Matching Items (11)
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- All Subjects: Educational Psychology
- Creators: Thompson, Marilyn
- Status: Published
Description
Designing studies that use latent growth modeling to investigate change over time calls for optimal approaches for conducting power analysis for a priori determination of required sample size. This investigation (1) studied the impacts of variations in specified parameters, design features, and model misspecification in simulation-based power analyses and (2) compared power estimates across three common power analysis techniques: the Monte Carlo method; the Satorra-Saris method; and the method developed by MacCallum, Browne, and Cai (MBC). Choice of sample size, effect size, and slope variance parameters markedly influenced power estimates; however, level-1 error variance and number of repeated measures (3 vs. 6) when study length was held constant had little impact on resulting power. Under some conditions, having a moderate versus small effect size or using a sample size of 800 versus 200 increased power by approximately .40, and a slope variance of 10 versus 20 increased power by up to .24. Decreasing error variance from 100 to 50, however, increased power by no more than .09 and increasing measurement occasions from 3 to 6 increased power by no more than .04. Misspecification in level-1 error structure had little influence on power, whereas misspecifying the form of the growth model as linear rather than quadratic dramatically reduced power for detecting differences in slopes. Additionally, power estimates based on the Monte Carlo and Satorra-Saris techniques never differed by more than .03, even with small sample sizes, whereas power estimates for the MBC technique appeared quite discrepant from the other two techniques. Results suggest the choice between using the Satorra-Saris or Monte Carlo technique in a priori power analyses for slope differences in latent growth models is a matter of preference, although features such as missing data can only be considered within the Monte Carlo approach. Further, researchers conducting power analyses for slope differences in latent growth models should pay greatest attention to estimating slope difference, slope variance, and sample size. Arguments are also made for examining model-implied covariance matrices based on estimated parameters and graphic depictions of slope variance to help ensure parameter estimates are reasonable in a priori power analysis.
ContributorsVan Vleet, Bethany Lucía (Author) / Thompson, Marilyn S. (Thesis advisor) / Green, Samuel B. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Epilepsy is a chronic illness impacting the lives of over 300,000 children nationally. Sexson and Madan-Swain offer a theory that addresses successful school reentry in children that are chronically ill. Their theory posits that successful school reentry is influenced by school personnel with appropriate attitudes, training experiences, and by factors relating to the child's illness. The parents of 74 students, between second and twelfth grades, completed a questionnaire addressing their child's epilepsy and their current level of seizure control. Each child's homeroom teacher also completed a survey regarding their training experiences about epilepsy and their attitudes towards individuals with epilepsy. Additional information was gathered from the child's school regarding attendance rates, most recent Terra Nova test scores (a group achievement test), and special education enrollment status. Data were analyzed via four multiple regression analyses and one logistic regression analysis. It was found that seizure control was a significant predictor for attendance, academic achievement (i.e., mathematics, writing, and reading), and special education enrollment. Additionally, teachers' attitudes towards epilepsy were a significant predictor of academic achievement (writing and reading) and special education enrollment. Teacher training experience was not a significant predictor in any of the analyses.
ContributorsBohac, Genevieve (Author) / Wodrich, David L (Thesis advisor) / Lavoie, Michael (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2011
Description
Chronic illness can affect multiple domains of functioning, yet scientific understanding of the effects across the lifespan and under multiple contexts is still developing. For instance, research consistently indicates the early years of a child's life are pivotal for early intervening to positively affect physical, cognitive, and socio-emotional development; unfortunately, the impact of chronic illnesses, and thus appropriate interventions, during this time are not well-established. Academic achievement is one area in which children with chronic illness are negatively affected and research suggests that the effects of illness can be exacerbated by certain social determinants of health and demographic characteristics; however, no recent studies have examined these relationships for children at school entry. The current study utilized the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) to examine variations in early academic readiness in reading and mathematics by diabetes status, race, and social determinants, specifically mother's education and access to early childhood education, among children born in 2001. Results of the current study indicated that children with diabetes scored lower on reading and mathematics relative to their non-diabetic peers. Significant interactions were evident for diabetes status by mother's education, race/ethnicity, and by early childhood education. Children in homes whose mothers had the lowest level of education did not score as high as children in homes with mothers who had higher levels of education. Among children without diabetes, those identified as Asian, Pacific Islander, or Native Hawaiian outperformed White, Black, Hispanic, American Indian, and multi-race groups on measures of reading and mathematics, whereas among children with diabetes, those identified as multiracial scored highest. Regardless of diabetes status, children who attended preschool outperformed those who did not, yet children without diabetes who had not attended preschool outperformed diabetic children who did receive such services. Findings support the need for targeted early intervention as preschool alone did not mitigate the effects of diabetes on academic performance.
