Matching Items (7)

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Torah-Observant Jewish Married Couples: The Influence of Mandated Abstinence of Physical Touch and Marital Maintenance

Description

Maintaining sexual desire as the marriage endures is a challenge, especially as it involves the interplay of seemingly opposing tensions of novelty, autonomy, and closeness. Difficulties can arise when autonomy,

Maintaining sexual desire as the marriage endures is a challenge, especially as it involves the interplay of seemingly opposing tensions of novelty, autonomy, and closeness. Difficulties can arise when autonomy, which requires spousal distancing, is perceived as a martial threat and therefore suppressed. This dissertation investigates whether prosocial marital distancing can nurture autonomy and promote sexual desire.

Torah-observant Jewish married couples practice family purity, a Jewish law forbidding sexual relations during menstruation and shortly thereafter. During this time couples often avoid sleeping in the same bed, physical touch, and behaviors that can instigate a sexual encounter. These distancing restrictions are lifted when the wife immerses in a ritual bath. The process repeats at the next menstruation.

This research examined the effects of family purity’s marital distancing through two studies. The first involved qualitative interviews of family purity wives (N = 10) guided by relational dialectics theory (Baxter & Montgomery, 1996). Study one findings suggest that family purity wives navigate the three tensions of integration, expression, and certainty. Study one also revealed a new tension, the dialect of restraint. The dialectic of restraint appears to enhance marital communication, heighten the appreciation for the mundane, and help sustain sexual desire.

Study two, the quantitative phase of the research, applied self-expansion theory (Aron & Aron, 1986) to investigate differences between family purity and non-family purity couples. A sample of 90 married Jewish dyads (N = 180) participated in a cross-sectional online questionnaire. Findings suggest that while non-practicing couples report greater self-expansion, family purity couples report greater sexual closeness. Family purity couples also report the same closeness and sexual closeness ideals, whereas non-practicing couples reported divergent ideals. Non-practicing family purity husbands had the greatest reported discrepancy between ideal and actual sexual closeness.

The combined findings suggest that sanctioned prosocial distancing as practiced by family purity couples enables the integration of cognitive growth and mitigates the threat of autonomy. Prosocial distancing within the family purity marriage appears to provide the wife space for autonomy that in turn provokes novelty and sexual desire. Findings are discussed in relation to theoretical contributions, study limitations, and future directions.

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Created

Date Created
  • 2020

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Multiple imputation for two-level hierarchical models with categorical variables and missing at random data

Description

Accurate data analysis and interpretation of results may be influenced by many potential factors. The factors of interest in the current work are the chosen analysis model(s), the presence of

Accurate data analysis and interpretation of results may be influenced by many potential factors. The factors of interest in the current work are the chosen analysis model(s), the presence of missing data, and the type(s) of data collected. If analysis models are used which a) do not accurately capture the structure of relationships in the data such as clustered/hierarchical data, b) do not allow or control for missing values present in the data, or c) do not accurately compensate for different data types such as categorical data, then the assumptions associated with the model have not been met and the results of the analysis may be inaccurate. In the presence of clustered
ested data, hierarchical linear modeling or multilevel modeling (MLM; Raudenbush & Bryk, 2002) has the ability to predict outcomes for each level of analysis and across multiple levels (accounting for relationships between levels) providing a significant advantage over single-level analyses. When multilevel data contain missingness, multilevel multiple imputation (MLMI) techniques may be used to model both the missingness and the clustered nature of the data. With categorical multilevel data with missingness, categorical MLMI must be used. Two such routines for MLMI with continuous and categorical data were explored with missing at random (MAR) data: a formal Bayesian imputation and analysis routine in JAGS (R/JAGS) and a common MLM procedure of imputation via Bayesian estimation in BLImP with frequentist analysis of the multilevel model in Mplus (BLImP/Mplus). Manipulated variables included interclass correlations, number of clusters, and the rate of missingness. Results showed that with continuous data, R/JAGS returned more accurate parameter estimates than BLImP/Mplus for almost all parameters of interest across levels of the manipulated variables. Both R/JAGS and BLImP/Mplus encountered convergence issues and returned inaccurate parameter estimates when imputing and analyzing dichotomous data. Follow-up studies showed that JAGS and BLImP returned similar imputed datasets but the choice of analysis software for MLM impacted the recovery of accurate parameter estimates. Implications of these findings and recommendations for further research will be discussed.

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Created

Date Created
  • 2016

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Individual and combined impact of institutional student support strategies on first-time, full-time, degree-seeking community college students

Description

Although U.S. rates of college enrollment among 18-24 year olds have reached historic highs, rates of degree completion have not kept pace. This is especially evident at community colleges, where

Although U.S. rates of college enrollment among 18-24 year olds have reached historic highs, rates of degree completion have not kept pace. This is especially evident at community colleges, where a disproportionate number of students from groups who, historically, have had low college-completion rates enroll. One way community colleges are attempting to address low completion rates is by implementing institutional interventions intended to increase opportunities for student engagement at their colleges. Utilizing logistic and linear regression analyses, this study focused on community college students, examining the association between participation in institutional support activities and student outcomes, while controlling for specific student characteristics known to impact student success in college. The sample included 746 first-time, full-time, degree-seeking students at a single community college located in the U.S. Southwest. Additional analyses were conducted for the 440 first-time, full-time, degree-seeking students in this sample who placed into at least one developmental education course. Findings indicate that significant associations exist between different types of participation in institutional interventions and various student outcomes: Academic advising was found to be related to increased rates of Fall to Spring and Fall to Fall persistence and, for developmental education students, participation in a student success course was found to be related to an increase in the proportion of course credit hours earned. The results of this study provide evidence that student participation in institutional-level support may relate to increased rates of college persistence and credit hour completion; however, additional inquiry is warranted to inform specific policy and program decision-making at the college and to determine if these findings are generalizable to populations outside of this college setting.

