Human Development & Quantitative Methodology Theses and Dissertations
http://hdl.handle.net/1903/2794
2016-04-30T16:54:32ZLOW-INCOME LATINO IMMIGRANT MOTHERS AND THEIR TODDLERS: HOW DOES SOCIALIZATION PROMOTE INHIBITORY CONTROL SKILLS?
http://hdl.handle.net/1903/17306
LOW-INCOME LATINO IMMIGRANT MOTHERS AND THEIR TODDLERS: HOW DOES SOCIALIZATION PROMOTE INHIBITORY CONTROL SKILLS?
Aldoney Ramirez, Daniela
Executive function (EF), cognitive skills involved in planning and problem solving, includes inhibitory control as one of its major components. Inhibitory control skills and overall EF has been positively related to social, literacy, and math skills. Research on contextual factors has identified the quality of parenting and parental practices as important predictors of children’s EF skills. An emerging line of studies suggests that parental beliefs may also influence children’s EF. However, the literature has mostly focused on White middle-class children, so less is known about the way in which minority children living in low-income environments develop EF skills. Based on Bronfenbrenner’s bioecological model, I examined how low-income Latino mothers’ beliefs (familism and self-efficacy) relate to the quality of the mother-child interaction (scaffolding and intrusiveness) and practices (routines in the home) and how these, in turn, relate to their toddlers’ inhibitory control skills. I also examined whether maternal warmth moderated the association between the quality of the mother-child interaction and children’s inhibitory control skills. I used a multi-method design to collect observational and self-reported data on 51 low-income Latino mothers and their toddlers. Using multiple regression analysis, I found that self-efficacy was positively related to having routines in the home. Familism was not related to the quality of the mother-child interaction or practices. Controlling for scaffolding, intrusiveness was negatively associated with children’s inhibitory control skills. Warmth did not moderate this association, supporting the notion that intrusiveness, even in low levels, has negative consequences for toddlers regardless of whether their mothers are also warm. Findings from this study help to further the understanding of how the early experiences of Latino toddlers support the development of inhibitory control skills.
2015-01-01T00:00:00ZMODELING CLUSTERED DATA WITH FEW CLUSTERS: A CROSS-DISCIPLINE COMPARISON OF SMALL SAMPLE METHODS
http://hdl.handle.net/1903/17293
MODELING CLUSTERED DATA WITH FEW CLUSTERS: A CROSS-DISCIPLINE COMPARISON OF SMALL SAMPLE METHODS
McNeish, Daniel
Small sample inference with clustered data has received increased attention recently in the methodological literature with several simulation studies being presented on the small sample behavior of various methods. There are several different classes of methods that can be implemented to account for clustering and disciplinary allegiances are quite rigid: for instance, recent reviews have found that 94% of psychology studies use multilevel models whereas only 3% of economics studies use multilevel models. In economics, fixed effects models are far more popular and in biostatistics there is a tendency to employ generalized estimating equations. As a result of these strong disciplinary preferences, methodological studies tend to focus only a single class of methods (e.g., multilevel models in psychology) while largely ignoring other possible methods. Therefore, the performance of small sample methods have been investigated within classes of methods but studies have not expanded investigations across disciplinary boundaries to more broadly compare the performance of small sample methods that exist in the various classes of methods to accommodate clustered data.
Motivated by an applied educational psychology study with a few clusters, in this dissertation the various methods to accommodate clustered data and their small sample extensions are introduced. Then a wide ranging simulation study is conducted to compare 12 methods to model clustered data with a small number of clusters. Many small sample studies generate data from fairly unrealistic models that only feature a single predictor at each level so this study generates data from a more complex model with 8 predictors that is more reminiscent of data researchers might have in an applied study. Few studies have also investigated extremely small numbers of clusters (less than 10) that are quite common in many researchers areas where clusters contain many observations and are there expensive to recruit (e.g., schools, hospitals) and the simulation study lowers the number of clusters well into the single digits. Results show that some methods such as fixed effects models and Bayes estimation clearly perform better than others and that researchers may benefit from considering methods outside those typically employed in their specific discipline.
