This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs . We conclude with a discussion of issues surrounding causal inference.

This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.

This editorial offers new ways to ethically practice, evaluate, and use quantitative research (QR). Our central claim is that ready-made formulas for QR, including 'best practices' and common notions of 'validity' or 'objectivity', are often divorced from the ethical and practical implications of doing, evaluating, and using QR for specific purposes. To focus on these implications, we critique common theoretical foundations for QR and then recommend approaches to QR that are 'built for purpose', by which we mean designed to ethically address specific problems or situations on terms that are contextually relevant. For this, we propose a new tool for evaluating the quality of QR, which we call 'relational validity'. Studies, including their methods and results, are relationally valid when they ethically connect researchers' purposes with the way that QR is oriented and the ways that it is doneincluding the concepts and units of analysis invoked, as well as what its 'methods' imply more generally. This new way of doing QR can provide the liberty required to address serious worldly problems on terms that are both practical and ethically informed in relation to the problems themselves rather than the confines of existing QR logics and practices.

In spite of their widespread use in policy making in the UK and elsewhere, there is a relatively sparse literature specifically devoted to policy pilots. Recent research on policy piloting has focused on the role of pilots in making policy work in accordance with national agendas. Taking this as a point of departure, the present paper develops the notion of pilots doing policy work. It does this by situating piloting within established theories of policy formulation and implementation, and illustrating using an empirical case. Our case is drawn from a qualitative policy ethnography of a local government pilot programme aiming to extend access to healthcare services. Our case explores the collective entrepreneurship of regional policy makers together with local pilot volunteers. We argue that pilots work to mobilise and manage the ambiguity and conflict associated with particular policy goals, and in their structure and design, shape action towards particular outcomes. We conclude with a discussion of the generative but managed role which piloting affords to local implementers.

This paper explores the material turn in sociology and the tools it provides for understanding organizational problems highlighted by the Royal Commission into the 2009 'Black Saturday' bushfires during which 173 people died in the Australian State of Victoria. Often inspired by Bruno Latour's material-semiotic sociology of associations, organization scholars employing these tools focus on the messy details of organization otherwise overlooked by approaches assuming a macroscopic frame of analysis. In Latour's approach no object is reducible to something else - such as nature, the social, or atoms - it is instead a stabilized set of relations. A Latourian approach allows us to highlight how the Royal Commission and macroscopic models of organizing do unwitting damage to their objects of inquiry by purifying the 'natural' from the 'social'. Performative elements in their schemas are mistaken for descriptive ones. However, a long standing critique of this approach claims that it becomes its own form of reduction, to nothing but relations. Graham Harman, in his object-oriented philosophy develops this critique by showing that a 'relationist' metaphysics cannot properly accommodate the capacity of 'objects' to cause or mediate surprises. Through our case of the Victorian Bushfires Royal Commission, we argue that a purely relational model of objects loosens a productive tension between the structural and ephemeral that drives sociological analysis. By drawing on elements of Harman's ontology of objects we argue that it is necessary for material-semiotic sociology to retain a central place for the emergence of sociological objects.

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and Dan Woodman. For help with historical insights, the authors thank James March, Steven Schlossman, William Starbuck, and the many independent presses that have made it possible to investigate the roots of our quantitative practices.

Algorithms are increasingly being adopted in healthcare settings, promising increased safety, productivity and efficiency. The growing sociological literature on algorithms in healthcare shares an assumption that algorithms are introduced to ‘support’ decisions within an interactive order that is predominantly human-oriented. This article presents a different argument, calling attention to the manner in which organisations can end up introducing a non-negotiable disjuncture between human-initiated care work and work that supports algorithms, which the authors call algorithmic work. Drawing on an ethnographic study, the authors describe how two hospitals in England implemented an Acute Kidney Injury (AKI) algorithm and analyse ‘interruptions’ to the algorithm’s expected performance. When the coordination of algorithmic work occludes care work, the study finds a ‘dismembered’ organisation that is algorithmically-oriented rather than human-oriented. In the discussion, the authors examine the consequences of coordinating human and non-human work in each hospital and conclude by urging sociologists of organisation to attend to the importance of the formal in algorithmic work. As the use of algorithms becomes widespread, the analysis provides insight into how organisations outside of healthcare can also end up severing tasks from human experience when algorithmic automation is introduced.