Computerized adaptive test strategies for the matrix reasoning subtest of the Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV).

OBJECTIVE: Most neuropsychological tests were developed without the benefit of modern psychometric theory. We used item response theory (IRT) methods to determine whether a widely used test - the 26-item Matrix Reasoning subtest of the WAIS-IV - might be used more efficiently if it were administered using computerized adaptive testing (CAT). METHOD: Data on the Matrix Reasoning subtest from 2197 participants enrolled in the National Neuropsychology Network (NNN) were analyzed using a two-parameter logistic (2PL) IRT model. Simulated CAT results were generated to examine optimal short forms using fixed-length CATs of 3, 6, and 12 items and scores were compared to the original full subtest score. CAT models further explored how many items were needed to achieve a selected precision of measurement (standard error ≤ .40). RESULTS: The fixed-length CATs of 3, 6, and 12 items correlated well with full-length test results (with r = .90, .97 and .99, respectively). To achieve a standard error of .40 (approximate reliability = .84) only 3-7 items had to be administered for a large percentage of individuals. CONCLUSIONS: This proof-of-concept investigation suggests that the widely used Matrix Reasoning subtest of the WAIS-IV might be shortened by more than 70% in most examinees while maintaining acceptable measurement precision. If similar savings could be realized in other tests, the accessibility of neuropsychological assessment might be markedly enhanced, and more efficient time use could lead to broader subdomain assessment.

Clinical Validity of the PROMIS Healthcare Engagement 8-Item Short Form.

BACKGROUND: Healthcare engagement is a key measurement target for value-based healthcare, but a reliable and valid patient-reported measure has not yet been widely adopted. OBJECTIVE: To assess the validity of a newly developed patient-reported measure of healthcare engagement, the 8-item PROMIS Healthcare Engagement (PHE-8a). DESIGN: Prospective cohort study of the association between healthcare engagement and quality of care over 1 year. We fit mixed effects models of quality indicators as a function of engagement scores, adjusting for age, race/ethnicity, rural residence, and risk scores. PARTICIPANTS: National stratified random sample of 9552 Veterans receiving Veterans Health Administration care for chronic conditions (hypertension, diabetes) or mental health conditions (depression, post-traumatic stress disorder). MAIN MEASURES: Patient experience: Consumer Assessment of Health Plans and Systems communication and self-management support composites; no-show rates for primary care and mental health appointments; use of patient portal My HealtheVet; and Healthcare Effectiveness Data and Information Set electronic quality measures: HbA1c poor control, controlling high blood pressure, and hyperlipidemia therapy adherence. KEY RESULTS: Higher engagement scores were associated with better healthcare quality across all outcomes, with each 5-point increase (1/2 standard deviation) in engagement scores associated with statistically significant and clinically meaningful gains in quality. Across the continuum of low to high engagement scores, we observed a concomitant reduction in primary care no-show rates of 37% and 24% for mental health clinics; an increased likelihood of My HealtheVet use of 15.4%; and a decreased likelihood of poor diabetes control of 44%. CONCLUSIONS: The PHE-8a is a brief, reliable, and valid patient-reported measure of healthcare engagement. These results confirm previously untested hypotheses that patient engagement can promote healthcare quality.

Estimating individual health-related quality of life changes in low back pain patients.

BACKGROUND: There is a need to evaluate different options for estimating individual change in health-related quality of life for patients with low back pain. METHODS: Secondary analysis of data collected at baseline and 6 weeks later in a randomized trial of 749 adults with low back pain receiving usual medical care (UMC) or UMC plus chiropractic care at a small hospital at a military training site or two large military medical centers. The mean age was 31; 76% were male and 67% were White. The study participants completed the Patient-Reported Outcomes Measurement Information System (PROMIS®)-29 v 1.0 physical function, pain interference, pain intensity, fatigue, sleep disturbance, depression, anxiety, satisfaction with participation in social roles, physical summary, and mental health summary scores (T-scored with mean = 50 and standard deviation (SD) = 10 in the U.S. general population). RESULTS: Reliability estimates at the baseline ranged from 0.700 to 0.969. Six-week test-retest intraclass correlation estimates were substantially lower than these estimates: the median test-retest intraclass correlation for the two-way mixed-effects model was 0. 532. Restricting the test-retest reliability estimates to the subset who reported they were about the same as at baseline on a retrospective rating of change item increased the median test-retest reliability to 0.686. The amount of individual change that was statistically significant varied by how reliability was estimated, and which SD was used. The smallest change needed was found when internal consistency reliability and the SD at baseline were used. When these values were used, the amount of change needed to be statistically significant (p < .05) at the individual level ranged from 3.33 (mental health summary scale) to 12.30 (pain intensity item) T-score points. CONCLUSIONS: We recommend that in research studies estimates of the magnitude of individual change needed for statistical significance be provided for multiple reliability and standard deviation estimates. Whenever possible, patients should be classified based on whether they 1) improved significantly and perceived they got better, 2) improved significantly but did not perceive they were better, 3) did not improve significantly but felt they got better, or 4) did not improve significantly or report getting better.

