Search

In latent growth modeling, measurement invariance across groups has received little attention. Considering that a group difference is commonly of interest in social science, a Monte Carlo study explored the performance of multigroup second-order latent growth modeling (MSLGM) in testing measurement invariance. True positive and false positive rates in detecting noninvariance across groups in addition to bias estimates of major MSLGM parameters were investigated. Simulation results support the suitability of MSLGM for measurement invariance testing when either forward or iterative likelihood ratio procedure is applied. [ABSTRACT FROM AUTHOR]

This Monte Carlo study investigated the impacts of measurement noninvariance across groups on major parameter estimates in latent growth modeling when researchers test group differences in initial status and latent growth. The average initial status and latent growth and the group effects on initial status and latent growth were investigated in terms of Type I error and bias. The location and magnitude of noninvariance across groups was related to the location and magnitude of bias and Type I error in the parameter estimates. That is, noninvariance in factor loadings and intercepts was associated with the Type I error inflation and bias in the parameter estimates of the slope factor (or latent growth) and the intercept factor (or initial status), respectively. As noninvariance became large, the degree of Type I error and bias also increased. On the other hand, a correctly specified second-order latent growth model yielded unbiased parameter estimates and correct statistical inferences. Other findings and implications on future studies were discussed. [ABSTRACT FROM PUBLISHER]

The effects of misspecifying intercept-covariate interactions in a 4 time-point latent growth model were the focus of this investigation. The investigation was motivated by school growth studies in which students' entry-level skills may affect their rate of growth. We studied the latent interaction of intercept and a covariate in predicting growth with respect to 3 factors: sample size (100, 200, and 500), 4 levels of magnitude of interaction effect, and 3 correlation values between intercept and covariate (.3, .5, and .7). Correctly specified models were examined to determine power and Type I error rates, and misspecified models were examined to evaluate the effects on power, parameter estimation, bias, and fit indexes. Results showed that, under correctly specified models, power increased as expected with increasing sample size, larger magnitude of interaction, and larger intercept-covariate correlation. Under misspecification, omitting a non-null interaction results in significant change in the estimation of the direct effects of both covariate and intercept in both magnitude and direction, with results dependent on sign of parameter values for main effects and interaction. Including a spurious interaction does not affect estimation of direct effects of intercept and covariate but does increase standard errors. The primary problem in ignoring a non-null interaction lies in misinterpretation of the model, as interactions yield important insights into the nature of the processes being studied. [ABSTRACT FROM AUTHOR]

Both structural equation models and hierarchical linear models (HLMs) have been commonly used in multilevel analysis. This study utilized simulated data to investigate the power difference among 3 multilevel models: HLM, deviation structural equation models, and a hybrid approach of HLM and structural equation models. Two factors were examined: sample size and the second-level regression coefficient, each of which was varied independently to evaluate the empirical power of the 3 models. Results showed that large samples were crucial for HLM to perform well. The power of the other 2 methods was similar and generally higher than HLM, although the deviation structural equation model had the best overall performance. In addition, power did not always increase with larger second-level regression coefficient values. First-level unit size was an important component with an asymptotic efficiency at about n = 35. HLM power was more susceptible to change in second-level regression coefficient values than the other 2 methods. [ABSTRACT FROM AUTHOR]

Abstract: A sample of 283 Hispanic children with literacy performance at entrance to first grade below the median for their school district was studied as part of a larger research project on the predictors of grade retention in grade 1. Following retention decisions, 51 Hispanic students were retained in first grade. Low literacy skills, being young at entrance to first grade, low ego resilience, low support in the teacher–student relationship, and parents'' low sense of responsibility for their children''s educational outcomes predicted retention decisions. Hierarchical logistics regression investigated the contribution of six categories of variables (academic competencies; socio-demographic characteristics; social, emotional, and behavioral adjustment; resiliency, school context; and home environment) to retention. Controlling for literacy, only being young for grade and parents'' low sense of responsibility for their children''s adjustment to school made a direct contribution to retention. Early literacy skills were higher for children enrolled in bilingual classrooms than for children in non-bilingual classrooms. Implications for educational policy are discussed. [Copyright &y& Elsevier]

