Fatalism Revisited: Further Psychometric Testing Across Two Studies.

Cancer fatalism may impact outcomes, particularly for African American (AA) women with breast cancer (BrCa). We examined the psychometrics of the modified Powe Fatalism Inventory in sample of AA women with BrCa from two studies. Only the predetermination and God's will items satisfy the conditions to be classified as a strong subscale. Our analysis identified that five items had strong psychometric properties for measuring fatalism for AA women with BrCa. However, these items do not include all the defining attributes of fatalism. A strong measure of fatalism strengthens our understanding of how this concept influences AA patient outcomes.

Relationship of Mental Health and Illness in Substance Abuse Patients.

This study examined the latent structure of a number of measures of mental health (MH) and mental illness (MI) in substance use disorder outpatients to determine whether they represent two independent dimensions, as Keyes (2005) found in a community sample. Seven aspects of MI assessed were assessed - optimism, personal meaning, spirituality/religiosity, social support, positive mood, hope, and vitality. MI was assessed with two measures of negative psychological moods/states, a measure of antisociality, and the Addiction Severity Index's recent psychiatric and family-social problem scores. Correlational and exploratory factor analyses revealed that MH and MI appear to reflect two independent, but correlated, constructs. However, optimism and social support had relatively high loadings on both factors. Antisociality and the family-social problem score failed to load significantly on the MI factor. Confirmatory factor analysis supported the existence of two obliquely related, negatively correlated dimensions. Study findings, although generally supporting the independence of MH and MI, suggest that the specific answers to this question may be influenced by the constructs and assessments used to measure them.

Exploring app features with outcomes in mHealth studies involving chronic respiratory diseases, diabetes, and hypertension: a targeted exploration of the literature.

Objectives: Limited data are available on the correlation of mHealth features and statistically significant outcomes. We sought to identify and analyze: types and categories of features; frequency and number of features; and relationship of statistically significant outcomes by type, frequency, and number of features. Materials and Methods: This search included primary articles focused on app-based interventions in managing chronic respiratory diseases, diabetes, and hypertension. The initial search yielded 3622 studies with 70 studies meeting the inclusion criteria. We used thematic analysis to identify 9 features within the studies. Results: Employing existing terminology, we classified the 9 features as passive or interactive. Passive features included: 1) one-way communication; 2) mobile diary; 3) Bluetooth technology; and 4) reminders. Interactive features included: 1) interactive prompts; 2) upload of biometric measurements; 3) action treatment plan/personalized health goals; 4) 2-way communication; and 5) clinical decision support system. Discussion: Each feature was included in only one-third of the studies with a mean of 2.6 mHealth features per study. Studies with statistically significant outcomes used a higher combination of passive and interactive features (69%). In contrast, studies without statistically significant outcomes exclusively used a higher frequency of passive features (46%). Inclusion of behavior change features (ie, plan/goals and mobile diary) were correlated with a higher incident of statistically significant outcomes (100%, 77%). Conclusion: This exploration is the first step in identifying how types and categories of features impact outcomes. While the findings are inconclusive due to lack of homogeneity, this provides a foundation for future feature analysis.

Addiction Severity Index Recent and Lifetime summary indexes based on nonparametric item response theory methods.

Baseline Addiction Severity Index (5th ed.; ASI-5) data of 2,142 substance abuse patients were analyzed with two nonparametric item response theory (NIRT) methods: Mokken scaling and conditional covariance techniques. Nine reliable and dimensionally homogeneous Recent Problem indexes emerged in the ASI-5's seven areas, including two each in the Employment/Support and Family/Social Relationships areas. Lifetime Problem indexes were derived for five of the areas--Medical, Drug, Alcohol, Legal, and Psychiatric--but not for the Employment/Support and Family/Social Relationships areas. Correlational analyses conducted on a subsample of 586 patients revealed the indexes for the seven areas to be largely independent. At least moderate correlations were obtained between the Recent and Lifetime indexes within each area where both existed. Concurrent validity analyses conducted on this same subsample found meaningful relationships, except for the Employment/Support area. NIRT-based methods were able to add to findings produced previously by classical psychometric methods and appear to offer promise for the psychometric analysis of complex, mixed-format instruments such as the ASI-5.

Conditional Covariance-Based Subtest Selection for Polytomous DIMTEST.

