Matching Items (20)

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Examining reputation from a life history perspective

Description

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’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.

Contributors

Agent

Created

Date Created
  • 2020-12

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Impact of violations of longitudinal measurement invariance in latent growth models and autoregressive quasi-simplex models

Description

In order to analyze data from an instrument administered at multiple time points it is a common practice to form composites of the items at each wave and to fit

In order to analyze data from an instrument administered at multiple time points it is a common practice to form composites of the items at each wave and to fit a longitudinal model to the composites. The advantage of using composites of items is that smaller sample sizes are required in contrast to second order models that include the measurement and the structural relationships among the variables. However, the use of composites assumes that longitudinal measurement invariance holds; that is, it is assumed that that the relationships among the items and the latent variables remain constant over time. Previous studies conducted on latent growth models (LGM) have shown that when longitudinal metric invariance is violated, the parameter estimates are biased and that mistaken conclusions about growth can be made. The purpose of the current study was to examine the impact of non-invariant loadings and non-invariant intercepts on two longitudinal models: the LGM and the autoregressive quasi-simplex model (AR quasi-simplex). A second purpose was to determine if there are conditions in which researchers can reach adequate conclusions about stability and growth even in the presence of violations of invariance. A Monte Carlo simulation study was conducted to achieve the purposes. The method consisted of generating items under a linear curve of factors model (COFM) or under the AR quasi-simplex. Composites of the items were formed at each time point and analyzed with a linear LGM or an AR quasi-simplex model. The results showed that AR quasi-simplex model yielded biased path coefficients only in the conditions with large violations of invariance. The fit of the AR quasi-simplex was not affected by violations of invariance. In general, the growth parameter estimates of the LGM were biased under violations of invariance. Further, in the presence of non-invariant loadings the rejection rates of the hypothesis of linear growth increased as the proportion of non-invariant items and as the magnitude of violations of invariance increased. A discussion of the results and limitations of the study are provided as well as general recommendations.

Contributors

Agent

Created

Date Created
  • 2013

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Modeling relationships between cycles in psychology: potential limitations of sinusoidal and mass-spring models

Description

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

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.

Contributors

Agent

Created

Date Created
  • 2019

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Estimating the Causal Effect of Maternal Depression During Early Childhood on Child Externalizing and Internalizing Problems

Description

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

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.

Contributors

Agent

Created

Date Created
  • 2020

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Interaction effects in multilevel models

Description

Researchers are often interested in estimating interactions in multilevel models, but many researchers assume that the same procedures and interpretations for interactions in single-level models apply to multilevel models.

Researchers are often interested in estimating interactions in multilevel models, but many researchers assume that the same procedures and interpretations for interactions in single-level models apply to multilevel models. However, estimating interactions in multilevel models is much more complex than in single-level models. Because uncentered (RAS) or grand mean centered (CGM) level-1 predictors in two-level models contain two sources of variability (i.e., within-cluster variability and between-cluster variability), interactions involving RAS or CGM level-1 predictors also contain more than one source of variability. In this Master’s thesis, I use simulations to demonstrate that ignoring the four sources of variability in a total level-1 interaction effect can lead to erroneous conclusions. I explain how to parse a total level-1 interaction effect into four specific interaction effects, derive equivalencies between CGM and centering within context (CWC) for this model, and describe how the interpretations of the fixed effects change under CGM and CWC. Finally, I provide an empirical example using diary data collected from working adults with chronic pain.

Contributors

Agent

Created

Date Created
  • 2015

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Obtaining accurate estimates of the mediated effect with and without prior information

Description

Research methods based on the frequentist philosophy use prior information in a priori power calculations and when determining the necessary sample size for the detection of an effect, but not

Research methods based on the frequentist philosophy use prior information in a priori power calculations and when determining the necessary sample size for the detection of an effect, but not in statistical analyses. Bayesian methods incorporate prior knowledge into the statistical analysis in the form of a prior distribution. When prior information about a relationship is available, the estimates obtained could differ drastically depending on the choice of Bayesian or frequentist method. Study 1 in this project compared the performance of five methods for obtaining interval estimates of the mediated effect in terms of coverage, Type I error rate, empirical power, interval imbalance, and interval width at N = 20, 40, 60, 100 and 500. In Study 1, Bayesian methods with informative prior distributions performed almost identically to Bayesian methods with diffuse prior distributions, and had more power than normal theory confidence limits, lower Type I error rates than the percentile bootstrap, and coverage, interval width, and imbalance comparable to normal theory, percentile bootstrap, and the bias-corrected bootstrap confidence limits. Study 2 evaluated if a Bayesian method with true parameter values as prior information outperforms the other methods. The findings indicate that with true values of parameters as the prior information, Bayesian credibility intervals with informative prior distributions have more power, less imbalance, and narrower intervals than Bayesian credibility intervals with diffuse prior distributions, normal theory, percentile bootstrap, and bias-corrected bootstrap confidence limits. Study 3 examined how much power increases when increasing the precision of the prior distribution by a factor of ten for either the action or the conceptual path in mediation analysis. Power generally increases with increases in precision but there are many sample size and parameter value combinations where precision increases by a factor of 10 do not lead to substantial increases in power.

