Exploring differential item functioning in the SF-36 by demographic, clinical, psychological and social factors in an osteoarthritis population

The SF-36 is a very commonly used generic measure of health outcome in osteoarthritis (OA). An important, but frequently overlooked, aspect of validating health outcome measures is to establish if items work in the same way across subgroup of a population. That is, if respondents have the same 'true' level of outcome, does the item give the same score in different subgroups or is it biased towards one subgroup or another. Differential item functioning (DIF) can identify items that may be biased for one group or another and has been applied to measuring patient reported outcomes. Items may show DIF for different conditions and between cultures, however the SF-36 has not been specifically examined in an osteoarthritis population nor in a UK population. Hence, the aim of the study was to apply the DIF method to the SF-36 for a UK OA population. The sample comprised a community sample of 763 people with OA who participated in the Somerset and Avon Survey of Health. The SF-36 was explored for DIF with respect to demographic, social, clinical and psychological factors. Well developed ordinal regression models were used to identify DIF items. Results: DIF items were found by age (6 items), employment status (6 items), social class (2 items), mood (2 items), hip v knee (2 items), social deprivation (1 item) and body mass index (1 item). Although the impact of the DIF items rarely had a significant effect on the conclusions of group comparisons, in most cases there was a significant change in effect size. Overall, the SF-36 performed well with only a small number of DIF items identified, a reassuring finding in view of the frequent use of the SF-36 in OA. Nevertheless, where DIF items were identified it would be advisable to analyse data taking account of DIF items, especially when age effects are the focus of interest

Risky business: factor analysis of survey data – assessing the probability of incorrect dimensionalisation

This paper undertakes a systematic assessment of the extent to which factor analysis the correct number of latent dimensions (factors) when applied to ordered categorical survey items (so-called Likert items). We simulate 2400 data sets of uni-dimensional Likert items that vary systematically over a range of conditions such as the underlying population distribution, the number of items, the level of random error, and characteristics of items and item-sets. Each of these datasets is factor analysed in a variety of ways that are frequently used in the extant literature, or that are recommended in current methodological texts. These include exploratory factor retention heuristics such as Kaiser’s criterion, Parallel Analysis and a non-graphical scree test, and (for exploratory and confirmatory analyses) evaluations of model fit. These analyses are conducted on the basis of Pearson and polychoric correlations.We find that, irrespective of the particular mode of analysis, factor analysis applied to ordered-categorical survey data very often leads to over-dimensionalisation. The magnitude of this risk depends on the specific way in which factor analysis is conducted, the number of items, the properties of the set of items, and the underlying population distribution. The paper concludes with a discussion of the consequences of overdimensionalisation, and a brief mention of alternative modes of analysis that are much less prone to such problems

Exploring differential item functioning in the Western Ontario and McMaster Universities osteoarthritis index (WOMAC)

Background: The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) is a widely used patient reported outcome in osteoarthritis. An important, but frequently overlooked, aspect of validating health outcome measures is to establish if items exhibit differential item functioning (DIF). That is, if respondents have the same underlying level of an attribute, does the item give the same score in different subgroups or is it biased towards one subgroup or another. The aim of the study was to explore DIF in the Likert format WOMAC for the first time in a UK osteoarthritis population with respect to demographic, social, clinical and psychological factors. Methods: The sample comprised a community sample of 763 people with osteoarthritis who participated in the Somerset and Avon Survey of Health. The WOMAC was explored for DIF by gender, age, social deprivation, social class, employment status, distress, body mass index and clinical factors. Ordinal regression models were used to identify DIF items. Results: After adjusting for age, two items were identified for the physical functioning subscale as having DIF with age identified as the DIF factor for 2 items, gender for 1 item and body mass index for 1 item. For the WOMAC pain subscale, for people with hip osteoarthritis one item was identified with age-related DIF. The impact of the DIF items rarely had a significant effect on the conclusions of group comparisons. Conclusions: Overall, the WOMAC performed well with only a small number of DIF items identified. However, as DIF items were identified in for the WOMAC physical functioning subscale it would be advisable to analyse data taking into account the possible impact of the DIF items when weight, gender or especially age effects, are the focus of interest in UK-based osteoarthritis studies. Similarly for the WOMAC pain subscale in people with hip osteoarthritis it would be worthwhile to analyse data taking into account the possible impact of the DIF item when age comparisons are of primary interest

MassBuilt: effectiveness of an apprenticeship site-based smoking cessation intervention for unionized building trades workers

