Suchergebnisse

Intro -- MECHANICAL VIBRATIONS:MEASUREMENT, EFFECTSAND CONTROL -- MECHANICAL VIBRATIONS:MEASUREMENT, EFFECTSAND CONTROL -- CONTENTS -- PREFACE -- EXPERT COMMENTARIES -- TRENDS IN VIBRATION-BASED STRUCTURALHEALTH MONITORING OF RAILWAY SLEEPERS -- ON THE TRANSVERSE VIBRATION OF ATRANSVERSELY ISOTROPIC MAGNETO-ELECTROELASTICPLATE BY ADOPTING THE THIN-PLATETHEORY -- Abstract -- Introduction -- Formulations -- Numerical Evaluations and Discussions -- References -- SHORT COMMUNICATIONS -- VIBRATION ANALYSIS OF PRE-TWISTED BEAMSUSING THE DIFFERENTIAL QUADRATURE METHOD -- Abstract -- Introduction -- The Differential Quadrature Method -- Eigen Solutions of Pre-twisted Beams -- Numerical Results -- Conclusion -- References -- A MODEL TEST ON A CLAMPED CANTILEVER BEAM -- Abstract -- 1. Theory for Natural Frequency -- 2. Finite Element Analysis (ANSYS) -- 2.1. Natural Frequency -- 2.2. Model Shape -- 2.3. Input File of ANSYS -- 3. Damping Ratio Experiment -- 3.1 Logarithmic Decrement Method -- 3.2. Half-power Method -- References -- RESEARCH AND REVIEW STUDIES -- REGULAR AND CHAOTIC VIBRATIONSOF MECHANICAL SYSTEMS IN MODELSOF COUPLED ROTATORS -- Abstract -- 1. Introduction -- 2. Dynamics of Autonomous Rotator -- 3. Dynamics of Non-Autonomous Rotator -- 4. Dynamics of Autonomous Rotator with Aperiodic Load -- 5. Dynamics of Non-autonomous Rotator with Aperiodic Load -- 6. Dynamics of Rotator with Oscillatory Load -- 7. Nonlinear Resonance of Lateral Vibrations of Rotorin a System With Energy Source of Limited Power -- 8. Damping of Lateral Vibrations Using a Modulation of a MotorSpeed -- 9. Chaotic Dynamics of Simple Vibrational System with EnergySource of Limited Power -- Qualitative Forms of the Rotation Characteristic in the Resonance Zone -- 10. Chaotic Torsional Vibrations of a Shaft in System with EnergySource of Limited Power.

Studies of media effects on political knowledge have used exposure and attention to measure television watching and confidence and accuracy to measure knowledge. These studies have employed a variety of control strategies. Although measurements and control strategies are known to affect the magnitude of the resulting independent-dependent correlation, this study investigates whether they can affect research outcomes in any other way. The topic of substance, which we use as an example, is whether voters learn more from political advertisements than from television news. The results indicate that different methods can lead to different, or even opposite, conclusions.

In order to investigate the advantage of mixed-mode (MM) surveys, selection effects between the modes should be evaluated. Selection effects refer to differences in respondent compositions on the target variables between the modes. However, estimation of selection effects is not an easy task because they may be completely confounded with measurement effects between the modes (differences in measurement error). Publications concerning the estimation of these mode effects are scarce. This article presents and compares three methods that allow measurement effects and selection effects to be evaluated separately. The first method starts from existing publications that avoid the confounding problem by introducing a set of mode-insensitive variables into the analysis model. However, this article will show that this method involves unrealistic assumptions in most practical research. The second and the third methods make use of an MM sample extended by comparable single-mode data. The assumptions, advantages, and disadvantages of all three methods are discussed. Each method will further be illustrated using a set of six variables relating to opinions about surveys among the Flemish population. The results show large differences between the methods.

Abstract
With the increasing usage of dual-mode data collection, researchers of public opinion have shown considerable interest in understanding response differences across different interview modes. Are mode effects an outcome of representation or measurement differences across modes? We conducted a dual-mode survey (web and telephone) using Florida's voter file as the sampling frame, randomly assigning registered voters into one mode versus the other. Having a priori information about the respondents allows us to gauge whether and how sample composition differences may be driven by mode effects, and whether mode affects estimated models of political behavior. Survey mode effects are still significant for issue voting even when sampling design is similar for both modes.

