PURPOSEWe developed and validated a brief, yet sensitive, 33-item general cancer quality-of-life (QL) measure for evaluating patients receiving cancer treatment, called the Functional Assessment of Cancer Therapy (FACT) scale.METHODS AND RESULTSThe five-phase validation process involved 854 patients with cancer and 15 oncology specialists. The initial pool of 370 overlapping items for breast, lung, and colorectal cancer was generated by open-ended interview with patients experienced with the symptoms of cancer and oncology professionals. Using preselected criteria, items were reduced to a 38-item general version. Factor and scaling analyses of these 38 items on 545 patients with mixed cancer diagnoses resulted in the 28-item FACT-general (FACT-G, version 2). In addition to a total score, this version produces subscale scores for physical, functional, social, and emotional well-being, as well as satisfaction with the treatment relationship. Coefficients of reliability and validity were uniformly high. The ...

The Functional Assessment of Cancer Therapy scale: development and validation of the general measure.

A unidimensional latent trait model for responses scored in two or more ordered categories is developed. This “Partial Credit” model is a member of the family of latent trait models which share the property of parameter separability and so permit “specifically objective” comparisons of persons and items. The model can be viewed as an extension of Andrich's Rating Scale model to situations in which ordered response alternatives are free to vary in number and structure from item to item. The difference between the parameters in this model and the “category boundaries” in Samejima's Graded Response model is demonstrated. An unconditional maximum likelihood procedure for estimating the model parameters is developed.

A Rasch Model for Partial Credit Scoring.

Self-report measures of adult attachment are typically scored in ways (e.g., averaging or summing items) that can lead to erroneous inferences about important theoretical issues, such as the degree of continuity in attachment security and the differential stability of insecure attachment patterns. To determine whether existing attachment scales suffer from scaling problems, the authors conducted an item response theory (IRT) analysis of 4 commonly used self-report inventories: Experiences in Close Relationships scales (K. A. Brennan, C. L. Clark, & P. R. Shaver, 1998), Adult Attachment Scales (N. L. Collins & S. J. Read, 1990), Relationship Styles Questionnaire (D. W. Griffin & K. Bartholomew, 1994) and J. Simpson's (1990) attachment scales. Data from 1,085 individuals were analyzed using F. Samejima's (1969) graded response model. The authors' findings indicate that commonly used attachment scales can be improved in a number of important ways. Accordingly, the authors show how IRT techniques can be used to develop new attachment scales with desirable psychometric properties.

An item response theory analysis of self-report measures of adult attachment.

Two significant emerging policy directions put patients and consumers in a key role for influencing health care quality and costs. First, consumer-directed health plans rely on informed consumer choices to contain costs and improve the quality of care. This approach assumes that consumers will make more prudent health and health care choices when they are given financial incentives along with access to comparative cost and quality information. This approach also assumes that the combination of financial incentives and relevant information will increase their “activation” (Gabel, Lo Sasso, and Rice 2002). Second, the Chronic Illness Care Model (Bodenheimer et al. 2002) emphasizes patient-oriented care, with patients and their families integrated as members of the care team. A critical element in the model is activated patients, with the skills, knowledge, and motivation to participate as effective members of the care team (Von Korff et al. 1997).

A key health policy question is, what would it take for consumers to become effective and informed managers of their health and health care? What skills, knowledge, beliefs, and motivations do they need to become “activated” or more effectual health care actors? These are essential questions if we hope to improve the health care process, the outcomes of care, and control costs. This is true especially with regard to the 99 million Americans with a chronic disease. Because those with chronic illness need ongoing care, account for a large portion of health care costs, and must play an important role in maintaining their own functioning, encouraging their activation should be a priority.

Even though patient activation is a central concept in both the consumer driven health care approach and the chronic illness care models, it remains conceptually and empirically underdeveloped. There has been a lack of conceptual clarity regarding “activation,” and thus a lack of adequate measurement. There are a number of existing methods for assessing different aspects of activation, such as health locus of control (Wallston, Stein, and Smith), self-efficacy in self-managing behaviors (Lorig et al. 1996), and readiness to change health-related behaviors (DiClemente et al. 1991; Prochaska, Redding, and Evers 1997), but these measures tend to focus on the prediction of a single behavior. Moreover, there is no existing measure that includes the broad range of elements involved in activation, including the knowledge, skills, beliefs, and behaviors that a patient needs to manage a chronic illness.