Rationale and Design of the National Neuropsychology Network.

OBJECTIVE: The National Neuropsychology Network (NNN) is a multicenter clinical research initiative funded by the National Institute of Mental Health (NIMH; R01 MH118514) to facilitate neuropsychology's transition to contemporary psychometric assessment methods with resultant improvement in test validation and assessment efficiency. METHOD: The NNN includes four clinical research sites (Emory University; Medical College of Wisconsin; University of California, Los Angeles (UCLA); University of Florida) and Pearson Clinical Assessment. Pearson Q-interactive (Q-i) is used for data capture for Pearson published tests; web-based data capture tools programmed by UCLA, which serves as the Coordinating Center, are employed for remaining measures. RESULTS: NNN is acquiring item-level data from 500-10,000 patients across 47 widely used Neuropsychology (NP) tests and sharing these data via the NIMH Data Archive. Modern psychometric methods (e.g., item response theory) will specify the constructs measured by different tests and determine their positive/negative predictive power regarding diagnostic outcomes and relationships to other clinical, historical, and demographic factors. The Structured History Protocol for NP (SHiP-NP) helps standardize acquisition of relevant history and self-report data. CONCLUSIONS: NNN is a proof-of-principle collaboration: by addressing logistical challenges, NNN aims to engage other clinics to create a national and ultimately an international network. The mature NNN will provide mechanisms for data aggregation enabling shared analysis and collaborative research. NNN promises ultimately to enable robust diagnostic inferences about neuropsychological test patterns and to promote the validation of novel adaptive assessment strategies that will be more efficient, more precise, and more sensitive to clinical contexts and individual/cultural differences.

Friendship Network Satisfaction: A multifaceted construct scored as a unidimensional scale.

Although satisfying friendships are crucial for well-being throughout adulthood, measures of friendship satisfaction have been limited by: (1) item content relevant to children only, (2) a focus on single relationships rather than the friendship network, and (3) disagreement about the number of dimensions necessary to capture the construct. To overcome these limitations, we assembled an item pool from a number of existing measures, created additional items drawn from research on friendships, and then examined the structure and psychometric properties of those items in two online surveys of over 2000 respondents each. Factor analyses consistently identified two correlated factors-closeness and socializing-but bi-factor modeling revealed that scores on both subscales load strongly on a general factor, suggesting that the multifaceted content can be scored efficiently as a unidimensional composite. Analyses using item response theory (IRT) supported the creation of a reliable 14-item instrument that demonstrated adequate convergent and predictive validity. Thus, the Friendship Network Satisfaction (FNS) Scale is a psychometrically sound tool to advance research on friendships across the lifespan.

Psychometric evaluation of a patient-reported item bank for healthcare engagement.

PURPOSE: Healthcare engagement is a core measurement target for efforts to improve healthcare systems. This construct is broadly defined as the extent to which healthcare services represent collaborative partnerships with patients. Previous qualitative work operationalized healthcare engagement as generalized self-efficacy in four related subdomains: self-management, collaborative communication, health information use, and healthcare navigation. Building on this work, our objective was to establish a healthcare engagement instrument that is sufficiently unidimensional to yield a single score. METHOD: We conducted cognitive interviews followed by a nation-wide mail survey of US Veteran Administration (VA) healthcare users. Data were collected on 49 candidate healthcare engagement items, as well as measures of self-efficacy for managing symptoms, provider communication, and perceived access. Items were subjected to exploratory bifactor, statistical learning, and IRT analyses. RESULTS: Cognitive interviews were completed by 56 patients and 9552 VA healthcare users with chronic conditions completed the mail survey. Participants were mostly white and male but with sizable minority participation. Psychometric analyses and content considerations reduced the item pool to 23 items, which demonstrated a strong general factor (OmegaH of .89). IRT analyses revealed a high level of reliability across the trait range and little DIF across groups. Most health information use items were removed during analyses, suggesting a more independent role for this domain. CONCLUSION: We provide quantitative evidence for a relatively unidimensional measure of healthcare engagement. Despite developed with VA healthcare users, the measure is intended for general use. Future work includes short-form development and validation with other patient groups.

