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Description
The purpose of this dissertation was to develop a Compassionate Communication Scale (CCS) by conducting a series of studies. The first study used qualitative data to identify and develop initial scale items. A series of follow-up studies used exploratory factor analysis to investigate the underlying structure of the CCS. A

The purpose of this dissertation was to develop a Compassionate Communication Scale (CCS) by conducting a series of studies. The first study used qualitative data to identify and develop initial scale items. A series of follow-up studies used exploratory factor analysis to investigate the underlying structure of the CCS. A three-factor structure emerged, which included: Compassionate conversation, such as listening, letting the distressed person disclose feelings, and making empathetic remarks; compassionate touch, such as holding someone's hand or patting someone's back; and compassionate messaging, such as posting an encouraging message on a social networking site or sending a sympathetic email. The next study tested convergent and divergent validity by determining how the three forms of compassionate communication associate with various traits. Compassionate conversation was positively related to compassion, empathetic concern, perspective taking, emotional intelligence, social expressivity, emotional expressivity and benevolence, and negatively related to verbal aggressiveness and narcissism. Compassionate touch was positively correlated with compassion, empathetic concern, perspective taking, emotional intelligence, social expressivity, emotional expressivity, and benevolence, and uncorrelated with verbal aggressiveness and benevolence. Finally, compassionate messaging was positively correlated with social expressivity, emotional expressivity, and uncorrelated with verbal aggressiveness and narcissism. The next study focused on cross-validation and criterion-related validity. Correlations showing that self-reports of a person's compassionate communication were positively related to a friend or romantic partner's report of that person's compassionate communication provided cross-validation. The test for criterion-related validity examined whether compassionate communication predicts relational satisfaction. Regression analyses revealed that people were more relationally satisfied when they perceived themselves to use compassionate conversation, when they perceived their partner to use compassionate conversation, and when their partner reported using compassionate conversation. This finding did not extend to compassionate touch or compassionate messaging. In fact, in one regression analysis, people reported more relational satisfaction when they perceived that their partners used high levels of compassionate conversation and low levels of compassionate touch. Overall, the analyses suggest that of the three forms of compassionate communication, compassionate conversation is most strongly related to relational satisfaction. Taken together, this series of studies provides initial evidence for the validity of the CCS.
ContributorsRamos Salazar, Leslie (Author) / Guerrero, Laura (Thesis advisor) / Roberto, Anthony (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Daily dairies and other intensive measurement methods are increasingly used to study the relationships between two time varying variables X and Y. These data are commonly analyzed using longitudinal multilevel or bivariate growth curve models that allow for random effects of intercept (and sometimes also slope) but which do not

Daily dairies and other intensive measurement methods are increasingly used to study the relationships between two time varying variables X and Y. These data are commonly analyzed using longitudinal multilevel or bivariate growth curve models that allow for random effects of intercept (and sometimes also slope) but which do not address the effects of weekly cycles in the data. Three Monte Carlo studies investigated the impact of omitting the weekly cycles in daily dairy data under the multilevel model framework. In cases where cycles existed in both the time-varying predictor series (X) and the time-varying outcome series (Y) but were ignored, the effects of the within- and between-person components of X on Y tended to be biased, as were their corresponding standard errors. The direction and magnitude of the bias depended on the phase difference between the cycles in the two series. In cases where cycles existed in only one series but were ignored, the standard errors of the regression coefficients for the within- and between-person components of X tended to be biased, and the direction and magnitude of bias depended on which series contained cyclical components.
ContributorsLiu, Yu (Author) / West, Stephen G. (Thesis advisor) / Enders, Craig K. (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2013
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Description
In order to cope with the decreasing availability of symphony jobs and collegiate faculty positions, many musicians are starting to pursue less traditional career paths. Also, to combat declining audiences, musicians are exploring ways to cultivate new and enthusiastic listeners through relevant and engaging performances. Due to these challenges, many

