Andrew T. Jebb

Time series analysis for psychological research: examining and forecasting change

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials.

A Bayesian Primer for the Organizational Sciences The “Two Sources” and an Introduction to BugsXLA

When first learning Bayesian statistics, the organizational scholar may be confronted by a number of conceptual and practical challenges. The present article seeks to minimize these by first explicating how the Bayesian process can be understood simply as the combination of two complementary sources of information: prior beliefs and data. In turn, we describe how each source is derived from Bayes’s theorem and mathematically formalized, essential knowledge for the Bayesian analyst. However, the beginner can also be undermined by practical difficulties such as software implementation. To this end, we offer a walkthrough of how a Bayesian logistic regression model is coded within BugsXLA, a user-friendly Excel add-in for Bayesian estimation. The data for this example come from a previously published study that identified a subpopulation of “job hobos,” individuals characterized by their frequent voluntary turnover and positive attitudes toward quitting. In the original frequentist analysis, exploring the predictors of hoboism proved to be inefficient and inconclusive. We contrast this standard approach with Bayesian estimation, whose results provide rich and novel insights on the topic.

Introduction to Time Series Analysis for Organizational Research Methods for Longitudinal Analyses

Organizational science has increasingly recognized the need for integrating time into its theories. In parallel, innovations in longitudinal designs and analyses have allowed these theories to be tested. To promote these important advances, the current article introduces time series analysis for organizational research, a set of techniques that has proved essential in many disciplines for understanding dynamic change over time. We begin by describing the various characteristics and components of time series data. Second, we explicate how time series decomposition methods can be used to identify and partition these time series components. Third, we discuss periodogram and spectral analysis for analyzing cycles. Fourth, we discuss the issue of autocorrelation and how different structures of dependency can be identified using graphics and then modeled as autoregressive moving-average (ARMA) processes. Finally, we conclude by describing more time series patterns, the issue of data aggregation, and more sophisticated techniques that were not able to be given proper coverage. Illustrative examples based on topics relevant to organizational research are provided throughout, and a software tutorial in R for these analyses accompanies each section.

Happiness, income satiation and turning points around the world

Income is known to be associated with happiness1, but debates persist about the exact nature of this relationship2,3. Does happiness rise indefinitely with income, or is there a point at which higher incomes no longer lead to greater well-being? We examine this question using data from the Gallup World Poll, a representative sample of over 1.7 million individuals worldwide. Controlling for demographic factors, we use spline regression models to statistically identify points of ‘income satiation’. Globally, we find that satiation occurs at

A Closer Look at the Personality-Turnover Relationship Criterion Expansion, Dark Traits, and Time

95,000 for life evaluation and

Putting the “Person” in the Center: Review and Synthesis of Person-Centered Approaches and Methods in Organizational Science

60,000 to

Internet Searches for Affect-Related Terms: An Indicator of Subjective Well-Being and Predictor of Health Outcomes across US States and Metro Areas

75,000 for emotional well-being. However, there is substantial variation across world regions, with satiation occurring later in wealthier regions. We also find that in certain parts of the world, incomes beyond satiation are associated with lower life evaluations. These findings on income and happiness have practical and theoretical significance at the individual, institutional and national levels. They point to a degree of happiness adaptation4,5 and that money influences happiness through the fulfilment of both needs and increasing material desires6.Jebb et al. use data from the Gallup World Poll to show that happiness does not rise indefinitely with income: globally, income satiation occurs at US

Video capture of human behaviors: toward a Big Data approach

95,000 for life evaluation and US

The Need for Cognition: Key Concepts, Assessment, and Role in Educational Outcomes

60,000 to US

Establishing Construct Continua in Construct Validation: The Process of Continuum Specification:

75,000 for emotional well-being.