A child's parents are the primary source of knowledge and learned behaviors for developing children, and the benefits or repercussions of certain parental practices can be long lasting. Although parenting practices affect behavioral outcomes for children, families tend to be diverse in their circumstances and needs. Research attempting to ascertain cause and effect relationships between parental influences and child behavior can be difficult due to the complex nature of family dynamics and the intricacies of real life. Structural equation modeling (SEM) is an appropriate method for this research as it is able to account for the complicated nature of child-parent relationships. Both Frequentist and Bayesian methods are used to estimate the effect of latent parental behavior variables on child aggression and anxiety in order to allow for comparison and contrast between the two statistical paradigms in the context of structural equation modeling. Estimates produced from both methods prove to be comparable, but subtle differences do exist in those coefficients and in the conclusions to which a researcher would arrive. Although model estimates between the two paradigms generally agree, they diverge in the model selection process. The mother's behaviors are estimated to be the most influential on child aggression, while the influence of the father, socio-economic status, parental involvement, and the relationship quality of the couple also prove to be significant in predicting child aggression.

The purpose of this study was (a) to test a hypothesized model of motivation grounded in the Self-Determination Theory within the context of conceptual physical education (CPE), and (b) to explore the strength and directionality of perceived competence for physical activity as a possible mediator for health-related fitness knowledge and actual physical activity behaviors. Participants were 280 high school students who were at the end of a CPE course. Participants completed the Behavioural Regulation in Exercise Questionnaire–2, the Godin Leisure–Time Exercise Questionnaire, the Perceived Competence Scale, and a Health-Related Fitness Knowledge Questionnaire. Structural equation modeling analysis was used to explore the relationships between the variables of health-related fitness knowledge, perceived competence, motivation, and physical activity. The analysis resulted in a modified model that showed a relationship between perceived competence and physical activity, mediated by introjected and identified regulation. Implications and recommendations for physical education professionals are made.

It has been often argued that life events have an impact on our physical and psychological well-being. In general, research supports this connection between life events and general health, though some argue that simply experiencing life events has a measurable and predictable impact our health, while others contend that this effect is mediated by the appraisal process. Further, research has identified a number of different factors (hypothetically stratified into pre-existing beliefs, external resources and demands, and behavioral activation and coping strategies) that may influence appraisal and general health. The current study attempts to integrate these findings by testing structural models of the relationship between life events, life stress, and general health while considering the appraisal process and other potential moderators of appraisal and general health. University students (N=204) were tested using 17 assessment measures representing 7 latent constructs of Life Events, Life Stress, Appraisal, General Health, Beliefs, External, and Activation. Results of the measurement models required model respecification to combine Appraisal and Beliefs into one construct and External and Activation into another construct, resulting in a five-factor hypothetical structural model. The resulting empirical structural model is a partially-mediated model that suggests that appraisal and pre-existing beliefs influence the relationship between life events and life stress, and that life events significantly impact measured life stress. The empirical model also indicates that general health is significantly impacted by life stress, as well as behavioral activation and external resources and demands. Practical implications of the findings and recommendations for further research were discussed.

Research has shown that female physicians continue to earn less than their male counterparts. From both social justice and feminist perspectives, laws requiring equal pay should provide just income for females as compared to males. However, the literature continues to indicate that in general females earn less than males, a trend that is also true for physicians. Theoretically informed postulates are measured here with structural equation modeling to test the influence of the unique latent construct "specialization" on the income gap while controlling for demographic and contextual variables. The analysis tests the assumption that the influence of specialization is the same for females and males. If the influence of specialization and other variables differs by gender, gender bias in physician income may be conceptually implied. The study uses three waves of data from the Community Tracking Study Physician Survey (CTS). The study finds an income gap between females and males in three waves of the CTS. Gini coefficients show females continue to experience greater income inequality than males, with the Lorenz curves for males being closer to the equality lines. Using 1999 income data, there is a statistically significant income gap between female and male physicians when controlling for weeks worked. Information Technology (IT) use was found to be the most reliable construct measuring the unique latent variable specialization. Structural equation modeling showed indicators of specialization have an influence on the income gap. The variables in the CTS Physician Survey made for a poor construct that failed to measure specialization as a uni-dimensional construct. The variables that influence the income gap were different for females than for males. As policy makers revise or create better laws to protect income equality, gender differences must be taken into consideration.

