Parallel analysis (PA) is recommended as one of the best procedures to determine the number of factors but its theoretical justification has long been questioned. The current study discussed theoretical issues on the use of eigenvalues for dimensionality assessment and reviewed the development of PA and its recent variants proposed to address the issues. The performances of 13 different PAs including PA with minimum rank factor analysis, revised PA, and comparison data method were investigated through a Monte Carlo simulation under a wide range of factor structures that produce small factor-representing and nonrepresenting eigenvalues for different types of measurement scales. Results showed that the traditional PA using full correlation matrices performed best in most of the conditions, especially when population error was involved. However, the overall accuracy of PA was not satisfactory when factor-representing eigenvalues were small, that is, when factor loadings were low and factor correlations were high. From these results, we suggest that the original PA be used to determine the number of factors but the estimated number should not be taken as a fixed estimate. The number of factors within Ϯ1 range of the estimate can be considered as viable candidates and interpretational validity of the competing models should be consulted for the decision.

The psychometric process used to establish a relationship between the scores of two (or more) instruments is generically referred to as linking. When two instruments with the same content and statistical test specifications are linked, these instruments are said to be equated. Linking and equating procedures have long been used for practical benefit in educational testing. In recent years, health outcome researchers have increasingly applied linking techniques to patient-reported outcome (PRO) data. However, these applications have some noteworthy purposes and associated methodological questions. Purposes for linking health outcomes include the harmonization of data across studies or settings (enabling increased power in hypothesis testing), the aggregation of summed score data by means of score crosswalk tables, and score conversion in clinical settings where new instruments are introduced, but an interpretable connection to historical data is needed. When two PRO instruments are linked, assumptions for equating are typically not met and the extent to which those assumptions are violated becomes a decision point around how (and whether) to proceed with linking. We demonstrate multiple linking procedures-equipercentile, unidimensional IRT calibration, and calibrated projection-with the Patient-Reported Outcomes Measurement Information System Depression bank and the Patient Health Questionnaire-9. We validate this link across two samples and simulate different instrument correlation levels to provide guidance around which linking method is preferred. Finally, we discuss some remaining issues and directions for psychometric research in linking PRO instruments.

A common problem when using a variety of patient-reported outcomes (PROs) for diverse populations and subgroups is establishing a harmonized scale for the incommensurate outcomes. The lack of comparability in metrics (e.g., raw summed scores vs. scaled scores) among different PROs poses practical challenges in studies comparing effects across studies and samples. Linking has long been used for practical benefit in educational testing. Applying various linking techniques to PRO data has a relatively short history; however, in recent years, there has been a surge of published studies on linking PROs and other health outcomes, owing in part to concerted efforts such as the Patient-Reported Outcomes Measurement Information System (PROMIS®) project and the PRO Rosetta Stone (PROsetta Stone®) project ( www.prosettastone.org ). Many R packages have been developed for linking in educational settings; however, they are not tailored for linking PROs where harmonization of data across clinical studies or settings serves as the main objective. We created the PROsetta package to fill this gap and disseminate a protocol that has been established as a standard practice for linking PROs.

Background Behavioral medicine is showing growing theoretical and applied interest in multiple health-risk behaviors. Compared to engaging in a single health-risk behavior, multiple health-risk behaviors are linked to increased morbidity and mortality. A contextual determinant of multiple risk behaviors may be living with a smoker. Purpose This study investigated the role of living with a smoker in predicting multiple health-risk behaviors compared to a single health-risk behavior, as well as whether these multiple risk behaviors occur across both physical activity and dietary domains. Moreover, the study tested these effects across 3 years in longitudinal and prospective (controlling for health-risk behaviors at baseline) analyses. Methods Participants were 82,644 women (age M = 63.5, standard deviation = 7.36, age range = 49–81) from the Women’s Health Initiative Observational Study. Analyses used multinomial and binary logistic regression. Results Living with a smoker was more strongly associated with multiple health-risk behaviors than with a single health-risk behavior. These multiple risk behaviors occurred across both physical activity and dietary domains. The effects persisted across 3 years in longitudinal and prospective analyses. Living with a smoker, compared to not living with a smoker, increased the odds of multiple health-risk behaviors 82% cross-sectionally and, across 3 years, 94% longitudinally and 57% prospectively. Conclusions These findings integrate research on multiple health-risk behaviors and on living with a smoker and underscore an unrecognized public health risk of tobacco smoking. These results are relevant to household-level interventions integrating smoking-prevention and obesity-prevention efforts.

This study investigated: (a) the association between living with a smoker and weight-related health risk behaviors, and (b) the role of these behaviors in indirectly linking living with a smoker to general and central adiposity. Participants were 83,492 women (age M = 63.5, SD = 7.36) from the Women's Health Initiative Observational Study. In logistic regression analyses at baseline, living with a smoker was associated with increased odds of no exercise (29%), no walking (33%), high dietary fat (62%), and low fruit and vegetable consumption (43%). Using structural equation modeling, bootstrap confidence intervals confirmed a significant indirect effect from living with a smoker to adiposity through health risk behaviors at baseline and prospectively across 3 and 8 years. Health risk behaviors fully explained the living with a smoker-adiposity relationship. These findings integrate clustering and contagion theoretical perspectives on health behaviors and contribute to understanding a novel pathway to adiposity.