Alternative Approaches to Addressing Non-Normal Distributions in the Application of IRT Models to Personality Measures.

It is generally assumed that the latent trait is normally distributed in the population when estimating logistic item response theory (IRT) model parameters. This assumption requires that the latent trait be fully continuous and the population homogenous (i.e., not a mixture). When this normality assumption is violated, models are misspecified, and item and person parameter estimates are inaccurate. When normality cannot be assumed, it might be appropriate to consider alternative modeling approaches: (a) a zero-inflated mixture, (b) a log-logistic, (c) a Ramsay curve, or (d) a heteroskedastic-skew model. The first 2 models were developed to address modeling problems associated with so-called quasi-continuous or unipolar constructs, which apply only to a subset of the population, or are meaningful at one end of the continuum only. The second 2 models were developed to address non-normal latent trait distributions and violations of homogeneity of error variance, respectively. To introduce these alternative IRT models and illustrate their strengths and weaknesses, we performed real data application comparing results to those from a graded response model. We review both statistical and theoretical challenges in applying these models and choosing among them. Future applications of these and other alternative models (e.g., unfolding, diffusion) are needed to advance understanding about model choice in particular situations.

The global therapist competence scale for youth psychosocial treatment: Development and initial validation.

OBJECTIVE: We describe the development and initial psychometric properties of the observer-rated Global Therapist Competence Scale for Youth Psychosocial Treatment (G-COMP) in the context of cognitive-behavioral treatment (CBT) for youth anxiety disorders. METHOD: Independent coders rated 744 sessions from a sample of 68 youth (mean age = 10.56 years) using the G-COMP and the instruments of alliance, involvement, CBT adherence, CBT competence. RESULTS: Inter-rater reliability coefficients, ICC(2,2), were greater than .60 for the 5 G-COMP domain scores. G-COMP scores yielded small to medium correlations with instruments of alliance (rs = .17-.44) and youth involvement in treatment (rs = .08-.53), and medium to large correlations with instruments of CBT competence and adherence (rs = .26-.63). Therapists in the research setting were rated higher compared to newly trained therapists in community clinics. CONCLUSION: Preliminary reliability and validity of the G-COMP are promising, but future research is needed with non-CBT samples.

Responsiveness to Change and Minimally Important Differences of the Patient-Reported Outcomes Measurement Information System Gastrointestinal Symptoms Scales.

BACKGROUND: The NIH-sponsored Patient-Reported Outcomes Measurement Information System (PROMIS) Gastrointestinal (GI) Symptoms scales were developed to assess patients' GI symptoms in clinical settings. AIMS: To assess responsiveness to change and provide minimally important difference (MID) estimates for the PROMIS GI Symptoms scales. METHODS: A sample of 256 GI outpatients self-administered the eight PROMIS GI Symptoms scales (gastroesophageal reflux, disrupted swallowing, diarrhea, bowel incontinence/soilage, nausea and vomiting, constipation, belly pain, and gas/bloating/flatulence) at two visits. Patient self-reported and physician-reported assessments of the subjects' overall GI condition were employed as change anchors. In addition, we prospectively assessed change at both visits using a GI-symptom anchor, the Gastrointestinal Symptom Rating Scale (GSRS). Responsiveness to change was assessed using F-statistics. The minimally changed group was those somewhat better or somewhat worse on the retrospective anchors and changing by one category on the modified GSRS (e.g., from slight to mild discomfort to moderate to moderately severe discomfort). RESULTS: Responsiveness to change was statistically significant for 6 of 8 PROMIS scales using the self-report GI anchor, 3 of 8 scales using the physician-reported anchor, and 5 of 5 scales using the corresponding GSRS scales as anchors. The MID estimates for scales for improvement and worsening were about 0.5-0.6 SD using the GSRS anchor and generally larger in magnitude than the change for the "about the same" group. CONCLUSIONS: The responsiveness and MID estimates provided here for the PROMIS GI Symptoms scales can aid in scale score interpretation in clinical trials and observational studies.

