This dissertation introduces a new method, Pratt's measure matrix, for interpreting multidimensional oblique factor models in both exploratory and confirmatory contexts. Overall, my thesis, supported by empirical evidence, refutes the currently recommended and practiced methods for understanding an oblique factor model; that is, interpreting the pattern matrix or structure matrix alone or juxtaposing both without integrating the information. Chapter Two reviews the complexities of interpreting a multidimensional factor solution due to factor correlation (i.e., obliquity). Three major complexities highlighted are (1) the inconsistency between the pattern and structure coefficients, (2) the distortion of additive properties, and (3) the inappropriateness of the traditional cut-off rules as being "meaningful". Chapter Three provides the theoretical rationale for adapting Pratt's importance measures from their use in multiple regression to that of factor analysis. The new method is demonstrated and tested with both continuous and categorical data in exploratory factor analysis. The results show that Pratt's measures are applicable to factor analysis and are able to resolve three interpretational complexities arising from factor obliquity. In the context of confirmatory factor analysis, Chapter Four warns researchers that a structure coefficient could be entirely spurious due to factor obliquity as well as zero constraint on its corresponding pattern coefficient. Interpreting such structure coefficients as Graham et al. (2003) suggested can be problematic. The mathematically more justified method is to transform the pattern and structure coefficients into Pratt's measures. The last chapter describes eight novel contributions in this dissertation. The new method is the first attempt ever at ordering the importance of latent variables for multivariate data. It is also the first attempt at demonstrating and explicating the existence, mechanism, and implications of the suppression effect in factor analyses. Specifically, the new method resolves the three interpretational problems due to factor obliquity, assists in identifying a better-fitting exploratory factor model, proves that a structure coefficient in a confirmatory factor analysis with a zero pattern constraint is entirely spurious, avoids the debate over the choice of oblique and orthogonal factor rotation, and last but not least, provides a tool for consolidating the role off actors as the underlying causes. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate

Performance appraisal satisfaction is the extent to which the employee perceives performance ratings, which reflect those behaviours that contribute to the organisation. Even though performance appraisal satisfaction is the most frequently measured appraisal reaction, there are relatively few meta-analysis studies which link determinants of appraisal system to satisfaction with employee performance. The focus of this research is to examine the determinants affecting employee performance appraisal satisfaction in the Brunei public sector using data collected from among public sector employees, with particular emphasis on how performance is viewed and measured in the public sector. Data for this research were gathered across ten government ministries in Brunei. This research study adopts a ‘mixed method approach’, which utilises quantitative data supported by qualitative data. The qualitative interviews involved 14 participants, while the main quantitative data had 355 samples. Quantitative data was analysed using descriptive analysis and exploratory factor analysis run on SPSS, while confirmatory factor analysis, path analysis and structural equation modelling were also employed on applied analysis of moment structure (AMOS) to assess the model fit of the study and hypotheses testing. Results indicated that latent constructs (goal-setting and the purposes of performance appraisal; alignment of personal objectives with organisational goals; fairness of the appraisal system; types of performance evaluation measures; format of rating scales; appraiser-appraisee relationship and credibility of appraiser; in-group collectivism; power-distance; and pay-for-performance constructs) were positively and significantly correlated to performance appraisal satisfaction. The results also showed that the goodness of fit indices offered an acceptable fit to Brunei’s data. The study findings advance current knowledge in the performance management domain by extending individual level theory of performance appraisal satisfaction and provide empirical evidence for performance appraisal and employee satisfaction at the individual level in the public sector. This study contributes theoretically by highlighting the unique effects of latent factors on employee performance appraisal satisfaction. The research also contributes in terms of methodology, in that this study contributes to the examination of the predictors of established models of performance management in a country which is culturally different from the environments in which these constructs were developed. This research has filled gaps by testing predictor variables in cross-cultural work settings, which may be useful in generalising these predictors. Furthermore, the examination of the conceptual framework using structural equation modelling is a methodological contribution in its own right. The presence of multivariate normality encourages the assessment of the measurement model by a confirmatory factor approach, using maximum likelihood estimation, which is an additional contribution to the method analysis.

<p> Fearlessness is often discussed in relation to clinical and personality research (e.g., Davies & Craske, 2018; Lilienfeld & Widows, 2005). However, there is a paucity of research focusing on its empirical structure, in particular with from self-report measures. The present study examined the hierarchical structure of self-reported fearlessness and compared this structure to external criterion measures. Using a pre-registered analytical approach, we found evidence for the multidimensionality of fearlessness, and that a six-factor model fit the data best. Criterion variables measuring boldness, fear, anxiety, psychopathy, personality, and impulsivity, were correlated with the factor scores at each factor level of the model. The six-factor solution emerged as comprehensive and labeled Boldness, Anxiety, Surgency, Recklessness, Adventurousness, and Daring. The findings from this study elucidate how trait fearlessness unfolds at varying levels and how these factors relate to and diverge from various outcomes.</p> <p><br></p>