Contributors

Agent

Created

Date Created
  • 2014

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Stress, depression, and the mother-infant relationship across the first year

Description

Postpartum depression (PPD) is a significant public health concern affecting up to half a million U.S. women annually. Mexican-American women experience substantially higher rates of PPD, and represent an underserved

Postpartum depression (PPD) is a significant public health concern affecting up to half a million U.S. women annually. Mexican-American women experience substantially higher rates of PPD, and represent an underserved population with significant health disparities that put these women and their infants at greater risk for substantial psychological and developmental difficulties. The current study utilized data on perceived stress, depression, maternal parenting behavior, and infant social-emotional and cognitive development from 214 Mexican-American mother-infant dyads. The first analysis approach utilized a latent intercept (LI) model to examine how overall mean levels and within-person deviations of perceived stress, depressive symptoms, and maternal parenting behavior are related across the postpartum period. Results indicated large, positive between- and within-person correlations between perceived stress and depression. Neither perceived stress nor depressive symptoms were found to have significant between- or within-person associations with the parenting variables. The second analysis approach utilized an autoregressive cross-lagged model with tests of mediation to identify underlying mechanisms among perceived stress, postpartum depressive symptoms, and maternal parenting behavior in the prediction of infant social-emotional and cognitive development. Results indicated that increased depressive symptoms at 12- and 18-weeks were associated with subsequent reports of increased perceived stress at 18- and 24-weeks, respectively. Perceived stress at 12-weeks was found to be negatively associated with subsequent non-hostility at 18-weeks, and both sensitivity and non-hostility were found to be associated with infant cognitive development and social-emotional competencies at 12 months of age (52-weeks), but not with social-emotional problems. The results of the mediation analyses showed that non-hostility at 18- and 24-weeks significantly mediated the association between perceived stress at 12-weeks and infant cognitive development and social-emotional competencies at 52-weeks. The findings extend research that sensitive parenting in early childhood is as important to the development of cognitive ability, social behavior, and emotion regulation in ethnic minority cultures as it is in majority culture families; that maternal perceptions of stress may spillover into parenting behavior, resulting in increased hostility and negatively influencing infant cognitive and social-emotional development; and that symptoms of depressed mood may influence the experience of stress.

Contributors

Agent

Created

Date Created
  • 2014

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Statistical properties of the single mediator model with latent variables in the bayesian framework

Description

Statistical mediation analysis has been widely used in the social sciences in order to examine the indirect effects of an independent variable on a dependent variable. The statistical properties of

Statistical mediation analysis has been widely used in the social sciences in order to examine the indirect effects of an independent variable on a dependent variable. The statistical properties of the single mediator model with manifest and latent variables have been studied using simulation studies. However, the single mediator model with latent variables in the Bayesian framework with various accurate and inaccurate priors for structural and measurement model parameters has yet to be evaluated in a statistical simulation. This dissertation outlines the steps in the estimation of a single mediator model with latent variables as a Bayesian structural equation model (SEM). A Monte Carlo study is carried out in order to examine the statistical properties of point and interval summaries for the mediated effect in the Bayesian latent variable single mediator model with prior distributions with varying degrees of accuracy and informativeness. Bayesian methods with diffuse priors have equally good statistical properties as Maximum Likelihood (ML) and the distribution of the product. With accurate informative priors Bayesian methods can increase power up to 25% and decrease interval width up to 24%. With inaccurate informative priors the point summaries of the mediated effect are more biased than ML estimates, and the bias is higher if the inaccuracy occurs in priors for structural parameters than in priors for measurement model parameters. Findings from the Monte Carlo study are generalizable to Bayesian analyses with priors of the same distributional forms that have comparable amounts of (in)accuracy and informativeness to priors evaluated in the Monte Carlo study.

Contributors

Agent

Created

Date Created
  • 2017

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Performance of contextual multilevel models for comparing between-person and within-person effects

Description

The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may

The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been explicated mostly for cross-sectional data, but they can also be applied to longitudinal data where level-1 effects represent within-person relations and level-2 effects represent between-person relations. With longitudinal data, estimating the contextual effect allows direct evaluation of whether between-person and within-person effects differ. Furthermore, these models, unlike single-level models, permit individual differences by allowing within-person slopes to vary across individuals. This study examined the statistical performance of the contextual model with a random slope for longitudinal within-person fluctuation data.

A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.

Contributors

Agent

Created

Date Created
  • 2016

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Comparison of methods for estimating longitudinal indirect effects

Description

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

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.

Contributors

Agent

Created

Date Created
  • 2018