Although there is extensive research on temperament and its relation to psychopathology, most studies to date have used a dimensional approach to temperament, assessing one or two temperament traits at a time, which does not account for the interrelatedness between temperament dimensions. Recent studies have identified statistical methods that can capture these heterogenous relations that take a more typological approach. The current study used data from a racially/ethnically and socioeconomically diverse sample of twins to investigate whether using these different analytic methods together to capture temperament contribute to the conceptualization of phenotypic and genetic temperamental risk for later cross-reporter factors of anxiety, depression, externalizing, and ADHD. In breaking psychopathology down into its respective components, as measured by multiple reporters, I found that temperamental genetic risk for psychopathology differed depending on the psychopathology factors assessed. In using both dimensional and typological approaches to temperament, I found that high negative emotionality may only act as a risk factor for anxiety and ADHD when children also have low effortful control and high impulsivity. Results also showed an inconsistent role of genetics and the environment that explain the relation between temperament and psychopathology depending on the approach used. Examining typological and dimensional approaches together supported well-known findings and challenged others, highlighting importance of taking multiple approaches in investigating the phenotypic relation between temperament and psychopathology, and its etiology.

This paper examines the effect of a weekly student self-assessment assignment on student performance in an undergraduate math course. Self-assessment is an increasingly popular type of formative assessment with close ties to self-regulated learning theory. In this randomized controlled trial, 88 students enrolled in MAT 142 were divided into four treatment groups, receiving the self-assessment assignment for either half the semester, the full semester, or not at all. There was no main effect of the treatment on students’ course performance (F(3,80) = 0.154, p = 0.999). However, students’ level of compliance with the assignments (F(1, 63) = 6.87, p = 0.011) and class attendance (F(1, 83) = 12.34, p < 0.001) both significantly predicted exam scores, suggesting that conscientiousness predicts performance. I conducted focus groups to understand how students felt toward the self-assessments. Participants expressed distaste toward the assignments and provided suggestions for improvements. I describe these improvements, among others, in an effort to outline future directions for this research. I also describe a new model of student self-assessment based on theories of adaptive testing and self-regulated learning.

Humans are highly interdependent, living and working in close proximity with many others. From an affordance management perspective, the goal of social perception is to assess and manage potential opportunities and threats afforded by these close others. Social perceivers are thus often motivated to assess particular affordance-relevant characteristics in a target. Frequently, perceivers assess these characteristics via passive observation. Sometimes, however, making such an assessment via observation can be difficult. In these cases, perceivers may instead “affordance test”: actively manipulate the target’s circumstances to reveal (or notably not reveal) cues to the characteristic of interest. There are multiple factors hypothesized to affect whether a perceiver is more likely to passively observe or affordance test that characteristic, including factors related to the characteristic of interest, the situation, the perceiver, and the target. Here, four core hypotheses of this affordance testing framework are tested. In a Preliminary Study (analyzed N = 1301), Study 1 (analyzed N = 559), and Study 2 (analyzed N = 572), highly consistent correlational and experimental evidence was found in support of Hypothesis 1, that the less observable a characteristic is believed to be, the more likely a perceiver is to assess it via affordance testing. In the Preliminary Study, evidence supported Hypothesis 2, that the more important a characteristic is believed to be, the more likely it is to be affordance tested. In Studies 1 and 2, mixed evidence supported Hypothesis 3, that the more urgency or time pressure a perceiver feels, the more likely they are to assess the characteristic of interest via affordance testing. And in Studies 1 and 2, evidence did not support Hypothesis 4, that believed observability and felt urgency interact, such that even characteristics of moderate believed observability are highly likely to be affordance tested under higher felt urgency. Implications of these findings for the affordance testing framework, limitations of the studies, and potential future directions are discussed. In sum, the present work provides promising initial progress in understanding foundational factors that affect when perceivers are likely to affordance test—an important, yet previously understudied, component of the social information-seeking process.

Perception of the future self (i.e., future self-identification) is an important indicator of outcomes over time and during different life-stages (e.g., adolescence, emerging adulthood, retirement). Although recent research established that future self-identification is comprised of three distinct but interrelated factors (i.e., relatedness, positivity, and vividness of the future self), the current research was the first to consider the stability of that factor structure (i.e., factorial invariance) over extended time and over the course of a major life-stage transition. Using a longitudinal design, this research investigated (1) longitudinal factorial invariance as young adults transitioned into, and became established in, their college education and (2) explored differences in factor stability across demographic groups (i.e., sex; college generation status). Results indicated that as students progressed through their first three semesters of college, future self-identification had a stable factor structure over the short-term. However, from the first week of college to when students were established in college, strong factorial invariance (i.e., invariance of the item intercepts) did not hold. In general, there were not differences in future self-identification factor structure by sex. However, from the first year of college to the second year, strict invariance was not supported (i.e., the item residual variances were not invariant between men and women). This sex difference appeared during the first stage of the transition into college and diminished as students became established in their college career. Finally, complete factorial invariance was established between first-generation and continuing-generation college students suggesting that the future self-identification factor structure did not differ based on college generation status. Findings provide crucial information regarding the validity of mean comparisons of future self-identification across a transition into a life-stage and across demographic groups. Future research may build on this foundation to better understand the sources of factorial non-invariance.

