Yanyun Yang

Symptoms of posttraumatic stress disorder, depression, and anxiety among adolescents following the 2008 Wenchuan earthquake in China

This study examined the symptoms of posttraumatic stress disorder (PTSD), anxiety, and depression among 2,250 adolescents 6 months after the 2008 Wenchuan earthquake in China. Results showed that 15.8%, 40.5%, and 24.5% of participants reported clinical symptoms of PTSD, anxiety, and depression, respectively. Posttraumatic stress disorder, anxiety, and depression were highly comorbid. Risk factors for symptoms of the 3 disorders were female gender, older age, and earthquake disaster exposure. In addition, the interaction effects of residence (urban/rural) and number of siblings of study subjects on symptoms of the 3 disorders were examined. Implications of findings on intervention and prevention of mental health problems among adolescents after experiencing earthquake disasters are discussed.

Coefficient Alpha: A Reliability Coefficient for the 21st Century?

Coefficient alpha is almost universally applied to assess reliability of scales in psychology. We argue that researchers should consider alternatives to coefficient alpha. Our preference is for structural equation modeling (SEM) estimates of reliability because they are informative and allow for an empirical evaluation of the assumptions underlying them. An example is presented to illustrate the advantages of SEM estimates of reliability.

A Note on Structural Equation Modeling Estimates of Reliability

Reliability can be estimated using structural equation modeling (SEM). Two potential problems with this approach are that estimates may be unstable with small sample sizes and biased with misspecified models. A Monte Carlo study was conducted to investigate the quality of SEM estimates of reliability by themselves and relative to coefficient alpha. The SEM approach showed minimal bias when the model was correctly specified if items were relatively well defined by their underlying factor(s). They tended to demonstrate somewhat greater bias when the model was misspecified, particularly underspecified. Overall, SEM estimates were more stable than anticipated. Researchers are more likely to obtain accurate estimates of reliability using SEM by conducting large-sample studies with well-constructed scales and critically assessing model fit.

Faculty Perceptions of Web-Based Resources in Higher Education

Recent increases in the use of Web-based resources by faculty in higher education appear to be reaching a plateau. To better understand how to encourage the use of Web resources a survey was designed to examine faculty perceptions about the navigability and content of a specific Web-based resource and their attitudes about the instructional use of Web-based resources in general. Results of the survey indicated that faculty with more years of experience and those teaching at research and doctoral institutions expressed the most favorable attitudes toward the provided Web resource. In addition, gender differences were found pertaining to the factors most likely to influence the use of Web resources. These findings are discussed in terms of their implications for increasing the use of Web-based resources in higher education.

Age and gender differences in behavioral problems in Chinese children: Parent and teacher reports

This study examined age and gender differences in internalizing and externalizing behavioral problems in a large sample of Chinese children aged 6-15 (N=4472). The Chinese Child Behavior Checklist (CBCL) and Teacher Report Form (TRF) were used to assess these problems. Results showed that boys were scored higher than girls on externalizing problems by both parents and teachers, while girls were rated higher than boys on somatic problems by teachers. Parent reported externalizing problems tended to decline with age but there was no age effect on internalizing problems except slight increases with age on somatic problems. Older children tended to have higher scores than younger children on anxious and somatic problems as reported by teachers, while aggressive problems showed quadratic association with age, declining until age 10 and thereafter increasing. Parents and teachers tended to report more consistently with boys on externalizing problems but less consistently with boys on anxious and somatic syndromes. Directions for future research were provided.

