Exploratory Bifactor Analysis Research Articles

Measuring psychological distress using the 12-item general health questionnaire and the six-item Kessler psychological distress scale. Psychometric comparison and equipercentile equating of the two scales.

This study aimed to examine if the General Health Questionnaire (GHQ)-12 and Kessler 6 (K6) assess the same underlying construct and to develop a score conversion table for the two scales. A random sample of 4303 people who completed both the GHQ-12 and K6 in 2021 were analyzed. Exploratory bifactor analysis evaluated if both scales measured the same construct, and Rasch analysis assessed item severities. The scales were transformed using Equipercentile equivalence for comparability and score conversion. Agreement was estimated with Cohen's Kappa coefficient, along with raw positive and negative agreement. We found that the two scales measure the same phenomenon to the extent that they can be made equivalent. Conversion tables between GHQ-12 and K6 are presented. Applying the commonly used cut-off of ≥3 on the GHQ-12 bi-modal scoring, we found that the best corresponding cut-off on the K6 would be ≥8. The prevalence of psychological distress was then 22% with GHQ-12% and 21% with K6. The GHQ-12 and K6 measure the same construct and corresponding cut-off scores on one scale were found for the other scale. This is valuable for longitudinal studies or time series where one scale has replaced the other scale.

Associations Between a General Factor and Group Factor from the Spanish-Language Eysenck Personality Questionnaire–Revised Short Form’s Neuroticism Scale and the Revised NEO Personality Inventory Domains and Facets

Prior studies used exploratory bifactor analyses to examine the structure of the Neuroticism scale from the Short-scale Eysenck Personality Questionnaire–Revised (EPQ–RS). These studies revealed a general factor and two group factors—Anxious-Tense and Worried-Vulnerable. These factors were related to poorer mental health, but their associations with physical health differed, as did their genetic and neurobiological underpinnings. A later study found that their associations with the Big Five Inventory-2 Short Form’s factors and facets differed. We reanalyzed data on 1,006 Spanish students who completed Spanish-language versions of the EPQ–RS and the Revised NEO Personality Inventory (NEO PI–R). Using confirmatory factor analysis, we showed that a model comprising the general factor and a group factor—Anxious-Tense—fit well. In later correlations, a joint factor analysis, and simultaneous multiple regressions, we showed that the EPQ-RS’s general factor and the group factor had different patterns of associations with the NEO PI–R domains and facets. These associations were consistent with the definition of the EPQ–RS Neuroticism scale’s general factor and that of the group factor. Further investigation into the EPQ–RS Neuroticism scale’s structure can improve our understanding of neuroticism’s relationship with health and other outcomes.

Misbegotten methodologies and forgotten lessons from Tom Swift's electric factor analysis machine: A demonstration with competing structural models of psychopathology.

Confirmatory factor analysis (CFA) and its bifactor models are popular in empirical investigations of the factor structure of psychological constructs. CFA offers straightforward hypothesis testing but has notable pitfalls, such as the imposition of strict assumptions (i.e., simple structure) that obscure unmodeled complexity. Due to the limitations of bifactor CFAs, they have yielded anomalous results across samples and studies that suggest model misspecification (e.g., evaporating specific factors and unexpected loadings). We propose the use of exploratory factor analysis (EFA) to evaluate the structural validity of CFA solutions-either before or after the estimation of more restrictive CFA models-to (a) identify model misspecifications that may drive anomalous estimates and (b) confirm CFA models by examining whether hypothesized structures emerge with limited researcher input. We evaluated the degree to which predominant factor structures were invariant across contexts along the exploratory-confirmatory continuum and demonstrate how poor methodological choices can distort results and impede theory development. All CFA models fit well, but there were numerous differences in replicability and substantive interpretability. Several similarities emerged between bifactor CFA and EFA models, including evidence of overextraction, the collapse of specific factors onto the general factor, and subsequent shifts in how the general factor was defined. We situate these methodological shortcomings within the broader literature on structural models of psychopathology, articulate implications for theories (such as the p-factor) that are borne out of factor analysis, outline several remedies for problems encountered when performing exploratory bifactor analysis, and propose alternative specifications for confirmatory bifactor models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cognitive impairments predict the behavioral and psychological symptoms of dementia.