A critical review of the 24-step procedure of Miller and Rohling's (in press) proposed standardization of clinician's use of neuropsychological assessment batteries is presented. Each step is examined for statistical sources of invalidity. It was concluded that parts of the procedure are quite vulnerable to between-battery variability that cannot be easily estimated or controlled, leading to significant errors in analysis and classification. A second fatal flaw is the failure to distinguish in the procedures between standard error measurement and standard error of the estimate in calculations in several steps. The purpose of the process remains viable, however, and is an important contribution toward the improvement of clinical diagnosis. [ABSTRACT FROM AUTHOR]

The methods and conclusions of Van den Broeck (in this issue) are evaluated from two perspectives: (a) statistical considerations and (b) theoretical models of IQ and achievement, specifically reading achievement. We consider the statistical model proposed by Van den Broeck for the regression-based discrepancy model (RDM) to be either irrelevant or conceptually inconsistent with current models of IQ and achievement. The resulting simulation produced exemplar cases that are not realistic in terms of practice. The theoretical representations of IQ and achievement were, in our understanding, inconsistent with contemporary models of either. We suggest that acceptable models support the use of the RDM as it has been proposed by us and by others as one component of the determination of the presence or absence of a learning disability. [ABSTRACT FROM AUTHOR]

This research was designed to investigate how much more suitable moving average (MA) and autoregressive-moving average (ARMA) models are for longitudinal panel data in which measurement errors correlate than AR, quasi-simplex, and 1-factor models. The conclusions include (a) when testing for a stochastic process hypothesized to occur in a longitudinal data set, testing for other processes is necessary, because incorrect models often fit other processes well enough to be deceiving; (b) when measurement error correlations are flagged to be relatively high in panel data, the fit and propriety of an MA or ARMA model should be considered and compared to the fit and propriety of other models; (c) when an MA model is fit to AR data, measurement error correlations may nonetheless be deceptively high, though fortunately MA model fit indexes are almost always lower than those for an AR model; and (d) the assumption that longitudinal panel data always contain measurement error correlations is patently false. In summary, whenever evaluating longitudinal panel data, the fit, propriety, and parsimony of all 5 models should be considered jointly and compared before a particular model is endorsed as most suitable. [ABSTRACT FROM AUTHOR]

This article focuses on the normative data on the Wechsler Adult Intelligence Scale (WAIS)-R for Selz and Reitan's index of scatter. The Selz and Reitan scatter index was computed for each person using all 11 WAIS-R within-age standardized subtest scores. Descriptive statistics were computed. One-way analysis of variance on the scatter index was performed for each demographic variable: education, occupation, age, sex, race and urban-rural residence. The Selz and Reitan index appears less useful for adult populations than for children. Only levels of scatter that are infrequent in a normal population should be entertained as potentially indicative or suggestive of neurological dysfunction.

Compares the rauding theory with that of a model of reading acquisition which supports stage and phase theories of reading development. Analysis of the relations among rauding variables using structural equation modeling; Fit indices produced among elementary students; Contribution of word recognition to comprehension.

Presents a model of reading comprehension development from a cognitive perspective. Factors that affect reading comprehension; Prediction of reading comprehension for both narrative and expository text.

"Meta-analysis" is defined as a cross-discipline research method. Formally a form of literature review, its primary purpose is the synthesis of research studies already reported. It may be used for description or theory testing, and statistical tests may be applied to the units of analysis, termed effects, to estimate relationships between independent and dependent variables found in research studies. [ABSTRACT FROM AUTHOR]

Tests empirically the tenability of the hypothesis that tests tend to be systematically biased against ethnic groups with smaller or no representation in the test standardization sample. Differential representation of ethnic groups and maximum representation of one ethnic group test construction models; Standardization fallacy.