With the rapid growth of juvenile offender diversion programs, which use many nontraditional sanctions, the effectiveness of sanction combinations in juvenile diversion programs and in each individual program needs to be evaluated. Those making sanctioning decisions currently do so based on intuition rather than using an evidence- or theory-based approach. Considerable research has examined the relationship between offender risk factors and recidivism (who is more likely to reoffend?) and between offender risk factors and sanctions (who is more likely to receive what sanctions?), but little is known about the relationship between sanctions and recidivism (which sanctions best reduce recidivism and for whom?). Furthermore, recidivism studies vary drastically in how they measure or quantify recidivism. This variability of approach makes comparing studies difficult and provides a less-than-complete picture of recidivism in general. The present study used data from one specific youth diversion program to test certain hypotheses of sanctioning by developing and testing a model for assigning sanction combinations to certain offenders on the basis of their individual characteristics. The study first developed measurement models for Offender Risk Propensity, Multiplicity of Sanctions, and Recidivism using structural equation modeling (SEM). Then predictive models were developed to test specific relationships. Understanding the effectiveness of certain sanction packages on certain offenders can form the basis for effective sanctioning in youth diversion programs. This study sought to answer three research questions: What is the best way to measure recidivism? Does completion of a restorative justice program reduce recidivism? Which sanctions, if any, reduce recidivism for specific offender types? To answer the first question: a iv multi-indicator latent construct of recidivism did a very good job of measuring variation in recidivism. Multiple indicators analyzed simultaneously produced a robust tool that can be used in other recidivism studies and help to reduce comparability issues between studies. The recidivism construct, when tested as a function of completion of the restorative justice program, was seen to produce a significant model having an overall good fit with the data. Thus to answer the second research question: offenders’ completion status for the restorative justice program was shown to be a significant predictor of the latent construct of recidivism at the 0.05 level (two-tailed), with those who failed to complete (or chose not to participate) having higher recidivism than did those who completed the program. To answer the third research question: the assignment of specific sanctions (both those suggested by research and theory and those traditionally assigned by this and similar programs) on the entire data set (and on various subsets) of this study have no statistically significant impact on recidivism at the 0.05 level (two-tailed). The findings suggest many policy implications. Consistency is all but nonexistent in recidivism measurements in the academic literature and in program review studies. A multiindicator latent construct of recidivism, such as the one proposed and proven effective in this study, provides a more complete picture than simply conceptualizing recidivism by one dummy variable. This recidivism model can be used as the endogenous variable to evaluate programs and their practices and could reduce the problem of study comparability. This could lead to a better understanding of program characteristics and their impact on offender success. This study also found that completion of the Neighborhood Restorative Justice Program was a significant predictor of recidivism, yet none of the eleven most commonly assigned v sanctions were seen to have a significant impact on recidivism for any subgroup. Proponents of restorative justice argue that it is the programs’ characteristics and not their specific activities that make the programs successful. Reintegrative Shaming Theory and Labeling Theory support this claim and suggest the best approach to address youth criminal behavior is to admonish the act and not the actor, have the offender and community agree on a plan to make the community whole after that criminal act, and prevent repeated interaction with the formal criminal justice system which encourages the youth to see themself as a deviant and engage in further deviant behavior. These characteristics should be further examined and widely employed if confirmed.

