Marilyn S. Thompson

Longitudinal change in language production: effects of aging and dementia on grammatical complexity and propositional content.

Mixed modeling was used to examine longitudinal changes in linguistic ability in healthy older adults and older adults with dementia. Language samples, vocabulary scores, and digit span scores were collected annually from healthy older adults and semiannually from older adults with dementia. The language samples were scored for grammatical complexity and propositional content. For the healthy group, age-related declines in grammatical complexity and propositional content were observed. The declines were most rapid in the mid 70s. For the group with dementia, grammatical complexity and propositional content also declined over time, regardless of age. Rates of decline were uniform across individuals. These analyses reveal how both grammatical complexity and propositional content are related to late-life changes in cognition in healthy older adults aswell as those with dementia. Alzheimers disease accelerates this decline, regardless of age.

Evaluation of Parallel Analysis Methods for Determining the Number of Factors.

Population and sample simulation approaches were used to compare the performance of parallel analysis using principal component analysis (PA-PCA) and parallel analysis using principal axis factoring (PA-PAF) to identify the number of underlying factors. Additionally, the accuracies of the mean eigenvalue and the 95th percentile eigenvalue criteria were examined. The 95th percentile criterion was preferable for assessing the first eigenvalue using either extraction method. In assessing subsequent eigenvalues, PA-PCA tended to perform as well as or better than PA-PAF for models with one factor or multiple minimally correlated factors; the relative performance of the mean eigenvalue and the 95th percentile eigenvalue criteria depended on the number of variables per factor. PA-PAF using the mean eigenvalue criterion generally performed best if factors were more than minimally correlated or if one or more strong general factors as well as group factors were present.

Peer Victimization and Effortful Control: Relations to School Engagement and Academic Achievement

The relations among peer victimization, effortful control, school engagement, and academic achievement were examined in a group of 390 (212 boys and 178 girls) racially diverse (38.20% Latino and 46.70% White) 6- to 10-year old children. Specifically, a multimethod, multi-informant approach was used in which data were gathered using self-report, peer-report, and teacher-report questionnaires at three points in time: twice during the initial year of the study when children were in first and third grades and once in the fall of their second-grade and fourth-grade years, respectively. Findings showed that peer victimization was negatively correlated with effortful control; however, longitudinal analyses conducted to examine causal priority were inconclusive. Results from structural equation modeling were consistent with the hypotheses that school engagement mediated the relations between peer victimization and academic achievement, as well as between effortful control and academic achievement.

Exploratory analyses to improve model fit: Errors due to misspecification and a strategy to reduce their occurrence

Two Lagrange multiplier (LM) methods may be used in specification searches for adding parameters to models: one based on univariate LM tests and respecification of the model (LM‐respecified method) and the other based on a partitioning of multivariate LM tests (LM‐incremental method). These methods may result in extraneous parameters being included in models due to either sampling error or the model being misspecified. A 2‐stage specification search may be used to reduce errors due to misspecification. In the 1st stage, parameters are added to models based on LM tests to maximize fit. Second, parameters added in the 1st stage are deleted if they are no longer necessary to maintain model fit. Illustrations are presented to demonstrate that errors due to misspecification occur with the LM‐respecified method and are even more likely with the LM‐incremental approach. These illustrations also show how the deletion stage can help eliminate some of these errors.

Effortful Control and Impulsivity as Concurrent and Longitudinal Predictors of Academic Achievement

The goal of this study was to test if both effortful control (EC) and impulsivity, a reactive index of temperament, uniquely predict adolescents’ academic achievement, concurrently and longitudinally (Time 1: N = 168, x ¯ age = 12 years). At Time 1, parents and teachers reported on students’ EC and impulsivity. At both time points, spaced 2 years apart, parents and teachers reported on students’ achievement. In a concurrent regression, both EC and impulsivity were positively related to achievement. At T1, there was evidence of a nonlinear relation between impulsivity and achievement, and the shape of the quadratic was dependent on if EC was simultaneously considered. Results from a longitudinal analysis demonstrated that although parent-reported impulsivity was generally negatively correlated with achievement, EC, but not impulsivity, was prospectively, uniquely related to achievement. The discussion highlights the value of considering adolescents’ EC and impulsivity in models of school success.

