20 results on '"Brian W. Junker"'
Search Results
2. Analysis of Longitudinal Advice-Seeking Networks Following Implementation of High Stakes Testing
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Brian W. Junker, Tracy M. Sweet, and Samrachana Adhikari
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Statistics and Probability ,Economics and Econometrics ,Medical education ,Computer science ,Statistics, Probability and Uncertainty ,Advice (complexity) ,Social Sciences (miscellaneous) - Abstract
Teacher interactions around instructional practices have been a topic of study for a long time. Previous studies concerning such interactions have focused on questions pertaining to cross-sectional networks. In fact, very few studies have considered longitudinal networks and still fewer have employed longitudinal network models to study changes in such interactions. We analyse teachers’ advice-seeking networks, observed annually between 2010 and 2013, in schools within a district where several initiatives were implemented starting in 2011. We assess whether formal structures, teaching assignment and leadership position, and teacher characteristics, gender and experience, are associated with advice-seeking ties, and the extent to which these associations change over time. To analyse the advice-seeking networks, we implement a Bayesian longitudinal latent space network model with covariates and random sender-receiver effects. Within the Bayesian framework, we address practical aspects of a principled network analysis such as missing ties and yearly immigration and emigration of teachers. Goodness of model fit assessment is conducted using posterior predictive checks. Our results demonstrate that while some of the associations between observed covariates and teachers’ interactions varied in 2011, most were otherwise stable. In 2011, we found decreases in the associations with same grade assignment, leadership position, and teaching in the same school.
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- 2021
3. Psychometric analysis of forensic examiner behavior
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Brian W. Junker, Amanda Luby, and Anjali Mazumder
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FOS: Computer and information sciences ,Psychometrics ,Computer science ,Bayesian probability ,Decision tree ,Experimental and Cognitive Psychology ,Sample (statistics) ,computer.software_genre ,Statistics - Applications ,Methodology (stat.ME) ,03 medical and health sciences ,0302 clinical medicine ,0504 sociology ,Item response theory ,Applications (stat.AP) ,030216 legal & forensic medicine ,Statistics - Methodology ,Cultural consensus theory ,Rasch model ,business.industry ,Applied Mathematics ,05 social sciences ,050401 social sciences methods ,Clinical Psychology ,Identification (information) ,Artificial intelligence ,business ,computer ,Analysis ,Natural language processing - Abstract
Forensic science often involves the comparison of crime-scene evidence to a known-source sample to determine if the evidence and the reference sample came from the same source. Even as forensic analysis tools become increasingly objective and automated, final source identifications are often left to individual examiners’ interpretation of the evidence. Each source identification relies on judgements about the features and quality of the crime-scene evidence that may vary from one examiner to the next. The current approach to characterizing uncertainty in examiners’ decision-making has largely centered around the calculation of error rates aggregated across examiners and identification tasks, without taking into account these variations in behavior. We propose a new approach using IRT and IRT-like models to account for differences among examiners and additionally account for the varying difficulty among source identification tasks. In particular, we survey some recent advances (Luby 2019a) in the application of Bayesian psychometric models, including simple Rasch models as well as more elaborate decision tree models, to fingerprint examiner behavior.
