Albert Maydeu-Olivares

Assessing the dimensionality of optimism and pessimism using a multimeasure approach

Weighted least-squares confirmatory factor analysis and exploratory factor-analytic procedures were used to assess the dimensionality of three self-report instruments designed to measure optimism and pessimism: the Life Orientation Test (LOT), the Hopelessness Scale (HS), and the Optimism and Pessimism Scale (OPS). The LOT was found to be bidimensional, the HS unidimensional, and the OPS multidimensional. The HS was interpreted as measuring a unipolar pessimism dimension. Factor analyses performed on an item subset from the OPS that fit the definition of optimism and pessimism as generalized outcome expectancies also supported the two-dimensional model of optimism and pessimism. Differential correlations between separate optimism and pessimism indices and a measure of psychological stress provided partial further support for a two-dimensional model of optimism and pessimism.

Conceptual and methodological issues in social problem-solving assessment

Several promising instruments are currently available to researchers and clinicians who require a reliable and valid measure of social problem-solving abilities. However, all of these measures have shortcomings and none has definitive, unequivocal support for its construct validity at the present time. The conceptual and methodological issues that are most directly related to the validity of social problem-solving measures were discussed. The strengths and weaknesses of the major current instruments were examined with respect to these issues. Empirical evidence related to convergent and discriminant validity was also reviewed. Recommendations were made for the improvement of these measures as well as the future development of new and better measures of social problem-solving processes and outcomes.

Limited‐information goodness‐of‐fit testing of item response theory models for sparse 2P tables

Bartholomew and Leung proposed a limited-information goodness-of-fit test statistic (Y) for models fitted to sparse 2(P ) contingency tables. The null distribution of Y was approximated using a chi-squared distribution by matching moments. The moments were derived under the assumption that the model parameters were known in advance and it was conjectured that the approximation would also be appropriate when the parameters were to be estimated. Using maximum likelihood estimation of the two-parameter logistic item response theory model, we show that the effect of parameter estimation on the distribution of Y is too large to be ignored. Consequently, we derive the asymptotic moments of Y for maximum likelihood estimation. We show using a simulation study that when the null distribution of Y is approximated using moments that take into account the effect of estimation, Y becomes a very useful statistic to assess the overall goodness of fit of models fitted to sparse 2(P) tables.

Structural Equation Modeling of Paired-Comparison and Ranking Data.

L. L. Thurstones (1927) model provides a powerful framework for modeling individual differences in choice behavior. An overview of Thurstonian models for comparative data is provided, including the classical Case V and Case III models as well as more general choice models with unrestricted and factor-analytic covariance structures. A flow chart summarizes the model selection process. The authors show how to embed these models within a more familiar structural equation modeling (SEM) framework. The different special cases of Thurstones model can be estimated with a popular SEM statistical package, including factor analysis models for paired comparisons and rankings. Only minor modifications are needed to accommodate both types of data. As a result, complex models for comparative judgments can be both estimated and tested efficiently.

Limited information estimation and testing of Thurstonian models for paired comparison data under multiple judgment sampling

We relate Thurstonian models for paired comparisons data to Thurstonian models for ranking data, which assign zero probabilities to all intransitive patterns. We also propose an intermediate model for paired comparisons data that assigns nonzero probabilities to all transitive patterns and to some but not all intransitive patterns.There is a close correspondence between the multidimensional normal ogive model employed in educational testing and Thurstones model for paired comparisons data under multiple judgment sampling with minimal identification restrictions. Alike the normal ogive model, Thurstonian models have two formulations, a factor analytic and an IRT formulation. We use the factor analytic formulation to estimate this model from the first and second order marginals of the contingency table using estimators proposed by Muthén. We also propose a statistic to assess the fit of these models to the first and second order marginals of the contingency table. This is important, as a model may reproduce well the estimated thresholds and tetrachoric correlations, yet fail to reproduce the marginals of the contingency table if the assumption of multivariate normality is incorrect.A simulation study is performed to investigate the performance of three alternative limited information estimators which differ in the procedure used in their final stage: unweighted least squares (ULS), diagonally weighted least squares (DWLS), and full weighted least squares (WLS). Both the ULS and DWLS show a good performance with medium size problems and small samples, with a slight better performance of the ULS estimator.

