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This study's first goal is to investigate whether a 23-item multidimensional scale is a valid and reliable measure of benefits private club members perceive to be important. Seven theoretically plausible model structures are empirically tested: a unidimensional model, a two oblique first-order factors model, a four oblique first-order factors model, a two oblique second-order factors model, a bifactor model with two domain-specific factors, a bifactor model with four domain-specific factors, and two oblique bifactor models. The second goal is to examine the benefits members receive most often from their membership clubs. The multidimensional scale is based on four dimensions: member-to-employee relationship, member-to-member relationship, confidence, and reduced anxiety. Member-to-employee relationship and member-to-member relationship subscales are aligned with social benefits while confidence and reduced anxiety subscales are aligned with psychological benefits. The study participants (N = 114) were recruited through a commercial crowdsourcing platform, Prolific. The results of a Bayesian confirmatory factor analysis (BCFA) provided support for the two oblique bifactor models. Additionally, the social benefits and psychological benefits bifactor scales displayed acceptable reliability. A comparison of the means for each type of benefit revealed that no statistically significant differences existed between the general social benefits factor and the general psychological benefits factor as well as between member-to-employee relationship and member-to-member relationship benefits. However, the mean of reduced anxiety benefits was statistically significantly higher than the mean of confidence benefits. The findings of this study contribute to the theoretical understanding and measurement of private club membership value by examining various dimensions of benefits members perceive to be important. The findings also provide private club managers with a valid and reliable scale for assessing benefits their members perceive to be important.

Most research on confirmatory factor analysis using global fit indices (AIC, BIC, AICc, and CAIC) has been in the structural equation modeling framework. Little research has been done concerning application of these indices to item response models, especially within the framework of multidimensional Rasch analysis. The results of two simulations studies that investigated how sample size, between-dimension correlation, and test length affect the accuracy of these indices in model recovery using a multidimensional Rasch analysis are described in this dissertation. The first study analyzed dichotomous data, with model-to-data misfit as an additional independent variable. The second study analyzed polytomous data, with rating scale structure as an additional independent variable. The interaction effect between global fit index and between-dimension correlation had very large effect sizes in both studies. At higher values of between-dimension correlation, AIC indicated the correct two-dimension generating structure slightly more often than does the BIC or CAIC. The correlation by test length interaction had an odds ratio indicating practical importance in the polytomous study but not the dichotomous study. The combination of shorter tests and higher correlations resulted in a difficult-to-detect distinction being modeled with less statistical information. The correlation by index interaction in the dichotomous study had an odds ratio indicating practical importance. As expected, the results demonstrated that violations of the Rasch model assumptions are magnified at higher between-dimension correlations. Recommendations for practitioners working with highly correlated multidimensional data include creating moderate length (roughly 40 items) instruments, minimizing data-to-model misfit in the choice of model used for confirmatory factor analysis (MRCMLM or other MIRT models), and making decisions based on multiple global indices instead of depending on one index in particular. / Ph. D.

In social science research, data is often collected using questionnaires with Likert scales, resulting in ordinal data. Confirmatory factor analysis (CFA) is the most common type of analysis, which assumes continuous data and multivariate normality, the assumptions violated for ordinal data. Simulation studies have shown that Robust Maximum Likelihood (RML) works well when the normality assumption is violated. Diagonally Weighted Least Squares (DWLS) estimation is especially recommended for categorical data. Bayesian estimation (BE) methods are also potentially effective for ordinal data. The current study employs a CFA model and Monte Carlo simulation to evaluate the performance of three estimation methods with ordinal data under various conditions in terms of the levels of asymmetry, sample sizes, and number of categories. The results indicate that, for ordinal data, DWLS outperforms RML and BE. RML is effective for ordinal data when the category numbers are sufficiently large. Bayesian methods do not demonstrate a significant advantage with different values of factor loadings, and category distributions had minimal impact on the estimation results.

This research explores the relationship between organisational climate and job satisfaction in an Information and Communication Technology (ICT) organisation within South Africa by means of quantitative research. An organisational climate questionnaire was developed to measure the organisational climate and job satisfaction of the organisation and was administered to a sample of 696 employees across three regions. The results indicate that there was a strong positive correlation (0.813 at the 0.01 level) between organisational climate and job satisfaction, therefore supporting the research hypothesis. A stepwise regression was conducted and nine dimensions of organisational climate were found to predict 71% variance in job satisfaction. The interaction of biographical and organisational variables on organisational climate and job satisfaction was studied by means of t-tests and ANOVA. Although statistical significant differences were found, in terms of practical significance, the effect sizes were generally found to be small. / Industrial and Organisational Psychology / M.A. (Industrial and Organisational Psychology)

This research explores the relationship between organisational climate and job satisfaction in an Information and Communication Technology (ICT) organisation within South Africa by means of quantitative research. An organisational climate questionnaire was developed to measure the organisational climate and job satisfaction of the organisation and was administered to a sample of 696 employees across three regions. The results indicate that there was a strong positive correlation (0.813 at the 0.01 level) between organisational climate and job satisfaction, therefore supporting the research hypothesis. A stepwise regression was conducted and nine dimensions of organisational climate were found to predict 71% variance in job satisfaction. The interaction of biographical and organisational variables on organisational climate and job satisfaction was studied by means of t-tests and ANOVA. Although statistical significant differences were found, in terms of practical significance, the effect sizes were generally found to be small. / Industrial and Organisational Psychology / M.A. (Industrial and Organisational Psychology)