Dimensionality assessment is an important component of test validation. While the vast majority of statewide standardized tests contain both dichotomous and polytomous items, much of the work in dimensionality assessment has focused on the case of dichotomous item exams. Poly-NEWDIM is a polytomous version of DIMTEST that is a nonparametric hypothesis testing procedure for dimensionality assessments based on comparison of an assessment subtest (AT) with a partitioning subtest (PT). A good AT and PT selection is vital to the performance of Poly-NEWDIM. The proposed two new AT-selection procedures, HCA/CCPROX-PolyDIMTEST (HCP) and HCA/CCPROX-PolyNEWDIM (HCN), perform better than the previous AT-selection method (HCD) in terms of power. Moreover, HCN shows less sensitivity than HCP to sample size, correlation, and structural complexity. The results also indicate that 70% of data for AT selection is appropriate for all three kinds of tests (dichotomous only, polytomous only, and mixed) with large sample sizes and dichotomous tests with small sample sizes, while 50% is good for polytomous tests and mixed tests with small sample sizes.

Recent status scores for version 6 of the Addiction Severity Index (ASI-6).

AIMS: To describe the derivation of recent status scores (RSSs) for version 6 of the Addiction Severity Index (ASI-6). DESIGN: 118 ASI-6 recent status items were subjected to nonparametric item response theory (NIRT) analyses followed by confirmatory factor analysis (CFA). Generalizability and concurrent validity of the derived scores were determined. SETTING AND PARTICIPANTS: A total of 607 recent admissions to variety of substance abuse treatment programs constituted the derivation sample; a subset (n = 252) comprised the validity sample. MEASUREMENTS: The ASI-6 interview and a validity battery of primarily self-report questionnaires that included at least one measure corresponding to each of the seven ASI domains were administered. FINDINGS: Nine summary scales describing recent status that achieved or approached both high scalability and reliability were derived; one scale for each of six areas (medical, employment/finances, alcohol, drug, legal, psychiatric) and three scales for the family/social area. Intercorrelations among the RSSs also supported the multi-dimensionality of the ASI-6. Concurrent validity analyses yielded strong evidence supporting the validity of six of the RSSs (medical, alcohol, drug, employment, family/social problems, psychiatric). Evidence was weaker for the legal, family/social support and child problems RSSs. Generalizability analyses of the scales to males versus females and whites versus blacks supported the comparability of the findings, with slight exceptions. CONCLUSIONS: The psychometric analyses to derive Addiction Severity Index version 6 recent status scores support the multi-dimensionality of the Addiction Severity Index version 6 (i.e. the relative independence of different life functioning areas), consistent with research on earlier editions of the instrument. In general, the Addiction Severity Index version 6 scales demonstrate acceptable scalability, reliability and concurrent validity. While questions remain about the generalizability of some scales to population subgroups, the overall findings coupled with updated and more extensive content in the Addiction Severity Index version 6 support its use in clinical practice and research.

Maximum likelihood estimation with binary-data regression models: small-sample and large-sample features.

Many inferential procedures for generalized linear models rely on the asymptotic normality of the maximum likelihood estimator (MLE). Fahrmeir & Kaufmann (1985, Ann. Stat., 13, 1) present mild conditions under which the MLEs in GLiMs are asymptotically normal. Unfortunately, limited study has appeared for the special case of binomial response models beyond the familiar logit and probit links, and for more general links such as the complementary log-log link, and the less well-known complementary log link. We verify the asymptotic normality conditions of the MLEs for these models under the assumption of a fixed number of experimental groups and present a simple set of conditions for any twice differentiable monotone link function. We also study the quality of the approximation for constructing asymptotic Wald confidence regions. Our results show that for small sample sizes with certain link functions the approximation can be problematic, especially for cases where the parameters are close to the boundary of the parameter space.

Reliability and validity of the alcohol short index of problems and a newly constructed drug short index of problems.

OBJECTIVE: This study evaluated the psychometric properties of the 15-item alcohol Short Index of Problems (SIP) instrument and those of a newly constructed 15-item drug Short Index of Problems (SIP-D) instrument in 277 newly entered substance-abuse patients. METHOD: The SIP is derived from the longer, 50-item Drinker Inventory of Consequences (DrInC), which was designed to assess adverse consequences of alcohol use. The SIP-D was constructed by substituting the term "drug use" for the term "drinking" in each SIP item. A 3-month recall interval was employed. RESULTS: Factor analyses of each of the instruments revealed similar solutions, with only one main factor accounting for the majority of variance. Nonparametric item response theory methods produced the same finding. Internal consistency reliability estimates for the SIP and SIP-D total scores were .98 and .97, respectively. Concurrent validity was demonstrated by examining the correlations of the total scores for each of the instruments with the recent summary indexes of the newly revised Addiction Severity Index (ASI-Version 6): alcohol, drug, medical, economic, legal, family/social, and psychiatric problems. CONCLUSIONS: This study is the first to confirm the psychometric validity of the SIP when used as an independent instrument unembedded within the DrInC. The study also supports the use of the SIP-D as a brief measure of adverse consequences of drug use. The findings strongly support the unidimensional structure of both measures.