The purpose of this study was to (1) validate the Thai version of the Neuropsychiatric Inventory Questionnaire (NPI-Q) as a screening tool for behavioral and psychological symptoms of dementia (BPSD), and (2) examine the relationship between cognitive performance and BPSD in an elderly population with amnestic mild cognitive impairment (aMCI) and dementia of Alzheimer's type (DAT). One hundred and twenty participants, comprising 80 aMCI and 40 DAT patients, and their respective caregivers were included in the study. Participants completed the NPI-Q and the Neuropsychiatric Inventory (NPI) within 2 weeks of each other and cognitive performance was primarily assessed using the Montreal Cognitive Assessment (MoCA). The Thai NPI-Q had good validity and reliability. Pure exploratory bifactor analysis revealed that a general factor and a single-group factor (with high loadings on delusions, hallucinations, apathy, and appetite) underpinned the NPI-Q domains. Significant negative correlations between the MoCA total score and the general and single-group NPI-Q scores were found in all subjects (aMCI + DAT combined) and DAT alone, but not in aMCI. Cluster analysis allocated subjects with BPSD (10% of aMCI and 50% of DAT participants) into a distinct "DAT + BPSD" class. The NPI-Q is an appropriate instrument for assessing BPSD and the total score is largely predicted by cognitive deficits. It is plausible that aMCI subjects with severe NPI-Q symptoms (10% of our sample) may have a poorer prognosis and constitute a subgroup of aMCI patients who will likely convert into probable dementia.

Exploratory Bi-factor Analysis with Multiple General Factors

Exploratory bi-factor analysis (EBFA) is a very popular approach to estimate models where specific factors are concomitant to a single, general dimension. However, the models typically encountered in fields like personality, intelligence, and psychopathology involve more than one general factor. To address this circumstance, we developed an algorithm (GSLiD) based on partially specified targets to perform exploratory bi-factor analysis with multiple general factors (EBFA-MGF). In EBFA-MGF, researchers do not need to conduct independent bi-factor analyses anymore because several bi-factor models are estimated simultaneously in an exploratory manner, guarding against biased estimates and model misspecification errors due to unexpected cross-loadings and factor correlations. The results from an exhaustive Monte Carlo simulation manipulating nine variables of interest suggested that GSLiD outperforms the Schmid-Leiman approximation and is robust to challenging conditions involving cross-loadings and pure items of the general factors. Thereby, we supply an R package (bifactor) to make EBFA-MGF readily available for substantive research. Finally, we use GSLiD to assess the hierarchical structure of a reduced version of the Personality Inventory for DSM-5 Short Form (PID-5-SF).

Dublin Anti-Bullying Self-Efficacy Scales: Bifactor and Item Response Theory Models.

Dublin Anti-Bullying Self-Efficacy Scales aim to measure the effectiveness of school anti-bullying programs in promoting five steps that victims and bystanders take against online and offline bullying behaviors. These steps are anti-bullying self-efficacy beliefs to recognize bullying behaviors, comprehend emergency, take responsibility, know what to do, and intervene. However, when an anti-bullying program is very effective for the majority of participants who give high scores, a considerable number of participants who give low scores are very likely to be detected as outliers. This raises two measurement issues. First, high scores create highly negatively skewed data and lead to measuring a unidimensional rather than multidimensional construct. This could be one reason why recent research has been unclear about the extent to which the scales measure a unidimensional, multidimensional, or bifactor construct. Second, should outliers be removed or be considered as participants for whom the program was ineffective? If the scales had measurement invariance across the group of outliers and non-outliers or low and high self-efficacy, it could be concluded that the anti-bullying program was ineffective for some participants. The current research aims to address these issues by testing both measurement invariance as well as unidimensional and bifactor models of anti-bullying self-efficacy. Results of Pure Exploratory Bifactor (PEBI) Analyses and Item Response Theory (IRT) with Two-Parameter-Logistic (2PL) Models of data from a convenience sample of 14-year-old students in Ireland (N = 1,222) indicated sufficient psychometric properties of both unidimensional and multidimensional scales for victim offline, victim online, bystander offline, and bystander online. Further research can use these scales for measuring the bifactor model of anti-bullying self-efficacy as well as the cut-off score for distinguishing between low and high anti-bullying self-efficacy.