Social problem solving and trait anxiety as predictors of worry in a college student population

This study examined the relations between trait anxiety, social problem-solving ability, and two different measures of worry in a sample of 353 college students. The worry measures were the Penn State Worry Questionnaire (PSWQ), which measures worry frequency, uncontrollability, and distress, and the Catastrophic Worry Questionnaire (CWQ), which assesses extreme negative outcome expectancies associated with worry. Results of hierarchical multiple regression analyses showed that social problem-solving ability accounted for a significant amount of variance in both worry measures even after trait anxiety was controlled. Three different dimensions of social problem-solving ability were found to be significantly associated with worry. Negative problem orientation was positively related to both worry measures after controlling for trait anxiety. In addition, rational problem solving and impulsivity/carelessness style were both positively related to the CWQ after controlling for trait anxiety and problem orientation, which suggests that catastrophic worry has both constructive and dysfunctional problem-solving aspects that cannot be accounted for by the persons problem orientation. The implications of these findings for theory, research, and practice were discussed.

Thurstonian modeling of ranking data via mean and covariance structure analysis

Although Thurstonian models provide an attractive representation of choice behavior, they have not been extensively used in ranking applications since only recently efficient estimation methods for these models have been developed. These, however, require the use of special-purpose estimation programs, which limits their applicability. Here we introduce a formulation of Thurstonian ranking models that turns an idiosyncratic estimation problem into an estimation problem involving mean and covariance structures with dichotomous indicators. Well-known standard solutions for the latter can be readily applied to this specific problem, and as a result any Thurstonian model for ranking data can be fitted using existing general purpose software for mean and covariance structure analysis. Although the most popular programs for covariance structure analysis (e.g., LISREL and EQS) cannot be presently used to estimate Thurstonian ranking models, other programs such as MECOSA already exist that can be straightforwardly used to estimate these models.

Psychometric properties of the Spanish adaptation of the Social Problem-Solving Inventory-Revised (SPSI-R)

The Social Problem Solving Inventory-Revised (SPSI-R) has been translated and adapted to a Spanish population. Covariance structure analysis was used to replicate the five factor model for this questionnaire and to assess whether the Spanish and English versions were factorially invariant. The questionnaire was found to be only partially factorially invariant, as one of the dimensions measured by the questionnaire, impulsivity/carelessness style (ICS), appears to be measured diAerently across populations. As a result, the correlations between the ICS scale and the remaining SPSI-R scales diAer across populations. The correlations among the remaining SPSI-R scales are comparable across populations. Furthermore, the scales’ means were found to be linearly related across populations and so were the scales’ standard deviations. Hence, the scales’ metrics can be linked linearly across populations. The scales of the Spanish version of the SPSI-R showed adequate reliability and, as in North American samples, gender diAerences were found in NPO in the Spanish sample. 7 2000 Elsevier Science Ltd. All rights reserved.

A Cautionary Note on Using G(2)(dif) to Assess Relative Model Fit in Categorical Data Analysis.

The likelihood ratio test statistic G2(dif) is widely used for comparing the fit of nested models in categorical data analysis. In large samples, this statistic is distributed as a chi-square with degrees of freedom equal to the difference in degrees of freedom between the tested models, but only if the least restrictive model is correctly specified. Yet, this statistic is often used in applications without assessing the adequacy of the least restrictive model. This may result in incorrect substantive conclusions as the above large sample reference distribution for G2(dif) is no longer appropriate. Rather, its large sample distribution will depend on the degree of model misspecification of the least restrictive model. To illustrate this, a simulation study is performed where this statistic is used to compare nested item response theory models under various degrees of misspecification of the least restrictive model. G2(dif) was found to be robust only under small model misspecification of the least restrictive model. Consequently, we argue that some indication of the absolute goodness of fit of the least restrictive model is needed before employing G2(dif) to assess relative model fit.

Impulsive/careless problem solving style as predictor of subsequent academic achievement

A previous study (D’Zurilla, T. J. & Sheedy, C. F. (1992). The relation between social problemsolving ability and subsequent level of academic competence in college students. Cognitive Therapy and Research, 16, 589‐599) has shown that social problem-solving ability significantly predicts academic performance in college students after accounting for their academic aptitude. In this study we use a recently proposed five-dimensional model of social problem solving to investigate which social problem solving dimension is responsible for this eAect. To further assess the cross-cultural validity of previous findings, our study was performed in a diAerent educational system (that of Spain). Also, we used a measure of previous academic achievement instead of one of academic aptitude. Despite these diAerences, our results are remarkably similar to previous ones. Furthermore, we found that the dimension responsible for this relationship was impulsive/careless problem-solving. This establishes some interesting links between social problem solving theory and existing research on impulsivity as predictors of GPA. # 2000 Elsevier Science Ltd. All rights reserved.