different data-collection modes, multitrait-multimethod (MTMM) experiments have been implemented in a mixed-mode experiment parallel to the European Social Survey (ESS) fourth round (2008/2009). Special interest lies in measurement effects between the modes which refer to the pure impact of a data-collection mode on the quality. Nevertheless, mere comparison between quality estimates of the different modes does not allow drawing conclusions about measurement effects. Indeed, measurement effects may be completely confounded with selection effects which refer to differences in respondents compositions across the modes. However, by comparing the mixed-mode data with the main ESS data and treating the dataset of origin as an instrumental variable, some conditional measurement effects and selection effects can be disentangled. This paper provides a preliminary exploratory analysis of this approach. The results generally yield low to fair measurement effects while the selection effects on some items are rather large.

Does a stepped approach using mixed-mode data collection reduce attrition problems in a longitudinal mental health study?

According to epidemiological standards for longitudinal studies, any appropriate attempt should be made to reach a high response rate. This also concerns re-contacting respondents in follow-up waves. In the Netherlands Study of Depression and Anxiety (NESDA), respondents, who refused to visit the study site for a follow-up interview and would have been lost in a single step approach, were offered an interview at their home (in a second step) or an interview by telephone (in a third step). The stepped approach intended to increase response rates, but as a by-product, the switching of the interview response setting introduced effects due to the three different modes for obtaining data. This study aimed to evaluate the benefits and detriments of the introduction of a stepped approach using mixed-mode data collection. The attained attrition in the stepped approach was compared to the attrition rate that would have been achieved if the design had remained in a single mode. Logistic regression models were used to study if the attrition was related to patient characteristics, i.e. the attrition was selective. Propensity score matching was used to study if similar respondents reacted differently to different modes. The stepped approach using mixed-mode collection reduced the attrition from 22% to 13%, while the selectivity of the attrition was reduced on almost all socio-demographic variables, although the selectivity increased in variables that reflect the respondents’ mental health. Propensity score matching demonstrated no evidence of mode effects. Although the introduction of the stepped approach using mixed mode data collection did not completely solve the problem of attrition, we conclude that adding different modes was worthwhile.

Alternative Sequential Mixed-Mode Designs: Effects on Attrition Rates, Attrition Bias, and Costs

This article considers the effect that a wave of mixed-mode data collection (telephone and face-to-face), in an otherwise face-to-face survey, can have on panel attrition and the extent to which this effect is dependent on the nature of the mode-switch protocol. Findings are reported from an experiment carried out at wave 2 of the UK Household Longitudinal Study Innovation Panel, a survey in which the objective is to interview each adult member of the household. One protocol involves making intensive efforts to interview each household member by telephone before switching to face-to-face, and the other involves switching a household to face-to-face as soon as it is apparent that an interviewer visit will be needed for at least one household member. I assess effects on response at waves 2, 3, and 4. With both protocols response rate is lower at wave 2 than with face-to-face single-mode data collection, but with the protocol involving intensive efforts, this response differential is eroded by wave 4, whereas with the other protocol the difference remains. This difference in effect may be caused by intra-household communications.

Re-engineering Dutch social surveys: From single-purpose surveys to an integrated design

Statistics Netherlands and its division of Socio-economic and Spatial Statistics are facing increasing quality demands by customers and stakeholders. Information is needed at more detailed levels and more and more crossings need to be made among different variables of interest. Also timeliness and coherence of statistics are important issues. Next to these demands there is a strong incentive to collect data more efficiently and to reduce the costs of data collection. The growing possibilities of administrative data sources, the rise of the internet as a survey mode and the availability of new methods and techniques provide opportunities to answer these demands. In 2010 as part of a large scale re-engineering pro- gramme of Dutch household surveys, an integrated design of household surveys combined with optimal use of administrative data and mixed mode data collection was introduced. This integrated design that consists of core questionnaire and specific topic modules can make social statistics richer, more cost-effective and more useful as a tool to answer policy needs.

Clustering-oriented privacy-preserving data publishing

Privacy-preserving data publishing has attracted considerable research interests in recent years. One of the problems in such practices is how to trade-off between data utility and privacy protection. This problem heavily deteriorates when the published data are used to do cluster analysis; clustering demands differences between singles for grouping while privacy preserving aims to hide single identifications. In this paper, a mixed mode data obfuscation method AENDO is proposed, which provides a tradeoff strategy from a novel view. The underlying principle is to keep nearest neighborhood structures of data points while data are obfuscated. In particular, for each data point, AENDO differentiates its attributes into neighboring dispersed attributes and neighboring concentrated ones. Furthermore, pertinent statistical data substitution and data swapping strategies are applied to these attributes, respectively. An extensive set of experiments on UCI data sets are provided to assess the effectiveness of our solution, including comparing AENDO with RBT which is one of the best methods on maintaining data usability for clustering. Our results demonstrate that AENDO behaves similarly with RBT on maintaining data utility for clustering, while it outperforms NeNDS by a factor of approximate 10%. Meanwhile, it delivers better anti-inferring effect compared with RBT and NeNDS.