ContributorsKucera, Miranda (Author) / Sullivan, Amanda L (Thesis advisor) / Wodrich, David (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2012
Description
Dimensionality assessment is an important component of evaluating item response data. Existing approaches to evaluating common assumptions of unidimensionality, such as DIMTEST (Nandakumar & Stout, 1993; Stout, 1987; Stout, Froelich, & Gao, 2001), have been shown to work well under large-scale assessment conditions (e.g., large sample sizes and item pools; see e.g., Froelich & Habing, 2007). It remains to be seen how such procedures perform in the context of small-scale assessments characterized by relatively small sample sizes and/or short tests. The fact that some procedures come with minimum allowable values for characteristics of the data, such as the number of items, may even render them unusable for some small-scale assessments. Other measures designed to assess dimensionality do not come with such limitations and, as such, may perform better under conditions that do not lend themselves to evaluation via statistics that rely on asymptotic theory. The current work aimed to evaluate the performance of one such metric, the standardized generalized dimensionality discrepancy measure (SGDDM; Levy & Svetina, 2011; Levy, Xu, Yel, & Svetina, 2012), under both large- and small-scale testing conditions. A Monte Carlo study was conducted to compare the performance of DIMTEST and the SGDDM statistic in terms of evaluating assumptions of unidimensionality in item response data under a variety of conditions, with an emphasis on the examination of these procedures in small-scale assessments. Similar to previous research, increases in either test length or sample size resulted in increased power. The DIMTEST procedure appeared to be a conservative test of the null hypothesis of unidimensionality. The SGDDM statistic exhibited rejection rates near the nominal rate of .05 under unidimensional conditions, though the reliability of these results may have been less than optimal due to high sampling variability resulting from a relatively limited number of replications. Power values were at or near 1.0 for many of the multidimensional conditions. It was only when the sample size was reduced to N = 100 that the two approaches diverged in performance. Results suggested that both procedures may be appropriate for sample sizes as low as N = 250 and tests as short as J = 12 (SGDDM) or J = 19 (DIMTEST). When used as a diagnostic tool, SGDDM may be appropriate with as few as N = 100 cases combined with J = 12 items. The study was somewhat limited in that it did not include any complex factorial designs, nor were the strength of item discrimination parameters or correlation between factors manipulated. It is recommended that further research be conducted with the inclusion of these factors, as well as an increase in the number of replications when using the SGDDM procedure.
ContributorsReichenberg, Ray E (Author) / Levy, Roy (Thesis advisor) / Thompson, Marilyn S. (Thesis advisor) / Green, Samuel B. (Committee member) / Arizona State University (Publisher)
Created2013
Description
The five-factor model of personality is a conceptual model for describing personality, and represents five traits which are theorized to interact with each other to form personality. The Big Five Questionnaire-Children (BFQ-C) was developed by Barbaranelli, Caprara, Rabasca and Pastorelli (2003) specifically to measure the five factor model in children. The original version was in Italian, but it has subsequently been translated and used in Dutch, German, and Spanish samples. Given that the BFQ-C has support in Europe, obtained in four different languages it seems promising as an assessment of personality for English speaking children and adolescents. The BFQ-C was translated into English utilizing translation and back translation in order to maintain a high conceptual equivalency. The current study utilizes principal components analysis in order to examine the structure of the English language translation of the BFQ-C in a sample of American adolescents. Results indicate that in contrast to the Italian study, findings from this study suggest a six component solution as the most effective interpretation of the data.