Presents a critique of the technical, conceptual and utilitarian aspects of E. Burns' standardized grade equivalents (SGE) score system. Method's failure to remediate most of the well-known problems of grade equivalent scales; Recreation of some of the problems of the original mental age concept; Scores' underestimating of above-average performance.

The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the higher-level predictor was not included and that standard errors of the regression coefficients from the higher-level were underestimated when a regular LGCM was used. Nevertheless, random effect estimates, regression coefficients, and standard error estimates were consistent with those from the true MLGCM when the design-based LGCM included the higher-level predictor. They discussed implication for the study with empirical data illustration. [ABSTRACT FROM PUBLISHER]

The 1999 Standards for Educational and Psychological Testing adopted by AERA, APA, and NCME requires examiners to make reasonable accommodations for individuals with disabilities when administering psychological tests to such persons. Changes in test administration may be required, but the Standards also require the examiner to provide evidence associated with the validity of test score interpretation in the face of such changes in administration. Departures from standard procedures during test administration may change the meaning of test scores, because scores based on norms derived from standardized procedures may not be appropriate; error terms and rates may also be affected. Several United States Supreme Court decisions also discuss the concept of error and reliability of data as relevant to the admissibility of expert testimony. There is a significant body of literature, but one scattered among many venues and journals, addressing modifications of standardized procedures and their effects on test performance. This paper reviews this extensive literature on the effects of various forms of departure from standardized procedures on test performance and such test score variables as reliability, all areas relevant to neuropsychological practice. [ABSTRACT FROM AUTHOR]

This study examined differential item function on the K-ABC for gifted and nongifted subjects on the basis of race and gender using the technique for partial correlation proposed by Stricker (1982) and Reynolds, Willson, and Chatman (1984). It was determined that there were no items biased against gifted Black children and that 8 items were biased against gifted White children. Three items were found to be biased against nongifted Black children, while 4 items were biased against nongifted White children. When gender was considered, 2 items were found to be biased against gifted males, and 2 items also were found to be biased against gifted females. There were 10 items that were biased against nongifted Black males and 6 items biased against nongifted females. Systematic bias against race or gender was not found. It appears that the K-ABC is a relatively nonbiased test suitable for the evaluation of both gifted and nongifted children regardless of race or gender. [ABSTRACT FROM AUTHOR]

Illustrates and compares two longitudinal statistical methods, the Tuckerized growth curve analysis (TGCA) and an extension of the clustering technique, K means analysis (KMA). Distinctions between TGCA and KMA; Basics of TGCA and KMA; Applications.

Growth mixture modeling (GMM) is a relatively new technique for analyzing longitudinal data. However, when applying GMM, researchers might assume that the higher level (nonrepeated measure) units (e.g., students) are independent from each other even though it might not always be true. This article reports the results of a simulation study examining the impact of ignoring a higher level nesting structure in multilevel GMM. Three-level longitudinal clustered data were generated and then analyzed with the correct 3-level model and the incorrect model that ignored the highest level of nesting structure separately. The simulation results showed that although over 90% of the replications resulted in the correct class solution under both true and misspecified models, ignoring a higher level nesting structure could still result in lower classification accuracy, overestimated lower level variance components, and biased standard errors. The biased standard errors in turn affect the tests of significance for the fixed effects. [ABSTRACT FROM AUTHOR]

This study tested a dual-mediation model of the relations among harsh parenting, hostile social information processing, and level of child aggression in a sample of 239 (150 male, 89 female) 2nd- to 4th-grade children. The theoretical model posited that harsh parenting has both direct and indirect effects on child level of aggression, with the indirect effects mediated through children's social goals. The model further posited that the impact of social goals on aggression is mediated through other social cognitive processes (i.e., attributions of hostile intent, hostile solution generation, and positive outcome expectancies for aggression). We tested the dual-mediation model with structural equation modeling and found it to be a good fit to the data. Results were consistent with the view that parenting affects children's goal orientation and that children's goal orientation affects their behavior via online processing in social situations. [ABSTRACT FROM AUTHOR]