Matching Items (30)

137333-Thumbnail Image.png

Predicting and Promoting Healthy Diet in College-Aged Women Using the Health Action Process Approach Model

Description

One of the nation's most pressing health related issues is that of healthy diet and proper nutrition. Because much research has shown that many Americans are in poor health or

One of the nation's most pressing health related issues is that of healthy diet and proper nutrition. Because much research has shown that many Americans are in poor health or are at risk to become so due to poor diet and nutrition, understanding the psychological factors of a healthy diet or lack thereof is of the utmost importance. In order to understand the adoption and maintenance of health related behaviors, the link between intentions and behaviors must be evaluated. Of current health behavior models, the model utilized in this study was the Health Action Process Approach model (HAPA), which addressed this "intention-behavior gap." The HAPA model proposes that planning is the key mediator of the link between intentions and behavior. The current research was performed in two stages. The first stage evaluated the psychosocial constructs of the HAPA model, and their predictive utility for current diet and the second stage evaluated a planning-based intervention that aimed to increase proper nutrition in college-aged women. All HAPA constructs were found to be significantly correlated with one another, and planning was found to significantly and fully mediate the link between intention and healthy diet. The intervention did not lead to an increase in healthy diet relative to a standard-of-care control, although all participants across conditions reported increased intention, self-efficacy, and healthy diet from pre-test to follow-up.

Contributors

Agent

Created

Date Created
  • 2013-12

Facial Expressions and Deception in the Court Room

Description

Evidence thus far has not lent credence to facilitate lie detection by the average person. According to studies, there are five major signs of lying: lip pursing, narrowed eyebrows, shoulder

Evidence thus far has not lent credence to facilitate lie detection by the average person. According to studies, there are five major signs of lying: lip pursing, narrowed eyebrows, shoulder shrugs, looking to the left, and smirking. The present study aims to determine whether training people in detecting the five signs of lying will facilitate lie detection in the average person. We analyzed the accuracy of lie detection by examining the verdicts of 155 undergraduate students during simulated police interrogations. Comparisons between the trained and untrained subjects support the hypothesis that the average person is no better than chance at detecting lies through non-verbal cues.

Contributors

Agent

Created

Date Created
  • 2014-12

129409-Thumbnail Image.png

Participation Patterns Among Mexican-American Parents Enrolled in a Universal Intervention and Their Association with Child Externalizing Outcomes

Description

This study used growth mixture modeling to examine attendance trajectories among 292 Mexican–American primary female caregivers enrolled in a universal preventive intervention and the effects of health beliefs, participation intentions,

This study used growth mixture modeling to examine attendance trajectories among 292 Mexican–American primary female caregivers enrolled in a universal preventive intervention and the effects of health beliefs, participation intentions, cultural influences, and intervention group cohesion on trajectory group membership as well as trajectory group differences on a distal outcome, immediate posttest teacher report of child externalizing (T2). Results supported four trajectory groups—early terminators (ET), mid-program terminators (MPT), low-risk persistent attenders (LRPA), and high-risk persistent attenders (HRPA). Compared with LRPAs, caregivers classified as HRPAs had weaker familism values, less parenting efficacy, and higher externalizing children with lower GPAs. Caregivers in the two persistent attender groups reported strong group cohesion and providers rated these caregivers as having strong participation intentions. Children of caregivers in the LRPA group had the lowest T2 child externalizing. Children of caregivers in the MPT group had lower T2 externalizing than did those of the ET group, suggesting partial intervention dosage can benefit families. Despite high levels of attendance, children of caregivers in the HRPA had the highest T2 externalizing, suggesting this high-risk group needed either more intensive services or a longer period for parents to implement program skills to evidence change in child externalizing.

Contributors

Agent

Created

Date Created
  • 2014-12-01

149984-Thumbnail Image.png

Behavioral and subjective participant responsiveness to a manualized preventive intervention

Description

The effects of preventive interventions are found to be related to participants' responsiveness to the program, or the degree to which participants attend sessions, engage in the material, and use

The effects of preventive interventions are found to be related to participants' responsiveness to the program, or the degree to which participants attend sessions, engage in the material, and use the program skills. The current study proposes a multi-dimensional method for measuring responsiveness to the Family Bereavement Program (FBP), a parenting-focused program to prevent mental health problems for children who experienced the death of a parent. It examines the relations between individual-level risk-factors and responsiveness to the program, as well as the relations between responsiveness and program outcomes. The sample consists of 90 caregivers and 135 children assigned to the intervention condition of an efficacy trial of the FBP. Caregivers' responsiveness to the 12-week program was measured using a number of indicators, including attendance, completion of weekly "homework" assignments, overall program skill use, perceived helpfulness of the program and program skills, and perceived group environment. Three underlying dimensions of responsiveness were identified: Skill Use, Program Liking, and Perceived Group Environment. Positive parenting and child externalizing problems at baseline were found to predict caregiver Skill Use. Skill Use and Perceived Group Environment predicted changes in caregiver grief and reports of child behavior problems at posttest and 11-month follow-up. Caregivers with better Skill Use had better positive parenting outcomes. Skill use mediated the relation between baseline positive parenting and improvements in positive parenting at 11-month follow-up.