ContributorsGaio, Vanesa M (Author) / Caterino, Linda C (Thesis advisor) / Thompson, Marilyn (Committee member) / Miller, Paul A. (Committee member) / Arizona State University (Publisher)
Created2012
Description
ABSTRACT This study investigated the possibility of item parameter drift (IPD) in a calculus placement examination administered to approximately 3,000 students at a large university in the United States. A single form of the exam was administered continuously for a period of two years, possibly allowing later examinees to have prior knowledge of specific items on the exam. An analysis of IPD was conducted to explore evidence of possible item exposure. Two assumptions concerning items exposure were made: 1) item recall and item exposure are positively correlated, and 2) item exposure results in the items becoming easier over time. Special consideration was given to two contextual item characteristics: 1) item location within the test, specifically items at the beginning and end of the exam, and 2) the use of an associated diagram. The hypotheses stated that these item characteristics would make the items easier to recall and, therefore, more likely to be exposed, resulting in item drift. BILOG-MG 3 was used to calibrate the items and assess for IPD. No evidence was found to support the hypotheses that the items located at the beginning of the test or with an associated diagram drifted as a result of item exposure. Three items among the last ten on the exam drifted significantly and became easier, consistent with item exposure. However, in this study, the possible effects of item exposure could not be separated from the effects of other potential factors such as speededness, curriculum changes, better test preparation on the part of subsequent examinees, or guessing.
ContributorsKrause, Janet (Author) / Levy, Roy (Thesis advisor) / Thompson, Marilyn (Thesis advisor) / Gorin, Joanna (Committee member) / Arizona State University (Publisher)
Created2012
Description
National assessment data indicate that the large majority of students in America perform below expected proficiency levels in the area of writing. Given the importance of writing skills, this is a significant problem. Curriculum-based measurement, when used for progress monitoring and intervention planning, has been shown to lead to improved academic achievement. However, researchers have not yet been able to establish the validity of curriculum-based measures of writing (CBM-W). This study examined the structural validity of CBM-W using exploratory factor analysis. The participants for this study were 253 third, 154 seventh, and 154 tenth grade students. Each participant completed a 3-minute writing sample in response to a narrative prompt. The writing samples were scored for fifteen different CBM-W indices. Separate analyses were conducted for each grade level to examine differences in the CBM-W construct across grade levels. Due to extreme multicollinearity, principal components analysis rather than common factor analysis was used to examine the structure of writing as measured by CBM-W indices. The overall structure of CBM-W indices was found to remain stable across grade levels. In all cases a three-component solution was supported, with the components being labeled production, accuracy, and sentence complexity. Limitations of the study and implications for progress monitoring with CBM-W are discussed, including the recommendation for a combination of variables that may provide more reliable and valid measurement of the writing construct.
