Hair sampling for cortisol analysis with mother-toddler dyads living in low-income homes

BackgroundA first step to advance stress science research in young children is understanding the relationship between chronic stress in a mother and chronic stress in her child. One non-invasive measure of chronic stress is hair cortisol. However, little is known about strategies for hair sampling in mother-toddler dyads living in low-income homes in the U.S. To address prior limitations, the purpose of this study was to understand the feasibility of sampling hair for cortisol analysis in mother-toddler dyads living in low-income homes in the U.S. We examined feasibility related to participation, eligibility, and gathering an adequate hair sample weight.MethodsWe approached 142 low-income, racially diverse, urban-dwelling mothers who were participating in an ongoing longitudinal birth cohort study for informed consent to cut approximately 150 hairs from the posterior vertex of their scalp and their toddlers’ (20–24 months) scalp. We demonstrated the process of sampling hair with a hairstyling doll during home visits to the mother and toddler using rounded-end thinning shears.ResultsOverall, 94 of 142 mother-toddler dyads (66 %) participated in hair sampling. The most common reason for participation refusal was related to hairstyle. All but three hair samples were of adequate weight for cortisol extraction.DiscussionThe findings from this study can help researchers address sampling feasibility concerns in hair for cortisol analysis research in mother-toddler dyads living in low-income homes in the U.S.     

A Process Model of Causal Reasoning

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process-level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it.     

Methods for second order meta-analysis and illustrative applications

This paper presents methods for second order meta-analysis along with several illustrative applications. A second order meta-analysis is a meta-analysis of a number of statistically independent and methodologically comparable first order meta-analyses examining ostensibly the same relationship in different contexts. First order meta-analysis greatly reduces sampling error variance but does not eliminate it. The residual sampling error is called second order sampling error. The purpose of a second order meta-analysis is to estimate the proportion of the variance in mean meta-analytic effect sizes across multiple first order meta-analyses attributable to second order sampling error and to use this information to improve accuracy of estimation for each first order meta-analytic estimate. We present equations and methods based on the random effects model for second order meta-analysis for three situations and three empirical applications of second order meta-analysis to illustrate the potential value of these methods to the pursuit of cumulative knowledge.     

Leaping to Conclusions: Why Premise Relevance Affects Argument Strength

Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non‐monotonicity, in which adding premises to a category‐based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category‐based induction taking premise sampling assumptions and category similarity into account complements such theories and yields two important predictions: First, that sensitivity to premise relationships can be violated by inducing a weak sampling assumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manipulates people's assumptions in this regard, showing that people draw qualitatively different conclusions in each case.     

Dysfunctional implications of narrow window theory: variability in the intuitive assessment of correlation

Narrow Window theory, suggested by Y. Kareev ten years ago, has so far focused on one central implication of the limited capacity of working memory on intuitive correlation estimation, namely, overestimation of the distal population correlation. This paper points to additional and perhaps more dramatic implications due to the large dispersion of intuitive estimates: (a) large estimation errors, possibly causing overestimation of negligible rhos, misses of strong rhos, and distorted hierarchies of the rhos between different pairs of variables; and (b) large interpersonal differences in the estimation of any given rho and highly incongruent hierarchies of estimated correlations between different pairs of variables. These implications impede both individuals' adaptation to the empirical world and communication among themselves.     

Changing the logic of replication: A case from infant studies

Among infant researchers there is growing concern regarding the widespread practice of undertaking studies that have small sample sizes and employ tests with low statistical power (to detect a wide range of possible effects). For many researchers, issues of confidence may be partially resolved by relying on replications. Here, we bring further evidence that the classical logic of confirmation, according to which the result of a replication study confirms the original finding when it reaches statistical significance, could be usefully abandoned. With real examples taken from the infant literature and Monte Carlo simulations, we show that a very wide range of possible replication results would in a formal statistical sense constitute confirmation as they can be explained simply due to sampling error. Thus, often no useful conclusion can be derived from a single or small number of replication studies. We suggest that, in order to accumulate and generate new knowledge, the dichotomous view of replication as confirmatory/disconfirmatory can be replaced by an approach that emphasizes the estimation of effect sizes via meta-analysis. Moreover, we discuss possible solutions for reducing problems affecting the validity of conclusions drawn from meta-analyses in infant research.     

Decision by sampling

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute's subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision's context and also the background, real-world distribution of attribute values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities.     

Information repetition in evaluative judgments: Easy to monitor,hard to control

The sampling approach [Fiedler, K. (2000a). Beware of samples! A cognitive-ecological sampling approach to judgment biases. Psychological Review, 107(4), 659–676.] attributes judgment biases to the information given in a sample. Because people usually do not monitor the constraints of samples and do not control their judgments accordingly, systematic judgment biases occur. Three experiments demonstrate this for an obvious sampling constraint, the emptiness of merely repeated information. When evaluating stock market shares, participants did not correct for the repetition of positive or negative information about a share. Although original and repeated information was reliably distinguished in estimates of occurrence (successful monitoring), preferences were misled by mere repetition of success and failure reports (unsuccessful control). This effect could even override a share’s actual success rate. Explicit instructions to ignore repetitions provided no remedy; however, a cognitive load manipulation reduced repetition’s undue influence. Possible reasons for and benefits of this lack of direct metacognitive control are discussed.     

The measure of a nation: The USDA and the rise of survey methodology

Survey research has played a major role in American social science. An outgrowth of efforts by the United States Department of Agriculture in the 1930s, the Division of Program Surveys (DPS) played an important role in the development of survey methodology. The DPS was headed by the ambitious and entrepreneurial Rensis Likert, populated by young and talented social scientists getting their first practical experience, and fed by the needs of the US government fighting World War II. The DPS innovations included open-ended interviewing and area probability sampling methodology as illustrated in the War Bond studies and the Master Sample of Agriculture. This paper examines the creation of the DPS, its work, and its legacy.