Contributors

Agent

Created

Date Created
  • 2012

149687-Thumbnail Image.png

Modern psychometric theory in clinical assessment

Description

Item response theory (IRT) and related latent variable models represent modern psychometric theory, the successor to classical test theory in psychological assessment. While IRT has become prevalent in the assessment

Item response theory (IRT) and related latent variable models represent modern psychometric theory, the successor to classical test theory in psychological assessment. While IRT has become prevalent in the assessment of ability and achievement, it has not been widely embraced by clinical psychologists. This appears due, in part, to psychometrists' use of unidimensional models despite evidence that psychiatric disorders are inherently multidimensional. The construct validity of unidimensional and multidimensional latent variable models was compared to evaluate the utility of modern psychometric theory in clinical assessment. Archival data consisting of 688 outpatients' presenting concerns, psychiatric diagnoses, and item level responses to the Brief Symptom Inventory (BSI) were extracted from files at a university mental health clinic. Confirmatory factor analyses revealed that models with oblique factors and/or item cross-loadings better represented the internal structure of the BSI in comparison to a strictly unidimensional model. The models were generally equivalent in their ability to account for variance in criterion-related validity variables; however, bifactor models demonstrated superior validity in differentiating between mood and anxiety disorder diagnoses. Multidimensional IRT analyses showed that the orthogonal bifactor model partitioned distinct, clinically relevant sources of item variance. Similar results were also achieved through multivariate prediction with an oblique simple structure model. Receiver operating characteristic curves confirmed improved sensitivity and specificity through multidimensional models of psychopathology. Clinical researchers are encouraged to consider these and other comprehensive models of psychological distress.

Contributors

Agent

Created

Date Created
  • 2011

149935-Thumbnail Image.png

Assessing dimensionality in complex data structures: a performance comparison of DETECT and NOHARM procedures

Description

The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in compensatory and noncompensatory multidimensional item response models (MIRT) of assessment data using dimensionality

The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in compensatory and noncompensatory multidimensional item response models (MIRT) of assessment data using dimensionality assessment procedures based on conditional covariances (i.e., DETECT) and a factor analytical approach (i.e., NOHARM). The DETECT-based methods typically outperformed the NOHARM-based methods in both two- (2D) and three-dimensional (3D) compensatory MIRT conditions. The DETECT-based methods yielded high proportion correct, especially when correlations were .60 or smaller, data exhibited 30% or less complexity, and larger sample size. As the complexity increased and the sample size decreased, the performance typically diminished. As the complexity increased, it also became more difficult to label the resulting sets of items from DETECT in terms of the dimensions. DETECT was consistent in classification of simple items, but less consistent in classification of complex items. Out of the three NOHARM-based methods, χ2G/D and ALR generally outperformed RMSR. χ2G/D was more accurate when N = 500 and complexity levels were 30% or lower. As the number of items increased, ALR performance improved at correlation of .60 and 30% or less complexity. When the data followed a noncompensatory MIRT model, the NOHARM-based methods, specifically χ2G/D and ALR, were the most accurate of all five methods. The marginal proportions for labeling sets of items as dimension-like were typically low, suggesting that the methods generally failed to label two (three) sets of items as dimension-like in 2D (3D) noncompensatory situations. The DETECT-based methods were more consistent in classifying simple items across complexity levels, sample sizes, and correlations. However, as complexity and correlation levels increased the classification rates for all methods decreased. In most conditions, the DETECT-based methods classified complex items equally or more consistent than the NOHARM-based methods. In particular, as complexity, the number of items, and the true dimensionality increased, the DETECT-based methods were notably more consistent than any NOHARM-based method. Despite DETECT's consistency, when data follow a noncompensatory MIRT model, the NOHARM-based method should be preferred over the DETECT-based methods to assess dimensionality due to poor performance of DETECT in identifying the true dimensionality.

Contributors

Agent

Created

Date Created
  • 2011

150765-Thumbnail Image.png

Testing the limits of latent class analysis

Description

The purpose of this study was to examine under which conditions "good" data characteristics can compensate for "poor" characteristics in Latent Class Analysis (LCA), as well as to set forth

The purpose of this study was to examine under which conditions "good" data characteristics can compensate for "poor" characteristics in Latent Class Analysis (LCA), as well as to set forth guidelines regarding the minimum sample size and ideal number and quality of indicators. In particular, we studied to which extent including a larger number of high quality indicators can compensate for a small sample size in LCA. The results suggest that in general, larger sample size, more indicators, higher quality of indicators, and a larger covariate effect correspond to more converged and proper replications, as well as fewer boundary estimates and less parameter bias. Based on the results, it is not recommended to use LCA with sample sizes lower than N = 100, and to use many high quality indicators and at least one strong covariate when using sample sizes less than N = 500.

