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Item response theory (IRT) models are often compared with respect to predictive performance to determine the dimensionality of rating scale data. However, such model comparisons could be biased toward nested-dimensionality IRT models (e.g., the bifactor model) when comparing those models with non-nested-dimensionality IRT models (e.g., a unidimensional or a between-item-dimensionality model). The reason is that, compared with non-nested-dimensionality models, nested-dimensionality models could have a greater propensity to fit data that do not represent a specific dimensional structure. However, it is unclear as to what degree model comparison results are biased toward nested-dimensionality IRT models when the data represent specific dimensional structures and when Bayesian estimation and model comparison indices are used. We conducted a simulation study to add clarity to this issue. We examined the accuracy of four Bayesian predictive performance indices at differentiating among non-nested- and nested-dimensionality IRT models. The deviance information criterion (DIC), a commonly used index to compare Bayesian models, was extremely biased toward nested-dimensionality IRT models, favoring them even when non-nested-dimensionality models were the correct models. The Pareto-smoothed importance sampling approximation of the leave-one-out cross-validation was the least biased, with the Watanabe information criterion and the log-predicted marginal likelihood closely following. The findings demonstrate that nested-dimensionality IRT models are not automatically favored when the data represent specific dimensional structures as long as an appropriate predictive performance index is used.

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Using the bifactor item response theory model to analyze data arising from educational and psychological studies has gained popularity over the years. Unfortunately, using this model in practice comes with challenges. One such challenge is an empirical identification issue that is seldom discussed in the literature, and its impact on the estimates of the bifactor model's parameters has not been demonstrated. This issue occurs when an item's discriminations on the general and specific dimensions are approximately equal (i.e., the within-item discriminations are similar in strength), leading to difficulties in obtaining unique estimates for those discriminations. We conducted three simulation studies to demonstrate that within-item discriminations being similar in strength creates problems in estimation stability. The results suggest that a large sample could alleviate but not resolve the problems, at least when considering sample sizes up to 4,000. When the discriminations within items were made clearly different, the estimates of these discriminations were more consistent across the data replicates than that observed when the discriminations within the items were similar. The results also show that the similarity of an item's discriminatory magnitudes on different dimensions has direct implications on the sample size needed in order to consistently obtain accurate parameter estimates. Although our goal was to provide evidence of the empirical identification issue, the study further reveals that the extent of similarity of within-item discriminations, the magnitude of discriminations, and how well the items are targeted to the respondents also play factors in the estimation of the bifactor model's parameters.

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The majority of research on accessing and utilizing mental health services has focused on patient barriers to care. Few studies have explored possible provider biases that may impact client access at point of entry. Using the audit method, we conducted an email-based field experiment to investigate the responsiveness of psychotherapy providers to inquiries from simulated patients with different backgrounds (i.e., race, gender, diagnosis, and ability to pay). A total of 725 therapists (176 men, 549 women) practicing in Chicago, Illinois were identified from an online therapist directory and randomized to receive emails requesting therapy appointments. Overall, 21.7% of providers did not return prospective client email inquiries; 32.5% of providers were somewhat responsive in that they returned an email despite not being able to take on the client; and 45.7% were highly responsive in that they returned an email and offered an appointment or the opportunity to discuss the matter further. Male providers were less responsive to African American and Latinx simulated clients and most responsive to White clients, whereas female providers were more likely to respond similarly to all simulated clients. Moreover, regardless of the providers' gender, they were more responsive to simulated patients with depression than to simulated patients with schizophrenia or borderline personality disorder (BPD). Finally, providers were more responsive to those who could pay the full fee than to those who requested a sliding scale. Educating providers on these possible biases is important because it could help reduce biased behaviors and improve access to care for vulnerable populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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Although item response theory (IRT) models such as the bifactor, two-tier, and between-item-dimensionality IRT models have been devised to confirm complex dimensional structures in educational and psychological data, they can be challenging to use in practice. The reason is that these models are multidimensional IRT (MIRT) models and thus are highly parameterized, making them only suitable for data provided by large samples. Unfortunately, many educational and psychological studies are conducted on a small scale, leaving the researchers without the necessary MIRT models to confirm the hypothesized structures in their data. To address the lack of modeling options for these researchers, we present a general Bayesian MIRT model based on adaptive informative priors. Simulations demonstrated that our MIRT model could be used to confirm a two-tier structure (with two general and six specific dimensions), a bifactor structure (with one general and six specific dimensions), and a between-item six-dimensional structure in rating scale data representing sample sizes as small as 100. Although our goal was to provide a general MIRT model suitable for smaller samples, the simulations further revealed that our model was applicable to larger samples. We also analyzed real data from 121 individuals to illustrate that the findings of our simulations are relevant to real situations.

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Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the aspects of another source (i.e., a nested method-source interaction). For this study, then, a Bayesian IRT model is proposed, one that accounts for such interaction among method sources while controlling for the clustering of individuals within the sample. The proposed model accomplishes these tasks by specifying a multilevel trifactor structure for the latent trait space. Details of simulations are also reported. These simulations demonstrate that this model can identify when item response data represent a multilevel trifactor structure, and it does so in data from samples as small as 250 cases nested within 50 clusters. Additionally, the simulations show that misleading estimates for the item discriminations could arise when the trifactor structure reflected in the data is not correctly accounted for. The utility of the model is also illustrated through the analysis of empirical data.