In order to cope with the decreasing availability of symphony jobs and collegiate faculty positions, many musicians are starting to pursue less traditional career paths. Also, to combat declining audiences, musicians are exploring ways to cultivate new and enthusiastic listeners through relevant and engaging performances. Due to these challenges, many community-based chamber music ensembles have been formed throughout the United States. These groups not only focus on performing classical music, but serve the needs of their communities as well. The problem, however, is that many musicians have not learned the business skills necessary to create these career opportunities. In this document I discuss the steps ensembles must take to develop sustainable careers. I first analyze how groups build a strong foundation through getting to know their communities and creating core values. I then discuss branding and marketing so ensembles can develop a public image and learn how to publicize themselves. This is followed by an investigation of how ensembles make and organize their money. I then examine the ways groups ensure long-lasting relationships with their communities and within the ensemble. I end by presenting three case studies of professional ensembles to show how groups create and maintain successful careers. Ensembles must develop entrepreneurship skills in addition to cultivating their artistry. These business concepts are crucial to the longevity of chamber groups. Through interviews of successful ensemble members and my own personal experiences in the Tetra String Quartet, I provide a guide for musicians to use when creating a community-based ensemble.
ContributorsDalbey, Jenna (Author) / Landschoot, Thomas (Thesis advisor) / McLin, Katherine (Committee member) / Ryan, Russell (Committee member) / Solis, Theodore (Committee member) / Spring, Robert (Committee member) / Arizona State University (Publisher)
Created2013
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Description
American Primitive is a composition written for wind ensemble with an instrumentation of flute, oboe, clarinet, bass clarinet, alto, tenor, and baritone saxophones, trumpet, horn, trombone, euphonium, tuba, piano, and percussion. The piece is approximately twelve minutes in duration and was written September - December 2013. American Primitive is absolute

American Primitive is a composition written for wind ensemble with an instrumentation of flute, oboe, clarinet, bass clarinet, alto, tenor, and baritone saxophones, trumpet, horn, trombone, euphonium, tuba, piano, and percussion. The piece is approximately twelve minutes in duration and was written September - December 2013. American Primitive is absolute music (i.e. it does not follow a specific narrative) comprising blocks of distinct, contrasting gestures which bookend a central region of delicate textural layering and minimal gestural contrast. Though three gestures (a descending interval followed by a smaller ascending interval, a dynamic swell, and a chordal "chop") were consciously employed throughout, it is the first gesture of the three that creates a sense of unification and overall coherence to the work. Additionally, the work challenges listeners' expectations of traditional wind ensemble music by featuring the trumpet as a quasi-soloist whose material is predominately inspired by transcriptions of jazz solos. This jazz-inspired material is at times mimicked and further developed by the ensemble, also often in a soloistic manner while the trumpet maintains its role throughout. This interplay of dialogue between the "soloists" and the "ensemble" further skews listeners' conceptions of traditional wind ensemble music by featuring almost every instrument in the ensemble. Though the term "American Primitive" is usually associated with the "naïve art" movement, it bears no association to the music presented in this work. Instead, the term refers to the author's own compositional attitudes, education, and aesthetic interests.
ContributorsJandreau, Joshua (Composer) / Rockmaker, Jody D (Thesis advisor) / Rogers, Rodney I (Committee member) / Demars, James R (Committee member) / Arizona State University (Publisher)
Created2014
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Description
This project is a practical annotated bibliography of original works for oboe trio with the specific instrumentation of two oboes and English horn. Presenting descriptions of 116 readily available oboe trios, this project is intended to promote awareness, accessibility, and performance of compositions within this genre.

The annotated bibliography focuses

This project is a practical annotated bibliography of original works for oboe trio with the specific instrumentation of two oboes and English horn. Presenting descriptions of 116 readily available oboe trios, this project is intended to promote awareness, accessibility, and performance of compositions within this genre.

The annotated bibliography focuses exclusively on original, published works for two oboes and English horn. Unpublished works, arrangements, works that are out of print and not available through interlibrary loan, or works that feature slightly altered instrumentation are not included.

Entries in this annotated bibliography are listed alphabetically by the last name of the composer. Each entry includes the dates of the composer and a brief biography, followed by the title of the work, composition date, commission, and dedication of the piece. Also included are the names of publishers, the length of the entire piece in minutes and seconds, and an incipit of the first one to eight measures for each movement of the work.

In addition to providing a comprehensive and detailed bibliography of oboe trios, this document traces the history of the oboe trio and includes biographical sketches of each composer cited, allowing readers to place the genre of oboe trios and each individual composition into its historical context. Four appendices at the end include a list of trios arranged alphabetically by composer's last name, chronologically by the date of composition, and by country of origin and a list of publications of Ludwig van Beethoven's oboe trios from the 1940s and earlier.
ContributorsSassaman, Melissa Ann (Author) / Schuring, Martin (Thesis advisor) / Buck, Elizabeth (Committee member) / Holbrook, Amy (Committee member) / Hill, Gary (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as those denoted by sex or ethnicity. While establishing factorial invariance across observed groups is a requisite step in making meaningful

Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as those denoted by sex or ethnicity. While establishing factorial invariance across observed groups is a requisite step in making meaningful cross-group comparisons, failure to attend to possible sources of latent class heterogeneity in the form of class-based differences in factor structure has the potential to compromise conclusions with respect to observed groups and may result in misguided attempts at instrument development and theory refinement. The present studies examined the sensitivity of two widely used confirmatory factor analytic model fit indices, the chi-square test of model fit and RMSEA, to latent class differences in factor structure. Two primary questions were addressed. The first of these concerned the impact of latent class differences in factor loadings with respect to model fit in a single sample reflecting a mixture of classes. The second question concerned the impact of latent class differences in configural structure on tests of factorial invariance across observed groups. The results suggest that both indices are highly insensitive to class-based differences in factor loadings. Across sample size conditions, models with medium (0.2) sized loading differences were rejected by the chi-square test of model fit at rates just slightly higher than the nominal .05 rate of rejection that would be expected under a true null hypothesis. While rates of rejection increased somewhat when the magnitude of loading difference increased, even the largest sample size with equal class representation and the most extreme violations of loading invariance only had rejection rates of approximately 60%. RMSEA was also insensitive to class-based differences in factor loadings, with mean values across conditions suggesting a degree of fit that would generally be regarded as exceptionally good in practice. In contrast, both indices were sensitive to class-based differences in configural structure in the context of a multiple group analysis in which each observed group was a mixture of classes. However, preliminary evidence suggests that this sensitivity may contingent on the form of the cross-group model misspecification.
ContributorsBlackwell, Kimberly Carol (Author) / Millsap, Roger E (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2011
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Description
The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in compensatory and noncompensatory multidimensional item response models (MIRT) of assessment data using dimensionality assessment procedures based on conditional covariances (i.e., DETECT) and a factor analytical approach (i.e., NOHARM). The DETECT-based methods typically outperformed

The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in compensatory and noncompensatory multidimensional item response models (MIRT) of assessment data using dimensionality assessment procedures based on conditional covariances (i.e., DETECT) and a factor analytical approach (i.e., NOHARM). The DETECT-based methods typically outperformed the NOHARM-based methods in both two- (2D) and three-dimensional (3D) compensatory MIRT conditions. The DETECT-based methods yielded high proportion correct, especially when correlations were .60 or smaller, data exhibited 30% or less complexity, and larger sample size. As the complexity increased and the sample size decreased, the performance typically diminished. As the complexity increased, it also became more difficult to label the resulting sets of items from DETECT in terms of the dimensions. DETECT was consistent in classification of simple items, but less consistent in classification of complex items. Out of the three NOHARM-based methods, χ2G/D and ALR generally outperformed RMSR. χ2G/D was more accurate when N = 500 and complexity levels were 30% or lower. As the number of items increased, ALR performance improved at correlation of .60 and 30% or less complexity. When the data followed a noncompensatory MIRT model, the NOHARM-based methods, specifically χ2G/D and ALR, were the most accurate of all five methods. The marginal proportions for labeling sets of items as dimension-like were typically low, suggesting that the methods generally failed to label two (three) sets of items as dimension-like in 2D (3D) noncompensatory situations. The DETECT-based methods were more consistent in classifying simple items across complexity levels, sample sizes, and correlations. However, as complexity and correlation levels increased the classification rates for all methods decreased. In most conditions, the DETECT-based methods classified complex items equally or more consistent than the NOHARM-based methods. In particular, as complexity, the number of items, and the true dimensionality increased, the DETECT-based methods were notably more consistent than any NOHARM-based method. Despite DETECT's consistency, when data follow a noncompensatory MIRT model, the NOHARM-based method should be preferred over the DETECT-based methods to assess dimensionality due to poor performance of DETECT in identifying the true dimensionality.
ContributorsSvetina, Dubravka (Author) / Levy, Roy (Thesis advisor) / Gorin, Joanna S. (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Two models of motivation are prevalent in the literature on sport and exercise participation (Deci & Ryan, 1991; Vallerand, 1997, 2000). Both models are grounded in self-determination theory (Deci & Ryan, 1985; Ryan & Deci, 2000) and consider the relationship between intrinsic, extrinsic, and amotivation in explaining behavior choice and