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- 2020
4. Psychometrics for Forensic Fingerprint Comparisons
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Anjali Mazumder, Brian W. Junker, and Amanda Luby
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Bayesian statistics ,Identification (information) ,Information retrieval ,Psychometrics ,Computer science ,Sample (material) ,Fingerprint (computing) ,Item response theory ,Suspect ,Set (psychology) - Abstract
Forensic science often involves the evaluation of crime-scene evidence to determine whether it matches a known-source sample, such as whether a fingerprint or DNA was left by a suspect or if a bullet was fired from a specific firearm. Even as forensic measurement and analysis tools become increasingly automated and objective, final source decisions are often left to individual examiners’ interpretation of the evidence. Furthermore, forensic analyses often consist of a series of steps. While some of these steps may be straightforward and relatively objective, substantial variation may exist in more subjective decisions. The current approach to characterizing uncertainty in forensic decision-making has largely centered around conducting error rate studies (in which examiners evaluate a set of items consisting of known-source comparisons) and calculating error rates aggregated across examiners and identification tasks. We propose a new approach using Item Response Theory (IRT) and IRT-like models to account for differences in examiner behavior and for varying difficulty among identification tasks. There are, however, substantial differences between forensic decision-making and traditional IRT applications such as educational testing. For example, the structure of the response process must be considered, “answer keys” for comparison tasks do not exist, and information about participants and items is not available due to privacy constraints. In this paper, we provide an overview of forensic decision-making, outline challenges in applying IRT in practice, and survey some recent advances in the application of Bayesian psychometric models to fingerprint examiner behavior.
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- 2021
5. Faster MCMC for Gaussian Latent Position Network Models
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Neil A. Spencer, Brian W. Junker, and Tracy M. Sweet
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Methodology (stat.ME) ,FOS: Computer and information sciences ,ComputingMethodologies_PATTERNRECOGNITION ,Sociology and Political Science ,Social Psychology ,Statistics - Machine Learning ,Communication ,Statistics::Methodology ,Machine Learning (stat.ML) ,Statistics - Computation ,Statistics - Methodology ,Computation (stat.CO) ,Statistics::Computation - Abstract
Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node's latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it is well-known that Metropolis within Gibbs is inefficient for large networks; the acceptance ratios are expensive to compute, and the resultant posterior draws are highly correlated. In this article, we propose an alternative Markov chain Monte Carlo strategy -- defined using a combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo -- that leverages the posterior distribution's functional form for more efficient posterior computation. We demonstrate that these strategies outperform Metropolis within Gibbs and other algorithms on synthetic networks, as well as on real information-sharing networks of teachers and staff in a school district., Comment: 41 pages, 8 figures
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- 2020
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6. The Development and Testing of an Instrument to Measure Youth Social Capital in the Domain of Postsecondary Transitions
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Brian W. Junker and Sarah Ryan
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Economic growth ,Sociology and Political Science ,business.industry ,Field (Bourdieu) ,05 social sciences ,Measure (physics) ,050401 social sciences methods ,050301 education ,General Social Sciences ,Construct validity ,Peer group ,Context (language use) ,Public relations ,Structural equation modeling ,Domain (software engineering) ,0504 sociology ,Sociology ,business ,0503 education ,Social Sciences (miscellaneous) ,Social capital - Abstract
Through the development and field testing of an instrument designed to measure youth social capital in the context of postsecondary transitions, this research addresses the need for theory-driven measures of social capital among youth. The results offer preliminary evidence that dimensions of youth social capital, including network structure and network content, can be reliably measured and that these dimensions of social capital are interrelated in a manner consistent with theory. The results also offer initial support for the validity of the social capital construct within the domain of youth postsecondary transitions. Taken together, the findings provide a foundation for continued research that might surmount inadequate measures and theoretical disputes to encompass more careful and rigorous empirical scrutiny when it comes to the measurement of social capital among children and adolescents.
