The aim of this research was to establish what black adults' perceptions are of factors that contribute to healthy family functioning. Qualitative research was conducted. Random sampling was used to obtain eighteen black participants between the ages of 20 and 50. These participants responded in writing to the following open ended question: "What factors do you think contribute to healthy family functioning?" Semi-structured interviews were also conducted with the eight participants who presented with the richest data. Analysis of the data yielded 10 prevalent themes and eleven other themes. The 10 prevalent themes were: respect, love, communication, family time/spending time together, trust, understanding, discipline, availability for each other, boundaries and religion. The other eleven themes were: personal space, responsibility, hierarchy, family rules, conflict handling, morality, roles, maturity, intelligence, culture and forgiveness. The above mentioned 21 themes were grouped in seven broad categories, namely communication, conflict handling, affectionate involvement, family rules, boundaries, religion and other dimensions. All these themes were compared to existing research results. It appeared that themes of this study correspond with many dimensions of family functioning as indicated by family therapy models and existing research. Participants also indicated new dimensions not mentioned by the existing literature. Recommendations were made concerning future research. / Thesis (M.A. (Clinical Psychology))--North-West University, Potchefstroom Campus, 2005.

The aim of this research was to establish what black adults' perceptions are of factors that contribute to healthy family functioning. Qualitative research was conducted. Random sampling was used to obtain eighteen black participants between the ages of 20 and 50. These participants responded in writing to the following open ended question: "What factors do you think contribute to healthy family functioning?" Semi-structured interviews were also conducted with the eight participants who presented with the richest data. Analysis of the data yielded 10 prevalent themes and eleven other themes. The 10 prevalent themes were: respect, love, communication, family time/spending time together, trust, understanding, discipline, availability for each other, boundaries and religion. The other eleven themes were: personal space, responsibility, hierarchy, family rules, conflict handling, morality, roles, maturity, intelligence, culture and forgiveness. The above mentioned 21 themes were grouped in seven broad categories, namely communication, conflict handling, affectionate involvement, family rules, boundaries, religion and other dimensions. All these themes were compared to existing research results. It appeared that themes of this study correspond with many dimensions of family functioning as indicated by family therapy models and existing research. Participants also indicated new dimensions not mentioned by the existing literature. Recommendations were made concerning future research. / Thesis (M.A. (Clinical Psychology))--North-West University, Potchefstroom Campus, 2005.