An integral concern across care settings is the prompt intervention for patients suffering with pain. Long-term care (LTC) settings present with unique challenges to assess and manage pain in resident populations. Pain assessment is especially challenging, because residents have varying degrees of cognition to communicate their pain, and clinician/staff knowledge of pain symptoms may be lacking. The purpose of this research was to improve the measurement of pain and outcomes of care for the elderly residing in skilled nursing care, especially those with cognitive-impairment. The specific aims of this study were to: 1) Determine the magnitude of the relationship between pain behaviors and a measurement model hypothesized for pain; 2) Test the construct validity of a pain measurement model; 3) Examine the concomitance of pain and cognition in a three-year longitudinal analysis. The research questions answered: 1) Is there a difference in the prevalence of pain in cognitively intact versus cognitively-impaired residents; 2) Can a theoretically derived model of pain aid in detecting pain across all cognitive levels; and 3) Do pain and cognitive status concomitantly correlate? The goal was to examine the covariance model of concomitance of pain and cognition to more accurately construct theoretical models of pain to then include additional resident care factors in future research. Traditional self-reports of pain are often under-assessed and under-treated in the cognitively-impaired (CI) elderly resident. Having additional measures to detect pain beyond self-reports of pain intensity and frequency increases the likelihood of detecting pain in populations with complex symptom presentation. Data collected from skilled nursing facilities offer exceptional opportunities to study resident demographics, characteristics, symptoms, medication use, quality indicators, and care outcomes. The Minimum Data Set-Resident Assessment Instrument (MDS-RAI) 2.0, a nationally required resident assessment tool, must be completed on every resident in a Medicare LTC facility within 14 days of admission, quarterly, annually and with significant changes in resident status. Because the MDS is widely used and recognized in LTC settings, core items from MDS [i.e., pain frequency (J2a) and pain intensity (J2b)] along with additional MDS items hypothesized to signify pain were analyzed in the pilot measurement model. Ten core items from MDS were used: 1) Inappropriate behavior frequency (E4da); 2) Repetitive physical movements; 3) Repetitive verbalizations (E1c); 4) Sad facial expressions (E1l); 5) Crying (E1m); 6) Change in mood (E3); 7) Negative statements (E1a); 8) Pain frequency (J2a); 9) Pain intensity (J2b); and 10) Cumulative pain sites scores. All indicators of pain were significant at the <.01 level. A longitudinal cohort design was used to answer if a concomitance exists between pain and cognition. Data were collected from MDS annual assessments from 2001, 2002 and 2003 for residents across the United States. The sample consisted of 56,494 residents age 65 years and older with an average age of 83 [plus or minus] 8.2 years. Descriptive statistics, ANOVA and a covariance model were used to evaluate cognition and pain at the three time intervals. ANOVA indicated a significant effect (<.01) for pain and cognition with protected t-tests indicating scores decreased significantly over time with resident measures of pain and cognition. Results from this study suggest that: 1) Using only pain intensity and frequency, pain prevalence was found in 30% of the pilot population, while 47.7% of cognitively intact residents had documented pain and only 18.2% of the severely CI had documented pain, supporting previous research that pain is potentially under-reported in the CI; 2) Parsimonious measurements models of pain should include dimensions beyond self-reports of pain (i.e., cognitive, affective, behavioral and inferred pain indicators); 3) Model fit was improved by using specific MDS items in the pain construct; 4) Longitudinal analysis revealed relative stability for pain and cognition measures over time (e.g., larger stability or consistency was found in cognitive measures than the measures of pain over the three-year period); 5) Crossed-legged effects between pain and cognition were not consistent; 6) A concomitant relationship was not found between pain and cognition. The relationship was significant (<.01), but associations were weak (r=0.03 to 0. 08). Pain or cognition should not be used as a predictor of the other in theoretical models for similar populations. The MDS is a reliable instrument to follow resident attributes, quality of care, and patient outcomes over time. The development of more accurate assessments of pain may improve resident care outcomes. Ineffectively intervening on the pain cycle is posited to cause secondary unmet needs that affect the resident's quality of life. Findings support the importance of improving clinical outcomes in the management of pain in the elderly residing in long-term care. Deficits in the treatment of pain highlight the impetus to support health policy change that includes pain treatment as a top health priority and a quality indicator for federally funded programs supporting eldercare.

Fit indices and fit measures commonly used to determine the accuracy and desirability of structural equation models are expected to be insensitive to nonlinearity in the data. This includes measures as ubiquitous as the CFI, TLI, RMSEA, SRMR, AIC, and BIC. Despite this, some software will report these measures when certain models are used. Consequently, some researchers may be led to use these fit measures without realizing the impropriety of the act. Alternative fit measures have been proposed, but these measures require further testing. As part of this thesis, a large simulation study was carried out to investigate alternative fit measures and to confirm whether the traditional measures are practically blind to nonlinearity in the data. The results of the simulation provide conclusive evidence that fit statistics and fit indices based on the chi-square distribution or the residual covariance matrix are entirely insensitive to nonlinearity. The posterior predictive p-value was also insensitive to nonlinearity. Only fit measures based on the structural residuals (i.e., HFI and R-squared) showed any sensitivity to nonlinearity. Of these, the R-squared was the only reliable measure of nonlinear model misspecification. This thesis shows that an effective strategy for determining whether a nonlinear model is preferable to a linear one involves using the R-squared to compare models that have been fit to the same data. An R-squared that is much larger for the nonlinear model than the linear model suggests that the linear model may be less desirable than the nonlinear model. The proposed method is intended to be supplementary to substantive theory. It is argued that any dependence on fit indices or fit statistics that places these measures on a higher pedestal than substantive theory will invariably lead to blindness on the part of the researcher. In other words, unwavering adherence to goodness-of-fit measures limits the researchers vision to what the measures themselves can detect.