Differential item functioning of the patient-reported outcomes information system (PROMIS®) pain interference item bank by language (Spanish versus English).

BACKGROUND: About 70% of Latinos, 5 years old or older, in the United States speak Spanish at home. Measurement equivalence of the PROMIS® pain interference (PI) item bank by language of administration (English versus Spanish) has not been evaluated. METHODS: A sample of 527 adult Spanish-speaking Latinos completed the Spanish version of the 41-item PROMIS® pain interference item bank. We evaluate dimensionality, monotonicity and local independence of the Spanish-language items. Then we evaluate differential item functioning (DIF) using ordinal logistic regression with item response theory scores estimated from DIF-free "anchor" items. RESULTS: One of the 41 items in the Spanish version of the PROMIS® PI item bank was identified as having significant uniform DIF. CONCLUSIONS: English- and Spanish-speaking subjects with the same level of pain interference responded differently to 1 of the 41 items in the PROMIS® PI item bank. This item was not retained due to proprietary issues. The original English language item parameters can be used when estimating PROMIS® PI scores.

Exploratory Bifactor Analysis: The Schmid-Leiman Orthogonalization and Jennrich-Bentler Analytic Rotations.

Analytic bifactor rotations have been recently developed and made generally available, but they are not well understood. The Jennrich-Bentler analytic bifactor rotations (bi-quartimin and bi-geomin) are an alternative to, and arguably an improvement upon, the less technically sophisticated Schmid-Leiman orthogonalization. We review the technical details that underlie the Schmid-Leiman and Jennrich-Bentler bifactor rotations, using simulated data structures to illustrate important features and limitations. For the Schmid-Leiman, we review the problem of inaccurate parameter estimates caused by the linear dependencies, sometimes called "proportionality constraints," that are required to expand a p correlated factors solution into a (p + 1) (bi)factor space. We also review the complexities involved when the data depart from perfect cluster structure (e.g., item cross-loading on group factors). For the Jennrich-Bentler rotations, we describe problems in parameter estimation caused by departures from perfect cluster structure. In addition, we illustrate the related problems of (a) solutions that are not invariant under different starting values (i.e., local minima problems) and (b) group factors collapsing onto the general factor. Recommendations are made for substantive researchers including examining all local minima and applying multiple exploratory techniques in an effort to identify an accurate model.

Is the Bifactor Model a Better Model or Is It Just Better at Modeling Implausible Responses? Application of Iteratively Reweighted Least Squares to the Rosenberg Self-Esteem Scale.

Although the structure of the Rosenberg Self-Esteem Scale (RSES) has been exhaustively evaluated, questions regarding dimensionality and direction of wording effects continue to be debated. To shed new light on these issues, we ask (a) for what percentage of individuals is a unidimensional model adequate, (b) what additional percentage of individuals can be modeled with multidimensional specifications, and (c) what percentage of individuals respond so inconsistently that they cannot be well modeled? To estimate these percentages, we applied iteratively reweighted least squares (IRLS) to examine the structure of the RSES in a large, publicly available data set. A distance measure, ds, reflecting a distance between a response pattern and an estimated model, was used for case weighting. We found that a bifactor model provided the best overall model fit, with one general factor and two wording-related group factors. However, on the basis of dr values, a distance measure based on individual residuals, we concluded that approximately 86% of cases were adequately modeled through a unidimensional structure, and only an additional 3% required a bifactor model. Roughly 11% of cases were judged as "unmodelable" due to their significant residuals in all models considered. Finally, analysis of ds revealed that some, but not all, of the superior fit of the bifactor model is owed to that model's ability to better accommodate implausible and possibly invalid response patterns, and not necessarily because it better accounts for the effects of direction of wording.

Applying Bifactor Statistical Indices in the Evaluation of Psychological Measures.

The purpose of this study was to apply a set of rarely reported psychometric indices that, nevertheless, are important to consider when evaluating psychological measures. All can be derived from a standardized loading matrix in a confirmatory bifactor model: omega reliability coefficients, factor determinacy, construct replicability, explained common variance, and percentage of uncontaminated correlations. We calculated these indices and extended the findings of 50 recent bifactor model estimation studies published in psychopathology, personality, and assessment journals. These bifactor derived indices (most not presented in the articles) provided a clearer and more complete picture of the psychometric properties of the assessment instruments. We reached 2 firm conclusions. First, although all measures had been tagged "multidimensional," unit-weighted total scores overwhelmingly reflected variance due to a single latent variable. Second, unit-weighted subscale scores often have ambiguous interpretations because their variance mostly reflects the general, not the specific, trait. Finally, we review the implications of our evaluations and consider the limits of inferences drawn from a bifactor modeling approach.