During the COVID-19 pandemic, customer shopping habits have changed. Some industries experienced an abrupt shift during the pandemic outbreak while others navigate in new normal states. For some merchants, the highly-uncertain new phenomena of COVID-19 expresses as outliers in time series of volume of sales. As forecasting models tend to replicate past behavior of a series, outliers complicates the procedure of forecasting; the abnormal events tend to unreliably replicate in forecasts of the subsequent year(s). In this thesis, we investigate how to forecast volume of sales during the abnormal time period of COVID-19, where the classical ARIMA family of models produce unreliable forecasts. The research revolved around three time series exhibiting three types of outliers: a level shift, a transient change and an additive outlier. Upon detecting the time period of the abnormal behavior in each series, two experiments were carried out as attempts for increasing the predictive accuracy for the three extreme cases. The first experiment was related to imputing the abnormal data in the series and the second was related to using a combined model of a pre-pandemic and a post-abnormal forecast. The results of the experiments pointed at significant improvement of the mean absolute percentage error at significance level alpha=0.05 for the level shift when using a combined model compared to the pre-pandemic best-fit SARIMA model. Also, at significant improvement for the additive outlier when using a linear impute. For the transient change, the results pointed at no significant improvement in the predictive accuracy of the experimental models compared to the pre-pandemic best-fit SARIMA model. For the purpose of generalizing to large-scale conclusions of methods' superiority or feasibility for particular abnormal behaviors, empirical evaluations are required. The proposed experimental models were discussed in terms of reliability, validity and quality. By residual diagnostics, it was argued that the models were valid; however, that further improvements can be made. Also, it was argued that the models fulfilled desired attributes of simplicity, scaleability and flexibility. Due to the uncertain phenomena of the COVID-19 pandemic, it was suggested not to take the outputs as long-term reliable solutions. Rather, as temporary solutions requiring more frequent updating of forecasts. / Under coronapandemin har kundbeteenden och köpvanor förändrats. I vissa branscher upplevdes ett plötsligt skifte vid pandemiutbrottet och i andra navigerar handlare i nya normaltillstånd. För vissa handlare är förändringarna så pass distinkta att de yttrar sig som avvikelser i tidsserier över försäljningsvolym. Dessa avvikelser komplicerar prognosering. Då prognosmodeller tenderar att replikera tidsseriers tidigare beteenden, tenderas det avvikande beteendet att replikeras i försäljningsprognoser för nästkommande år. I detta examensarbete ämnar vi att undersöka tillvägagångssätt för att estimera försäljningsprognoser under den abnorma tidsperioden av COVID-19, då klassiska tidsseriemodeller felprognoserar. Detta arbete kretsade kring tre tidsserier som uttryckte tre avvikelsertyper: en nivåförskjutning, en övergående förändring och en additiv avvikelse. Efter att ha definierat en specifik tidsperiod relaterat till det abnorma beteendet i varje tidsserie, utfördes två experiment med syftet att öka den prediktiva noggrannheten för de tre extremfallen. Det första experimentet handlade om att ersätta den abnorma datan i varje serie och det andra experimentet handlade om att använda en kombinerad pronosmodell av två estimerade prognoser, en pre-pandemisk och en post-abnorm. Resultaten av experimenten pekade på signifikant förbättring av ett absolut procentuellt genomsnittsfel för nivåförskjutningen vid användande av den kombinerade modellen, i jämförelse med den pre-pandemiskt bäst passande SARIMA-modellen. Även, signifikant förbättring för den additiva avvikelsen vid ersättning av abnorm data till ett motsvarande linjärt polynom. För den övergående förändringen pekade resultaten inte på en signifikant förbättring vid användande av de experimentella modellerna. För att generalisera till storskaliga slutsatser giltiga för specifika avvikande beteenden krävs empirisk utvärdering. De föreslagna modellerna diskuterades utifrån tillförlitlighet, validitet och kvalitet. Modellerna uppfyllde önskvärda kvalitativa attribut såsom enkelhet, skalbarhet och flexibilitet. På grund av hög osäkerhet i den nuvarande abnorma tidsperioden av coronapandemin, föreslogs det att inte se prognoserna som långsiktigt pålitliga lösningar, utan snarare som tillfälliga tillvägagångssätt som regelbundet kräver om-prognosering.

Neste trabalho desenvolvemos um estudo sobre modelos auto-regressivos com heterocedasticidade (ARCH) e modelos auto-regressivos com erros ARCH (AR-ARCH). Apresentamos os procedimentos para a estimação dos modelos e para a seleção da ordem dos mesmos. As estimativas dos parâmetros dos modelos são obtidas utilizando duas técnicas distintas: a inferência Clássica e a inferência Bayesiana. Na abordagem de Máxima Verossimilhança obtivemos intervalos de confiança usando a técnica Bootstrap e, na abordagem Bayesiana, adotamos uma distribuição a priori informativa e uma distribuição a priori não-informativa, considerando uma reparametrização dos modelos para mapear o espaço dos parâmetros no espaço real. Este procedimento nos permite adotar distribuição a priori normal para os parâmetros transformados. As distribuições a posteriori são obtidas através dos métodos de simulação de Monte Carlo em Cadeias de Markov (MCMC). A metodologia é exemplificada considerando séries simuladas e séries do mercado financeiro brasileiro / In this work we present a study of autoregressive conditional heteroskedasticity models (ARCH) and autoregressive models with autoregressive conditional heteroskedasticity errors (AR-ARCH). We also present procedures for the estimation and the selection of these models. The estimates of the parameters of those models are obtained using both Maximum Likelihood estimation and Bayesian estimation. In the Maximum Likelihood approach we get confidence intervals using Bootstrap resampling method and in the Bayesian approach we present informative prior and non-informative prior distributions, considering a reparametrization of those models in order to map the space of the parameters into real space. This procedure permits to choose prior normal distributions for the transformed parameters. The posterior distributions are obtained using Monte Carlo Markov Chain methods (MCMC). The methodology is exemplified considering simulated and Brazilian financial series

