Examining Gender Differences in Writing Skill with Latent Factor Modeling

Thesis (Master's)--University of Washington, 2016-06

The productivity-inflation nexus: The case of the Australian mining sector

This paper examines the causal links between productivity growth and two price series given by domestic inflation and the price of mineral products in Australia's mining sector for the period 1968/1969 to 1997/1998. The study also uses a stochastic translog cost frontier to generate improved estimates of total factor productivity (TFP) growth. The results indicate negative unidirectional causality running from both price series to mining productivity growth. Regression analysis further shows that domestic inflation has a small but adverse effect on mining productivity growth, thus providing some empirical support for Australia's 'inflation first' monetary policy, at least with respect to the mining sector. Inflation in mineral price, on the other hand, has a greater negative effect on mining productivity growth via mineral export growth.

Depression in young adolescents: Investigations using 2 and 3 factor versions of the parental bonding instrument

Associations between parenting style and depressive symptomatology in a community sample of young adolescents (N = 2596) were investigated using self-report measures including the Parental Bonding Instrument and the Center for Epidemiologic Studies Depression Scale. Specifically, the 25-item 2-factor and 3-factor models by Parker et al. (1979), Kendler's (1996) 16-item 3-factor model, and Parker's (1983) quadrant model for the Parental Bonding Instrument were compared. Data analysis included analysis of variance and logistic regression. Reanalysis of Parker's original scale indicates that overprotection is composed of separate factors: intrusiveness (at the individual level) and restrictiveness (in the social context). All models reveal significant independent contributions from paternal care, maternal care, and maternal overprotection (2-factor) or intrusiveness (3-factor) to moderate and serious depressive symptomatology, controlling for sex and family living arrangement. Additive rather than multiplicative interactions between care and overprotection were found. Regardless of the level of parental care and affection, clinicians should note that maternal intrusiveness is strongly associated with adverse psychosocial health in young adolescents.

The gambling related cognitions scale (GRCS): development, confirmatory factor validation and psychometric properties

Aims The aims of this study are to develop and validate a measure to screen for a range of gambling-related cognitions (GRC) in gamblers. Design and participants A total of 968 volunteers were recruited from a community-based population. They were divided randomly into two groups. Principal axis factoring with varimax rotation was performed on group one and confirmatory factor analysis (CFA) was used on group two to confirm the best-fitted solution. Measurements The Gambling Related Cognition Scale (GRCS) was developed for this study and the South Oaks Gambling Screen (SOGS), the Motivation Towards Gambling Scale (MTGS) and the Depression Anxiety Stress Scale (DASS-2 1) were used for validation. Findings Exploratory factor analysis performed using half the sample indicated five factors, which included interpretative control/bias (GRCS-IB), illusion of control (GRCS-IC), predictive control (GRCS-PC), gambling-related expectancies (GRCS-GE) and a perceived inability to stop gambling (GRCS-IS). These accounted for 70% of the total variance. Using the other half of the sample, CFA confirmed that the five-factor solution fitted the data most effectively. Cronbach's alpha coefficients for the factors ranged from 0.77 to 0.91, and 0.93 for the overall scale. Conclusions This paper demonstrated that the 23-item GRCS has good psychometric properties and thus is a useful instrument for identifying GRC among non-clinical gamblers. It provides the first step towards devising/adapting similar tools for problem gamblers as well as developing more specialized instruments to assess particular domains of GRC.

The gambling urge scale: Development, confirmatory factor validation, and psychometric properties

The urge to gamble is a physiological, psychological, or emotional motivational state, often associated with continued gambling. The authors developed and validated the 6-item Gambling Urge Questionnaire (GUS), which was based on the 8-item Alcohol Urge Questionnaire (M. J. Bohn, D. D. Krahn, & B. A. Staehler, 1995), using 968 community-based participants. Exploratory factor analysis using half of the sample indicated a 1-factor solution that accounted for 55.18% of the total variance. This was confirmed using confirmatory factor analysis with the other half of the sample. The GUS had a Cronbach's alpha coefficient of .81. Concurrent, predictive, and criterion-related validity of the GUS were good, suggesting that the GUS is a valid and reliable instrument for assessing gambling urges among nonclinical gamblers.

Determining the effective dimensionality of the genetic variance-covariance matrix

Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by AMEMIYA (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In, contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.

Statnote 10: the two-way analysis of variance

The two-way design has been variously described as a matched-sample F-test, a simple within-subjects ANOVA, a one-way within-groups ANOVA, a simple correlated-groups ANOVA, and a one-factor repeated measures design! This confusion of terminology is likely to lead to problems in correctly identifying this analysis within commercially available software. The essential feature of the design is that each treatment is allocated by randomization to one experimental unit within each group or block. The block may be a plot of land, a single occasion in which the experiment was performed, or a human subject. The ‘blocking’ is designed to remove an aspect of the error variation and increase the ‘power’ of the experiment. If there is no significant source of variation associated with the ‘blocking’ then there is a disadvantage to the two-way design because there is a reduction in the DF of the error term compared with a fully randomised design thus reducing the ‘power’ of the analysis.

