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Applied Missing Data Analysis

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Abstract
 


Objective

The purpose of this study was to finalize the development of the International Trauma Questionnaire (ITQ), a self‐report diagnostic measure of post‐traumatic stress disorder (PTSD) and complex PTSD (CPTSD), as defined in the 11th version of the International Classification of Diseases (ICD‐11). 


Method

The optimal symptom indicators of PTSD and CPTSD were identified by applying item response theory (IRT) analysis to data from a trauma‐exposed community sample (n = 1051) and a trauma‐exposed clinical sample (n = 247) from the United Kingdom. The validity of the optimized 12‐item ITQ was assessed with confirmatory factor analyses. Diagnostic rates were estimated and compared to previous validation studies. 


Results

The latent structure of the 12‐item, optimized ITQ was consistent with prior findings, and diagnostic rates of PTSD and CPTSD were in line with previous estimates.


Conclusion

The ITQ is a brief, simply worded measure of the core features of PTSD and CPTSD. It is consistent with the organizing principles of the ICD‐11 to maximize clinical utility and international applicability through a focus on a limited but central set of symptoms. The measure is freely available and can be found in the body of this paper.

/pdf/the-international-trauma-questionnaire-development-of-a-self-s1mfqioo6r.pdf

The International Trauma Questionnaire: development of a self‐report measure of ICD‐11 PTSD and complex PTSD

Based on recent psychometric developments, this paper presents a conceptual and practical guide for estimating internal consistency reliability of measures obtained as item sum or mean. The internal consistency reliability coefficient is presented as a by-product of the measurement model underlying the item responses. A three-step procedure is proposed for its estimation, including descriptive data analysis, test of relevant measurement models, and computation of internal consistency coefficient and its confidence interval. Provided formulas include: (a) Cronbach’s alpha and omega coefficients for unidimensional measures with quantitative item response scales, (b) coefficients ordinal omega, ordinal alpha and nonlinear reliability for unidimensional measures with dichotomic and ordinal items, (c) coefficients omega and omega hierarchical for essentially unidimensional scales presenting method effects. The procedure is generalized to weighted sum measures, multidimensional scales, complex designs with multilevel and/or missing data and to scale development. Four illustrative numerical examples are fully explained and the data and the R syntax are provided.

/pdf/a-journey-around-alpha-and-omega-to-estimate-internal-3nu962e02p.pdf

A journey around alpha and omega to estimate internal consistency reliability

Thank you for reading item response theory principles and applications. As you may know, people have look hundreds times for their chosen novels like this item response theory principles and applications, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop. item response theory principles and applications is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the item response theory principles and applications is universally compatible with any devices to read.

Item Response Theory Principles And Applications

This longitudinal study focuses on the transition from higher education to working life. Research has hitherto described the transition in rather general terms, and there is still only limited knowledge about how graduates construe themselves as professionals, or how they experience the transition to the sociocultural contexts of working life. In this study, the transition is viewed as a trajectory between different communities of practice. Three different Master’s programmes at Linköping University are focused on and compared: political science, psychology and mechanical engineering. The specific aims are to: (i) identify aspects of identity and knowledge formation as reported by informants, both as senior students and later as novice workers with 18 months of work experience; (ii) identify features of discourses of knowledge and competence operating in the programmes and working life; and (iii) to relate the results to differences in the way the programmes are designed. The results indicate that the psychology programme prepares for working life in a rational way, that is, the generic skills and substantive knowledge acquired seem to correspond to the demands of professional work. The other programmes stand out as preparing for working life either by providing generic skills that need to be transformed in professional work, or by containing elements that mainly play a ritual role rather than corresponding to the demands of working life.