2015-01-01T00:00:00ZEVALUATING MODEL FIT FOR LINEAR-LINEAR PIECEWISE MULTILEVEL LATENT GROWTH CURVE MODELS
http://hdl.handle.net/1903/17259
EVALUATING MODEL FIT FOR LINEAR-LINEAR PIECEWISE MULTILEVEL LATENT GROWTH CURVE MODELS
Zhang, Yuan
This dissertation examines the sensitivity of six fit indices in detecting various types of misspecifications in the application of a linear-linear piecewise multilevel latent growth curve model that uses continuous multivariate normal data. The study results show that all fit indices are more sensitive to misspecifications on the within level than those on the between level structure of the model. On the within level, all fit indices are more sensitive to the misspecification in the covariance structure than that in the residual structure; on the between level, all fit indices are more sensitive to the misspecification in the marginal mean structure than that in the covariance structure. Actually, none of the fit indices are practically significantly sensitive to the misspecification in the between-level covariance structure. Partially-saturated estimation method helps NFI, TLI, and Mc to be sensitive to the appropriate sample size when evaluating the misspecification in the between-level covariance structure; however, it helps none of the fit indices when detecting models misspecified in the between-level covariance structure.
All fit indices are principally influenced by the severity of misfit if it happens on the within level; however, they are primarily affected by group size if the misspecification occurs at the between level. When severity level increases, all fit indices have more power to detect misspecification in the within-level covariance structure. When group size increases, NFI, TLI, CFI, Mc, and RMSEA are more likely to commit Type II errors in detecting misspecifications in the marginal mean structure and in both the marginal mean and the covariance structures. Compared with other fit indices, NFI is most vulnerable to sample size and least sensitive to severity level of misfit. SRMR, however, behaves differentially from all other fit indices in that it is most sensitive to the intraclass correlation coefficient when detecting studied misspecifications on the between level structure. Furthermore, the recommended cutoff values lead to high Type II errors for all fit indices in detecting various types of misspecifications, and it is infeasible to find a substitute new set of criteria based on the current data conditions.
2015-01-01T00:00:00ZPreschoolers' Early Math Experiences in Varying Contexts: Parent and Child Math Talk During Playful and Didactic Activities
http://hdl.handle.net/1903/17244
Preschoolers' Early Math Experiences in Varying Contexts: Parent and Child Math Talk During Playful and Didactic Activities
Eason, Sarah
The home numeracy environment, particularly parent math talk, are predictive of children’s early math development, yet it is not clear what contexts produce high-quality parent-child exchanges about math. Both formal math learning activities and informal activities where math is embedded in the task have been linked to children's math knowledge; however, there is a need for experimental studies investigating the contextual factors that contribute to how parents and children engage in math talk during joint activities. The current study investigated parent and child talk about fractions and numbers during didactic and playful math activities as well as an unguided play context.
Seventy-two dyads of parents and preschoolers were assigned to one of three conditions (Didactic Instruction, Guided Play, Unguided Play) to participate in an activity intended to promote understanding of fractions. The conditions varied in the extent to which the activity was structured, as well as the instructions and materials provided. The quantity and quality of parent and child math talk were analyzed; children’s fraction knowledge was assessed before and after the activity. Parents also completed a survey reporting enjoyment of the task and whether they believed it could promote math learning.
Dyads in the more structured didactic and playful math contexts engaged in greater proportions of, and more diverse, math talk than dyads in the unguided play context. Dyads in the didactic math context also used a greater proportion of, and more diverse, math talk than dyads in the playful math context. Despite the differences found in math talk, no change in children’s fraction knowledge was found after participating in the parent-child interaction. Interestingly, parents in the playful math activity context rated the interaction as being as enjoyable as did the parents in the unguided play activity; however, parents in both structured math contexts (playful and didactic) were equally likely to indicate that their respective activities would promote math learning. These findings support the importance of providing guidance to parents for engaging their children in high-quality math talk and highlight the need for further research investigating qualitative differences in parent-child interactions in didactic and playful contexts.
2015-01-01T00:00:00Z