Statnote 12: the split-plot analysis of variance

In some experimental situations, the factors may not be equivalent to each other and replicates cannot be assigned at random to all treatment combinations. A common case, called a ‘split-plot design’, arises when one factor can be considered to be a major factor and the other a minor factor. Investigators need to be able to distinguish a split-plot design from a fully randomized design as it is a common mistake for researchers to analyse a split-plot design as if it were a fully randomised factorial experiment.

Data analysis methods in optometry Part 8: Principal components analysis (PCA) and factor analysis (FA)

PCA/FA is a method of analyzing complex data sets in which there are no clearly defined X or Y variables. It has multiple uses including the study of the pattern of variation between individual entities such as patients with particular disorders and the detailed study of descriptive variables. In most applications, variables are related to a smaller number of ‘factors’ or PCs that account for the maximum variance in the data and hence, may explain important trends among the variables. An increasingly important application of the method is in the ‘validation’ of questionnaires that attempt to relate subjective aspects of a patients experience with more objective measures of vision.

Data methods in optometry Part 9: experimental design and analysis of variance

The key to the correct application of ANOVA is careful experimental design and matching the correct analysis to that design. The following points should therefore, be considered before designing any experiment: 1. In a single factor design, ensure that the factor is identified as a 'fixed' or 'random effect' factor. 2. In more complex designs, with more than one factor, there may be a mixture of fixed and random effect factors present, so ensure that each factor is clearly identified. 3. Where replicates can be grouped or blocked, the advantages of a randomised blocks design should be considered. There should be evidence, however, that blocking can sufficiently reduce the error variation to counter the loss of DF compared with a randomised design. 4. Where different treatments are applied sequentially to a patient, the advantages of a three-way design in which the different orders of the treatments are included as an 'effect' should be considered. 5. Combining different factors to make a more efficient experiment and to measure possible factor interactions should always be considered. 6. The effect of 'internal replication' should be taken into account in a factorial design in deciding the number of replications to be used. Where possible, each error term of the ANOVA should have at least 15 DF. 7. Consider carefully whether a particular factorial design can be considered to be a split-plot or a repeated measures design. If such a design is appropriate, consider how to continue the analysis bearing in mind the problem of using post hoc tests in this situation.

Higher-order factor structures and intercorrelations of the 16PF5 and FIRO-B

This study investigated the intercorrelations and the independent and combined factor structures of the Sixteen Personality Factor Questionnaire Fifth Edition (16PF5) and the Fundamental Interpersonal Orientation-Behaviour Scale (FIRO-B). Four thousand four hundred and fourteen U.S. participants completed these measures as part of executive assessments between 1994 and 2003. Exploratory factor analyses supported the five-factor higher-order structure of the 16PF5; however, the three-component structure for the FIRO-B was not supported. A six-factor structure was found to underlie the variance in the measures in combination. Five of these were close to the 16PF5 higher-order structure, but a sixth factor labelled Social Independence also emerged. This new factor consisted of the 16PF5 primaries of Liveliness and Social Boldness, and the FIRO-B Wanted Control scale.

Multiresponse Robust Engineering: Case with Errors in Factor Levels

2000 Mathematics Subject Classification: 62J05, 62J10, 62F35, 62H12, 62P30.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Factor structure of the General Health Questionnaire-28 (GHQ-28) from infertile women attending the Yazd Research and Clinical Center for Infertility

Background: Nowadays, infertility problems have become a social concern, and are associated with multiple psychological and social problems. Also, it affects the interpersonal communication between the individual, familial, and social characteristics. Since women are exposed to stressors of physical, mental, social factors, and treatment of infertility, providing a psychometric screening tool is necessary for disorders of this group. Objective: The aim of this study was to determine the factor structure of the general health questionnaire-28 to discover mental disorders in infertile women. Materials and Methods: In this study, 220 infertile women undergoing treatment of infertility were selected from the Yazd Research and Clinical Center for Infertility with convenience sampling in 2011. After completing the general health questionnaire by the project manager, validity and, reliability of the questionnaire were calculated by confirmatory factor structure and Cronbach's alpha, respectively. Results: Four factors, including anxiety and insomnia, social dysfunction, depression, and physical symptoms were extracted from the factor structure. 50.12% of the total variance was explained by four factors. The reliability coefficient of the questionnaire was obtained 0.90. Conclusion: Analysis of the factor structure and reliability of General Health Questionnaire-28 showed that it is suitable as a screening instrument for assessing general health of infertile women.