Treatment of overweight children: The relationship between dependent measures and a clinically significant outcome

Behavioral treatments for overweight children have not fared well in achieving or maintaining clinically significant weight losses. The use of more appropriate dependent measures which also take height, sex, and age into consideration is suggested. A multiple-baseline analysis of the behavioral treatment of seven overweight children was conducted. Results support the contention that a more clinically significant outcome emerges when appropriate measures are used.     

Component models for three-way data: An alternating least squares algorithm with optimal scaling features

A review of the existing techniques for the analysis of three-way data revealed that none were appropriate to the wide variety of data usually encountered in psychological research, and few were capable of both isolating common information and systematically describing individual differences. An alternating least squares algorithm was proposed to fit both an individual difference model and a replications component model to three-way data which may be defined at the nominal, ordinal, interval, ratio, or mixed measurement level; which may be discrete or continuous; and which may be unconditional, matrix conditional, or row conditional. This algorithm was evaluated by a Monte Carlo study. Recovery of the original information was excellent when the correct measurement characteristics were assumed. Furthermore, the algorithm was robust to the presence of random error. In addition, the algorithm was used to fit the individual difference model to a real, binary, subject conditional data set. The findings from this application were consistent with previous research in the area of implicit personality theory and uncovered interesting systematic individual differences in the perception of political figures and roles.This paper is part of a Thesis performed by Richard Sands under the direction of Forrest Young at the L. L. Thurstone Psychometric Laboratory, University of North Carolina at Chapel Hill. Thanks are extended to Drs. Charles Schmidt and Andrea Sedlak for the use of their political role data set.     

Structural analysis of subjective categorical data

A general approach to the analysis of subjective categorical data is considered, in which agreement matrices of two or more raters are directly expressed in terms of error and agreement parameters. The method provides focused analyses of ratings from several raters for whom ratings have measurement error distributions that may induce bias in the evaluation of substantive questions of interest. Each rater's judgment process is modeled as a mixture of two components: an error variable that is unique for the rater in question as well as an agreement variable that operationalizes the true values of the units of observation. The statistical problems of identification, estimation, and testing of such measurement models are discussed.The general model is applied in several special cases. The most simple situation is that underlying Cohen's Kappa, where two raters place units into unordered categories. The model provides a generalization and systematization of the Kappa-idea to correct for agreement by chance. In applications with typical research designs, including a between-subjects design and a mixed within-subjects, between-subjects design, the model is shown to disentangle structural and measurement components of the observations, thereby controlling for possible confounding effects of systematic rater bias. Situations considered include the case of more than two raters as well as the case of ordered categories. The different analyses are illustrated by means of real data sets.The authors wish to thank Lawrence Hubert and Ivo Molenaar for helpful and detailed comments on a previous draft of this paper. Thanks are also due to Jens Möller und Bernd Strauß for the data from the 1992 Olympic Games. We thank the editor and three anonymous reviewers for valuable comments on an earlier draft.     

The development of an autonomy scale

The purpose of this study was to develop a valid and relaible instrument to measure four areas of autonomy. The instrument is composed of four subscales that measure the following constructs of autonomy: (a) family loyalty autonomy, (b) value autonomy, (c) emotional autonomy, and (d) behavioral autonomy. A detailed four-step procedure was used to provide the scale with construct, content, and predictive validity. Presented here is the four-stage process used to develop the Autonomy Scale, data supporting the validity and reliability of the scale, and the final version of the instrument.This article is based on dissertation research in partial fulfillment of degree requirements at Texas Woman's University.     

Psychological empowerment: Issues and illustrations

Discussed several issues related to psychological empowerment. The thesis of this paper is that the development of a universal and global measure of psychological empowerment may not be a feasible or appropriate goal. I begin by distinguishing between empowerment processes and outcomes. Underlying assumptions are discussed including the notion that empowerment differs across people, contexts, and times. A nomological network that includes intrapersonal, interactional, and behavioral components is also presented. Two examples of psychological empowerment for voluntary service organization members and members of a mutual help organization are described to help illustrate differences in the specific variables that may be used to measure psychological empowerment in different populations and settings. I thank Deborah A. Salem for her thoughtful comments on earlier drafts of this paper and for her persistence in pushing me to strengthen the ideas presented. I also thank Douglas Perkins, Julian Rappaport, David Altman, and James G. Kelly for their very helpful comments. They helped me articulate my ideas more carefully and clearly.     