A Monte Carlo Investigation of Methods for Controlling Type I Errors with Specification Searches in Structural Equation Modeling

A standard strategy in structural equation modeling is to conduct multiple Lagrange multiplier (LM) tests after rejection of an initial model. Controlling for Type 1 error across these tests minimizes the likelihood of including unnecessary additional parameters in the model. Three methods for controlling Type I errors are evaluated using simulated data for factor analytic models: the standard approach which involves testing each parameter at the .05 level, a Bonferroni approach, and a simultaneous test procedure (STP). In the first part of the study, all samples were generated from a population in which all null hypotheses associated with the LM tests were correct. Three factors were manipu1,~ted: factor weights, sample size, and number of parameters in the specification search. The standard and the STP approaches yielded overly liberal and overly conservative familywise error rates, respectively, while the Bonferroni approach yielded error rates closer to the nominal level. In the second part of the study, data were generated in which one or more null hypotheses associated with the LM test were incorrect, and the number of parameters in the search was manipulated. Again the Bonferroni method was the best approach in controlling familywise: error rate, particularly when the alpha level was adjusted for the number of parameters evaluated at each step.

A Proposed Solution to the Problem With Using Completely Random Data to Assess the Number of Factors With Parallel Analysis

A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to compare revised and traditional parallel analysis approaches. Five dimensions are manipulated in the study: number of observations, number of factors, number of measured variables, size of the factor loadings, and degree of correlation between factors. Based on the results, the revised parallel analysis method, using principal axis factoring and the 95th percentile eigenvalue rule, offers promise.

Psychosocial Mediators of a School-Based Tobacco Prevention Program in India: Results from the First Year of Project MYTRI

Each day in India, an estimated 5,500 youth initiate tobacco use, contributing to predictions that by 2020, tobacco will account for 13% of all deaths in India. Project MYTRI (Mobilizing Youth for Tobacco-Related Initiatives in India) is a multi-component school-based intervention designed to prevent and reduce tobacco use among adolescents in Delhi and Chennai, India. The intervention was implemented over the 2004–2006 school years and involved 6th and 8th grade students in 32 classrooms. Students participated in peer-led classroom activities involving games, competitions, and other activities intended to target a number of psychosocial risk factors believed to prevent tobacco use among urban Indian youth. To more fully understand how Project MYTRI influenced students’ intentions to smoke or chew tobacco, the current study used mediation analysis to investigate whether Project MYTRI altered the psychosocial risk factors as intended, and whether the changes in psychosocial risk factors were, in turn, responsible for altering students’ tobacco-use intentions. Multi-level mediation models were estimated using student data from baseline and 1-year follow-up surveys. Results indicated that the psychosocial risk factors Knowledge of Health Effects, Normative Beliefs, Reasons to Use Tobacco, and Perceived Prevalence were significant mediators between the intervention activities and students’ tobacco use intentions. Evidence of inconsistent mediation was observed for the Perceived Prevalence factor. These findings, combined with those from qualitative research and the second-year student data, will help to illuminate the impact of Project MYTRI on participating youth.

Relations of Student Perceptions of Teacher Oral Feedback With Teacher Expectancies and Student Self-Concept

In this article, the authors investigated the relations of students’ perceptions of teachers’ oral feedback with teacher expectancies and student self-concept. A sample of 1,598 Taiwanese children in Grades 3 to 6 completed measures of student perceptions of teacher oral feedback and school self-concept. Homeroom teachers identified students for whom they had high or low expectancies. Discriminant analysis indicated student perceptions of positive and negative academic oral feedback were more important than nonacademic feedback in predicting teacher expectancies. A 2-way multivariate analysis of variance showed that boys perceived more negative oral feedback than did girls, and fifth-grade students perceived more negative oral feedback on academic and nonacademic domains than did the third- and fourth-grade students. Furthermore, structural equation modeling results indicated a particularly strong relation between positive academic oral feedback and academic self-concept.

An Adjusted Bonferroni Method for Elimination of Parameters in Specification Addition Searches

An adjusted Bonferroni method for controlling familywise error rate when deleting parameters in specification addition searches was presented and evaluated in a 2-part study. First, data were generated based on a factor model and then analyzed using backward selection, with and without applying the adjusted Bonferroni method. Three factors were manipulated: sample size, magnitude of weights, and number of parameters in a search. Under most of the 30 explored conditions, the empirical familywise error rates were relatively close to the nominal alpha level of. 05 when the adjusted Bonferroni method was applied. Error rates that exceeded the. 05 level appeared to be a function of the multivariate Wald test and not of the adjusted Bonferroni method. Second, data were generated based on a path model and analyzed using 2-stage and backward selection methods when the initial model was misspecified. Controlling stringently for Type I errors in the initial addition stage of a 2-stage search created selection errors when N was small (200). The adjusted Bonferroni method controlled adequately for Type I errors in the deletion stage of the 2-stage search and with backward selection.