Factor Structure of the eHEALS

Abstract. The dimensionality of the eHEALS has been the subject of some controversy. Sample populations and language versions vary widely, as do the employed statistical methods to assess dimensionality. In previous research, we assessed the factor structure in two different samples testing 1 vs. 2 and 1 vs. 2 vs. 3 correlated factors. The objective of this reanalysis was to assess whether the 3-factor model fitted better than the 2- and 1-factor models. We analyzed data from a 2009 cross-sectional survey on health literacy in grade 12 ( n = 327) using CFA. All factor models of the eHEALS showed unsatisfactory model fit. A subsequent exploratory bifactor analysis confirmed multidimensionality and indicated that Item 2 was problematic. When this item was excluded from the correlated factor models, model fit improved, and the 3-factor model showed the best fit. The results in our sample of 12th-grade students offer some support to the German eHEALS having a 3-factor structure similar to the results from our previous research in women aged 50. The replicability of the fit pattern in a different sample and setting was limited by diverging results on Item 2.

A bifactor model of U.S. parents’ attitudes regarding mediation for the digital age

ABSTRACT Parents use digital-specific strategies to mitigate online risks and augment online benefits of digital technology in their children’s lives. The goal of this study was to develop and validate a measure of parents’ attitudes about mediation of digital technology. An internet-based survey was administered to 460 parents of children and adolescents in the United States. Exploratory bifactor analysis revealed one general factor, reflecting general parenting attitudes, and four digital-specific factors: discursive mediation, restrictive mediation and monitoring, participatory mediation, and mediation by modeling. Confirmatory factor analysis supported a bifactor model of the Digital Parental Mediation Attitudes Scale (DPMAS); the general factor explained shared variance related to parenting style and skills in general, while the mediation factors represented digital-specific attitudes. Construct validity was evidenced in differential associations between mediation factors and parenting efficacy, influence, child age, and parent and child technology use patterns.

Reliability and omega hierarchical in multidimensional data: A comparison of various estimators.

The current guidelines for estimating reliability recommend using two omega combinations in multidimensional data. One omega is for factor analysis (FA) reliability estimators, and the other omega is for omega hierarchical estimators (i.e., ωh). This study challenges these guidelines. Specifically, the following three questions are asked: (a) Do FA reliability estimators outperform non-FA reliability estimators? (b) Is it always desirable to estimate ωh? (c) What are the best reliability and ωh estimators? This study addresses these issues through a Monte Carlo simulation of reliability and ωh estimators. The conclusions are given as follows. First, the performance differences among most reliability estimators are small, and the performance of FA estimators is comparable to that of non-FA estimators. However, the current, most-recommended estimators, that is, estimators based on the bifactor model and exploratory factor analysis, tend to overestimate reliability. Second, the accuracy of ωh estimators is much lower than that of reliability estimators, so we should perform ωh estimation selectively only on data that meet several requirements. Third, exploratory bifactor analysis is more accurate than confirmatory bifactor analysis only in the presence of cross-loading; otherwise, exploratory bifactor analysis is less accurate than confirmatory bifactor analysis. Fourth, techniques known to improve the Schmid-Leiman (SL) transformation are not superior to SL transformation but have different advantages. This study provides an R Shiny app that allows users to obtain multidimensional reliability and ωh estimates with a few mouse clicks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The North Italian Longitudinal Study Assessing the Mental Health Effects of SARS-CoV-2 Pandemic Health Care Workers-Part II: Structural Validity of Scales Assessing Mental Health.

It is unclear if the factor structure of the questionnaires that were employed by studies addressing the impact of COVID-19 on the mental health of Healthcare Workers (HCW) did not change due to the pandemic. The aim of this study is to assess the factor structure and longitudinal measurement invariance of the Maslach Burnout Inventory (MBI) and the factor structure of the General Health Questionnare-12 (GHQ-12), PTSD Checklist for DSM-5-Short Form (PCL-5-SF), Connor-Davidson Resilience Scale-10 (CD-RISC-10) and Post-Traumatic Growth Inventory-Short Form (PTGI-SF). Out of n = 805 HCWs from a University hospital who responded to a pre-COVID-19 survey, n = 431 were re-assessed after the COVID-19 outbreak. A Confirmatory Factor Analysis (CFA) on the MBI showed adequate fit and good internal consistency only after removal of items 2, 6, 12 and 16. The assumptions of configural and metric longitudinal invariance were met, whereas scalar longitudinal invariance did not hold. CFAs and exploratory bifactor analyses performed using data from the second wave confirmed that the GHQ-12, the PCL-5-SF, the PTGI-SF and the CD-RISC-10 were unidimensional. In conclusion, we found support for a refined version of the MBI. The comparison of mean MBI values in HCWs before and after the pandemic should be interpreted with caution.

