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Survey score interpretations are often plagued by sources of construct-irrelevant variation, such as response styles. In this study, we propose the use of an IRTree Model to account for response styles by making use of self-report items and anchoring vignettes. Specifically, we investigate how the IRTree approach with anchoring vignettes compares to traditional approaches that either do not include anchoring vignettes or do not account for response styles. We analyze secondary data using four different models: 1) total score; 2) graded response model; 3) IRTree without the consideration of anchoring vignettes, and 4) IRTree considering anchoring vignettes. We found significant differences in trait estimates from models that account for response styles compared to those that do not. Additionally, we found differences in trait estimates between the IRTree Models when considering anchoring vignettes and when not. Model comparisons suggest that trait differences are due to adjusting for acquiescence response style.

Response styles introduce construct-irrelevant variance as a result of respondents systematically responding to Likert-type items regardless of content. Methods to account for response styles through data analysis as well as approaches to mitigating the effects of response styles during data collection have been well-documented. Recent approaches to modeling Likert responses, such as the IRTree model, rely on the response process individuals take when answering item responses. In this study, we advocate for the use of IRTrees to analyze Likert items in addition to using the hypothesized response process to design new items. Combining these two approaches facilitates answering Likert item design questions that have plagued researchers. These include the interpretation of a middle response option, the optimal number of response options, and how to label the response options. We present 7 research questions that could be answered using this new approach, outline methods of data collection and analysis for each, and present results from an empirical example to address one of these seven questions.

In this study, we examined the results and interpretations produced from two different IRTree models--one using paths consisting of only dichotomous decisions, and one using paths consisting of both dichotomous and polytomous decisions. We used data from two versions of an impulsivity measure. In the first version, all the response options had labels; in the second version, only the endpoints were labeled. Based on past research, we hypothesized that the endpoints would be selected more frequently in the endpoint-only labeled condition, and the midpoint response option would be selected more frequently in the fully labeled condition. Results from the two models (dichotomous and polytomous) were similar and indicated that our hypotheses were partially supported--specifically, there was no consistent pattern in terms of which condition saw a higher frequency of midpoint response selection. However, our hypotheses regarding extreme responding in the endpoint-only labeling condition were supported.

We conducted a comprehensive survey of the educational measurement profession to assist recruiting to the measurement profession in the United States. Specifically, in April and May 2019, we surveyed measurement professionals and graduate students by targeting six areas to assist recruitment efforts: demographics of the field, compensation for professionals, graduate student funding, where to work, what work life entails, and mentoring opportunities. We evidence a profession trending toward being more diverse, professionals having a median annual salary between $110,000 and $130,000 (U.S.) dollars, varying graduate student funding opportunities, and flexibility in work location and tasks performed. For each of the six target areas, we present detailed results from the survey as well as implications for practice. We conclude the manuscript by discussing a change needed in the field that would assist in recruitment efforts.

Standard setting is arguably one of the most subjective techniques in test development and psychometrics. The decisions when scores are compared to standards, however, are arguably the most consequential outcomes of testing. Providing licensure to practice in a profession has high stake consequences for the public. Denying graduation or forcing remediation has high-impact consequences for students. Unfortunately, tests that classify individuals are subjected to false positive and false negative misclassifications. When determining a standard, standard setting panelists implicitly consider the negative consequences of the decisions made from test use. We propose the conscious weight method and subconscious weight method to bring more objectivity to the standard setting process. To do this, these methods quantify the relative harm of the negative consequences of false positive and false negative misclassification.

Data simulation and Monte Carlo simulation studies are important skills for researchers and practitioners of educational and psychological measurement, but there are few resources on the topic specific to item response theory. Even fewer resources exist on the statistical software techniques to implement simulation studies. This article presents an introduction to the questions that simulation studies can, and cannot answer, along with the primary steps of the simulation study. We primarily focus on the parts of the simulation for which statistical software is used, presenting syntax for simulating 2PL data, and discuss how to extend the basic syntax to more complex models. We focus on SAS software in the article to build upon an existing syntax base, as most universities have SAS licenses. Two small simulations serve as didactic illustrations and to demonstrate parameter recovery of the SAS IRT procedure and an evaluation of the procedure's item-fit options.

When rating performance assessments, raters may ascribe different scores for the same performance when rubric application does not align with the intended application of the scoring criteria. Given performance assessment score interpretation assumes raters apply rubrics as rubric developers intended, misalignment between raters' scoring processes and the intended scoring processes may lead to invalid inferences from these scores. In an effort to standardize raters' scoring processes, an alternative scoring method was used. With this method, rubric developers' intended scoring processes are made explicit by requiring raters to respond to a series of selected-response statements resembling a decision tree. To determine if raters scored essays as intended using a traditional rubric and the alternative scoring method, an IRT model with a tree-like structure (IRTree) was specified to depict the intended scoring processes and fit to data from each scoring method. Results suggest raters using the alternative method may be better able to rate as intended and thus the alternative method may be a viable alternative to traditional rubric scoring. Implications of the IRTree model are discussed.