Purpose - The purpose of this paper is twofold: first, to study the environmental and competitive factors in the service organizations and second, to investigate the extent of effective implementation of quality improvement initiatives in different operational settings. Design/methodology/approach - In this paper factor analysis is used to determine the underlying factors associated with the changes in the competitive environment. Proportional measures are used to study the implementation of quality improvement initiatives. Findings - The paper finds that quality improvement initiatives are not implemented uniformly by all the service industries. Organizations implementing quality improvement initiatives face varying degrees of effectiveness. Positive operational and strategic outcomes have been observed by organizations implementing the quality improvement initiatives. Practical implications - The results of this paper show that implementation of different types of quality improvement initiatives has a positive impact on operational and strategic aspects of service organizations. Originality/value - The empirical investigation in this paper shows the practical and theoretical value of issues related to the performance of service organizations.

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The primary purpose of this study was to confirm the four-factor structure of the 30-item Adlerian Personality Priority Assessment (APPA) using a split-sample cross-validation confirmatory factor analysis (CFA). The APPA is an assessment, grounded in Adlerian theory, used to conceptualize clients based on the four personality priorities most commonly used in the Adlerian literature: superiority, pleasing, control, and comfort. The secondary purpose of this study was to provide evidence for discriminant validity, examine predictive qualities of demographics, and explore the prevalence of the four priorities across demographics. For the cross validation CFA, I randomly divided the sample, 1210 undergraduates, at a large public research university (53% Caucasian, 13.1% Hispanic/Latino(a), 21.4% African American, 5.4% American Indian, and 5.8% biracial; mean age =19.8; 58.9% females), into two equal subsamples. I used Subsample 1 (n = 605) to conduct the initial CFA. I held out Subsample 2 (n = 605) to test any possible model changes resulting from Subsample 1 results and to provide further confirmation of the APPA's construct validity. Findings from the split-sample cross-validation CFA confirmed the four-factor structure of the APPA and provided support for the factorial/structure validity of the APPA's scores. Results also present initial evidence of discriminant validity and support the applicability of the instrument across demographics. Overall, these findings suggest Adlerian counselors can confidently use the APPA as a tool to conceptualize clients.

The construct of guilt has been a subject of debate among philosophers, theologians, sociologists and psychologists for centuries. Disagreements concerning guilt have emerged on the definitional level, measurement level, and conceptual level due to the various ways guilt can be experienced and interpreted. Researchers continue to empirically investigate various aspects of guilt in an effort to advance and refine our understanding of the construct; however, differences among researchers in assessing the impact of guilt on psychological well-being still exist. The purpose of this study is to investigate the internal factor structure of three prominent measures of guilt. This will enable us to develop a more concise guilt measure en route to reconciling these differences and better conceptualizing the construct.

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Microarray gene expression data for thousands of genes in many organisms is quickly becoming available. The information this data can provide the experimental biologist is powerful. This data may provide information clarifying the regulatory linkages between genes within a single metabolic pathway, or alternative pathway routes under different environmental conditions, or provide information leading to the identification of genes for selection in animal and plant genetic improvement programs or targets for drug therapy. Many analysis methods to unlock this information have been both proposed and utilized, but not evaluated under known conditions (e.g. simulations). Within this dissertation, an analysis method is proposed and evaluated for identifying independent and linked metabolic pathways and compared to a popular analysis method. Also, this same analysis method is investigated for its ability to identify regulatory linkages within a single metabolic pathway. Lastly, a variant of this same method is used to analyze time series microarray data. In Chapter 2, Factor Analysis is shown to identify and group genes according to membership within independent metabolic pathways for steady state microarray gene expression data. There were cases, however, where the allocation of all genes to a pathway was not complete. A competing analysis method, Hierarchical Clustering, was shown to perform poorly when negatively correlated genes are assumed unrelated, but performance improved when the sign of the correlation coefficient was ignored. In Chapter 3, Factor Analysis is shown to identify regulatory relationships between genes within a single metabolic pathway. These relationships can be explained using metabolic control analysis, along with external knowledge of the pathway structure and activation and inhibition of transcription regulation. In this chapter, it is also shown why factor analysis can group genes by metabolic pathway using metabolic control analysis. In Chapter 4, a Bayesian exploratory factor analysis is developed and used to analyze microarray gene expression data. This Bayesian model differs from a previous implementation in that it is purely exploratory and can be used with vague or uninformative priors. Additionally, 95% highest posterior density regions can be calculated for each factor loading to aid in interpretation of factor loadings. A correlated Bayesian exploratory factor analysis model is also developed in this chapter for application to time series microarray gene expression data. While this method is appropriate for the analysis of correlated observation vectors, it fails to group genes by metabolic pathway for simulated time series data. / Ph. D.