Keep Calm and Learn Multilevel Linear Modeling: A Three-Step Procedure Using SPSS, Stata, R, and Mplus

This piece is meant to help you understand and master two-level linear modeling in an accessible, swift, and fun way (while being based on rigorous and up-to-date research). It is divided into four parts: PART 1 presents the three key principles of two-level linear modeling. PART 2 presents a three-step procedure for conducting two-level linear modeling using SPSS, Stata, R, or Mplus (from centering variables to interpreting the cross-level interactions). PART 3 presents the results from a series of simulations comparing the performances of SPSS, Stata, R, and Mplus. PART 4 gives a Q&A addressing multilevel modeling issues pertaining to statistical power, effect sizes, complex design, and nonlinear two-level regression. The empirical example used in this tutorial is based on genuine data pertaining to ʼ90s and post-ʼ00s boy band member hotness and Instagram popularity. In reading this paper, you will have the opportunity to win a signed picture of Justin Timberlake

Achievement goals, reasons for goal pursuit, and achievement goal complexes as predictors of beneficial outcomes: Is the influence of goals reducible to reasons?

In the present research, we proposed a systematic approach to disentangling the shared and unique variance explained by achievement goals, reasons for goal pursuit, and specific goal-reason combinations (i.e., achievement goal complexes). Four studies using this approach (involving nearly 1,800 participants) led to 3 basic sets of findings. First, when testing goals and reasons separately, mastery (-approach) goals and autonomous reasons explained variance in beneficial experiential (interest, satisfaction, positive emotion) and self-regulated learning (deep learning, help-seeking, challenging tasks, persistence) outcomes. Second, when testing goals and reasons simultaneously, mastery goals and autonomous reasons explained independent variance in most of the outcomes, with the predictive strength of each being diminished. Third, when testing goals, reasons, and goal complexes together, the autonomous mastery goal complex explained incremental variance in most of the outcomes, with the predictive strength of both mastery goals and autonomous reasons being diminished. Comparable results were observed for performance (-approach) goals, the autonomous performance goal complex, and performance goal-relevant outcomes. These findings suggest that achievement goals and reasons are both distinct and overlapping constructs, and that neither unilaterally eliminates the influence of the other. Integrating achievement goals and reasons offers the most promising avenue for a full account of competence motivation

The effects of U.S. county and state income inequality on self-reported happiness and health are equivalent to zero

PURPOSE: A popular idea in the social sciences is that contexts with high income inequality undermine people’s well-being and health. However, existing studies documenting this phenomenon typically compare a small number of higher-level units (countries/regions). Here, we use local income inequality indicators and temporal designs to provide the most highly powered test to date of the associations between income inequality and self-reported happiness and health in the USA METHOD: We combined county-level income inequality data (county-level Gini coefficients) with the responses from the General Social Survey (GSS) Cross-sectional dataset (13,000 + participants from ≈1000 county-waves) and Panels (3 × 3000 + participants from 3 × ≈500 county-waves); we used the GSS happiness (“not too happy,” “pretty happy,” or “very happy”) and health (“poor,” “fair,” “good,” or “excellent”) variables. RESULTS: Multilevel-ordered logistic models and equivalence tests revealed that the within-county effects of income inequality on self-reported happiness and health were systematically equivalent to zero. Additional analyses revealed that the within-state effects were identical, that using alternative measures of state income inequality led to the same conclusions, and that lagged effects (between + 1 and + 12 years) were never significant and always equivalent to zero. CONCLUSION: The present work suggests that—at least in the USA—income inequality is likely neither associated with self-reported happiness nor with self-reported health. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11136-022-03137-8