In this paper we describe the development of the Patient Activation Measure (PAM), a measure of activation that is grounded in rigorous conceptualization and appropriate psychometric methods. The PAM was developed in four stages:


Stage 1.Conceptually defining activation involved a literature review, systematic consultation with experts using a “consensus method,” and consultation with individuals with chronic disease using focus groups.


Stage 2.Preliminary scale development began by building on the domains identified in stage one and operationalizing them with survey items within each domain. Steps included generating, refining, and testing a large item pool. We used Rasch psychometric methods to develop the scale and test the preliminary measure's psychometric properties.


Stage 3.Stage three involved exploring the possibility of extending the range of the measure, refining the response categories, and testing whether the measure could be used with respondents who had no chronic illnesses.


Stage 4.In the fourth and final stage a national probability sample was used to assess the performance of the measure across different subsamples in the population and to assess the construct validity of the measure.

/pdf/development-of-the-patient-activation-measure-pam-50q63ng0ok.pdf

Development of the Patient Activation Measure (PAM): Conceptualizing and Measuring Activation in Patients and Consumers

Two studies address the sequence of understandings evident in preschoolers' developing theory of mind. The first, preliminary study provides a meta-analysis of research comparing different types of mental state understandings (e.g., desires vs. beliefs, ignorance vs. false belief). The second, primary study tests a theory-of-mind scale for preschoolers. In this study 75 children (aged 2 years, 11 months to 6 years, 6 months) were tested on 7 tasks tapping different aspects of understanding persons' mental states. Responses formed a consistent developmental progression, where for most children if they passed a later item they passed all earlier items as well, as confirmed by Guttman and Rasch measurement model analyses.

/pdf/scaling-of-theory-of-mind-tasks-2dse1d8c4s.pdf

Scaling of Theory-of-Mind Tasks

Rating scale analysis

Rating Scale Analysis. Rasch Measurement.

Five members of the Rasch family of latent trait models which have appeared more or less independently in the literature are brought together and identified as one model. In addition to sharing the distinguishing characteristic of the dichotomous Rasch model—separable person and item parameters and hence sufficient statistics—all five models share a common algebraic form and have as their basic element the fundamental process defined by Rasch's simple logistic expression. In these models, the sufficient statistics for person and item parameters are counts of events constructed to be indicative of the variable being measured, and the measures they enable are ‘fundamental’.

The essential process in a family of measurement models

High item discrimination can be a symptom of a special kind of measurement disturbance introduced by an item that gives persons of high ability a special advantage over and above their higher abilities. This type of disturbance, which can be interpreted as aform of item "bias," can be encouraged by methods that routinely interpret highly discriminating items as the "best" items on a test and may be compounded by procedures that weight items by their discrimination. The type of measurement disturbance described and illustrated in this paper occurs when an item is sensitive to individual differences on a second, undesired dimension that is positively correlated with the variable intended to be measured. Possible secondary influences of this type include opportunity to learn, opportunity to answer, and test wiseness.

https://www.jstor.org/stable/pdfplus/1435021.pdf

Item Discrimination: When More Is Worse

This paper brings together and compares two developments in the analysis of Likert attitude scales. The first is the generalization of latent class models to ordered response categories. The second is the introduction of latent trait models with multiplicative parameter structures for the analysis of rating scales. Key similarities and differences between these two methods are described and illustrated by applying a latent trait model and a latent class model to the analysis of a set of “life satisfaction” data. The way in which the latent trait model defines a unit of measurement, takes into account the order of the response categories, and scales the latent classes, is discussed. While the latent class model provides better fit to these data, this is achieved at the cost of a logically inconsistent assignment of individuals to latent classes.

Geofferey N. Masters

Papers

Rating scale analysis

Rating Scale Analysis. Rasch Measurement.

The essential process in a family of measurement models

Item Discrimination: When More Is Worse

A comparison of latent trait and latent class analyses of Likert-type data