This study applies ordinal confirmatory factor analysis for multiple groups to assess equivalence of scale, random errors and systematic (nonrandom) errors of attitudinal questions surveyed on rating scales under different survey modes (Face-to-Face [F2F], Telephone, Paper, and Web). Empirical findings from a large-scale experiment are presented. Consistent with theoretical expectations, interviewer- and self-administered surveys measured all assessed questions on systematically different scales, with different systematic bias, and with differing extents of random error. These measurement effects were absent when comparing Paper with Web or F2F with Telephone. It is concluded that modes impact primarily systematic measurement effects affecting multiple items equally. Interviewer- and self-administered modes should only be combined with great care in mixed-mode surveys that focus on attitudinal constructs. Combining Paper and Web or Telephone and F2F are the viable options. Thereby choosing the self-administered modes appears more efficient, because these modes exhibited higher indicator reliabilities (smaller random error) than the interviewer modes.

Abstract
Effective evaluation of data quality between data collected in different modes is complicated by the confounding of selection and measurement effects. This study evaluates the utility of propensity score matching (PSM) as a method that has been proposed as a means of removing selection effects across surveys conducted in different modes. Our results show large differences in estimates for the same variables between parallel face-to-face and online surveys, even after matching on standard demographic variables. Moreover, discrepancies in estimates are still present after matching between surveys conducted in the same (online) mode, where differences in measurement properties can be ruled out a priori. Our findings suggest that PSM has substantial limitations as a method for separating measurement and selection differences across modes and should be used only with caution.

"Mixed mode surveys are presented as a solution to increasing survey costs and decreasing response rates. The disadvantage of such designs is the lack of control over mode effects and the interaction between selection and measurement effects. In a mixed mode survey, measurement effects can put into doubt data comparability between subgroups, or similarly between waves or rounds of a survey conducted using different modes. To understand the extent of measurement effects, selection and measurement effects between modes have to be disentangled. Almost all techniques to separate these effects depend on covariates that are assumed to be mode-insensitive and to fully explain selection effects. Most of the time, these covariates are sociodemographic variables that might be mode-insensitive, but fail to sufficiently explain selection effects. The aim of this research is to assess the performance of mode preference variables as covariates to evaluate selection and measurement effects between modes. In 2012, a mixed mode survey - a web questionnaire followed by face-to-face interviews - was conducted alongside the face-to-face European Social Survey in Estonia (Ainsaar et al., 2013). The questionnaire included mode preference items. In this paper, the effects of the trade-offs between the two assumptions on the precision of estimated selection and measurement effects are compared. The results show that while adding the mode preference to the propensity score model seems to increase the explanatory power of web participation, it decreases the correlation between propensity scores and target variables. In addition, the estimated selection and measurement effects do not always fit the expectation that more selection effects are explained and more measurement effects are detected." (author's abstract)

AbstractPapers that aim to measure peer effects on the educational outcomes of an individual face at least two problems in estimating them convincingly. The first is a sorting problem: in education, individuals who are already alike tend to sort together into similar schools or institutions. The second is the reflection problem: if my peers affect me, do I not also affect them? Solutions to these problems are not straightforward and the methods often used in the literature are contentious, as this review aims to make clear. External manipulation of peer groups, via quasi‐ or full randomisation, provides a necessary starting point.

Der vorliegende Beitrag entwickelt ein Modell der kognitiven Prozesse, die den Kontexteffekten bei der Einstellungsmessung zugrunde liegen. Das Modell erlaubt die folgenden Aussagen bzw. Prognosen: (1) die Bedingungen unter denen Kontexteffekte wahrscheinlich entstehen; (2) ihre Richtung (z.B. Assimilation oder Kontrast); (3) ihre Größe; (4) ihre Generalisierung über verwandte items und (5) ihre Abhängigkeit von der Art der Datenerhebung. Die Literatur der experimentellen Forschung zu diesem Thema wird referiert und die methodologischen Implikationen für die Fragebogenkonstruktion diskutiert. (pmb)