Matching Items (3)
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- All Subjects: Quantitative Methods
- Creators: Mackinnon, David P
- Creators: Grabowski, Jane
Description
Code-switching, a bilingual language phenomenon, which may be defined as the concurrent use of two or more languages by fluent speakers is frequently misunderstood and stigmatized. Given that the majority of the world's population is bilingual rather than monolingual, the study of code-switching provides a fundamental window into human cognition and the systematic structural outcomes of language contact. Intra-sentential code-switching is said to systematically occur, constrained by the lexicons of each respective language. In order to access information about the acceptability of certain switches, linguists often elicit grammaticality judgments from bilingual informants. In current linguistic research, grammaticality judgment tasks are often scrutinized on account of the lack of stability of responses to individual sentences. Although this claim is largely motivated by research on monolingual strings under a variety of variable conditions, the stability of code-switched grammaticality judgment data given by bilingual informants has yet to be systematically investigated. By comparing grammaticality judgment data from 3 groups of German-English bilinguals, Group A (N=50), Group B (N=34), and Group C (N=40), this thesis investigates the stability of grammaticality judgments in code-switching over time, as well as a potential difference in judgments between judgment data for spoken and written code-switching stimuli. Using a web-based survey, informants were asked to give ratings of each code-switched token. The results were computed and findings from a correlated groups t test attest to the stability of code-switched judgment data over time with a p value of .271 and to the validity of the methodologies currently in place. Furthermore, results from the study also indicated that no statistically significant difference was found between spoken and written judgment data as computed with an independent groups t test resulting in a p value of .186, contributing a valuable fact to the body of data collection practices in research in bilingualism. Results from this study indicate that there are significant differences attributable to language dominance for specific token types, which were calculated using an ANOVA test. However, when using group composite scores of all tokens, the ANOVA measure returned a non-significant score of .234, suggesting that bilinguals with differing language dominances rank in a similar manner. The findings from this study hope to help clarify current practices in code-switching research.
ContributorsGrabowski, Jane (Author) / Gilfillan, Daniel (Thesis advisor) / Macswan, Jeff (Thesis advisor) / Ghanem, Carla (Committee member) / Arizona State University (Publisher)
Created2011
Description
Including a covariate can increase power to detect an effect between two variables. Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase the power to detect a relation between two variables has not been investigated. The first study identified situations where empirical and analytical power of two tests of significance for a single mediator model was greater than power of a bivariate significance test. Results from the first study indicated that including a mediator increased statistical power in small samples with large effects and in large samples with small effects. Next, a study was conducted to assess when power was greater for a significance test for a two mediator model as compared with power of a bivariate significance test. Results indicated that including two mediators increased power in small samples when both specific mediated effects were large and in large samples when both specific mediated effects were small. Implications of the results and directions for future research are then discussed.
ContributorsO'Rourke, Holly Patricia (Author) / Mackinnon, David P (Thesis advisor) / Enders, Craig K. (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2013
Description
Time metric is an important consideration for all longitudinal models because it can influence the interpretation of estimates, parameter estimate accuracy, and model convergence in longitudinal models with latent variables. Currently, the literature on latent difference score (LDS) models does not discuss the importance of time metric. Furthermore, there is little research using simulations to investigate LDS models. This study examined the influence of time metric on model estimation, interpretation, parameter estimate accuracy, and convergence in LDS models using empirical simulations. Results indicated that for a time structure with a true time metric where participants had different starting points and unequally spaced intervals, LDS models fit with a restructured and less informative time metric resulted in biased parameter estimates. However, models examined using the true time metric were less likely to converge than models using the restructured time metric, likely due to missing data. Where participants had different starting points but equally spaced intervals, LDS models fit with a restructured time metric resulted in biased estimates of intercept means, but all other parameter estimates were unbiased, and models examined using the true time metric had less convergence than the restructured time metric as well due to missing data. The findings of this study support prior research on time metric in longitudinal models, and further research should examine these findings under alternative conditions. The importance of these findings for substantive researchers is discussed.
ContributorsO'Rourke, Holly P (Author) / Grimm, Kevin J. (Thesis advisor) / Mackinnon, David P (Thesis advisor) / Chassin, Laurie (Committee member) / Aiken, Leona S. (Committee member) / Arizona State University (Publisher)
Created2016