An individual’s reputation can be beneficial or detrimental to their exchanges with others,
and these exchanges may be critical for achieving evolutionary goals, such as reproduction.
Depending on their reputation, an individual may or may not gain access to resources in order to
achieve their evolutionary goals. Reputation is typically described as being “positive” and
“negative,” but the current study aimed to identify potential nuances to reputations beyond the
traditional dichotomy. It was hypothesized that different types of reputations (such as “friendly”,
“dishonest”, and “aggressive”) would group together in categories beyond “positive” and
“negative.” Additionally, individuals with different life history strategies might find different
reputations important, because the reputations they find most important may help them get the
kinds of resources they need to attain their specific evolutionary goals. Therefore, it was also
predicted that the importance individuals place on different types of reputations would vary as a
function of life history strategy. Exploratory factor analysis identified a five factor structure for
reputations. Individuals also placed varying levels of importance on different types of
reputations, and found some reputations more important than others depending on their life
history strategy. This study demonstrates that reputational information is more nuanced than
previously thought and future research should consider that there may be more than just
“positive” and “negative” reputations in social interactions.

Background. Hundreds of studies have linked maternal depression to negative child outcomes. However, these studies have been correlational, so they cannot rule out alternative explanations such as that child characteristics evoke maternal depression or that confounding variables are causes of both phenomena. Design. I applied a propensity score approach to data from the Early Steps Multisite Trial, a sample of 731 low-income families tracked approximately annually from ages 2 through 16. Families were equated on propensity scores based on a large set of baseline characteristics, producing two groups that were similar across all measured characteristics except for the presence of clinically significant symptoms of maternal depression during toddlerhood. Children’s longitudinal behavioral outcomes from parent-, teacher-, and self-report measures were compared across the equated groups in order to estimate the causal effects of maternal depression. Results. Both matching and weighting were successful in equating families with depressed and non-depressed mothers on a set of 89 potential confounding variables measured at child age 2. Prior to any adjustment for confounding, the effect of maternal depression was statistically significant for 41 of 48 mother-, secondary-caregiver-, and teacher-reported outcomes. Effect sizes were consistent with the larger literature and in the small to medium range. After matching or weighting to equate families with depressed versus non-depressed mothers, the effects of maternal depression at age 2 was statistically significant for 6 of 48 mother-, secondary-caregiver-, and teacher-reported outcomes. Adjusted effect sizes were in the very small to small range. Conclusions. Findings are consistent with the claim that there is a very small causal effect of exposure to maternal depression at child age 2 on child externalizing and internalizing behavior in early childhood, middle childhood, and adolescence. While awaiting replication, results suggest (a) that treatment of maternal depression should not be expected to substantially reduce child externalizing and internalizing behavior problems; (b) that very large sample sizes are needed to adequately investigate causal developmental processes that link maternal depression to child behavior; and (c) that causal inference methods can be an important addition to the toolbox of developmental psychopathologists.

With improvements in technology, intensive longitudinal studies that permit the investigation of daily and weekly cycles in behavior have increased exponentially over the past few decades. Traditionally, when data have been collected on two variables over time, multivariate time series approaches that remove trends, cycles, and serial dependency have been used. These analyses permit the study of the relationship between random shocks (perturbations) in the presumed causal series and changes in the outcome series, but do not permit the study of the relationships between cycles. Liu and West (2016) proposed a multilevel approach that permitted the study of potential between subject relationships between features of the cycles in two series (e.g., amplitude). However, I show that the application of the Liu and West approach is restricted to a small set of features and types of relationships between the series. Several authors (e.g., Boker & Graham, 1998) proposed a connected mass-spring model that appears to permit modeling of more general cyclic relationships. I showed that the undamped connected mass-spring model is also limited and may be unidentified. To test the severity of the restrictions of the motion trajectories producible by the undamped connected mass-spring model I mathematically derived their connection to the force equations of the undamped connected mass-spring system. The mathematical solution describes the domain of the trajectory pairs that are producible by the undamped connected mass-spring model. The set of producible trajectory pairs is highly restricted, and this restriction sets major limitations on the application of the connected mass-spring model to psychological data. I used a simulation to demonstrate that even if a pair of psychological time-varying variables behaved exactly like two masses in an undamped connected mass-spring system, the connected mass-spring model would not yield adequate parameter estimates. My simulation probed the performance of the connected mass-spring model as a function of several aspects of data quality including number of subjects, series length, sampling rate relative to the cycle, and measurement error in the data. The findings can be extended to damped and nonlinear connected mass-spring systems.

