On the use of likert-type scales in multilevel data:Influence on aggregate variables

In multilevel analyses, problems may arise when using Likert-type scales at the lowest level of analysis. Specifically, increases in variance should lead to greater censoring for the groups whose true scores fall at either end of the distribution. The current study used simulation methods to examine the influence of single-item Likert-type scale usage on ICC(1), ICC(2), and group-level correlations. Results revealed substantial underestimation of ICC(1) when using Likert-type scales with common response formats (e.g., 5 points). ICC(2) and group-level correlations were also underestimated, but to a lesser extent. Finally, the magnitude of underestimation was driven in large part to an interaction between Likert-type scale usage and the amounts of within- and between-group variance. © Sage Publications.

Time series analysis in the assessment of ICT impact at the aggregate level – lessons and implications for the new economy

The major role of information and communication technology (ICT) in the new economy is well documented: countries worldwide are pouring resources into their ICT infrastructure despite the widely acknowledged “productivity paradox”. Evaluating the contribution of ICT investments has become an elusive but important goal of IS researchers and economists. But this area of research is fraught with complexity and we have used Solow's Residual together with time-series analysis tools to overcome some methodological inadequacies of previous studies. Using this approach, we conduct a study of 20 countries to determine if there was empirical evidence to support claims that ICT investments are worthwhile. The results show that ICT contributes to economic growth in many developed countries and newly industrialized economies (NIEs), but not in developing countries. We finally suggest ICT-complementary factors, in an attempt to rectify possible flaws in ICT policies as a contribution towards improvement in global productivity.

When promoting positive feelings pays:Aggregate job satisfaction, work design features, and innovation in manufacturing organizations

This study investigates the relationship between aggregate job satisfaction and organizational innovation. In a sample of manufacturing companies, data were gathered from 3717 employees in 28 UK manufacturing organizations about their job satisfaction and aggregated to the organizational level. Data on innovation in technology/processes were gathered from multiple respondents in the same organizations 24 months later. The results revealed that aggregate job satisfaction was a significant predictor of subsequent organizational innovation, even after controlling for prior organizational innovation and profitability. Moreover the data indicated that the relationship between aggregate job satisfaction and innovation in production technology/processes was moderated by two factors: job variety and a commitment to "single status". Unlike previous studies, we conceptualize job satisfaction at the aggregate rather than the individual level and examine innovation rather than creativity. We propose that where the majority of employees experience job satisfaction, they will endorse rather than resist innovation and work collaboratively to implement as well as to generate creative ideas.

Can we observe collective neuronal activity from macroscopic aggregate signals?

The fundamental problem faced by noninvasive neuroimaging techniques such as EEG/MEG1 is to elucidate functionally important aspects of the microscopic neuronal network dynamics from macroscopic aggregate measurements. Due to the mixing of the activities of large neuronal populations in the observed macroscopic aggregate, recovering the underlying network that generates the signal in the absence of any additional information represents a considerable challenge. Recent MEG studies have shown that macroscopic measurements contain sufficient information to allow the differentiation between patterns of activity, which are likely to represent different stimulus-specific collective modes in the underlying network (Hadjipapas, A., Adjamian, P., Swettenham, J.B., Holliday, I.E., Barnes, G.R., 2007. Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex. NeuroImage 35, 518–530). The next question arising in this context is whether aspects of collective network activity can be recovered from a macroscopic aggregate signal. We propose that this issue is most appropriately addressed if MEG/EEG signals are to be viewed as macroscopic aggregates arising from networks of coupled systems as opposed to aggregates across a mass of largely independent neural systems. We show that collective modes arising in a network of simulated coupled systems can be indeed recovered from the macroscopic aggregate. Moreover, we show that nonlinear state space methods yield a good approximation of the number of effective degrees of freedom in the network. Importantly, information about hidden variables, which do not directly contribute to the aggregate signal, can also be recovered. Finally, this theoretical framework can be applied to experimental MEG/EEG data in the future, enabling the inference of state dependent changes in the degree of local synchrony in the underlying network.