Cohesion, team mental models, and collective efficacy: towards an integrated framework of team dynamics in sport

Abstract A nomological network on team dynamics in sports consisting of a multiframework perspective is introduced and tested. The aim was to explore the interrelationship among cohesion, team mental models (TMMs), collective efficacy (CE) and perceived performance potential (PPP). Three hundred and forty college-aged soccer players representing 17 different teams (8 female and 9 male) participated in the study. They responded to surveys on team cohesion, TMMs, CE and PPP. Results are congruent with the theoretical conceptualisation of a parsimonious view of team dynamics in sports. Specifically, cohesion was found to be an exogenous variable predicting both TMMs and CE beliefs. TMMs and CE were correlated and predicted PPP, which in turn accounted for 59% of the variance of objective performance scores as measured by teams’ season record. From a theoretical standpoint, findings resulted in a parsimonious view of team dynamics, which may represent an initial step towards clarifying the epistemological roots and nomological network of various team-level properties. From an applied standpoint, results suggest that team expertise starts with the establishment of team cohesion. Following the establishment of cohesiveness, teammates are able to advance team-related schemas and a collective sense of confidence. Limitations and key directions for future research are outlined.

An evaluation of WLSMV and Bayesian methods for confirmatory factor analysis with categorical indicators

This Monte Carlo study evaluated the performance of three estimation methods in fitting confirmatory factor analysis (CFA) models for ordered categorical data, with the focus on data with non-normal underlying distributions and small sample sizes. The three methods are: weighted least squares with mean and variance adjusted (WLSMV), Bayesian with non-informative priors (BN), and Bayesian with informative priors (BI). Design factors included sample sizes, factor structures, underlying continuous distributions, and categorical distributions. Results were evaluated based on the model-data fit, point estimates, and standard errors of point estimates. Results showed that Bayesian methods encountered less convergence problems than WLSMV. Bayesian methods were robust to the non-normality of underlying continuous distributions. WLSMV tended to perform equally well or slightly better than Bayesian methods except for some conditions with small sample sizes or highly non-normal underlying distributions.

On the number of factors to retain in exploratory factor analysis for ordered categorical data.

Conducting exploratory factor analysis (EFA) using statistical extraction methods has been recommended, but little is known about the accuracy of the decisions regarding the number of factors to retain for ordered categorical item data by considering a chi-square test, fit indices, and conventional criteria, such as eigenvalue >1 and parallel analysis. With computer-generated data, the authors examined the accuracy of decisions regarding the number of factors to retain for categorical item data, by combining these pieces of information using weighted least-square with mean and variance adjustment estimation methods based on polychoric correlations. A chi-square difference test was also conducted to compare nested EFA models. The results showed that the eigenvalue >1 criterion resulted in too many factors, in general. The chi-square test, chi-square difference test, fit indices, and parallel analysis performed reasonably well when the number of scale points was four, the number of items was 24, the sample size was at least 200, and the categorical distributions were similar across items. However, parallel analysis had a tendency toward factor underextraction when the correlation among factors was .50, particularly for two-point and 12-item scales.

Assessment of the Maximal Split-Half Coefficient to Estimate Reliability

The maximal split-half coefficient is computed by calculating all possible split-half reliability estimates for a scale and then choosing the maximal value as the reliability estimate. Osburn compared the maximal split-half coefficient with 10 other internal consistency estimates of reliability and concluded that it yielded the most consistently accurate estimate of reliability across the conditions explored. In this simulation study, the authors demonstrate that the maximal split-half coefficient can, under some conditions, badly overestimate reliability because of capitalization on chance and under other conditions appreciably underestimate reliability. Cross-validation can correct the former problem. A difficulty with the cross-validated coefficient is that it is a relatively inefficient estimator of reliability.

Use of internal consistency coefficients for estimating reliability of experimental task scores

Reliabilities of scores for experimental tasks are likely to differ from one study to another to the extent that the task stimuli change, the number of trials varies, the type of individuals taking the task changes, the administration conditions are altered, or the focal task variable differs. Given that reliabilities vary as a function of the design of these tasks and the characteristics of the individuals taking them, making inferences about the reliability of scores in an ongoing study based on reliability estimates from prior studies is precarious. Thus, it would be advantageous to estimate reliability based on data from the ongoing study. We argue that internal consistency estimates of reliability are underutilized for experimental task data and in many applications could provide this information using a single administration of a task. We discuss different methods for computing internal consistency estimates with a generalized coefficient alpha and the conditions under which these estimates are accurate. We illustrate use of these coefficients using data for three different tasks.