From senior student to novice worker: learning trajectories in political science, psychology and mechnical engineering

Developing a diagnostic tool within the diagnostic measurement framework is the optimal approach to obtain multidimensional and classification-based feedback on examinees. However, end users may seek to obtain diagnostic feedback from existing item responses to assessments that have been designed under either the classical test theory or item response theory frameworks. Retrofitting diagnostic classification models to existing assessments designed under other psychometric frameworks could be a plausible approach to obtain more actionable scores or understand more about the constructs themselves. This study (a) discusses the possibility and problems of retrofitting, (b) proposes a step-by-step retrofitting framework, and (c) explores the information one can gain from retrofitting through an empirical application example. While retrofitting may not always be an ideal approach to diagnostic measurement, this article aims to invite discussions through presenting the possibility, challenges, process, and product of retrofitting.

/pdf/retrofitting-diagnostic-classification-models-to-responses-1s2rkp69ax.pdf

Retrofitting Diagnostic Classification Models to Responses From IRT-Based Assessment Forms.

Online learning platforms integrating open educational resources (OERs) are increasingly adopted in secondary education as supplemental resources for teaching and learning. However, students report difficulties sustaining their engagement because of the self-paced nature of OER-supported learning environments. We noted that little attention has been paid to factors related to student perseverance and attrition in the learning environment. Little knowledge about these factors prevents discussion on how to promote OERs as pedagogical tools that complement the formal school curriculum. To address this research gap, we analyzed student- and teacher-level usage data, and demographic information. The purpose was to explore student- and teacher-level factors associated with the duration of student usage in Algebra Nation, an OER-supported online learning platform adopted in many secondary schools. The results revealed that at the student level, student engagement with video lectures, self-assessment, social tools, and additional videos relevant to solved test items significantly predicted student usage duration. At the teacher level, teachers’ use of teacher resources was positively associated with student usage duration. An additional analysis of student and teacher total usage time indicated that, compared with heavy users, light and medium users were more likely to discontinue their engagement in the long term if their teachers did not use the platform. Based on our findings, we provide recommendations for promoting student engagement with OER-supported online learning in secondary education contexts.

Exploring student and teacher usage patterns associated with student attrition in an open educational resource-supported online learning platform

There is an increasing demand for assessments that can provide more fine-grained information about examinees. In response to the demand, diagnostic measurement provides students with feedback on their strengths and weaknesses on specific skills by classifying them into mastery or nonmastery attribute categories. These attributes often form a hierarchical structure because student learning and development is a sequential process where many skills build on others. However, it remains to be seen if we can use information from the attribute structure and work that into the design of the diagnostic tests. The purpose of this study is to introduce three approaches of Q-matrix design and investigate their impact on classification results under different attribute structures. Results indicate that the adjacent approach provides higher accuracy in a shorter test length when compared with other Q-matrix design approaches. This study provides researchers and practitioners guidance on how to design the Q-matrix in diagnostic tests, which are in high demand from educators.

/pdf/the-impact-of-q-matrix-designs-on-diagnostic-classification-3gei8paone.pdf

The Impact of Q-Matrix Designs on Diagnostic Classification Accuracy in the Presence of Attribute Hierarchies.

Staying on target: A systematic literature review on learner-facing learning analytics dashboards

Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC), and absolute fit indices (RMSEA2, ABS(fcor) and MAX(χ2jj′)). These fit indices were assessed under different CDM settings with respect to Q-matrix misspecification and CDM misspecification. Results showed that relative fit indices selected the correct DINA model most of the times and selected the correct G-DINA model well across most conditions. Absolute fit indices rejected the true DINA model if the Q-matrix was misspecified in any way. Absolute fit indices rejected the true G-DINA model whenever the Q-matrix was under-specified. RMSEA2 could be artificially low when the Q-matrix was over-specified.

Anne Corinne Huggins-Manley

Papers

Retrofitting Diagnostic Classification Models to Responses From IRT-Based Assessment Forms.

Exploring student and teacher usage patterns associated with student attrition in an open educational resource-supported online learning platform

The Impact of Q-Matrix Designs on Diagnostic Classification Accuracy in the Presence of Attribute Hierarchies.

Staying on target: A systematic literature review on learner-facing learning analytics dashboards

Evaluation of Model Fit in Cognitive Diagnosis Models