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- 2016
7. Brain structural correlates of trajectories to cognitive impairment in men with and without HIV disease
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Eileen M. Martin, James T. Becker, Andrew J. Levine, Lawrence A. Kingsley, Ned Sacktor, Cynthia A. Munro, Brian W. Junker, Mikhail Popov, Samantha Molsberry, Eric C. Seaberg, Ann B. Ragin, Eric N. Miller, and Fabrizio Lecci
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Premature aging ,Male ,medicine.medical_specialty ,Cognitive Neuroscience ,Multicenter AIDS Cohort Study ,Precuneus ,HIV Infections ,Audiology ,computer.software_genre ,050105 experimental psychology ,Article ,Cohort Studies ,03 medical and health sciences ,Behavioral Neuroscience ,Cellular and Molecular Neuroscience ,0302 clinical medicine ,Voxel ,mental disorders ,Medicine ,Dementia ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Cognitive Dysfunction ,Gray Matter ,business.industry ,05 social sciences ,Neuropsychology ,Brain ,Cognition ,Middle Aged ,medicine.disease ,Magnetic Resonance Imaging ,Psychiatry and Mental health ,medicine.anatomical_structure ,Neurology ,Brain size ,Neurology (clinical) ,business ,computer ,030217 neurology & neurosurgery - Abstract
BACKGROUND: There are distinct trajectories to cognitive impairment among participants in the Multicenter AIDS Cohort Study (MACS). Here we analyzed the relationship between regional brain volumes and the individual trajectories to impairment in a subsample (n = 302) of the cohort. METHODS: 302 (167 HIV-infected; mean age = 55.7 yrs.; mean education: 16.2 yrs.) of the men enrolled in the MACS MRI study contributed data to this analysis. We used voxel-based morphometry (VBM) to segment the brain images to analyze gray and white matter volume at the voxel-level. A Mixed Membership Trajectory Model had previously identified three distinct profiles, and each study participant had a membership weight for each of these three trajectories. We estimated VBM model parameters for 100 imputations, manually performed the post-hoc contrasts, and pooled the results. RESULTS: We examined the associations between brain volume at the voxel level and the MMTM membership weights for two profiles: one considered “unhealthy” and the other considered “Premature aging.” The unhealthy profile was linked to the volume of the posterior cingulate gyrus/precuneus, the inferior frontal cortex, and the insula, whereas the premature aging profile was independently associated with the integrity of a portion of the precuneus. CONCLUSIONS: Trajectories to cognitive impairment are the result, in part, of atrophy in cortical regions linked to normal and pathological aging. These data suggest the possibility of predicting cognitive morbidity based on patterns of CNS atrophy.
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- 2019
8. Power to Detect Intervention Effects on Ensembles of Social Networks
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Tracy M. Sweet and Brian W. Junker
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Theoretical computer science ,05 social sciences ,Multilevel model ,050401 social sciences methods ,050301 education ,Education ,Bayesian statistics ,0504 sociology ,Sample size determination ,Covariate ,Convergence (routing) ,Econometrics ,Hierarchical network model ,0503 education ,Social network analysis ,Social Sciences (miscellaneous) ,Network analysis ,Mathematics - Abstract
The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention effect using the hierarchical latent space model, an important subfamily of HNMs. We derive basic convergence results and asymptotic bounds on power, showing that standard error for the treatment effect is inversely proportional to the product of the number of ties and the number of networks; a result rather different from the usual effect of cluster size in hierarchical linear models, for example. We explore these results with a simulation study and suggest a tentative approach to power for practical applications.
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- 2016
9. Factors Associated with Alignment between Teacher Survey Reports and Classroom Observation Ratings of Mathematics Instruction
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Julia H. Kaufman, Mary Kay Stein, and Brian W. Junker
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Teaching method ,05 social sciences ,050401 social sciences methods ,050301 education ,Cognition ,Collegiality ,Academic standards ,Education ,Educational research ,0504 sociology ,Mathematics education ,Mathematics instruction ,0503 education ,Curriculum ,Qualitative research - Abstract
We investigated the alignment between a teacher survey self-report measure and classroom observation measure of ambitious mathematics instructional practice among teachers in two urban school districts using two different standards-based mathematics curricula. Survey reports suggested mild differences in teachers’ instructional practices between the two districts, whereas observation ratings indicated starker differences. That said, teachers’ survey and observer ratings were significantly correlated in both districts. Factors significantly predicting the extent of survey-observation alignment included teachers’ grade level, Mathematical Knowledge for Teaching, cognitive demand, and—for one district—teachers’ adherence to the surface-level aspects of their curriculum. Qualitative analyses suggested that teachers’ survey-observation alignment could be a function of their interaction with colleagues who provided instructional models against which they could gauge the extent of their standards-based i...