Mediation analysis is used to investigate how an independent variable, X, is related to an outcome variable, Y, through a mediator variable, M (MacKinnon, 2008). If X represents a randomized intervention it is difficult to make a cause and effect inference regarding indirect effects without making no unmeasured confounding assumptions using the potential outcomes framework (Holland, 1988; MacKinnon, 2008; Robins & Greenland, 1992; VanderWeele, 2015), using longitudinal data to determine the temporal order of M and Y (MacKinnon, 2008), or both. The goals of this dissertation were to (1) define all indirect and direct effects in a three-wave longitudinal mediation model using the causal mediation formula (Pearl, 2012), (2) analytically compare traditional estimators (ANCOVA, difference score, and residualized change score) to the potential outcomes-defined indirect effects, and (3) use a Monte Carlo simulation to compare the performance of regression and potential outcomes-based methods for estimating longitudinal indirect effects and apply the methods to an empirical dataset. The results of the causal mediation formula revealed the potential outcomes definitions of indirect effects are equivalent to the product of coefficient estimators in a three-wave longitudinal mediation model with linear and additive relations. It was demonstrated with analytical comparisons that the ANCOVA, difference score, and residualized change score models’ estimates of two time-specific indirect effects differ as a function of the respective mediator-outcome relations at each time point. The traditional model that performed the best in terms of the evaluation criteria in the Monte Carlo study was the ANCOVA model and the potential outcomes model that performed the best in terms of the evaluation criteria was sequential G-estimation. Implications and future directions are discussed.

Understanding how adherence affects outcomes is crucial when developing and assigning interventions. However, interventions are often evaluated by conducting randomized experiments and estimating intent-to-treat effects, which ignore actual treatment received. Dose-response effects can supplement intent-to-treat effects when participants are offered the full dose but many only receive a partial dose due to nonadherence. Using these data, we can estimate the magnitude of the treatment effect at different levels of adherence, which serve as a proxy for different levels of treatment. In this dissertation, I conducted Monte Carlo simulations to evaluate when linear dose-response effects can be accurately and precisely estimated in randomized experiments comparing a no-treatment control condition to a treatment condition with partial adherence. Specifically, I evaluated the performance of confounder adjustment and instrumental variable methods when their assumptions were met (Study 1) and when their assumptions were violated (Study 2). In Study 1, the confounder adjustment and instrumental variable methods provided unbiased estimates of the dose-response effect across sample sizes (200, 500, 2,000) and adherence distributions (uniform, right skewed, left skewed). The adherence distribution affected power for the instrumental variable method. In Study 2, the confounder adjustment method provided unbiased or minimally biased estimates of the dose-response effect under no or weak (but not moderate or strong) unobserved confounding. The instrumental variable method provided extremely biased estimates of the dose-response effect under violations of the exclusion restriction (no direct effect of treatment assignment on the outcome), though less severe violations of the exclusion restriction should be investigated.

Time-to-event analysis or equivalently, survival analysis deals with two variables simultaneously: when (time information) an event occurs and whether an event occurrence is observed or not during the observation period (censoring information). In behavioral and social sciences, the event of interest usually does not lead to a terminal state such as death. Other outcomes after the event can be collected and thus, the survival variable can be considered as a predictor as well as an outcome in a study. One example of a case where the survival variable serves as a predictor as well as an outcome is a survival-mediator model. In a single survival-mediator model an independent variable, X predicts a survival variable, M which in turn, predicts a continuous outcome, Y. The survival-mediator model consists of two regression equations: X predicting M (M-regression), and M and X simultaneously predicting Y (Y-regression). To estimate the regression coefficients of the survival-mediator model, Cox regression is used for the M-regression. Ordinary least squares regression is used for the Y-regression using complete case analysis assuming censored data in M are missing completely at random so that the Y-regression is unbiased. In this dissertation research, different measures for the indirect effect were proposed and a simulation study was conducted to compare performance of different indirect effect test methods. Bias-corrected bootstrapping produced high Type I error rates as well as low parameter coverage rates in some conditions. In contrast, the Sobel test produced low Type I error rates as well as high parameter coverage rates in some conditions. The bootstrap of the natural indirect effect produced low Type I error and low statistical power when the censoring proportion was non-zero. Percentile bootstrapping, distribution of the product and the joint-significance test showed best performance. Statistical analysis of the survival-mediator model is discussed. Two indirect effect measures, the ab-product and the natural indirect effect are compared and discussed. Limitations and future directions of the simulation study are discussed. Last, interpretation of the survival-mediator model for a made-up empirical data set is provided to clarify the meaning of the quantities in the survival-mediator model.