When fit indices and residuals are incompatible

What are the consequences of ignoring cross-loadings in bifactor models? A simulation study assessing parameter recovery and sensitivity of goodness-of-fit indices

Bifactor latent models have gained popularity and are widely used to model construct multidimensionality. When adopting a confirmatory approach, a common practice is to assume that all cross-loadings take zero values. This article presents the results of a simulation study exploring the impact of ignoring non-zero cross-loadings on the performance of confirmatory bifactor analysis. The present work contributes to previous research by including study conditions that had not been examined before. For instance, a wider range of values of the factor loadings both for the group factors and the cross-loadings is considered. Parameter recovery is analyzed, but the focus of the study is on assessing the sensitivity of goodness-of-fit indices to detect the model misspecification that involves ignoring non-zero cross-loadings. Several commonly used SEM fit indices are examined: both biased estimators of the fit index (CFI, GFI, and SRMR) and unbiased estimators (RMSEA and SRMR). Results indicated that parameter recovery worsens when ignoring moderate and large cross-loading values and using small sample sizes, and that commonly used SEM fit indices are not useful to detect such model misspecifications. We recommend the use of the unbiased SRMR index with a cutoff value adjusted by the communality level (R²), as it is the only fit index sensitive to the model misspecification due to ignoring non-zero cross-loadings in the bifactor model. The results of the present study provide insights into modeling cross-loadings in confirmatory bifactor models but also practical recommendations to researchers.

The Spanish Version of the Child Medical Fear Questionnaire: Cross-Cultural Adaptation and Validation

Having valid and reliable tools that help health professionals to assess fear in children undergoing medical procedures is essential to offer humanised and quality of care in the paediatric population. The aim of this study was to develop the cross-cultural adaptation and the evaluation of the psychometric properties of the Spanish version of the "Child Medical Fear Scale" in its shortened version (CMFS-R). The design consisted of two phases: first, of cross-cultural adaptation and second, of the psychometric validation of the CMFS-R with a sample of 262 children from Spain, applying a cross-sectional design. Confirmatory factor analysis was conducted to assess construct validity and the Cronbach's alpha and the adjusted item-total score correlation coefficients were performed to study reliability. The results confirmed internal consistency and construct validity of the Spanish version of the CMFS-R, indicating that the scale has an acceptable level of validity and reliability. Therefore, this study brings a new version of the scale to assess fear related to medical procedures for use in the Spanish paediatric population.

Factor structure of the ARIC-NCS Neuropsychological Battery: An evaluation of invariance across vascular factors and demographic characteristics

Neuropsychological test batteries are designed to assess cognition in detail by measuring cognitive performance in multiple domains. This study examines the factor structure of tests from the ARIC-NCS battery overall and across informative subgroups defined by demographic and vascular risk factors in a population of older adults. We analyzed neuropsychological test scores from 6,413 participants in the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS) examined in 2011-2013. Confirmatory factor analysis (CFA) was used to assess the fit of an a priori hypothesized 3-domain model, and fit statistics were calculated and compared to 1- and 2-domain models. Additionally, we tested for stability (invariance) of factor structures among different subgroups defined by diabetes, hypertension, age, sex, race, and education. Mean age of participants was 76 years, 76% were White, and 60% were female. CFA on the a priori hypothesized 3-domain structure, including memory, sustained attention and processing speed, and language, fit the data better (comparative fit index [CFI] = 0.973, root mean square error of approximation [RMSEA] = 0.059) than the 2-domain (CFI = 0.960, RMSEA = 0.070) and 1-domain (CFI = 0.947, RMSEA = 0.080) models. Bayesian information criterion value was lowest, and quantile-quantile plots indicated better fit, for the 3-domain model. Additionally, multiple-group CFA supported a common structure across the tested demographic subgroups, and indicated strict invariance by diabetes and hypertension status. In this community-based population of older adults with varying levels of cognitive performance, the a priori hypothesized 3-domain structure fit the data well. The identified factors were configurally invariant by age, sex, race, and education, and strictly invariant by diabetes and hypertension status. (PsycINFO Database Record

Shame for disrespecting evidence: the personal consequences of insufficient respect for structural equation model testing

BACKGROUND

Inappropriate and unacceptable disregard for structural equation model (SEM) testing can be traced back to: factor-analytic inattention to model testing, misapplication of the Wilkinson task force's [Am Psychol 54:594-604, 1999] critique of tests, exaggeration of test biases, and uncomfortably-numerous model failures.

