Taking Up Offenses: Secondhand Forgiveness and Group Identification

When a person or group is mistreated, those not directly harmed by the transgression might still experience antipathy toward offenders, leading to secondhand forgiveness dynamics similar to those experienced by firsthand victims. Three studies examine the role of social identification in secondhand forgiveness. Study 1 shows that the effects of apologies on secondhand victims are moderated by level of identification with the wronged group. Study 2 shows that identification with the United States was associated with less forgiveness and greater blame and desire for retribution directed at the 9/11 terrorists, and these associations were primarily mediated by anger. Finally, Study 3 shows that participants whose assimilation needs were primed were less forgiving toward the perpetrators of an assault on ingroup members than participants whose differentiation needs were primed, an effect that was mediated by empathy for the victims.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

From Data to Causes II: Comparing Approaches to Panel Data Analysis

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

From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)

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

From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)

his 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. All authors: Michael J. Zyphur, Paul D. Allison, Louis Tay, Manuel C. Voelkle, Kristopher J. Preacher, Zhen Zhang, Ellen L. Hamaker, Ali Shamsollahi, Dean C. Pierides, Peter Koval, Ed Diene