Iteration of Partially Specified Target Matrices: Applications in Exploratory and Bayesian Confirmatory Factor Analysis.

We describe and evaluate a factor rotation algorithm, iterated target rotation (ITR). Whereas target rotation (Browne, 2001) requires a user to specify a target matrix a priori based on theory or prior research, ITR begins with a standard analytic factor rotation (i.e., an empirically informed target) followed by an iterative search procedure to update the target matrix. In Study 1, Monte Carlo simulations were conducted to evaluate the performance of ITR relative to analytic rotations from the Crawford-Ferguson family with population factor structures varying in complexity. Simulation results: (a) suggested that ITR analyses will be particularly useful when evaluating data with complex structures (i.e., multiple cross-loadings) and (b) showed that the rotation method used to define an initial target matrix did not materially affect the accuracy of the various ITRs. In Study 2, we: (a) demonstrated the application of ITR as a way to determine empirically informed priors in a Bayesian confirmatory factor analysis (BCFA; Muthén & Asparouhov, 2012) of a rater-report alexithymia measure (Haviland, Warren, & Riggs, 2000) and (b) highlighted some of the challenges when specifying empirically based priors and assessing item and overall model fit.

Scoring and modeling psychological measures in the presence of multidimensionality.

Confirmatory factor analytic studies of psychological measures showing item responses to be multidimensional do not provide sufficient guidance for applied work. Demonstrating that item response data are multifactorial in this way does not necessarily (a) mean that a total scale score is an inadequate indicator of the intended construct, (b) demand creating and scoring subscales, or (c) require specifying a multidimensional measurement model in research using structural equation modeling (SEM). To better inform these important decisions, more fine-grained psychometric analyses are necessary. We describe 3 established, but seldom used, psychometric approaches that address 4 distinct questions: (a) To what degree do total scale scores reflect reliable variation on a single construct? (b) Is the scoring and reporting of subscale scores justified? (c) If justified, how much reliable variance do subscale scores provide after controlling for a general factor? and (d) Can multidimensional item response data be represented by a unidimensional measurement model in SEM, or are multidimensional measurement models (e.g., second-order, bifactor) necessary to achieve unbiased structural coefficients? In the discussion, we provide guidance for applied researchers on how best to interpret the results from applying these methods and review their limitations.

Response Category Functioning on the Health Care Engagement Measure Using the Nominal Response Model.

As part of a scale development project, we fit a nominal response item response theory model to responses to the Health Care Engagement Measure (HEM). When using the original 5-point response format, categories were not ordered as intended for six of the 23 items. For the remaining, the category boundary discrimination between Categories 0 (not at all true) and 1 (a little bit true) was only weakly discriminating, suggesting uninformative categories. When the lowest two categories were collapsed, psychometric properties improved greatly. Category boundary discriminations within items, however, varied significantly. Specifically, higher response category distinctions, such as responding 3 (very true) versus 2 (mostly true) were considerably more discriminating than lower response category distinctions. Implications for HEM scoring and for improving measurement precision at lower levels of the construct are presented as is the unique role of the nominal response model in category analysis.

Construct identification in the neuropsychological battery: What are we measuring?

OBJECTIVE: Major obstacles to data harmonization in neuropsychology include lack of consensus about what constructs and tests are most important and invariant across healthy and clinical populations. This study addressed these challenges using data from the National Neuropsychology Network (NNN). METHOD: Data were obtained from 5,000 NNN participants and Pearson standardization samples. Analyses included variables from four instruments: Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV); Wechsler Memory Scale, 4th Edition (WMS-IV); California Verbal Learning Test, 3rd Edition (CVLT3); and Delis-Kaplan Executive Function System (D-KEFS). We used confirmatory factor analysis to evaluate models suggested by prior work and examined fit statistics and measurement invariance across samples. We examined relations of factor scores to demographic and clinical characteristics. RESULTS: For each instrument, we identified four first-order and one second-order factor. Optimal models in patients generally paralleled the best-fitting models in the standardization samples, including task-specific factors. Analysis of the NNN data prompted specification of a Recognition-Familiarity factor on the WMS-IV and an Inhibition-Switching factor on the D-KEFS. Analyses showed strong to strict factorial invariance across samples with expected differences in factor means and variances. The Recognition-Familiarity factor correlated with age more strongly in NNN than in the standardization sample. CONCLUSIONS: Factor models derived from healthy groups generally fit well in patients. NNN data helped identify novel Recognition-Familiarity and Inhibition-Switching factors that were also invariant across samples and may be clinically useful. The findings support efforts to identify evidence-based and optimally efficient measurements of neuropsychological constructs that are valid across groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Invited Paper: The Rediscovery of Bifactor Measurement Models.