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Created

Date Created
  • 2011

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Children's academic experiences during first grade as precursors of later academic performance

Description

Children's academic experiences during first grade have substantial implications for their academic performance both concurrently and longitudinally. Using two complementary studies, this dissertation utilizing data from the National Institute of

Children's academic experiences during first grade have substantial implications for their academic performance both concurrently and longitudinally. Using two complementary studies, this dissertation utilizing data from the National Institute of Child Development Study of Early Child Care and Youth Development helps create a better understanding of the importance of first-grade experiences for children's academic performance. The first study expands upon current literature by focusing on how children's academic experiences simultaneously influence children's academic performance through behavioral engagement. Specifically, study one examined the mediating role of first-grade behavioral engagement between first-grade academic experiences (i.e. parental involvement, positive peer interactions, student-teacher relationship, and instructional support) and second-grade academic performance. Using a panel model, results showed that behavioral engagement mediates relations between peer interactions and academic performance and relations between instructional support and academic performance. Implications for interventions focusing on children's positive peer interactions and teacher's high-quality instructional support in order to promote behavioral engagement during early elementary school are discussed.

The second study expands the current literature regarding instructional quality thresholds. Limited research has addressed the question of whether there is a minimum level of instructional quality that must be experienced in order to see significant changes in children's academic performance, and the limited research has focused primarily on preschoolers. The goal of study two was to determine if high-quality first-grade instructional support predicted children's first-, third-, and fifth-grade academic performance. Using piecewise regression analyses, results did not show evidence of a relation between first-grade instructional support quality and children's academic performance at any grade. Possible reasons for inconsistencies in findings from this study and previous research are discussed, including differences in sample characteristics and measurement tools. Because instructional quality remains at the forefront of discussions by educators and policy makers, the inconsistencies in research findings argue for further research that may clarify thresholds of instructional support quality that must be met in order for various subgroups of children to gain the skills needed for long-term academic success.

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Created

Date Created
  • 2015

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Applying academic analytics: developing a process for utilizing Bayesian networks to predict stopping out among community college students

Description

Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part

Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory and confirmatory techniques to reduce an initial pool of more than 50 potential predictor variables to a parsimonious final BN with only four predictor variables. The average in-sample classification accuracy rate for the model was 80% (Cohen's κ = 53%). The model was shown to be generalizable across samples with an average out-of-sample classification accuracy rate of 78% (Cohen's κ = 49%). The classification rates for the BN were also found to be superior to the classification rates produced by an analog frequentist discrete-time survival analysis model.

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Created

Date Created
  • 2015

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Developing a measure of cyberbullying perpetration and victimization

Description

This research addressed concerns regarding the measurement of cyberbullying and aimed to develop a reliable and valid measure of cyberbullying perpetration and victimization. Despite the growing body of literature on

This research addressed concerns regarding the measurement of cyberbullying and aimed to develop a reliable and valid measure of cyberbullying perpetration and victimization. Despite the growing body of literature on cyberbullying, several measurement concerns were identified and addressed in two pilot studies. These concerns included the most appropriate time frame for behavioral recall, use of the term "cyberbullying" in questionnaire instructions, whether to refer to power in instances of cyberbullying, and best practices for designing self-report measures to reflect how young adults understand and communicate about cyberbullying. Mixed methodology was employed in two pilot studies to address these concerns and to determine how to best design a measure which participants could respond to accurately and honestly. Pilot study one consisted of an experimental examination of time frame for recall and use of the term on the outcomes of honesty, accuracy, and social desirability. Pilot study two involved a qualitative examination of several measurement concerns through focus groups held with young adults. Results suggested that one academic year was the most appropriate time frame for behavioral recall, to avoid use of the term "cyberbullying" in questionnaire instructions, to include references to power, and other suggestions for the improving the method in the main study to bolster participants' attention. These findings informed the development of a final measure in the main study which aimed to be both practical in its ability to capture prevalence and precise in its ability to measure frequency. The main study involved examining the psychometric properties, reliability, and validity of the final measure. Results of the main study indicated that the final measure exhibited qualities of an index and was assessed as such. Further, structural equation modeling techniques and test-retest procedures indicated the measure had good reliability. And, good predictive validity and satisfactory convergent validity was established for the final measure. Results derived from the measure concerning prevalence, frequency, and chronicity are presented within the scope of findings in cyberbullying literature. Implications for practice and future directions for research with the measure developed here are discussed.

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Created

Date Created
  • 2012

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Developmental pathways to antisocial behavior in early-adolescence: examining changes in aggression and peer exclusion through childhood

Description

This study examined the influence of childhood aggression, peer exclusion and associating with deviant peers on the development of antisocial behavior in early adolescence. To gain a stronger understanding of

This study examined the influence of childhood aggression, peer exclusion and associating with deviant peers on the development of antisocial behavior in early adolescence. To gain a stronger understanding of how these factors are associated with antisocial behavior and delinquency, multiple alternative pathways were examined based on additive, mediation and incidental models. A parallel process growth model was specified to assess whether early childhood aggression and peer exclusion (in 1st grade) and intra-individual increases in aggressive behaviors and exclusion through childhood (grades 1 to 6) are predictive of associating with deviant peers (in 7th grade) and antisocial behavior (in 8th grade). Based on a sample of 383 children (193 girls and 190 boys), results showed the strongest support for an additive effects model in which early childhood aggression, increases in aggression, increases in peer exclusion and associating with more deviant peers all predicted antisocial behavior. These findings have implications for how children's psychological adjustment is impacted by their behavioral propensities and peer relational context and the importance of examining developmental processes within and between children over time.

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Created

Date Created
  • 2011