This study examines the relationship between organisational culture and TQM implementation barriers to gain a more comprehensive understanding of the factors affecting TQM implementation. For TQM implementation to take root effectively, the critical role of organisational culture is widely recognised. The existence of pitfalls and obstacles (barriers) to implementing TQM is also widely recognized, as is the importance of understanding these TQM implementation barriers. Nevertheless, whilst many TQM implementation models and frameworks have been designed and proposed, no study has been located in the literature that has systematically examined the relationship between organisational culture and TQM implementation barriers. This theoretical lapse in the TQM literature necessitates an investigation of the direction and significance of the relationship which can help in devising more informed TQM implementation models. In this context, a quantitative research methodology was adopted to examine the profiles of organisational culture and of TQM implementation barriers in organisations in Bahrain and to examine the relationship between these variables. Bahrain is presently going through a rapid expansion in quality management system adoption. Accordingly, the research uses four constructs of organisational culture as independent variables and six constructs of TQM implementation barriers identified through the literature as dependent variables. A set of hypotheses was developed describing the expected relationships between these two sets of variables. The study adopted a positivist, deductive approach using an online survey questionnaire to obtain quantitative data for hypothesis testing. The research instrument was assessed for validity and reliability through structured interviews. Responses to the survey were obtained from 325 organisations located in Bahrain. Analysis of Moment Structure (AMOS) version 16.0 was used to test the measurement model using Confirmatory Factor Analysis (CFA), and to test the structural model using Structural Equation Modelling (SEM). Both models showed a very good fit to the data, with good construct validity and reliability. The findings of the study showed that group culture, which is believed to be an ‘ideal’ culture for TQM implementation helps decrease employee barriers, information barriers and customer related barriers as predicted. However group culture wasn’t found to help decrease top management barriers. Rational culture was found to decrease top management barriers as predicted but it wasn’t found to help decrease employee and customer barriers. The findings confirm the significant impact of hierarchical culture in the Bahrain context in decreasing planning and process management barriers. Developmental culture’s potential to lower employee and customer barriers was observed but was not found to be statistically significant. This research makes several contributions in both academic and practical terms. Theoretically, positioning organisational culture as an antecedent of TQM implementation barriers, this study is the first holistic approach that attempts to empirically investigate which type of organisational culture is related to which TQM implementation barriers. Understanding the nature, strength and direction of these relationships can help to inform and support future TQM implementation attempts. Practically, this research will benefit organisations who have not been able to fully realise TQM, or who are in the process of planning the introduction of TQM. The findings of the study can help Bahraini organisations to realise the long term quality objectives of the Bahrain Centre of Excellence’s Vision 2030 programme. Furthermore, the study has contributed a new empirically tested scale for measuring TQM implementation barriers - a valuable tool on its own, or in conjunction with the organisational culture profile assessment tool - for both practitioners wishing to examine their readiness for TQM or progress in creating a TQM ethos, and for future researchers wishing to extend our understanding of the influence of TQM barriers and/or culture on major organisational improvement interventions. It is expected that replication of this study in other countries and regions with different culture and context may help in developing an improved model of TQM implementation. Implications for managers and future research are advanced.

Full weighted least squares (full WLS) and robust weighted least squares (robust WLS) are currently the two primary estimation methods designed for structural equation modeling with ordinal observed variables. These methods assume that continuous latent variables were coarsely categorized by the measurement process to yield the observed ordinal variables, and that the model proposed by the researcher pertains to these latent variables rather than to their ordinal manifestations. Previous research has strongly suggested that robust WLS is superior to full WLS when models are correctly specified. Given the realities of applied research, it was critical to examine these methods with misspecified models. This Monte Carlo simulation study examined the performance of full and robust WLS for two-factor, eight-indicator confirmatory factor analytic models that were either correctly specified, overspecified, or misspecified in one of two ways. Seven conditions of five-category indicator distribution shape at four sample sizes were simulated. These design factors were completely crossed for a total of 224 cells. Previously findings of the relative superiority of robust WLS with correctly specified models were replicated, and robust WLS was also found to perform better than full WLS given overspecification or misspecification. Robust WLS parameter estimates were usually more accurate for correct and overspecified models, especially at the smaller sample sizes. In the face of misspecification, full WLS better approximated the correct loading values whereas robust estimates better approximated the correct factor correlation. Robust WLS chi-square values discriminated between correct and misspecified models much better than full WLS values at the two smaller sample sizes. For all four model specifications, robust parameter estimates usually showed lower variability and robust standard errors usually showed lower bias. These findings suggest that robust WLS should likely remain the estimator of choice for applied researchers. Additionally, highly leptokurtic distributions should be avoided when possible. It should also be noted that robust WLS performance was arguably adequate at the sample size of 100 when the indicators were not highly leptokurtic. / text