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- 2016
10. CID Models on Real-world Social Networks and Goodness of Fit Measurements
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Tracy M. Sweet, Jun Hee Kim, Brian W. Junker, Qian Sha, and Eun Kyung Kwon
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FOS: Computer and information sciences ,Computer science ,Statistical model ,computer.software_genre ,Erdős–Rényi model ,Methodology (stat.ME) ,Goodness of fit ,Conditional independence ,Bayesian information criterion ,Stochastic block model ,Metric (mathematics) ,Data mining ,computer ,Statistics - Methodology ,Network analysis - Abstract
Assessing the model fit quality of statistical models for network data is an ongoing and under-examined topic in statistical network analysis. Traditional metrics for evaluating model fit on tabular data such as the Bayesian Information Criterion are not suitable for models specialized for network data. We propose a novel self-developed goodness of fit (GOF) measure, the `stratified-sampling cross-validation' (SCV) metric, that uses a procedure similar to traditional cross-validation via stratified-sampling to select dyads in the network's adjacency matrix to be removed. SCV is capable of intuitively expressing different models' ability to predict on missing dyads. Using SCV on real-world social networks, we identify the appropriate statistical models for different network structures and generalize such patterns. In particular, we focus on conditionally independent dyad (CID) models such as the Erdos Renyi model, the stochastic block model, the sender-receiver model, and the latent space model.
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- 2018
11. High-dimensional longitudinal classification with the multinomial fused lasso
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Samrachana Adhikari, Oscar L. Lopez, James T. Becker, Ryan J. Tibshirani, Brian W. Junker, Fabrizio Lecci, and Lewis H. Kuller
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Statistics and Probability ,Epidemiology ,Computer science ,Stability (learning theory) ,Machine learning ,computer.software_genre ,01 natural sciences ,Regularization (mathematics) ,Risk Assessment ,Article ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Lasso (statistics) ,Alzheimer Disease ,Risk Factors ,Multiple time dimensions ,Humans ,Computer Simulation ,030212 general & internal medicine ,Longitudinal Studies ,0101 mathematics ,Selection (genetic algorithm) ,business.industry ,Piecewise ,Disease Progression ,Multilevel Analysis ,Regression Analysis ,Multinomial distribution ,Artificial intelligence ,business ,Gradient descent ,computer ,Algorithms - Abstract
We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. We apply our proposed technique to a longitudinal data set on Alzheimer's disease from the Cardiovascular Health Study Cognition Study. Using data analysis and a simulation study, we motivate and demonstrate several practical considerations such as the selection of tuning parameters and the assessment of model stability. While race, gender, vascular and heart disease, lack of caregivers, and deterioration of learning and memory are all important predictors of dementia, we also find that these risk factors become more relevant in the later stages of life.