Should We Use a Total Score, Two Subscale Scores, or Six Subscale Scores for the Self-Compassion Scale? A Multi-faceted Assessment Beyond Model Fit Indices

ObjectivesThe aim of this study was to conduct a multi-faceted assessment of the psychometric properties of the Self-Compassion Scale (SCS). In addition to the goodness-of-fit, we aimed to assess the strength and replicability of three factorial solutions, and the quality and effectiveness of the three scoring schemes of the scale (i.e., total scale score, two subscale scores, and six subscale scores).MethodsParticipants were 1508 Spanish-speaking community-dwelling adults (M = 34.94 years, SD = 15.02). Data were examined by means of a conjoint strategy using Rasch modeling, non-linear factor analysis, exploratory bifactor analysis, and parallel analysis. A procedure for selecting the optimal set of items that must be used to compute individual’s scores was used.ResultsThe unidimensional solution showed a marginal model fit (RMSR = .089), and both the bifactor two-group and bifactor six-group solutions showed a good fit (RMSR = .043 and .019, respectively). However, only the unidimensional and the bifactor two-factor solutions showed interpretable and replicable factor structures, and high-quality and effective scores to be used for measurement purposes. Subscale scores derived from the six primary factors did not show adequate psychometric properties. It was observed that the information provided by 10 items was redundant and had already been provided by the other 16 items.ConclusionsGood model fit is neither sufficient nor necessary to justify the use of a scoring scheme. Goodness-of-fit statistics should be complemented by an assessment of the metric properties of the resulting scores when proposing SCS scoring schemes.

Longitudinal assessment of depression during the first year after stroke: Dimensionality and measurement invariance

ObjectiveRepeated measurement of depression using summative scores of commonly used depression scales assumes that the construct is unidimensional and invariant across time, but little is known about the validity of these assumptions in stroke patients. The study examined the assumptions during the first year after mild to moderate stroke using the 8-item Patient Health Questionnaire (PHQ-8). MethodsData were obtained from the 2014–2016 Brain Attack Surveillance in Corpus Christi project, a population-based stroke surveillance study with longitudinal follow-up in Texas, United States. Depressive symptoms were assessed with the PHQ-8 at 3, 6, and 12 months post-stroke (n = 648, 542, 533, respectively). Dimensionality of the PHQ-8 was examined using exploratory bifactor analysis and confirmatory factor analysis (CFA). Based on a one-factor CFA model, longitudinal measurement invariance was examined by fitting configural, weak, strong and strict invariance models sequentially. ResultsMean age was 65.6 (SD = 11.2), half were women, and 56.7% were Mexican American. Reliability measures suggested a generally unidimensional construct (Omega Hierarchical: 0.79–0.86), good internal consistency (Cronbach's alpha: 0.84–0.88) and good temporal stability (intraclass correlation coefficient: 0.838). Configural invariance for equality of item-factor structure (RMSEA = 0.038, CFI = 0.982, WRMR = 0.970) was supported. Partial weak invariance for equality of factor loadings (p = 0.11) was supported by chi-square difference tests, whereas full longitudinal invariance was supported by changes in comparative fit index and root mean square error of approximation. ConclusionsThe findings suggest the PHQ-8 may be a reasonable tool for repeated measurement of depression in stroke patients, and more research on its longitudinal psychometric properties is needed.

The Road to Cognitive Skill Acquisition: Psychometric Evaluation of the Competencies of Cognitive Therapy Scale.