ContributorsBrown, Alec Judd (Author) / Watkins, Marley (Thesis advisor) / Caterino, Linda (Thesis advisor) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2012
Modeling motivation: examining the structural validity of the Sport Motivation Scale-6 among runners
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 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
Description
Instrumentality is an important motivational construct that empathizes the connection between a present task and a future goal. Instrumentality is conceptualized as a task-specific variable. Reflecting context-dependent characteristics, two different types of instrumentality are distinguished: endogenous and exogenous instrumentality. Endogenous instrumentality is the perception that learning in a present task is useful to achieving valued future goals and exogenous instrumentality is the perception that outcome in a present task is instrumental to achieving valued future goals. This study investigated the differential relationships among each instrumentality type, academic achievements, and motivational variables. Three studies were conducted to investigate the relationship between each type of instrumentality and students’ achievement and motivational variables such as achievement goals, situational interests, and pressure and the moderating role of self-efficacy on the relationship. Study 1 investigated how endogenous and exogenous instrumentality was related to students’ achievement respectively. In addition, it was examined whether self-efficacy moderated in the relationship between each instrumentality and achievement. Study 2 was conducted to find that how each instrumentality was related to three different types of achievement goals, which were mastery, performance-approach, and performance-avoidance goals. Interaction between each type of instrumentality and self-efficacy was examined to find a moderating effect by self-efficacy on accounting for the relationship between instrumentality and achievement goals. Study 3 examined the role of each instrumentality on situational interest, pressure and achievement. The results showed that endogenous instrumentality predicted grade positively regardless students’ self-efficacy level, whereas exogenous instrumentality positively predicted grade of students with high self-efficacy and negatively predicted grade of students with low-self-efficacy. In addition, endogenous instrumentality predicted mastery goals positively and performance-avoidance goals negatively, whereas exogenous instrumentality predicted both performance-approach and performance avoidance goals positively. Moreover, students with high self-efficacy were less likely to adopt performance-avoidance goals when they perceived more endogenous instrumentality. It was also found that endogenous instrumentality was a positive predictor of situational interest and a negative predictor of pressure, whereas exogenous instrumentality was a negative predictor of situational interest and as a positive predictor of pressure. There was a mediating effect of pressure on the relationship between each instrumentality and grade.
ContributorsKim, Wonsik (Author) / Husman, Jenefer (Thesis advisor) / Thompson, Marilyn (Committee member) / Bong, Mimi (Committee member) / Arizona State University (Publisher)
Created2016
Description
Although models for describing longitudinal data have become increasingly sophisticated, the criticism of even foundational growth curve models remains challenging. The challenge arises from the need to disentangle data-model misfit at multiple and interrelated levels of analysis. Using posterior predictive model checking (PPMC)—a popular Bayesian framework for model criticism—the performance of several discrepancy functions was investigated in a Monte Carlo simulation study. The discrepancy functions of interest included two types of conditional concordance correlation (CCC) functions, two types of R2 functions, two types of standardized generalized dimensionality discrepancy (SGDDM) functions, the likelihood ratio (LR), and the likelihood ratio difference test (LRT). Key outcomes included effect sizes of the design factors on the realized values of discrepancy functions, distributions of posterior predictive p-values (PPP-values), and the proportion of extreme PPP-values.
In terms of the realized values, the behavior of the CCC and R2 functions were generally consistent with prior research. However, as diagnostics, these functions were extremely conservative even when some aspect of the data was unaccounted for. In contrast, the conditional SGDDM (SGDDMC), LR, and LRT were generally sensitive to the underspecifications investigated in this work on all outcomes considered. Although the proportions of extreme PPP-values for these functions tended to increase in null situations for non-normal data, this behavior may have reflected the true misfit that resulted from the specification of normal prior distributions. Importantly, the LR and the SGDDMC to a greater extent exhibited some potential for untangling the sources of data-model misfit. Owing to connections of growth curve models to the more fundamental frameworks of multilevel modeling, structural equation models with a mean structure, and Bayesian hierarchical models, the results of the current work may have broader implications that warrant further research.
In terms of the realized values, the behavior of the CCC and R2 functions were generally consistent with prior research. However, as diagnostics, these functions were extremely conservative even when some aspect of the data was unaccounted for. In contrast, the conditional SGDDM (SGDDMC), LR, and LRT were generally sensitive to the underspecifications investigated in this work on all outcomes considered. Although the proportions of extreme PPP-values for these functions tended to increase in null situations for non-normal data, this behavior may have reflected the true misfit that resulted from the specification of normal prior distributions. Importantly, the LR and the SGDDMC to a greater extent exhibited some potential for untangling the sources of data-model misfit. Owing to connections of growth curve models to the more fundamental frameworks of multilevel modeling, structural equation models with a mean structure, and Bayesian hierarchical models, the results of the current work may have broader implications that warrant further research.
ContributorsFay, Derek (Author) / Levy, Roy (Thesis advisor) / Thompson, Marilyn (Committee member) / Enders, Craig (Committee member) / Arizona State University (Publisher)
Created2015