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- 2018
12. Mixed membership trajectory models of cognitive impairment in the multicenter AIDS cohort study
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Sandra M. Reynolds, Karl Goodkin, Brian W. Junker, Andrew J. Levine, Lawrence A. Kingsley, Cynthia A. Munro, Eric N. Miller, James T. Becker, Fabrizio Lecci, Samantha Molsberry, Ann B. Ragin, Eileen M. Martin, and Ned Sacktor
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Adult ,Male ,medicine.medical_specialty ,Immunology ,Closeness ,Multicenter AIDS Cohort Study ,HIV-associated neurocognitive disorder ,Article ,Cohort Studies ,Normal cognition ,medicine ,Humans ,Immunology and Allergy ,Dementia ,Longitudinal Studies ,Cognitive impairment ,Psychiatry ,Aged ,Aged, 80 and over ,Acquired Immunodeficiency Syndrome ,Models, Statistical ,Homosexuality ,Middle Aged ,medicine.disease ,Infectious Diseases ,Trajectory ,Bisexuality ,Cognition Disorders ,Psychology ,Clinical psychology ,Cohort study - Abstract
The longitudinal trajectories that individuals may take from a state of normal cognition to HIV-associated dementia are unknown. We applied a novel statistical methodology to identify trajectories to cognitive impairment, and factors that affected the 'closeness' of an individual to one of the canonical trajectories.The Multicenter AIDS Cohort Study (MACS) is a four-site longitudinal study of the natural and treated history of HIV disease among gay and bisexual men.Using data from 3892 men (both HIV-infected and HIV-uninfected) enrolled in the neuropsychology substudy of the MACS, a Mixed Membership Trajectory Model (MMTM) was applied to capture the pathways from normal cognitive function to mild impairment to severe impairment. MMTMs allow the data to identify canonical pathways and to model the effects of risk factors on an individual's 'closeness' to these trajectories.First, we identified three distinct trajectories to cognitive impairment: 'normal aging' (low probability of mild impairment until age 60); 'premature aging' (mild impairment starting at age 45-50); and 'unhealthy' (mild impairment in 20s and 30s) profiles. Second, clinically defined AIDS, and not simply HIV disease, was associated with closeness to the premature aging trajectory, and, third, hepatitis-C infection, depression, race, recruitment cohort and confounding conditions all affected individual's closeness to these trajectories.These results provide new insight into the natural history of cognitive dysfunction in HIV disease and provide evidence for a potential difference in the pathophysiology of the development of cognitive impairment based on trajectories to impairment.
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- 2015
13. Loglinear Models for Observed-Score Distributions
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Brian W. Junker and Jodi M. Casabianca
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Statistics ,Multivariate normal distribution ,Mathematics - Published
- 2017
14. Markov Chain Monte Carlo for Item Response Models
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Brian W. Junker, Richard J. Patz, and Nathan M. VanHoudnos
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Restricted maximum likelihood ,Estimation theory ,Computer science ,Statistics ,Expectation–maximization algorithm ,Maximum a posteriori estimation ,Minimum chi-square estimation ,Maximum likelihood sequence estimation ,Likelihood function ,Marginal likelihood - Published
- 2017
15. Multivariate Normal Distribution
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Jodi M. Casabianca and Brian W. Junker
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Logit ,Econometrics ,Probit ,Mathematics - Published
- 2017
16. Discrete Distributions
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Jodi M. Casabianca and Brian W. Junker
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- 2017
17. Bayesian Model Fit and Model Comparison
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Richard J. Patz, Brian W. Junker, and Nathan VanHoudnos
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symbols.namesake ,Computer science ,symbols ,Markov chain Monte Carlo ,Statistical physics - Published
- 2017
18. A Hierarchical Rater Model for Longitudinal Data
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Ricardo Nieto, Jodi M. Casabianca, Brian W. Junker, and Mark Bond
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Statistics and Probability ,Psychometrics ,Longitudinal data ,media_common.quotation_subject ,Experimental and Cognitive Psychology ,Models, Psychological ,01 natural sciences ,010104 statistics & probability ,0504 sociology ,Arts and Humanities (miscellaneous) ,Statistics ,Item response theory ,Econometrics ,Humans ,Quality (business) ,Computer Simulation ,Longitudinal Studies ,0101 mathematics ,Duration (project management) ,media_common ,Observer Variation ,Models, Statistical ,05 social sciences ,050401 social sciences methods ,Reproducibility of Results ,General Medicine ,Outcome (probability) ,Autoregressive model ,Constructed response ,Data Interpretation, Statistical ,Research studies ,Psychology - Abstract
Research studies in psychology and education often seek to detect changes or growth in an outcome over a duration of time. This research provides a solution to those interested in estimating latent traits from psychological measures that rely on human raters. Rater effects potentially degrade the quality of scores in constructed response and performance assessments. We develop an extension of the hierarchical rater model (HRM), which yields estimates of latent traits that have been corrected for individual rater bias and variability, for ratings that come from longitudinal designs. The parameterization, called the longitudinal HRM (L-HRM), includes an autoregressive time series process to permit serial dependence between latent traits at adjacent timepoints, as well as a parameter for overall growth. We evaluate and demonstrate the feasibility and performance of the L-HRM using simulation studies. Parameter recovery results reveal predictable amounts and patterns of bias and error for most parameters across conditions. An application to ratings from a study of character strength demonstrates the model. We discuss limitations and future research directions to improve the L-HRM.
