A new Stata command for computing and graphing percentile shares

Percentile shares provide an intuitive and easy-to-understand way for analyzing income or wealth distributions. A celebrated example are the top income shares sported by the works of Thomas Piketty and colleagues. Moreover, series of percentile shares, defined as differences between Lorenz ordinates, can be used to visualize whole distributions or changes in distributions. In this talk, I present a new command called pshare that computes and graphs percentile shares (or changes in percentile shares) from individual level data. The command also provides confidence intervals and supports survey estimation.

A new Stata command for computing and graphing percentile shares

Percentile shares provide an intuitive and easy-to-understand way for analyzing income or wealth distributions. A celebrated example is the top income shares sported by the works of Thomas Piketty and colleagues. Moreover, series of percentile shares, defined as differences between Lorenz ordinates, can be used to visualize whole distributions or changes in distributions. In this talk, I present a new command called pshare that computes and graphs percentile shares (or changes in percentile shares) from individual level data. The command also provides confidence intervals and supports survey estimation.

PANELS: Stata module to count panels and apply a command to panel units

panels provides a quick way to count the number of panels (groups) in a dataset and display some basic information about the sizes of the panels. Furthermore, -panels- can be used as a prefix command to other Stata commands to apply them to panel units instead of individual observations. This is useful, for example, if you want to compute frequency distributions or summary statistics for panel characteristics.

Impaired pantomime in schizophrenia: Association with frontal lobe function

Gestures are important for nonverbal communication and were shown to be impaired in schizophrenia. Two categories of gestures can be differentiated: pantomime on verbal command and imitation of seen gestures. There is evidence that the neural basis of these domains may be distinct, pantomime being critically dependent on prefrontal cortex function. The aim of the study was to investigate gestural deficits in schizophrenia and their association with frontal lobe function and motor performance.

Impaired gesture performance in schizophrenia: particular vulnerability of meaningless pantomimes

Schizophrenia patients frequently present with subtle motor impairments, including higher order motor function such as hand gesture performance. Using cut off scores from a standardized gesture test, we previously reported gesture deficits in 40% of schizophrenia patients irrespective of the gesture content. However, these findings were based on normative data from an older control group. Hence, we now aimed at determining cut-off scores in an age and gender matched control group. Furthermore, we wanted to explore whether gesture categories are differentially affected in Schizophrenia. Gesture performance data of 30 schizophrenia patients and data from 30 matched controls were compared. Categories included meaningless, intransitive (communicative) and transitive (object related) hand gestures, which were either imitated or pantomimed, i.e. produced on verbal command. Cut-off scores of the age matched control group were higher than the previous cut-off scores in an older control group. An ANOVA tested effects of group, domain (imitation or pantomime), and semantic category (meaningless, transitive or intransitive), as well as their interaction. According to the new cut-off scores, 67% of the schizophrenia patients demonstrated gestural deficits. Patients performed worse in all gesture categories, however meaningless gestures on verbal command were particularly impaired (p = 0.008). This category correlated with poor frontal lobe function (p < 0.001). In conclusion, gestural deficits in schizophrenia are even more frequent than previously reported. Gesture categories that pose higher demands on planning and selection such as pantomime of meaningless gestures are predominantly affected and associated with the well-known frontal lobe dysfunction.

Plotting regression coefficients and other estimates in Stata

Graphical presentation of regression results has become increasingly popular in the scientific literature, as graphs are much easier to read than tables in many cases. In Stata such plots can be produced by the -marginsplot- command. However, while -marginsplot- is very versatile and flexible, it has two major limitations: it can only process results left behind by -margins- and it can only handle one set of results at the time. In this article I introduce a new command called -coefplot- that overcomes these limitations. It plots results from any estimation command and combines results from several models into a single graph. The default behavior of -coefplot- is to plot markers for coefficients and horizontal spikes for confidence intervals. However, -coefplot- can also produce various other types of graphs. The capabilities of -coefplot- are illustrated in this article using a series of examples.