Bifactor latent structures were introduced over 70 years ago, but only recently has bifactor modeling been rediscovered as an effective approach to modeling construct-relevant multidimensionality in a set of ordered categorical item responses. I begin by describing the Schmid-Leiman bifactor procedure (Schmid & Leiman, 1957), and highlight its relations with correlated-factors and second-order exploratory factor models. After describing limitations of the Schmid-Leiman, two newer methods of exploratory bifactor modeling are considered, namely, analytic bifactor (Jennrich & Bentler, 2011) and target bifactor rotations (Reise, Moore, & Maydeu-Olivares, 2011). In section two, I discuss limited and full-information estimation approaches to confirmatory bifactor models that have emerged from the item response theory and factor analysis traditions, respectively. Comparison of the confirmatory bifactor model to alternative nested confirmatory models and establishing parameter invariance for the general factor also are discussed. In the final section, important applications of bifactor models are reviewed. These applications demonstrate that bifactor modeling potentially provides a solid foundation for conceptualizing psychological constructs, constructing measures, and evaluating a measure's psychometric properties. However, some applications of the bifactor model may be limited due to its restrictive assumptions.

The Loneliness Questionnaire-Short Version: an evaluation of reverse-worded and non-reverse-worded items via item response theory.

Although reverse-worded items have often been incorporated in scale construction to minimize the effects of acquiescent reporting biases, some researchers have more recently begun questioning this approach and wondering whether the advantages associated with incorporating reverse-worded items is worth the complexities that they bring to measures (e.g., Brown, 2003 ; Marsh, 1996 ). In this study, we used item response theory (IRT) to determine whether there is statistical justification to eliminate the reverse-worded items (e.g., "I have lots of friends") from the Loneliness Questionnaire (LQ; Asher, Hymel, & Renshaw, 1984) and retain only the non-reverse-worded items (e.g., "I'm lonely") to inform the provision of a shortened LQ version. Using a large sample of children (Grades 2-7; n = 6,784) and adolescents (Grades 8-12; n = 4,941), we examined the psychometric properties of the 24-item LQ and found support for retaining the 9 non-reverse-worded LQ items to make up a shortened measure of loneliness in youth. We found that the non-reverse-worded items were associated with superior psychometric properties relative to the reverse-worded items with respect to reliability and IRT parameters (e.g., discrimination and item information). A 3-point Likert-type scale was also found to be more suitable for measuring loneliness across both children and adolescents compared to the original 5-point scale. The relative contributions of reverse-worded and non-reverse-worded items in scale development for youth instruments are also discussed.

The importance of modeling method effects: resolving the (uni)dimensionality of the loneliness questionnaire.

This study sought to resolve the dimensionality of the Loneliness Questionnaire (LQ; Asher, Hymel, & Renshaw, 1984) by applying recommended confirmatory factor analytic procedures that control for method effects (Brown, 2003). This study was needed given that inconsistent findings have been reported recently regarding the structure of this instrument (Bagner, Storch, & Roberti, 2004) and all models to date have not accounted for method effects due to the non-reversed-worded and reversed-worded items of this instrument. Using a large sample of youth in Grades 2 through 12 (N = 11,725), we compared the previously reported 1- and 2-factor models with a newly posited 1-factor model that incorporated correlated error terms to account for method effects. We found that the 1-factor model that included correlated error terms fit the data best, and that this factor structure evidenced measurement invariance across boys and girls in childhood, but not in adolescence. The meaning of the LQ indicators was also consistent for boys across development, but evidenced differences for girls in childhood versus adolescence. More generally, it was demonstrated that modeling method effects is vital to accurately understanding the dimensionality of loneliness when reversed-worded and non-reversed-worded items are used as indicators. The measurement and clinical implications of these findings are discussed.