Researchers frequently disagree about the latent structure of emotions. Taxometric analysis--a method for determining whether the latent structure of a construct is best defined as categorical or purely dimensional--can be a useful tool for resolving these debates. The present study used taxometric analysis to investigate the latent structure of envy. Scholars disagree about whether envy is necessarily malicious or whether it can also be benign. Van de Ven, Zeelenberg, and Pieters (2009) claim that benign envy exists, and that it is distinct from malicious envy. Much of their evidence for this claim relies on latent class analysis, which can be biased toward creating categories with data that actually vary dimensionally (Cleland, Rothschild, & Haslam, 2000; Uebersax, 1999). Therefore, taxometric analysis provides a more conservative test for an underlying categorical structure. A daily diary procedure was used to measure participants' day-to-day experiences of envy. The results support van de Ven et al.'s claim that benign envy exists, and that is distinct from malicious envy.

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Given limitations in the integrative scope of past research, basic questions about the organization and development of preschoolers' living kinds concept remain open to debate. This study was designed to address past limitations through use of a longitudinal design, extensive stimulus set, and alternate indices of understanding. Thirty-five English-speaking 3-year-olds from middle-class families in Albuquerque, NM participated in four testing sessions over 1 year. Indices of understanding included statements that preschoolers generated about various living and nonliving objects, biological properties they attributed to the objects, and their characterization of objects as "alive" or not. Results reveal a multifaceted picture of developmental change in preschoolers' living kinds concept involving both the construction and elaboration of a core biological understanding.

Abstract:Fluctuating asymmetry is hypothesized to predict developmental instability (DI) and fitness outcomes. While published studies largely support this prediction, publication bias remains an issue. Biologists have increasingly turned to meta-analysis to estimate true support for an effect. Van Dongen and Gangestad (VD&G) performed a meta-analysis on studies of fluctuating asymmetry (FA) and fitness-related qualities in humans. They found an average robust effect size, but estimates varied widely. Recently, psychologists have identified limitations in traditional meta-analyses and popular companion adjustments, and have advocated for alternative meta-analytic techniques. P-curve estimates true mean effects using significant published effects; it also detects the presence of p-hacking (where researchers exploit researcher "degrees of freedom"), not just publication bias. Alternative selection methods also provide a means to estimate average effect size correcting for publication bias, but may better account for heterogeneity in effect sizes and publication decisions than p-curve. We provide a demonstration by performing p-curve and selection method analyses on the set of effects from VD&G. We estimate an overall effect size range (r = 0.08-0.15) comparable to VD&G, but with notable differences between domains and techniques. Results from alternative estimation methods can provide corroborating evidence for, as well as insights beyond, traditional meta-analytic estimates.

It is unclear how mating strategies are distributed in humans; do they vary along a continuum from promiscuous (multiple short-term relationships) to monogamous (long-term pair-bonded relationship) or do these represent alternate phenotypes? If the latter, how are these phenotypes distributed? Wlodarski et al.[1] addressed these questions empirically, performing mixture models on sociosexuality and second-to-fourth digit ratio (2D : 4D), from which they inferred that both variables reflect two underlying normal distributions, rather than a single underlying normal distribution. Given this apparent evidence of two phenotypes for both sexes, they then estimated their distributional parameters. Questions arise, however. Was their normality assumption warranted? Measures of sociosexuality are generally skewed, so assuming normality biases against selection of a single-component model. Further, are sociosexuality and 2D : 4D associated as they should be if both are indicators of mating strategy phenotype? In fact, the literature offers scant evidence of such a relationship in humans [2]. Using a different modelling technique, and a sample with measures of both sociosexuality and 2D : 4D, I find no evidence that humans exhibit two sociosexuality phenotypes, or that sociosexuality and 2D : 4D are related.The present sample (353 men, 693 women; approx. 50% white non-Hispanic, 32% Hispanic, 18% other or mixed) was drawn from five previous studies [3][4][5][6][7]. Participants completed the original sociosexual orientation inventory (SOI) [8], not the revised version [9] used by Wlodarski et al. Four items are the same across both inventories (after responses are re-scaled), and two more items are similar. Wlodarski et al. used only the attitudes and desires items. The present data include one desire, and three attitude items that are comparable across both inventories. Digit ratio was measured on a portion of the sample.Taxometric analysis (TA) is a method for deciding between categorical and continuous models of psychological constructs. TA makes no distributional assumptions. Mixture models on skewed indicators tend to overestimate the number of latent categories [10]. TA outperforms mixture modelling when the underlying indicator distributions are non-normal [11].Instead of significance testing, TA uses consistency testing-multiple procedures converging on the same result. Two non-redundant taxometric procedures, maximum covariance (MAXCOV) and mean above minus below a cut (MAMBAC), were performed on the four attitude and desire items (for explanations of these procedures, see [12] and references therein).Cases with missing data were excluded. Analyses were conducted using Ruscio's taxometric programs for R. Indicators were standardized, and all indicators served in all roles. Tied cases were randomly re-sorted for 20 replications. The MAXCOVs used 25 windows with 90% overlap. The MAMBACs used 50 cuts with 25 cases maintained at each end.The comparison curve fit index (CCFI)-a relative fit statistic-was calculated for...