Cognitive therapy (CT) skills are an index of treatment progress. They predict changes in patients' acute depressive symptoms and symptom relapses. However, the psychometric properties of the various measures of CT skills are poorly understood. This study aimed to investigate the factor structure of the Competencies of Cognitive Therapy Scale-Self Report (CCTS-SR) and assess its concurrent validity. The psychometric properties of the CCTS-SR were explored by using data from a panel of online respondents (N=410). The fit of a one-factor solution was explored by using a confirmatory factor analysis. Exploratory bifactor analyses (EBFA) were then conducted to determine other possible factor structures. The one-factor solution did not fit the data well. Results of the EBFA suggested that the factor structure of the CCTS-SR may be characterized by a single underlying dimension capturing the general use of CT skills as well as by more specific factors the authors labeled "behavioral activation" and "CT comprehension." The variance captured by the factor initially labeled as CT comprehension was correlated with measures of depression and emotional dysregulation, suggesting that these items do not capture CT comprehension and should be removed from the scale. The CCTS-SR seems to be characterized by more than a single factor, and items that seemingly compose CT comprehension (i.e., items 13 and 14) may need to be removed. Although the CCTS-SR may be a valid index of therapy progress, more attention needs to be paid to its psychometric properties.

Will the Real Theoretical Structure of the WISC-V Please Stand Up? Implications for Clinical Interpretation

The Wechsler Intelligence Scale for Children’s (WISC) factorial\theoretical structure has undergone numerous substantive changes since it was first developed, and each of these changes has subsequently been questioned by assessment experts. Given remaining questions about the structure of the latest revision, the WISC-V, the present study used three different exploratory factor analytic techniques to investigate the factor structure of the 10 primary subtests in a large clinical sample (N = 5359). Results revealed that the WISC-V likely contains four factors (e.g., Gc, Gwm, Gs, and either Gv (via exploratory bifactor analysis) or a complexly determined perceptual reasoning factor (via the Schmid-Leiman procedure and oblique/higher-order factor analysis)), not the five factors proposed by the test publisher. Variance apportionment and omega estimates indicate that only secondary interpretive emphasis should be placed upon group factors, but only when there is structural validity support. This study suggests that the WISC-V measures four, not five, factors, although the four-factor configuration may be different than previously reported in the technical literature.

Regularized Exploratory Bifactor Analysis With Small Sample Sizes.

Several methods of factor extraction have recently gained popularity as a procedure for dealing with estimation problems associated with small sample sizes, which can be found in the various behavioral science disciplines, such as comparative psychology and behavior genetics. Two popular approaches for particularly small samples (below 50) include unweighted least squares factor analysis (ULS-FA) and regularized exploratory factor analysis (REFA). However, it is unclear how well each of the approaches performs with small samples in the context of exploratory bifactor modeling. In the current study, a comprehensive simulation study was conducted to evaluate the small sample behavior of the two approaches in terms of bifactor structure recovery under different sample size, factor loading, number of variables per factor, number of factors, and factor correlation experimental conditions. The results show that REFA is recommended for use over ULS-FA, particularly in the conditions involving low factor loadings, few group factors, or a small number of variables per factor.

The structure and predictive value of intrinsic capacity in a longitudinal study of ageing

ObjectivesTo assess the validity of the WHO concept of intrinsic capacity in a longitudinal study of ageing; to identify whether this overall measure disaggregated into biologically plausible and clinically useful...

The Component Structure of the Dizziness Handicap Inventory (DHI): A Reappraisal.

The Dizziness Handicap Inventory (DHI) is a 25-item self-report questionnaire developed to measure the disabling and handicapping impact of dizziness. The present investigation was conducted in an effort to re-assess the factor structure of the DHI. Retrospective study. Tertiary care center. Subjects were 1,991 patients who were evaluated in the Mayo Clinic-Rochester Vestibular and Balance Laboratory. Exploratory factor analysis: an exploratory bifactor analysis (EFA) with bifactor rotation was used to analyze a random sample of 999 patients. Analyses were used to determine the dominance of the general factor (i.e., total score) relative to the group factor (i.e., subscales). Confirmatory factor analysis: a confirmatory bifactor graded response model was fit with appropriate item-to-group relationships that was discovered by our exploratory analyses. To validate the bifactor model that was identified with the exploratory analyses, a bifactor model with three grouping factors (i.e., Physical manifestations, Catastrophic impact of dizziness, and the Emotional impact of dizziness) were fit to a different random sample of 992 patients using the new item-to-group factor specifications. In the confirmatory analyses, all items had a positive factor loading on the general factor. There were 14 items that loaded on the general factor only. The rest of the items (n = 11) loaded on both the general factor and one of three group factors. Conclusions of the study revealed several findings: 1) reporting the result as a total score (i.e., a single general factor) is warranted, and, 2) there is statistical support for the existence of three subscales representing: the Physical manifestations, Catastrophic impact, and Emotional impact of dizziness and vertigo.