Two models of motivation are prevalent in the literature on sport and exercise participation (Deci & Ryan, 1991; Vallerand, 1997, 2000). Both models are grounded in self-determination theory (Deci & Ryan, 1985; Ryan & Deci, 2000) and consider the relationship between intrinsic, extrinsic, and amotivation in explaining behavior choice and outcomes. Both models articulate the relationship between need satisfaction (i.e., autonomy, competence, relatedness; Deci & Ryan, 1985, 2000; Ryan & Deci, 2000) and various cognitive, affective, and behavioral outcomes as a function of self-determined motivation. Despite these comprehensive models, inconsistencies remain between the theories and their practical applications. The purpose of my study was to examine alternative theoretical models of intrinsic, extrinsic, and amotivation using the Sport Motivation Scale-6 (SMS-6; Mallett et al., 2007) to more thoroughly study the structure of motivation and the practical utility of using such a scale to measure motivation among runners. Confirmatory factor analysis was used to evaluate eight alternative models. After finding unsatisfactory fit of these models, exploratory factor analysis was conducted post hoc to further examine the measurement structure of motivation. A three-factor structure of general motivation, external accolades, and isolation/solitude explained motivation best, although high cross-loadings of items suggest the structure of this construct still lacks clarity. Future directions to modify item content and re-examine structure as well as limitations of this study are discussed.
ContributorsKube, Erin (Author) / Thompson, Marilyn (Thesis advisor) / Tracey, Terence (Thesis advisor) / Green, Samuel (Committee member) / Arizona State University (Publisher)
Created2012
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Description
This research addressed concerns regarding the measurement of cyberbullying and aimed to develop a reliable and valid measure of cyberbullying perpetration and victimization. Despite the growing body of literature on cyberbullying, several measurement concerns were identified and addressed in two pilot studies. These concerns included the most appropriate time frame

This research addressed concerns regarding the measurement of cyberbullying and aimed to develop a reliable and valid measure of cyberbullying perpetration and victimization. Despite the growing body of literature on cyberbullying, several measurement concerns were identified and addressed in two pilot studies. These concerns included the most appropriate time frame for behavioral recall, use of the term "cyberbullying" in questionnaire instructions, whether to refer to power in instances of cyberbullying, and best practices for designing self-report measures to reflect how young adults understand and communicate about cyberbullying. Mixed methodology was employed in two pilot studies to address these concerns and to determine how to best design a measure which participants could respond to accurately and honestly. Pilot study one consisted of an experimental examination of time frame for recall and use of the term on the outcomes of honesty, accuracy, and social desirability. Pilot study two involved a qualitative examination of several measurement concerns through focus groups held with young adults. Results suggested that one academic year was the most appropriate time frame for behavioral recall, to avoid use of the term "cyberbullying" in questionnaire instructions, to include references to power, and other suggestions for the improving the method in the main study to bolster participants' attention. These findings informed the development of a final measure in the main study which aimed to be both practical in its ability to capture prevalence and precise in its ability to measure frequency. The main study involved examining the psychometric properties, reliability, and validity of the final measure. Results of the main study indicated that the final measure exhibited qualities of an index and was assessed as such. Further, structural equation modeling techniques and test-retest procedures indicated the measure had good reliability. And, good predictive validity and satisfactory convergent validity was established for the final measure. Results derived from the measure concerning prevalence, frequency, and chronicity are presented within the scope of findings in cyberbullying literature. Implications for practice and future directions for research with the measure developed here are discussed.
ContributorsSavage, Matthew (Author) / Roberto, Anthony J (Thesis advisor) / Palazzolo, Kellie E (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2012
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Description
The goal of diagnostic assessment is to discriminate between groups. In many cases, a binary decision is made conditional on a cut score from a continuous scale. Psychometric methods can improve assessment by modeling a latent variable using item response theory (IRT), and IRT scores can subsequently be used to

The goal of diagnostic assessment is to discriminate between groups. In many cases, a binary decision is made conditional on a cut score from a continuous scale. Psychometric methods can improve assessment by modeling a latent variable using item response theory (IRT), and IRT scores can subsequently be used to determine a cut score using receiver operating characteristic (ROC) curves. Psychometric methods provide reliable and interpretable scores, but the prediction of the diagnosis is not the primary product of the measurement process. In contrast, machine learning methods, such as regularization or binary recursive partitioning, can build a model from the assessment items to predict the probability of diagnosis. Machine learning predicts the diagnosis directly, but does not provide an inferential framework to explain why item responses are related to the diagnosis. It remains unclear whether psychometric and machine learning methods have comparable accuracy or if one method is preferable in some situations. In this study, Monte Carlo simulation methods were used to compare psychometric and machine learning methods on diagnostic classification accuracy. Results suggest that classification accuracy of psychometric models depends on the diagnostic-test correlation and prevalence of diagnosis. Also, machine learning methods that reduce prediction error have inflated specificity and very low sensitivity compared to the data-generating model, especially when prevalence is low. Finally, machine learning methods that use ROC curves to determine probability thresholds have comparable classification accuracy to the psychometric models as sample size, number of items, and number of item categories increase. Therefore, results suggest that machine learning models could provide a viable alternative for classification in diagnostic assessments. Strengths and limitations for each of the methods are discussed, and future directions are considered.
ContributorsGonzález, Oscar (Author) / Mackinnon, David P (Thesis advisor) / Edwards, Michael C (Thesis advisor) / Grimm, Kevin J. (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Created2018