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Many item response theory (IRT) models take a multidimensional perspective to deal with sources that induce local item dependence (LID), with these models often making an orthogonal assumption about the dimensional structure of the data. One reason for this assumption is because of the indeterminacy issue in estimating the correlations among the dimensions in structures often specified to deal with sources of LID (e.g., bifactor and two-tier structures), and the assumption usually goes untested. Unfortunately, the mere fact that assessing these correlations is a challenge for some estimation methods does not mean that data seen in practice support such orthogonal structure. In this paper, a Bayesian multilevel multidimensional IRT model for locally dependent data is presented. This model can test whether item response data violate the orthogonal assumption that many IRT models make about the dimensional structure of the data when addressing sources of LID, and this test is carried out at the dimensional level while accounting for sampling clusters. Simulations show that the model presented is effective at carrying out this task. The utility of the model is also illustrated on an empirical data set.

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The acculturative family distancing (AFD) rating questionnaire measures the distancing that occurs between immigrant parents and their children, which is caused by breakdowns in communication and cultural value differences. The items included in the AFD questionnaire are indicators for more proximal and problem-focused aspects of the acculturation gap. In this study, a psychometric analysis is performed on the data gathered with the AFD instrument. One hundred twenty-three Chinese American families (112 mothers, 85 fathers, and 120 children for a total of 317) completed the questionnaire. In addition, the specifications for an extended 2-tier item factor analysis model within a Bayesian multidimensional item response theory framework are provided to psychometrically analyze the AFD data. Results suggest that the original 2 dimensions of Communication Difficulties and Cultural Values Incongruency can be further refined into 4 clinically relevant dimensions (i.e., Verbal Communication Difficulties, Nonverbal Communication Difficulties, General Cultural Values Incongruency, and Family Values Incongruency). Moreover, reducing the number of items can improve AFD estimates and reduce assessment burden.

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UNLABELLED: Finlayson ML, Peterson EW, Fujimoto KA, Plow MA. Rasch validation of the Falls Prevention Strategies Survey. OBJECTIVE: To validate the Falls Prevention Strategies Survey. DESIGN: Cross-sectional descriptive survey design. With the use of Rasch analysis, the following aspects of the Falls Prevention Strategies Survey were evaluated: rating scale structure, item quality, participant fit and participant ability, invariance structure, and the potential to change in response to intervention. SETTING: Community-based, national sample. PARTICIPANTS: Adult registrants of the North America Research Committee on Multiple Sclerosis volunteer patient registry (N=457). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The Falls Prevention Strategies Survey is a self-report instrument addressing protective behaviors related to fall risk among adults with multiple sclerosis (MS) (eg, monitoring MS symptoms, wearing proper footwear, modifying activities). Response options reflect the frequency with which the respondent engages in the behavior (ie, never, sometimes, regularly). RESULTS: Analysis indicated that the rating scale structure (ie, response options) was valid. Of the original 19 items, 8 of them misfit and needed to be dropped to obtain a valid instrument under the Rasch model. With the final 11 items, the instrument was able to distinguish participants of different ability levels across a range of 11.58 logits. Invariance structure analysis demonstrated that the instrument functioned equally for men and women, for mobility device users and nonusers, and for participants with diagnosed MS for less than or greater than 10 years. Findings indicated that approximately 50% of respondents would have room to improve on their Falls Prevention Strategies Survey scores over time. CONCLUSIONS: Rasch analysis supports the use of the Falls Prevention Strategies Survey to examine the frequency of engaging in protective behaviors related to fall risk among adults with MS. The instrument shows potential to track outcomes of behaviorally oriented fall reduction interventions in this population.

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BACKGROUND: Results from open-label and placebo-controlled trials suggest that the selective serotonin reuptake inhibitors reduce impulsive aggressive behavior. The objective of this open-label study was to investigate whether citalopram treatment has anti-aggressive effect on impulsive aggressive subjects meeting DSM-IV criteria for a cluster B personality disorder or intermittent explosive disorder. METHOD: In this 8-week trial, subjects were initiated on 20 mg/day of citalopram and titrated up to 60 mg/day by the fourth week, if tolerated. The primary outcome measure was the Overt Aggression Scale-Modified (OAS-M), a scale used to quantify verbal and physical aggression, subjective irritability, and overt irritability. Secondary outcome measures included the Barratt Impulsiveness Scale and Buss-Durkee Hostility Inventory. RESULTS: Of 25 subjects enrolled, 20 completed the study. The mean daily dose was 45.5 mg, and citalopram was generally well tolerated. Statistically significant decreases were found in the OAS-M aggression scores (32.82 +/- 19.76 to 4.73 +/- 7.57, p =.000), subjective irritability scores (3.50 +/- 0.60 to 1.45 +/- 1.18, p =.000), and overt irritability scores (3.23 +/- 0.81 to 0.91 +/- 1.02, p =.000). CONCLUSION: These results suggest that citalopram is an effective treatment for reducing impulsive aggressive behavior.

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