This dissertation consists of three studies that collectively investigate the factors influencing the consumer adoption intention towards emerging healthcare technologies. Essay 1 systematically reviews the extent literature on healthcare technology adoption and serves as the theoretical foundation of the dissertation. It investigates different models that have been previously applied to study healthcare technology acceptance. Meta-analysis method is used to quantitatively synthesize the findings from prior empirical studies. Essay 2 posits, develops, and tests a comprehensive biotechnology acceptance model from the end-user's perspective. Two new constructs, namely, perceived risk and trust in technology, are integrated into the unified theory of acceptance and use of technology. Research hypotheses are tested using survey data and partial least square – structural equation modeling (PLS-SEM). Essay 3 extends the findings from the Essay 2 and further investigates the consumer's trust initiation and its effect on behavioral adoption intention. To achieve this purpose, Essay 3 posits and develops a trust model. Survey data allows testing the model using PLS-SEM. The models developed in this dissertation reflect significant modifications specific to the healthcare context. The findings provide value for academia, practitioners, and policymakers.

Although behavioural security constructs have received extensive scholarly attention, the unpredictability of human behaviour has continued to add to the complexity of deploying effective countermeasures. Cross-cultural behavioural approach aimed at improving security compliance and practice have equally remained under-researched, a gap in literature this thesis addresses. This thesis argues that in order to establish a sound and efficient organisational security practice, it is necessary to integrate the various interdisciplinary component in a socio-cultural context. The goal of the thesis, therefore, is to examine the role of culture in cybersecurity development and organisational security practice. The study first used a group comparism approach, classification and examination of archival data from International Telecommunication Union (ITU), International Organisation of Standards (ISO), and Hofstede cultural score and further deployed structural equation modelling (SEM) to analyse data from a case study of 122 employees from three public sector information technology organisation in Nigeria. Drawing evidence from the case studies, an emergent conceptual model was developed from the traditional human behaviour and organisational security practice. The model contribute to information security management by by identifying organisational triggers related to cultural and cognitive dynamics in information security practice. The model was developed by combining three theoretical frameworks. The out- come of the case studies demonstrates that accountability to specific security practice is a product of integrated management of employee cognitive and cultural dynamics and by dimensionalising organisational culture, the individual capacity to respond to emerging security threats also evolved, while targeted security programs to enhance the efficiency of organisational security practice is achieved. This research, by exposing the underlying institutional drivers and ideational dynamics on individual security behaviour contribute to theory building of behavioural security research within organisational context. The research reports on the theory of integration as a demonstration of explanatory flexibility not normally associated with behaviour security models thereby providing a sound theoretical and ideational support for incorporating different theoretical frameworks into a single model. This is an original endeavour and s such makes a number of contribution to the scholarship. The findings in this research have implications for policymakers and practitioners by elevating the importance of cultural and cognitive dynamics by enhancing operational clarity in organisational security practice and presenting opportunity to develop a creative and robust solution to the challenge of organisational security in line with cultural peculiarities.

My dissertation served two primary purposes: 1) assess the measurement and structural model properties of the Wildlife Value Orientations (WVO) scale in a unique geographical context, and across two historically underserved groups in wildlife conservation, namely African American and females; and 2) examine Mississippians’ value orientations, beliefs, attitudes, norms, and behavior towards wildlife that could be used by wildlife professionals to create more salient and effective wildlife conservation programs. I conducted a statewide survey of the general public in Mississippi resulting in 1,335 respondents. Using this data, I conducted confirmatory factor analysis on the 4-construct WVO model and found, after model respecification, a 3-construct model met acceptable model fit thresholds. I determined the scale was metrically invariant across race and gender subgroups which was critical for unbiased cross-group comparisons. Structural equation modeling analysis of the WVO cognitive model resulted in an alternative model where wildlife-related recreation subjective norms were found to be a mediator between wildlife-related beliefs and recreational attitudes. Multi-group analysis found that African Americans and Caucasians similarly conceptualized the WVO cognitive model with the exception of wildlife-related recreation attitudes not being as strong of a behavioral predictor for African Americans compared to Caucasians. Similarly, wildlife-related subjective norms were a stronger predictor of recreation behavioral intentions for males than females. Females demonstrated a significantly stronger negative relationship between mutualism and wildlife management attitudes towards lethal control of wildlife. Lastly, I found three WVO types existed in Mississippi: utilitarian, pluralist, and mutualist. African Americans were found to be more mutualistic, possess less favorable attitudes towards wildlife-related recreation, and participate less in those activities than Caucasians. Similarly, females demonstrated more mutualistic orientations and in turn expressed less favorable attitudes towards lethal control of wildlife and wildlife-related recreation, and participated less in recreation compared to males. In sum, I found the WVO scale and WVO cognitive model were reliable and valid predictors of behavioral intention. While further refinement of the scale and model are needed, the instruments presented in this study should prove to be valuable in developing wildlife conservation programs that reflect the values and attitudes of Mississippians.