Contributors

Agent

Created

Date Created
  • 2012

152217-Thumbnail Image.png

Estimating causal direct and indirect effects in the presence of post-treatment confounders: a simulation study

Description

In investigating mediating processes, researchers usually use randomized experiments and linear regression or structural equation modeling to determine if the treatment affects the hypothesized mediator and if the mediator affects

In investigating mediating processes, researchers usually use randomized experiments and linear regression or structural equation modeling to determine if the treatment affects the hypothesized mediator and if the mediator affects the targeted outcome. However, randomizing the treatment will not yield accurate causal path estimates unless certain assumptions are satisfied. Since randomization of the mediator may not be plausible for most studies (i.e., the mediator status is not randomly assigned, but self-selected by participants), both the direct and indirect effects may be biased by confounding variables. The purpose of this dissertation is (1) to investigate the extent to which traditional mediation methods are affected by confounding variables and (2) to assess the statistical performance of several modern methods to address confounding variable effects in mediation analysis. This dissertation first reviewed the theoretical foundations of causal inference in statistical mediation analysis, modern statistical analysis for causal inference, and then described different methods to estimate causal direct and indirect effects in the presence of two post-treatment confounders. A large simulation study was designed to evaluate the extent to which ordinary regression and modern causal inference methods are able to obtain correct estimates of the direct and indirect effects when confounding variables that are present in the population are not included in the analysis. Five methods were compared in terms of bias, relative bias, mean square error, statistical power, Type I error rates, and confidence interval coverage to test how robust the methods are to the violation of the no unmeasured confounders assumption and confounder effect sizes. The methods explored were linear regression with adjustment, inverse propensity weighting, inverse propensity weighting with truncated weights, sequential g-estimation, and a doubly robust sequential g-estimation. Results showed that in estimating the direct and indirect effects, in general, sequential g-estimation performed the best in terms of bias, Type I error rates, power, and coverage across different confounder effect, direct effect, and sample sizes when all confounders were included in the estimation. When one of the two confounders were omitted from the estimation process, in general, none of the methods had acceptable relative bias in the simulation study. Omitting one of the confounders from estimation corresponded to the common case in mediation studies where no measure of a confounder is available but a confounder may affect the analysis. Failing to measure potential post-treatment confounder variables in a mediation model leads to biased estimates regardless of the analysis method used and emphasizes the importance of sensitivity analysis for causal mediation analysis.

Contributors

Agent

Created

Date Created
  • 2013

152407-Thumbnail Image.png

Exploring goodness of fit, mother-child relationships, and child risk

Description

Despite the compelling nature of goodness of fit and widespread recognition of the concept, empirical support has lagged, potentially due to complexities inherent in measuring such a complicated, relational construct.

Despite the compelling nature of goodness of fit and widespread recognition of the concept, empirical support has lagged, potentially due to complexities inherent in measuring such a complicated, relational construct. The present study examined two approaches to measuring goodness of fit in mother-child dyads and prospectively explored associations to mother-child relationship quality, child behavior problems, and parenting stress across the preschool period. In addition, as goodness of fit might be particularly important for children with developmental delays, child developmental risk status was considered as a moderator of goodness of fit processes. Children with (n = 110) and without (n = 137) developmental delays and their mothers were coded while interacting during a number of lab tasks at child age 36 months and during naturalistic home observations at child age 48 months. Mothers and father completed questionnaires at child ages 36 and 60 months assessing child temperamental characteristics, child behavior problems, and parenting stress. Results highlight child-directed effects on mother-child goodness of fit processes across the early child developmental period. Although there was some evidence that mother-child goodness of fit was associated with parenting stress 2 years later, goodness of fit remains an elusive concept. More precise models and expanded developmental perspectives are needed in order to fully capture the transactional and dynamic nature of goodness of fit in the parent-child relationship.

Contributors

Agent

Created

Date Created
  • 2014

151719-Thumbnail Image.png

Mediation as a novel method for increasing statistical power

Description

Including a covariate can increase power to detect an effect between two variables. Although previous research has studied power in mediation models, the extent to which the inclusion of a

Including a covariate can increase power to detect an effect between two variables. Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase the power to detect a relation between two variables has not been investigated. The first study identified situations where empirical and analytical power of two tests of significance for a single mediator model was greater than power of a bivariate significance test. Results from the first study indicated that including a mediator increased statistical power in small samples with large effects and in large samples with small effects. Next, a study was conducted to assess when power was greater for a significance test for a two mediator model as compared with power of a bivariate significance test. Results indicated that including two mediators increased power in small samples when both specific mediated effects were large and in large samples when both specific mediated effects were small. Implications of the results and directions for future research are then discussed.

Contributors

Agent

Created

Date Created
  • 2013