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- 2017
- Full Text
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19. Factor Analysis and Latent Structure: IRT and Rasch Models
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Brian W. Junker
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Rasch model ,business.industry ,Nonparametric statistics ,Latent variable ,Machine learning ,computer.software_genre ,Bayesian inference ,Bayes' theorem ,Item response theory ,Econometrics ,Artificial intelligence ,Psychology ,Set (psychology) ,business ,computer ,Parametric statistics - Abstract
Educational assessments, psychological scales, and social surveys often generate data in the form of a two-way array of discrete response data for N subjects (examinees, respondents, etc.) and J probes (test, scale, or survey items). Subjects' responses to the probes may be viewed as fallible measures of one or more underlying constructs, represented generically by a scalar or vector-valued index {latent variable} for each subject. When the response variables are discrete-valued, item response theory (IRT) and its variants provide a set of modeling and estimation tools for making inferences about the relationships between the Xij and the latent variable(s) θi and about subjects' location in the latent structure represented by θ. This article reviews standard parametric IRT models and estimation methods, extensions to compelx data analysis setting by way of a hierarchical Bayes formulation, nonparametric approaches to IRT, and selected applications in education, psychology, the social sciences, and biostatistics. Extensions of IRT to handle cognitively diagnostic assessment are briefly sketched, and similarities between IRT and Bayesian inference networks are pointed out.
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- 2015
20. Empirically Derived Trajectories to Dementia Over 15 Years of Follow-up Identified by Using Mixed Membership Models
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Lewis H. Kuller, James T. Becker, Oscar L. Lopez, Fabrizio Lecci, and Brian W. Junker
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Gerontology ,Apolipoprotein E Gene ,Male ,Epidemiology ,Practice of Epidemiology ,Apolipoprotein E4 ,Neuroimaging ,Comorbidity ,Age Distribution ,Normal cognition ,Alzheimer Disease ,Risk Factors ,mental disorders ,medicine ,Diabetes Mellitus ,Prevalence ,Dementia ,Humans ,Cognitive Dysfunction ,Sex Distribution ,Aged ,Proportional Hazards Models ,Aged, 80 and over ,business.industry ,Prodromal Stage ,Cognition ,medicine.disease ,Magnetic Resonance Imaging ,Natural history ,Radiography ,Potential difference ,Cardiovascular Diseases ,Disease Progression ,Female ,Alzheimer's disease ,business ,Follow-Up Studies - Abstract
Alzheimer disease is the most common form of dementia in the elderly, and the complex relationships among risk factors produce highly variable natural histories from normal cognition through the prodromal stage of mild cognitive impairment (MCI) to clinical dementia. We used a novel statistical approach, mixed membership trajectory models, to capture the variety of such pathways in 652 participants in the Cardiovascular Health Study Cognition Study over 22 years of follow-up (1992–2014). We identified 3 trajectories: a “healthy” profile with a peak probability of MCI between 95 and 100 years of age and only a 50% probability of dementia by age 100; an “intermediate” profile with a peak probability of MCI between 85 and 90 years of age and progression to dementia between 90 and 95 years; and an “unhealthy” profile with a peak probability of progressing to MCI between ages 75 and 80 years and to dementia between the ages of 80 and 85 years. Hypertension, education, race, and the ϵ4 allele of the apolipoprotein E gene all affected the closeness of an individual to 1 or more of the canonical trajectories. These results provide new insights into the natural history of Alzheimer disease and evidence for a potential difference in the pathophysiology of the development of dementia.
- Published
- 2014
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