Low-stakes test performance commonly reflects examinee ability and effort. Examinees exhibiting low effort may be identified through rapid guessing behavior throughout an assessment. There has been a plethora of methods proposed to adjust scores once rapid guesses have been identified, but these have been plagued by strong assumptions or the removal of examinees. In this study, we illustrate how an IRTree model can be used to adjust examinee ability for rapid guessing behavior. Our approach is flexible as it does not assume independence between rapid guessing behavior and the trait of interest (e.g., ability) nor does it necessitate the removal of examinees who engage in rapid guessing. In addition, our method uniquely allows for the simultaneous modeling of a disengagement latent trait in addition to the trait of interest. The results indicate the model is quite useful for estimating individual differences among examinees in the disengagement latent trait and in providing more precise measurement of examinee ability relative to models ignoring rapid guesses or accommodating it in different ways. A simulation study reveals that our model results in less biased estimates of the trait of interest for individuals with rapid responses, regardless of sample size and rapid response rate in the sample. We conclude with a discussion of extensions of the model and directions for future research. [ABSTRACT FROM AUTHOR]

Interest in Bayesian analysis of item response theory (IRT) models has grown tremendously due to the appeal of the paradigm among psychometricians, advantages of these methods when analyzing complex models, and availability of general-purpose software. Possible models include models which reflect multidimensionality due to designed test structure, construct-irrelevant variance, and mixed item formats. Using Markov Chain Monte Carlo methods, models can be estimated and evaluated for model-fit. In addition to discussing Bayesian analysis, analyses of three IRT models designed to account for extreme response style are illustrated: IRTree, multidimensional nominal response model (MNRM), and modified generalized partial credit model (MPCM). While results indicated there may be little impact of model choice on substantive trait estimates for the data set studied herein, model-fit results favored the MNRM and MPCM over the IRTree Model.

In this digital ITEMS module, Nikole Gregg and Dr. Brian Leventhal discuss strategies to ensure data visualizations achieve graphical excellence. Data visualizations are commonly used by measurement professionals to communicate results to examinees, the public, educators, and other stakeholders. To do so effectively, it is important that these visualizations communicate data efficiently and accurately. These visualizations can achieve graphical excellence when they simultaneously display data effectively, efficiently, and accurately. Unfortunately, measurement and statistical software default graphics typically fail to uphold these standards and are therefore not suitable for publication or presentation to the public. To illustrate best practices, the instructors provide an introduction to the graphical template language in SAS and show how elementary components can be used to make efficient, effective, and accurate graphics for a variety of audiences. The module contains audio-narrated slides, embedded illustrative videos, quiz questions with diagnostic feedback, a glossary, sample SAS code, and other learning resources.

Traditional psychometric models focus on studying observed categorical item responses, but these models often oversimplify the respondent cognitive response process, assuming responses are driven by a single substantive trait. A further weakness is that analysis of ordinal responses has been primarily limited to a single substantive trait at one time point. This study applies a significant expansion of this modeling framework to account for complex response processes across multiple waves of data collection using the item response tree (IRTree) framework. This study applies a novel model, the longitudinal IRTree, for response processes in longitudinal studies, and investigates whether the response style changes are proportional to changes in the substantive trait of interest. To do so, we present an empirical example using a six-item sexual knowledge scale from the National Longitudinal Study of Adolescent to Adult Health across two waves of data collection. Results show an increase in sexual knowledge from the first wave to the second wave and a decrease in midpoint and extreme response styles. Model validation revealed failure to account for response style can bias estimation of substantive trait growth. The longitudinal IRTree model captures midpoint and extreme response style, as well as the trait of interest, at both waves. [ABSTRACT FROM AUTHOR]

General education is an essential feature of American universities and colleges. While many educators value general education, many students do not. Students view general education requirements as interfering with their major requirements, perpetuating a negative sentiment about a disconnect between general education and academic degree programs. We aim to investigate the validity of this story using empirical data collected from robust assessment practice. We examined the extensive assessment records at a liberal arts university to evaluate overlap between the learning outcomes of the general education program and academic degree programs. Findings suggest that the outcomes-oriented general education program was well integrated -- nearly half of the academic degree learning outcomes were linked to a general education outcome. Further research should explore if the sentiment of disconnect stems from a lack of implementation fidelity and how findings regarding general education interrelatedness can contribute to the larger conversations and concerns surrounding higher education. [ABSTRACT FROM AUTHOR]

Traditional psychometric modeling focuses on observed categorical item responses, which can over-simplify the respondent cognitive response process. A further weakness is that analysis of ordinal responses has been primarily limited to a single substantive trait at one time point. We propose a significant expansion of this modeling framework to account for complex response processes across multiple waves of data collection using the beneficial item response tree framework. This study proposes a novel model, the longitudinal IRTree, for response processes in longitudinal studies, and investigates whether the response style changes are proportional to changes in the substantive trait of interest. A simulation study demonstrates adequate item parameter recovery in a Bayesian framework, especially with larger sample sizes of 2000. The longitudinal change parameters were recovered similarly well, with improved recovery using informative priors over default priors in Mplus. The empirical application demonstrates that relatively stable observed scores are due to a decrease in response styles offsetting an increase in the latent trait of interest. [ABSTRACT FROM AUTHOR]

Several multidimensional item response models have been proposed for survey responses affected by response styles. Through simulation, this study compares three models designed to account for extreme response tendencies: the IRTree Model, the multidimensional nominal response model, and the modified generalized partial credit model. The modified generalized partial credit model results in the lowest item mean squared error (MSE) across simulation conditions of sample size (500, 1,000), survey length (10, 20), and number of response options (4, 6). The multidimensional nominal response model is equally suitable for surveys measuring one substantive trait using responses to 10 four-option, forced-choice Likert-type items. Based on data validation, comparison of item MSE, and posterior predictive model checking, the IRTree Model is hypothesized to account for additional sources of construct-irrelevant variance. [ABSTRACT FROM AUTHOR]