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This chapter aims at making clear growth and distribution of China’s economy 1987–2000 with fixed capital on the input-output table basis. Since fixed capital data are not sufficiently available, one has to estimate fixed capital coefficients. In the outset, this chapter outlines the Sraffa–Fujimori method, which simulates the maximum growth path and estimates the marginal fixed capital coefficients on that path. In the second place, the marginal fixed capital coefficients of China’s economy are estimated. In the third place, the wage-profit curves of China’s economy will be drawn, and we discuss some further features obtained by our observations.

Every “structural model” is defined by the set of covariance and mean expectations. These expectations are the source of parameter estimates, fit statistics, and substantive interpretation. The recent chapter by Cortina, Pant, and Smith-Darden ((this volume). In: F. Dansereau & F. J. Yammarino (Eds), Research in multi-level issues (vol. 4). Oxford, England: Elsevier) shows how a formal investigation of the data covariance matrix of longitudinal data can lead to an improved understanding of the estimates of covariance terms among linear growth models. The investigations presented by Cortina et al. (this volume) are reasonable and potentially informative for researchers using linear change growth models. However, it is quite common for behavioral researchers to consider more complex models, in which case a variety of more complex techniques for the calculation of expectations will be needed. In this chapter we demonstrate how available computer programs, such as Maple, can be used to automatically create algebraic expectations for the means and the covariances of every structural model. The examples presented here can be used for a latent growth model of any complexity, including linear and nonlinear processes, and any number of longitudinal measurements.

This paper considers properties of an optimization-based sampler for targeting the posterior distribution when the likelihood is intractable. It uses auxiliary statistics to summarize information in the data and does not directly evaluate the likelihood associated with the specified parametric model. Our reverse sampler approximates the desired posterior distribution by first solving a sequence of simulated minimum distance problems. The solutions are then reweighted by an importance ratio that depends on the prior and the volume of the Jacobian matrix. By a change of variable argument, the output consists of draws from the desired posterior distribution. Optimization always results in acceptable draws. Hence, when the minimum distance problem is not too difficult to solve, combining importance sampling with optimization can be much faster than the method of Approximate Bayesian Computation that by-passes optimization.

The ability to analyze social action as it unfolds on micro time scales – particularly the 24-hour day – is central to understanding group processes. This chapter describes a new approach to this undertaking, which treats individuals’ involvement in specific activities at specific times as bases for: (1) sequential linkages between activities; as well as (2) connections to others who engage in similar action sequences. This makes it possible to examine the emergence and internal functioning of groups using existing network analysis techniques.

We illustrate this approach with a specific application – a quantitative and visual comparison of the daily activity patterns of employed and unemployed people. We use data from 13,310 24-hour time diaries from the 2010–2013 American Time Use Surveys.

Employed and unemployed people engage in significantly different types of activities and at different times. Beyond this, network analyses reveal that unemployed individuals experience much lower levels of synchrony with each other than do employed individuals and have much less organized action sequences. In short, there is a chronic lack of prevailing norms regarding how unemployed people organize the 24-hour day.

Future research that uses time-stamped data can employ network methods to analyze and visualize how group members sequence and synchronize social action. These methods make it possible to study how the structure of social action shapes group and individual-level outcomes.

We study organizational vocabularies as complex social structures emerging from the association between organizational participants and words they use to describe and make sense of their experiences at work. Using data that we have collected on the association between managers in a multi-unit international company and words they use to describe their organizational units and the overall company, we examine the relational micro-mechanisms underlying the observed network structure of organizational vocabularies. We find that members of the same subsidiary tend to become more similar in terms of the words they use to describe their units. Members of the same subsidiary, however, do not use the same words to describe the corporate group. Consequently, the structure of organizational vocabularies tends to support consistent local interpretations, but reveals the presence of divergent meanings that organizational participants associate with the superordinate corporate group.

This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the composite marginal likelihood (CML) approach in multivariate ordered-response situations. The ability of the two approaches to recover model parameters in simulated data sets is examined, as is the efficiency of estimated parameters and computational cost. Overall, the simulation results demonstrate the ability of the CML approach to recover the parameters very well in a 5–6 dimensional ordered-response choice model context. In addition, the CML recovers parameters as well as the MSL estimation approach in the simulation contexts used in this study, while also doing so at a substantially reduced computational cost. Further, any reduction in the efficiency of the CML approach relative to the MSL approach is in the range of nonexistent to small. When taken together with its conceptual and implementation simplicity, the CML approach appears to be a promising approach for the estimation of not only the multivariate ordered-response model considered here, but also for other analytically intractable econometric models.

Developments in the social neurosciences over the past two decades have rendered problematic the main knowledge elicitation techniques currently in use by strategy researchers, as a basis for revealing actors’ mental representations of strategic knowledge. Extant elicitation techniques were advanced during an era when cognitive scientists and organizational researchers alike were preoccupied with the basic information of processing limitations of decision makers and means of addressing them, predicated on an outmoded conception of strategists as affect-free, cognitive misers. The need to adapt these techniques to enable the investigation of the emotional content and structure of actors’ mental representations is now a pressing priority for the advancement of theory, research, and practice pertaining to several interrelated areas of strategic management, from dynamic capabilities development, to upper echelons theory, to strategic consensus formation. Accordingly, in this chapter, we report the findings of two studies that investigated the feasibility of adapting the repertory grid, a robust method, widely known and well used in strategic management, for this purpose. Study 1 elicited a series of commonly mentioned strategic issues (the elements) from a sample of senior managers similar in composition to the sample recruited to the second study. Study 2 participants evaluated the elements elicited in Study 1 in relation to a series of researcher-supplied bipolar attributes (the constructs), based on the well-known affective circumplex model of human emotions. In line with expectations, a series of vector-based multivariate analyses revealed a number of interesting similarities and variations among participants in terms of the basic structure and emotional salience of the issues under consideration.