Blue-collar workers are difficult to reach and less likely to successfully quit smoking. The objective of this study was to test a training site-based smoking cessation intervention. This study is a randomized-controlled trial of a smoking cessation intervention that integrated occupational health concerns and was delivered in collaboration with unions to apprentices at 10 sites (n = 1,213). We evaluated smoking cessation at 1 and 6 months post-intervention. The baseline prevalence of smoking was 41%. We observed significantly higher quit rates in the intervention versus control group (26% vs. 16.8%; p = 0.014) 1 month after the intervention. However, the effects diminished over time so that the difference in quit rate was not significant at 6 month post-intervention (9% vs. 7.2%; p = 0.48). Intervention group members nevertheless reported a significant decrease in smoking intensity (OR = 3.13; 95% CI: 1.55–6.31) at 6 months post-intervention, compared to controls. The study demonstrates the feasibility of delivering an intervention through union apprentice programs. Furthermore, the notably better 1-month quit rate results among intervention members and the greater decrease in smoking intensity among intervention members who continued to smoke underscore the need to develop strategies to help reduce relapse among blue-collar workers who quit smoking

An empirical examination of the factor structure of compassion

Compassion has long been regarded as a core part of our humanity by contemplative traditions, and in recent years, it has received growing research interest. Following a recent review of existing conceptualisations, compassion has been defined as consisting of the following five elements: 1) recognising suffering, 2) understanding the universality of suffering in human experience, 3) feeling moved by the person suffering and emotionally connecting with their distress, 4) tolerating uncomfortable feelings aroused (e.g., fear, distress) so that we remain open to and accepting of the person suffering, and 5) acting or being motivated to act to alleviate suffering. As a prerequisite to developing a high quality compassion measure and furthering research in this field, the current study empirically investigated the factor structure of the five-element definition using a combination of existing and newly generated self-report items. This study consisted of three stages: a systematic consultation with experts to review items from existing self-report measures of compassion and generate additional items (Stage 1), exploratory factor analysis of items gathered from Stage 1 to identify the underlying structure of compassion (Stage 2), and confirmatory factor analysis to validate the identified factor structure (Stage 3). Findings showed preliminary empirical support for a five-factor structure of compassion consistent with the five-element definition. However, findings indicated that the ‘tolerating’ factor may be problematic and not a core aspect of compassion. This possibility requires further empirical testing. Limitations with items from included measures lead us to recommend against using these items collectively to assess compassion. Instead, we call for the development of a new self-report measure of compassion, using the five-element definition to guide item generation. We recommend including newly generated ‘tolerating’ items in the initial item pool, to determine whether or not factor-level issues are resolved once item-level issues are addressed

Design of a randomized controlled trial for multiple cancer risk behaviors among Spanish-speaking Mexican-origin smokers

Background: Smoking, poor diet, and physical inactivity account for as much as 60% of cancer risk. Latinos experience profound disparities in health behaviors, as well as the cancers associated with them. Currently, there is a dearth of controlled trials addressing these health behaviors among Latinos. Further, to the best of our knowledge, no studies address all three behaviors simultaneously, are culturally sensitive, and are guided by formative work with the target population. Latinos represent 14% of the U. S. population and are the fastest growing minority group in the country. Efforts to intervene on these important lifestyle factors among Latinos may accelerate the elimination of cancer-related health disparities

Developing international business relationships in a Russian context

The collapse of the former Soviet Union has opened up a wealth of business opportunities for companies seeking new markets in the Russian Federation. Despite this, firms intending to do business in Russia have found themselves hampered by cultural differences in business practices and expectations. As Russia integrates into the global economy, understanding such practices and the managerial mindset of business people is crucial for managers who hope to navigate Russia's complex markets. This study draws on the trust literature and adopts quantitative tools to deconstruct the Russian 'Sviazi' system of social capital business networking. We develop a model isolating three dimensions of Sviazi: one an affective or emotional component; the second, a conative component; and the third, a cognitive component. The model provides a useful guide for helping foreign firms to succeed in Russia, while also serving as a basis for further research in the field. Keywords

The fallacy of placing confidence in confidence intervals

Interval estimates – estimates of parameters that include an allowance for sampling uncertainty – have long been touted as a key component of statistical analyses. There are several kinds of interval estimates, but the most popular are confidence intervals (CIs): intervals that contain the true parameter value in some known proportion of repeated samples, on average. The width of confidence intervals is thought to index the precision of an estimate; CIs are thought to be a guide to which parameter values are plausible or reasonable; and the confidence coefficient of the interval (e.g., 95 %) is thought to index the plausibility that the true parameter is included in the interval. We show in a number of examples that CIs do not necessarily have any of these properties, and can lead to unjustified or arbitrary inferences. For this reason, we caution against relying upon confidence interval theory to justify interval estimates, and suggest that other theories of interval estimation should be used instead