This paper aims to introduce multilevel logistic regression analysis in a simple and practical way. First, we introduce the basic principles of logistic regression analysis (conditional probability, logit transformation, odds ratio). Second, we discuss the two fundamental implications of running this kind of analysis with a nested data structure: In multilevel logistic regression, the odds that the outcome variable equals one (rather than zero) may vary from one cluster to another (i.e. the intercept may vary) and the effect of a lower-level variable may also vary from one cluster to another (i.e. the slope may vary). Third and finally, we provide a simplified three-step "turnkey" procedure for multilevel logistic regression modeling:• Preliminary phase: Cluster-or grand-mean centering variables • Step #1: Running an empty model and calculating the intraclass correlation coefficient (ICC) • Step #2: Running a constrained and an augmented intermediate model and performing a likelihood ratio test to determine whether considering the cluster-based variation of the effect of the lowerlevel variable improves the model fit • Step #3 Running a final model and interpreting the odds ratio and confidence intervals to determine whether data support your hypothesisCommand syntax for Stata, R, Mplus, and SPSS are included. These steps will be applied to a study on Justin Bieber, because everybody likes Justin Bieber.1

Objective Scholars disagree on whether income inequality has incentive or disincentive effects. In the present research, we move beyond such debate and focus on the motivational processes that income inequality predicts. First, income inequality makes economic stratification salient; therefore, it should promote perceived competitiveness. Second, competitiveness can be appraised as both a challenge and a threat; therefore, it should promote both approach and avoidance motivation. Method In three studies (N = 2,543), U.S. residents from various ZIP codes reported the extent to which they perceived competitiveness in their town/city (Studies 1–3), as well as their economic achievement goals, achievement motives, and self‐regulatory foci (Studies 2–3). Results Level of local income inequality was found to be a positive predictor—via increased perceived competitiveness—of other‐approach economic goals, need for achievement, and promotion focus, as well as other‐avoidance economic goals, fear of failure (specifically, the shame/embarrassment component), and prevention focus. Furthermore, actual and perceived income inequality were positively correlated. Conclusions The conceptual and empirical work herein is the first to show how the economic environment predicts individuals’ perceptions of competitiveness, influencing personal goals, motives, and orientations. It provides a more nuanced perspective on the implications of income inequality than perspectives currently available.

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 goalreason combinations (i.e., achievement goal complexes). Four studies using this approach (involving nearly 1,800 participants) led to three 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 present research seeks to disentangle the influence of "what" individuals want to achieve (type of goals), "why" they want to achieve (type of reasons), and specific "what" and "why" combinations (type of goal-reason combinations). In four studies, we showed that mastery goals (striving for task mastery), autonomous reasons (striving because it is stimulating and valued), and a specific mastery goal -autonomous reason combination (striving for task mastery because it is stimulating and valued) all made separate positive contributions to beneficial achievement-relevant outcomes (e.g., interest, positive emotion, deep learning).Comparable results were observed for performance goals (striving to outperform others) and a specific performance goal -autonomous reason combination (striving to outperform others because it is stimulating and valuable). The present findings indicate that both type of goals and type of reasons are important for a full understanding of achievement motivation.

Following the status-anxiety hypothesis, the psychological consequences of income inequality should be particularly severe for economically vulnerable individuals. Oddly, however, income inequality is often found to affect vulnerable low-income and advantaged high-income groups equally. We argue that economic vulnerability is better captured by a financial-scarcity measure and hypothesize that income inequality primarily impairs the psychological health of people facing scarcity. First, repeated cross-sectional international data (from the World Values Survey: 146,034 participants; 105 country waves) revealed that the within-country effect of national income inequality on feelings of unhappiness was limited to individuals facing scarcity (≈25% of the World Values Survey population). Second, longitudinal national data (Swiss Household Panel: 14,790 participants; 15,595 municipality years) revealed that the within-life-course effect of local income inequality on psychological health problems was also limited to these individuals (< 10% of the Swiss population). Income inequality by itself may not be a problem for psychological health but, rather, may be a catalyst for the consequences of financial scarcity.