Book Review: Regulating Code : Good Governance and Better Regulation in the Information Age

Digital technologies have profoundly changed not only the ways we create, distribute, access, use and re-use information but also many of the governance structures we had in place. Overall, "older" institutions at all governance levels have grappled and often failed to master the multi-faceted and multi-directional issues of the Internet. Regulatory entrepreneurs have yet to discover and fully mobilize the potential of digital technologies as an influential factor impacting upon the regulability of the environment and as a potential regulatory tool in themselves. At the same time, we have seen a deterioration of some public spaces and lower prioritization of public objectives, when strong private commercial interests are at play, such as most tellingly in the field of copyright. Less tangibly, private ordering has taken hold and captured through contracts spaces, previously regulated by public law. Code embedded in technology often replaces law. Non-state action has in general proliferated and put serious pressure upon conventional state-centered, command-and-control models. Under the conditions of this "messy" governance, the provision of key public goods, such as freedom of information, has been made difficult or is indeed jeopardized.The grand question is how can we navigate this complex multi-actor, multi-issue space and secure the attainment of fundamental public interest objectives. This is also the question that Ian Brown and Chris Marsden seek to answer with their book, Regulating Code, as recently published under the "Information Revolution and Global Politics" series of MIT Press. This book review critically assesses the bold effort by Brown and Marsden.

Projecting a relevant "next": Instruction sequences in driving lessons

Car interaction and the organisation of multi-activity in cars have become a fertile topic of research within CA and EM (Laurier 2005, Haddington & Keisanen 2009). While previous research has focused exclusively on everyday car rides, in this paper we will analyse a specific kind of car interaction, namely driving lessons. In addition to"driving" and"talking", as the two main parallel activities in everyday car rides (Mondada in press), in driving lessons a central activity is"instructing", that we understand to be a collaborative accomplishment (Sanchez Svensson et al. 2009). Drawing on a corpus of 7 video-recorded driving lessons, we will analyse the sequential organisation of"instruction sequences", i.e. of those actions that are initiated by the driving instructor with a turn projecting the next relevant action to be executed by the learner. Learners carry out next actions in two different ways: a) as"single" actions (e.g. using the indicator); b) as a complex series of overlapping or parallel actions. We will show that"single" actions occur as responses to instructions concerning the learner's command of the car, while complex actions occur when the instructors formulate direction indications. The aims of our analyses are twofold. Firstly, we will analyse how instruction sequences are fitted to the emerging contingencies of the car ride (movement in space, changing environment): we will show that a) the turn format of the instruction initiation displays the degree of"urgency" of the requested action; b) learners have the possibility to start the relevant"next" before the instruction initiation comes to completion. Secondly, we will focus on those"seconds" that the driving instructor treats as problematic by initiating a repair sequence (e.g. an improper use of the indicator). Our research contributes to the discussion about the multimodal resources that participants can employ to fulfil a projected action. In addition, it offers insights in a hitherto scarcely investigated topic, namely the organisation of instructions and the ecology of apprenticeship. References HADDINGTON, P. & KEISANEN, T. (2009) Location, mobility and the body as resources in selecting a route. Journal of Pragmatics 41 (10), 1938-1961. LAURIER, Eric (2005): Searching for a parking space. Intellectica 41-42/2-3: 101-116. MONDADA, Lorenza (in press). Talking and driving: multi-activity in the car. Semiotica. SANCHEZ SVENSSON, M. et al. (2009) "Embedding instruction in practice: contingency and collaboration during surgical training", Sociology of Health & Illness, 31/6: 889-906.

Plotting regression coefficients and other estimates

Graphical display of regression results has become increasingly popular in presentations and in scientific literature because graphs are often much easier to read than tables. Such plots can be produced in Stata by the marginsplot command (see [R] marginsplot). However, while marginsplot is versatile and flexible, it has two major limitations: it can only process results left behind by margins (see [R] margins), and it can handle only one set of results at a time. In this article, I introduce a new command called coefplot that overcomes these limitations. It plots results from any estimation command and combines results from several models into one graph. The default behavior of coefplot is to plot markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce other types of graphs. I illustrate the capabilities of coefplot by using a series of examples.

ADDPLOT: Stata module to add twoway plot objects to an existing twoway graph

addplot adds twoway plot objects to an existing twoway graph. This is useful if you want to add additional objects such as titles or extra data points to a twoway graph after it has been created. Most of what addplot can do, can also be done by rerunning the original graph command including additional options or plot statements. addplot, however, might be useful if you have to modify a graph for which you cannot rerun the original command, for example, because you only have the graph file but not the data that were used to create the graph. Furthermore, addplot can do certain things that would be difficult to achieve in a single graph command (e.g. customizing individual subgraphs within a by-graph). addplot also provides a substitute for some of the functionality of the graph editor.