Automatic processing, quality assurance and serving of real-time weather data over lightweight protocols

Recent advances in technology have produced a significant increase in the availability of free sensor data over the Internet. With affordable weather monitoring stations now available to individual meteorology enthusiasts a reservoir of real time data such as temperature, rainfall and wind speed can now be obtained for most of the United States and Europe. Despite the abundance of available data, obtaining useable information about the weather in your local neighbourhood requires complex processing that poses several challenges. This paper discusses a collection of technologies and applications that harvest, refine and process this data, culminating in information that has been tailored toward the user. In this case we are particularly interested in allowing a user to make direct queries about the weather at any location, even when this is not directly instrumented, using interpolation methods. We also consider how the uncertainty that the interpolation introduces can then be communicated to the user of the system, using UncertML, a developing standard for uncertainty representation.

The structural permeability of concrete at cryogenic temperatures

The thesis describes a programme of research designed to identify concretes for application at cryogenic temperature, in particular for storage of Liquefield Natural Gas which is maintained at a temperature of -165oC. The programme was undertaken in two stages. Stage 1 involved screening tests on seventeen concrete mixes to investigate the effects of strength grade (and water/cement ratio), air entrainment, aggregate type and cement type. Four mixes were selected on the basis of low temperature strength, residual strength after thermal cycling and permeability at ambient temperature. In Stage 2 the selected mixes were subjected to a comprehensive range of tests to measure those properties which determine the leak tightness of a concrete tank at temperatures down to -165oC. These included gas permeability; tensile strength, strain capacity, thermal expansion coefficient and elastic modulus, which in combination provide a measure of resistance to cracking; and bond to reinforcement, which is one of the determining factors regarding crack size and spacing. The results demonstrated that the properties of concrete were generally enhanced at cryogenic temperature, with reduced permeability, reduced crack proneness and, by virtue of increased bond to reinforcement, better control of cracking should it occur. Of the concretes tested, a lightweight mix containing sintered PFA aggregate exhibited the best performance at ambient and cryogenic temperature, having appreciably lower permeability and higher crack resistance than normal weight concretes of the same strength grade. The lightweight mix was most sensitive to thermal cycling, but there was limited evidence that this behaviour would not be significant if the concrete was prestressed. Relationships between various properties have been identified, the most significant being the reduction in gas permeability with increasing strain capacity. The structural implications of the changing properties of the concrete have also been considered.

An aggregate measure of financial ratios using a multiplicative DEA model

This paper examines the problems in the definition of the General Non-Parametric Corporate Performance (GNCP) and introduces a multiplicative linear programming as an alternative model for corporate performance. We verified and tested a statistically significant difference between the two models based on the application of 27 UK industries using six performance ratios. Our new model is found to be a more robust performance model than the previous standard Data Envelopment Analysis (DEA) model.

Why are aggregate equity payouts pro-cyclical?

We use two general equilibrium models to explain why changes in the external economic environment result in pro-cyclical aggregate dividend payout behavior. Both models that we consider endogenize low elasticity of investment. The first model incorporates capital adjustment costs, while the second one assumes that risk-averse managers maximize their own objective function rather than shareholder wealth. We show that, while both models generate pro-cyclical aggregate dividends, a feature consistent with the observed business-cycle pattern of payouts from well-diversified portfolios, the second model provides a more likely explanation for this effect. Our findings emphasize the importance of incorporating agency conflicts when considering the relationship between the external economic environment and the financial behavior of businesses.

State advances and private retreats? Evidence from the decomposition of the Chinese manufacturing aggregate productivity decomposition in China

This paper is motivated by the recent debate on the existence and scale of China's 'Guo Jin Min Tui' phenomenon, which is often translated as 'the state sector advances and the private sector retreats'. We argue that the profound implication of an advancing state sector is not the size expansion of the state ownership in the economy per se, but the likely retardation of the development of the already financially constrained private sector and the issues around the sustainability of the already weakening Chinese economy growth. Drawing on recent methodological advances, we provide a critical analysis of the contributions of the state and non-state sectors in the aggregate Total Factor Productivity and its growth over the period of 1998-2007 to verify the existence of GJMT and its possible impacts on Chinese economic growth. Overall, we find strong and consistent evidence of a systematic and worsening resource misallocation within the state sector and/or between the state sectors and private sectors over time. This suggests that non-market forces allow resources to be driven away from their competitive market allocation and towards the inefficient state sector. Crown Copyright © 2014.

Aggregate dividends and consumption smoothing

We show that net equity payouts from the corporate sector play a crucial role in helping individuals manage their consumption path across the business cycle. In particular, we show that, as investors' desire to smooth consumption increases, optimal aggregate dividends become both more volatile and more counter-cyclical to help counterbalance pro-cyclical labor income. These findings are robust to whether or not agency conflicts exist in the economy.