Neste trabalho desenvolvemos um estudo sobre modelos auto-regressivos com heterocedasticidade (ARCH) e modelos auto-regressivos com erros ARCH (AR-ARCH). Apresentamos os procedimentos para a estimação dos modelos e para a seleção da ordem dos mesmos. As estimativas dos parâmetros dos modelos são obtidas utilizando duas técnicas distintas: a inferência Clássica e a inferência Bayesiana. Na abordagem de Máxima Verossimilhança obtivemos intervalos de confiança usando a técnica Bootstrap e, na abordagem Bayesiana, adotamos uma distribuição a priori informativa e uma distribuição a priori não-informativa, considerando uma reparametrização dos modelos para mapear o espaço dos parâmetros no espaço real. Este procedimento nos permite adotar distribuição a priori normal para os parâmetros transformados. As distribuições a posteriori são obtidas através dos métodos de simulação de Monte Carlo em Cadeias de Markov (MCMC). A metodologia é exemplificada considerando séries simuladas e séries do mercado financeiro brasileiro / In this work we present a study of autoregressive conditional heteroskedasticity models (ARCH) and autoregressive models with autoregressive conditional heteroskedasticity errors (AR-ARCH). We also present procedures for the estimation and the selection of these models. The estimates of the parameters of those models are obtained using both Maximum Likelihood estimation and Bayesian estimation. In the Maximum Likelihood approach we get confidence intervals using Bootstrap resampling method and in the Bayesian approach we present informative prior and non-informative prior distributions, considering a reparametrization of those models in order to map the space of the parameters into real space. This procedure permits to choose prior normal distributions for the transformed parameters. The posterior distributions are obtained using Monte Carlo Markov Chain methods (MCMC). The methodology is exemplified considering simulated and Brazilian financial series

Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure’s response and performance in a probabilistic manner. The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric StId approach is employed without the use of an analytical model. The new methodology, which is iv successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions. It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels

The given Diploma Thesis focuses on social environment in the families with disabled children. The first three chapters describe the process of socialization and its impact on human development, then a family creating an environment for primary socialization and finally the third chapter describes examples of physical disabilities and specifics related to socialization of disabled people. This phenomena, examined and verified through the qualitative research, constitutes the issue of the fourth chapter. The qualitative survey is based on semi-structured interviews, followed by their open-coding assessment. The survey demonstrates changes, both in the family and in the family´s social environment, which occurred after the birth of a child with physical disability, or after detecting a diagnosis as a consequence of the acquired disability. The topic in question also discusses difficulties associated with care of a children with physical disability, as for the time, mental, social and other requirements.

The aim of this quantitative study was to determine whether there were significant relationships between cultural factors and academic achievement of secondary school female learners in the Chimanimani district of Manicaland in Zimbabwe. According to literature, home environment variables, school environment variables and learner variables influence academic achievement of learners. The home environment includes family's expectations, the family's socio-economic status, exposure to role models and child-rearing practices. The school environment includes teacher's attitudes and the curriculum. Learner variables encompass self-concept, gender role concepts as well as the learner's attitude and aspirations. The empirical research found significant correlations between all cultural factors and academic achievement, particularly in English and at times in mathematics. These correlations were low but positive. The investigation also revealed that diverse age groups did not differ significantly in academic achievement in mathematics or in English. However, females from diverse socio-economic backgrounds differed significantly in their academic achievements. / Psychology of Education / M.Ed.(Psychology of Education)

The aim of this quantitative study was to determine whether there were significant relationships between cultural factors and academic achievement of secondary school female learners in the Chimanimani district of Manicaland in Zimbabwe. According to literature, home environment variables, school environment variables and learner variables influence academic achievement of learners. The home environment includes family's expectations, the family's socio-economic status, exposure to role models and child-rearing practices. The school environment includes teacher's attitudes and the curriculum. Learner variables encompass self-concept, gender role concepts as well as the learner's attitude and aspirations. The empirical research found significant correlations between all cultural factors and academic achievement, particularly in English and at times in mathematics. These correlations were low but positive. The investigation also revealed that diverse age groups did not differ significantly in academic achievement in mathematics or in English. However, females from diverse socio-economic backgrounds differed significantly in their academic achievements. / Psychology of Education / M.Ed.(Psychology of Education)