Preferences for physical activity: a conjoint analysis involving people with chronic knee pain.

OBJECTIVE: To investigate individual preferences for physical activity (PA) attributes in adults with chronic knee pain, to identify clusters of individuals with similar preferences, and to identify whether individuals in these clusters differ by their demographic and health characteristics. DESIGN: An adaptive conjoint analysis (ACA) was conducted using the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method to determine preference weights representing the relative importance of six PA attributes. Cluster analysis was performed to identify clusters of participants with similar weights. Chi-square and ANOVA were used to assess differences in individual characteristics by cluster. Multinomial logistic regression was used to assess associations between individual characteristics and cluster assignment. RESULTS: The study sample included 146 participants; mean age 65, 72% female, 47% white, non-Hispanic. The six attributes (mean weights in parentheses) are: health benefit (0.26), enjoyment (0.24), convenience (0.16), financial cost (0.13), effort (0.11) and time cost (0.10). Three clusters were identified: Cluster 1 (n = 33): for whom enjoyment (0.35) is twice as important as health benefit; Cluster 2 (n = 63): for whom health benefit (0.38) is most important; and Cluster 3 (n = 50): for whom cost (0.18), effort (0.18), health benefit (0.17) and enjoyment (0.18) are equally important. Cluster 1 was healthiest, Cluster 2 most self-efficacious, and Cluster 3 was in poorest health. CONCLUSIONS: Patients with chronic knee pain have preferences for PA that can be distinguished effectively using ACA methods. Adults with chronic knee pain, clustered by PA preferences, share distinguishing characteristics. Understanding preferences may help clinicians and researchers to better tailor PA interventions.

Hierarchical modeling of paired comparison data.

The method of paired comparisons belongs to a small group of techniques that provide explicit information about the consistency of individual and aggregated choices. This article investigates the link between the individual- and group-level judgments by extending R. D. Luce's (1959) model, which was originally developed for individual choice behavior, to a mixed-effects paired comparison model. It is shown that standard multilevel software for binary data can be used to estimate the model. The interpretation of the paired comparison parameters and statistical model tests are discussed in detail. An extensive analysis of an experimental study illustrates the usefulness of a hierarchical approach in modeling multiple pairwise judgments.

Individual differences in paired comparison data.

Thurstonian models provide a flexible framework for the analysis of multiple paired comparison judgments because they allow a wide range of hypotheses about the judgments' mean and covariance structures to be tested. However, applications have been limited to a large extent by the computational intractability involved in fitting this class of models. This paper demonstrates that the Monte Carlo EM algorithm facilitates maximum likelihood estimation of Thurstonian paired comparison models even when the number of items is large. A paired comparison study is presented in detail to illustrate the estimation approach.

Modeling stage-sequential change in ordered categorical responses.

Although few would dispute the usefulness of looking at behavioral change from a stage-sequential perspective, until recently the lack of appropriate modeling techniques has hampered rigorous empirical tests of stage theories. In particular, for behavioral measurements that are ordinal, there is a need for methods that represent the underlying change processes in the form of qualitative and discontinuous shifts. This article introduces a stage-sequential ordinal model by postulating that at any point in time there are a finite number of latent stages. Panel members may shift among these stages over time. The authors show that the stage-sequential model provides a general approach for both the analysis of ordinal time-dependent data and tests of various competing theories and hypotheses about psychological change processes. An analysis of a 5-year study concerning attitudes toward alcohol consumption by teenagers is presented to illustrate the modeling approach.

Measuring change: mixed Markov models for ordinal panel data.

In an analysis of longitudinal data it is important to distinguish between dependencies caused by within- and between-subject variability. This paper presents mixed Markov models for ordinal data that take into account both sources of variation. In addition, covariates that may capture differences among panel members and time-specific changes are also incorporated in the model. The model is derived by specifying an observation-driven process at the individual level and allowing for parametric or semi-parametric representations of random parameter variation across the units of analysis. As a result, the approach is well suited for modelling ordinal panel data with a large number of time points. In an application a three-week diary study is analysed to test hypotheses about the relationships between emotions and personality factors over time.

Determinants of diagnostic hypothesis generation: effects of information, base rates, and experience.

Physicians generated diagnostic hypotheses for case histories for which 2 types of diagnoses were plausible, with one having a higher population base rate but less severe clinical consequences than the other. The number of clinical and background symptoms pointing towards the 2 diagnoses was factorially manipulated. The order and frequency with which physicians generated hypotheses varied with the amount of relevant clinical and background information and as a function of population incidence rates, with little evidence of base rate neglect. Availability of a hypothesis, made possible by diagnosis of a similar case before, also made doctors generate this diagnosis earlier and more frequently. Physicians' experience affected hypothesis generation solely by increasing the availability of similar cases. The results are consistent with the use of similarity-based hypothesis generation processes that operate on memory for prior cases.

Use of formal methods in medical decision making: a survey and analysis.

Apparent low usage of formal decision techniques by general clinicians has raised questions about dissemination methods and about the techniques' perceived usefulness. Two literature searches examined whether use of formal decision techniques among clinicians had indeed failed to increase from the 1970s to the 1980s. A general MEDLINE search for the period 1983-87 relative to 1973-77 indicated that usage of formal decision techniques had more than doubled. This increase, however, was due to increased coverage of formal decision techniques in specialist methods journals. A manual search of seven major clinical journals and a MEDLINE search restricted to the clinical journals of the manual search disclosed no increase in overall usage for the same time periods. MEDLINE detected only a small subset of the actual instances of formal method usage found by the manual search. Individual medical subspecialties were found to utilize different formal decision techniques to different degrees. The authors suggest interventions that may increase the usage of formal decision techniques among general clinicians.

Analyzing optima in the exploration of multiple response surfaces.

A method is presented for testing the equality of some or all (constrained or unconstrained) optima in a response surface analysis. An estimator of a common location of stationary points is obtained by a standard multivariate testing procedure and a confidence region associated with the common optimum is derived. The procedure is illustrated by the estimation of a common optimum in a multiple response experiment. The multivariate approach facilitates a more efficient estimation of the optimum than the usual univariate response surface analysis and provides additional tests of the response surface model.

Bootstrapping: applications to psychophysiology.

This paper presents the statistical technique known as the bootstrap to the general audience of psychophysiologists. The bootstrap, introduced by Efron (1979), allows data analysts to study the distribution of sample statistics that might otherwise be too complicated to consider. The technique, which requires simple calculations, involves drawing repeated samples (with replacement) from the empirical--or the actual--data distribution and then building a distribution for a statistic by calculating a value of the statistic for each sample. The bootstrap can be used to obtain confidence intervals, standard errors, and even higher moments for the statistic. It is similar to the well-known jackknife of Quenouille and Tukey. After discussing the history and theory of both the bootstrap and the jackknife, we illustrate the use of the bootstrap in the statistical analysis of correlation coefficients and the general linear model.