DISCUSSION

The arguments for disregarding structural equation model testing are reviewed and found to be misguided or flawed. The fundamental test-supporting observations are: a) that the null hypothesis of the χ2 structural equation model test is not nil, but notable because it contains substantive theory claims and consequences; and b) that the amount of covariance ill fit cannot be trusted to report the seriousness of model misspecifications. All covariance-based fit indices risk failing to expose model problems because the extent of model misspecification does not reliably correspond to the magnitude of covariance ill fit - seriously causally misspecified models can fit, or almost fit.

SUMMARY

The only reasonable research response to evidence of non-chance structural equation model failure is to diagnostically investigate the reasons for failure. Unfortunately, many SEM-based theories and measurement scales will require reassessment if we are to clear the backlogged consequences of previous deficient model testing. Fortunately, it will be easier for researchers to respect evidence pointing toward required reassessments, than to suffer manuscript rejection and shame for disrespecting evidence potentially signaling serious model misspecifications.

Development of a self-report physical function instrument for disability assessment: item pool construction and factor analysis

OBJECTIVES

To build a comprehensive item pool representing work-relevant physical functioning and to test the factor structure of the item pool. These developmental steps represent initial outcomes of a broader project to develop instruments for the assessment of function within the context of Social Security Administration (SSA) disability programs.

DESIGN

Comprehensive literature review; gap analysis; item generation with expert panel input; stakeholder interviews; cognitive interviews; cross-sectional survey administration; and exploratory and confirmatory factor analyses to assess item pool structure.

SETTING

In-person and semistructured interviews and Internet and telephone surveys.

PARTICIPANTS

Sample of SSA claimants (n=1017) and a normative sample of adults from the U.S. general population (n=999).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

Model fit statistics.

RESULTS

The final item pool consisted of 139 items. Within the claimant sample, 58.7% were white; 31.8% were black; 46.6% were women; and the mean age was 49.7 years. Initial factor analyses revealed a 4-factor solution, which included more items and allowed separate characterization of: (1) changing and maintaining body position, (2) whole body mobility, (3) upper body function, and (4) upper extremity fine motor. The final 4-factor model included 91 items. Confirmatory factor analyses for the 4-factor models for the claimant and the normative samples demonstrated very good fit. Fit statistics for claimant and normative samples, respectively, were: Comparative Fit Index=.93 and .98; Tucker-Lewis Index=.92 and .98; and root mean square error approximation=.05 and .04.

CONCLUSIONS

The factor structure of the physical function item pool closely resembled the hypothesized content model. The 4 scales relevant to work activities offer promise for providing reliable information about claimant physical functioning relevant to work disability.

Nonlinear growth curves in developmental research

Developmentalists are often interested in understanding change processes, and growth models are the most common analytic tool for examining such processes. Nonlinear growth curves are especially valuable to developmentalists because the defining characteristics of the growth process such as initial levels, rates of change during growth spurts, and asymptotic levels can be estimated. A variety of growth models are described beginning with the linear growth model and moving to nonlinear models of varying complexity. A detailed discussion of nonlinear models is provided, highlighting the added insights into complex developmental processes associated with their use. A collection of growth models are fit to repeated measures of height from participants of the Berkeley Growth and Guidance Studies from early childhood through adulthood.

Comparison of time and frequency domain measures of RSA in ambulatory recordings

The extent to which various measures of ambulatory respiratory sinus arrhythmia (RSA) capture the same information across conditions in different subjects remains unclear. In this study the root mean square of successive differences (RMSSD), peak valley RSA (pvRSA), and high frequency power (HF power) were assessed during ambulatory recording in 84 subjects, of which 64 were retested after about 3 years. We used covariance structure modeling to test the equality of the correlations among three RSA measures over two test days and three conditions (daytime sitting or walking and nighttime sleep) and in groups with low, medium, and high mean heart rate (HR), or low, medium, and high mean respiration rate (RR). Results showed that ambulatory RMSSD, pvRSA, and HF power are highly correlated and that their correlation is stable across time, ambulatory conditions, and a wide range of resting HR and RR values. RMSSD appears to be the most cost-efficient measure of RSA.