Validation of a new tool for evaluating subjects' satisfaction with medicine package leaflets: a cross-sectional descriptive study.

Package leaflets of medicines need to be intelligible, but tools for their evaluation are scarce. To validate a new tool for assessing subjects' satisfaction with medicine package leaflets (LiS-RPL). Cross-sectional descriptive study conducted in two regions of Portugal (Lisbon and Centre). 503 participants (53.1% male) were selected according to convenience and homogenously distributed into three groups: 1 to 6; 7 to 12; and > 12 years of schooling. LiS-RPL was developed based on international regulation guidelines and was initially composed of 14 items. Twelve package leaflets were tested. Dimensionality calculations included: exploratory factor analysis and minimum rank factor analysis; Kaiser-Meyer-Olkin index and Bartlett's sphericity test to assess matrix adequacy for exploratory factor analysis; exploratory bifactor analysis with Schmid-Leiman solution to detect possible existence of a broad second-order factor; and Bentler's Simplicity Index and Loading Simplicity Index to assess factor simplicity. Diverse coefficients were calculated to assess reliability. Minimum rank factor analysis detected a two-factor or single-factor structure. Exploratory factor analysis with 12 items showed a two-factor structure, explaining 69.11% of the variance. These items were strongly correlated with each other (r = 0.80). Schmid-Leiman: all items seemed to represent the general factor (loadings above 0.50), which was 76.4% of the extracted variance. Simplicity indices were good (percentile 99): Bentler's Simplicity Index of 0.99 and Loading Simplicity Index of 0.48. Internal consistency indexes indicated good reliability. LiS-RPL was shown to be homogenous. LiS-RPL is a validated tool for evaluating subjects' satisfaction with medicine package leaflets.

Proposal for Improvement of the Hospital Anxiety and Depression Scale for the Assessment of Emotional Distress in Patients With Chronic Musculoskeletal Pain: A Bifactor and Item Response Theory Analysis

The Hospital Anxiety and Depression Scale (HADS) is a scale originally developed for the assessment of anxiety and depression in hospitalized patients. Despite its wide diffusion, research on its factorial structure has displayed inconsistent results, leaving doubts about its use in chronic musculoskeletal pain. The purpose of this study was to thoroughly assess the factorial structure of the HADS in patients with chronic pain and to give guidance for a potential refinement. Data from 2,522 patients with chronic pain from the Amsterdam Pain (AMS-PAIN) cohort were analyzed through: 1) exploratory bifactor analysis based on a Schmid-Leiman orthogonalization, 2) confirmatory factor analysis comparing a unidimensional model, the original correlated factors model and a bifactor model, 3) item response theory (IRT) analysis based on the graded response model. The results of the confirmatory factor analysis and of the IRT analysis were then cross-validated in an independent sample of patients with chronic pain (n = 8,604). Both exploratory and confirmatory analyses revealed the presence of a strong general emotional distress factor, suggesting that the HADS can be used as a unidimensional scale. The IRT analysis led to the exclusion of three items and to the recoding of one item. The refined 11-item HADS scale was successfully cross-validated and confirmed as a unidimensional, locally independent, monotonic, and reliable scale. PerspectiveAn 11-item shorter version of the HADS could be used to measure emotional distress in patients with chronic musculoskeletal pain. Given its unidimensionality, the use of its total score seems appropriate.

Using Exploratory Bifactor Analysis to Understand the Latent Structure of Multidimensional Psychological Measures: An Example Featuring the WISC-V

Exploratory bifactor analysis (EBFA) represents a methodological advancement for implementing a bifactor model in exploratory factor analysis (EFA). However, little is known about how to properly employ the procedure. The current rotation criteria available for EBFA make it more likely to “get stuck” in local minima, contributing to possible group factor collapse, than more traditional EFA rotations. Thus, getting a proper solution is a more complex and involved process than typical EFA and may require a sensitivity analysis. This article examines EBFA through a sensitivity analysis and subsequent simulation of parameters thought to contribute to group factor collapse. Results support the use of sensitivity analysis, as the problematic variable was shown to greatly increase the likelihood of factor collapse. The hypothesis that estimation start values contribute to factor collapse was not supported. Accompanying R syntax for all analyses are provided to facilitate reproducibility.