Using Qualitative Methods to Develop Contextually Relevant Measures and Preventive Interventions: An Illustration

Qualitative methods were used to develop a contextually relevant quantitative measure of parenting stress and inform the design of family-focused interventions aimed at preventing adolescent mental and behavioral health problems. The study focused on the experiences of low-income one- and two-parent families representing three ethnic groups (Mexican, African, and European Americans) living in low-income neighborhoods in a large Southwestern city. This report describes the place of this study in a broader program of prevention research, the qualitative methods and data analysis procedures employed, and how the results were used to develop a new measure of parenting stress and inform future program design. The report also includes reflective comments on the methods used and lessons gained.     

Reliability and validity of two widely-used worry questionnaires: self-report and self-peer convergence

The reliability and validity of the Penn State Worry Questionnaire (PSWQ) and the Worry Domains Questionnaire (WDQ) were examined with self-ratings from a non-clinical sample of 148 students in a test-retest design across four weeks. Ratings from three well-acquainted peers were also obtained. With internal consistencies and test-retest correlations of at least 0.85, the present study confirmed the high reliability of the questionnaires. Moreover, both measures demonstrated substantial convergent validity: Average agreement among peers was 0.42 (PSWQ) and 0.47 (WDQ) and aggregated self-peer agreement was 0.55 (PSWQ) and 0.49 (WDQ). Self-peer agreement was not biased by social desirability. These findings challenge views that worry is an unreliable and unobservable phenomenon.     

The Construct Validity of Curriculum-Based Measurement of Reading: An Empirical Test of a Plausible Rival Hypothesis

Research has confirmed that curriculum-based measurement (CBM) of oral reading fluency and measures of reading comprehension are highly correlated, as predicted by developmental theories of reading. Research on CBM, however, has only begun to rule out plausible alternative explanations of this relationship—an important aspect of a strong program of construct validation (e.g., Messick, 1989). This study investigated one such rival hypothesis by examining the relative roles of general cognitive ability, speed and efficiency of elemental cognitive processing, and oral reading fluency in the prediction of reading comprehension. Results of simultaneous multiple regression analyses substantiate the construct validity of CBM oral reading fluency. These findings indicate that the significant relationship between oral reading fluency and reading comprehension cannot be explained by general cognitive ability or by processing speed and efficiency. CBM oral reading fluency also did not correlate significantly with any of the processing speed and efficiency tasks. Interestingly, however, CBM oral reading fluency accounted for less variance in reading comprehension (r² = .17) than expected based on the results of previous research and less than that explained by general cognitive ability (r² = .24). When controlling for psychometric g and processing speed in the regression analyses, CBM oral reading explained 11% of the variance in reading comprehension. Implications of these results for further research on the construct validity of CBM are discussed.     

On structural models for analyzing change

The recommendation to base the analysis of multi-wave data upon explicit models for change is advocated. Several univariate and multivariate models are described, which emerge from an interaction between the classical test theory and the structural equation modeling approach. The resulting structural models for analyzing change reflect in some of their parameters substantively interesting aspects of intra- and interindividual change in follow-up studies. The models are viewed as an alternative to an ANOVA-based analysis of longitudinal data, and are illustrated on data from a cognitive intervention study of old adults (Bakes et al , 1986). The approach presents a useful means of analyzing change over time, and is applicable for purposes of (latent) growth curve analysis when analysis of variance assumptions are violated (e.g., Schaie & Hertzog, 1982; Morrison, 1976).     

More “tricks of the trade”: Reparameterizing LISREL models using negative variances

This paper shows how LISREL may be used to estimate simplex models which impose constraints on the variances of endogenous variables. This technique allows us to estimate both the parameters and the standard errors of the correlated measurement error model proposed by Wiley and Wiley (1974).We would like to thank Jim Wiley for his many helpful comments and suggestions on an earlier draft. We are grateful also to an anonymous reviewer for supplying the EQS program presented in Figure 4.