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It is often claimed that only experiments can support strong causal inferences and therefore they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments results in their overuse both by researchers and decision makers and in an underappreciation of their shortcomings. Neglect of other methods often follows. Experiments can suggest whether X causes Y in a specific experimental setting; however, they often fail to elucidate either the mechanisms responsible for an effect or the strength of an effect in everyday natural settings. In this article, we consider two overarching issues. First, experiments have important limitations. We highlight problems with external, construct, statistical-conclusion, and internal validity; replicability; and conceptual issues associated with simple X causes Y thinking. Second, quasi-experimental and nonexperimental methods are absolutely essential. As well as themselves estimating causal effects, these other methods can provide information and understanding that goes beyond that provided by experiments. A research program progresses best when experiments are not treated as privileged but instead are combined with these other methods.

Goals are considered by some theories as essential for subjective well-being (i.e., telic theories, see Diener, 1984). In fact, achieving goals is beneficial for subjective well-being. However, just holding goals does not bring about goal achievement. Therefore, this chapter highlights the importance of goal pursuit (autotelic theories) in addition to goal achievement for a more comprehensive understanding of subjective well-being. We address the question of whether goal progress and attainment are differentially related to subjective well-being and examine how people best pursue their goals. Specifically, we posit that focusing more on the means of goal pursuit (i.e., adopting a process focus) is more beneficial for goal progress and subjective well-being than focusing more on its ends (i.e., adopting an outcome focus). There also exists some evidence that suggests that adopting a process focus is adaptive when people face a particular type of difficulty when intending to pursue a goal, namely procrastination. Given that procrastination hinders successful goal pursuit and subjective well-being, this finding again highlights the importance of goal focus for understanding how people best pursue their goals.

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.

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 Diener

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.

Subjective well-being is a main goal in modern society, thus it is worth knowing 1) how subjective well-being differs across nations, 2) how it develops over time, 3) what the determinants of subjective well-being in nations are and 4) what the consequences of more or less well-being are. In this chapter, I focus on one particular aspect of subjective well-being, that is, life-satisfaction. I take stock of the research on this matter, drawing on the World Database of Happiness. This reveals the following patterns: a) life-satisfaction differs much across nations, b) average life-satisfaction has risen over the past decade in most nations, while inequality in life-satisfaction in nations decreased, c) several societal determinants of life-satisfaction have been identified, many of which are part of modernity, and, d) high life-satisfaction in a nation has several positive effects and pursuit of greater happiness fits as such with wider policy aims. Key words: happiness, life-satisfaction, subjective well-being, comparative, cross-national, policy implication

The experience of positive emotion is closely linked to subjective well-being. For this reason, campaigns aimed at promoting the value of positive emotion have become widespread. What is rarely considered are the cultural implications of this focus on happiness. Promoting positive emotions as important for "the good life" not only has implications for how individuals value these emotional states, but for how they believe others around them value these emotions also. Drawing on data from over 9,000 college students across 47 countries we examined whether individuals' life satisfaction is associated with living in contexts in which positive emotions are socially valued. The findings show that people report more life satisfaction in countries where positive emotions are highly valued and this is linked to an increased frequency of positive emotional experiences in these contexts. They also reveal, however, that increased life satisfaction in countries that place a premium on positive emotion is less evident for people who tend to experience less valued emotional states: people who experience many negative emotions, do not flourish to the same extent in these contexts. The findings demonstrate how the cultural value placed on certain emotion states may shape the relationship between emotional experiences and subjective well-being.