scholarly journals Latent Variable Modeling and Adaptive Testing for Experimental Cognitive Psychopathology Research

2020 ◽  
pp. 001316442091989
Author(s):  
Michael L. Thomas ◽  
Gregory G. Brown ◽  
Virginie M. Patt ◽  
John R. Duffy
Keyword(s):  
Clinical Trials ◽  
Latent Variable ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Cognitive Testing ◽  
Cognitive Tasks ◽  
Latent Variable Modeling ◽  
Variable Model ◽  
Translational Studies ◽  
The Impact

The adaptation of experimental cognitive tasks into measures that can be used to quantify neurocognitive outcomes in translational studies and clinical trials has become a key component of the strategy to address psychiatric and neurological disorders. Unfortunately, while most experimental cognitive tests have strong theoretical bases, they can have poor psychometric properties, leaving them vulnerable to measurement challenges that undermine their use in applied settings. Item response theory–based computerized adaptive testing has been proposed as a solution but has been limited in experimental and translational research due to its large sample requirements. We present a generalized latent variable model that, when combined with strong parametric assumptions based on mathematical cognitive models, permits the use of adaptive testing without large samples or the need to precalibrate item parameters. The approach is demonstrated using data from a common measure of working memory—the N-back task—collected across a diverse sample of participants. After evaluating dimensionality and model fit, we conducted a simulation study to compare adaptive versus nonadaptive testing. Computerized adaptive testing either made the task 36% more efficient or score estimates 23% more precise, when compared to nonadaptive testing. This proof-of-concept study demonstrates that latent variable modeling and adaptive testing can be used in experimental cognitive testing even with relatively small samples. Adaptive testing has the potential to improve the impact and replicability of findings from translational studies and clinical trials that use experimental cognitive tasks as outcome measures.

Methods for Restricting Maximum Exposure Rate in Computerized Adaptative Testing

Methodology ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Juan Ramon Barrada ◽  
Julio Olea ◽  
Vicente Ponsoda
Keyword(s):  
Measurement Accuracy ◽  
Computerized Adaptive Testing ◽  
Computation Time ◽  
Adaptive Testing ◽  
Exposure Rate ◽  
Control Parameters ◽  
The Impact ◽  
Two Alternatives ◽  
Selection Of ◽  
Maximum Exposure

Abstract. The Sympson-Hetter (1985) method provides a means of controlling maximum exposure rate of items in Computerized Adaptive Testing. Through a series of simulations, control parameters are set that mark the probability of administration of an item on being selected. This method presents two main problems: it requires a long computation time for calculating the parameters and the maximum exposure rate is slightly above the fixed limit. Van der Linden (2003) presented two alternatives which appear to solve both of the problems. The impact of these methods in the measurement accuracy has not been tested yet. We show how these methods over-restrict the exposure of some highly discriminating items and, thus, the accuracy is decreased. It also shown that, when the desired maximum exposure rate is near the minimum possible value, these methods offer an empirical maximum exposure rate clearly above the goal. A new method, based on the initial estimation of the probability of administration and the probability of selection of the items with the restricted method ( Revuelta & Ponsoda, 1998 ), is presented in this paper. It can be used with the Sympson-Hetter method and with the two van der Linden's methods. This option, when used with Sympson-Hetter, speeds the convergence of the control parameters without decreasing the accuracy.

Application of Computerized Adaptive Testing to the Foot and Ankle Ability Measure

2020 ◽  
pp. 107110072097266
Author(s):  
Joseph T. O’Neil ◽  
Otho R. Plummer ◽  
Steven M. Raikin
Keyword(s):  
Computerized Adaptive Testing ◽  
Case Series ◽  
Adaptive Testing ◽  
Mean Difference ◽  
Foot And Ankle ◽  
Level Of Evidence ◽  
Patient Reported ◽  
The Mean ◽  
Ability Measure ◽  
The Impact

Background: Patient-reported outcome measures are an increasingly important tool for assessing the impact of treatments orthopedic surgeons render. Despite their importance, they can present a burden. We examined the validity and utility of a computerized adaptive testing (CAT) method to reduce the number of questions on the Foot and Ankle Ability Measure (FAAM), a validated anatomy-specific outcome measure. Methods: A previously developed FAAM CAT system was applied to the responses of patients undergoing foot and ankle evaluation and treatment over a 3-year period (2017-2019). A total of 15 902 responses for the Activities of Daily Living (ADL) subscale and a total of 14 344 responses for the Sports subscale were analyzed. The accuracy of the CAT to replicate the full-form score was assessed. Results: The CAT system required 11 questions to be answered for the ADL subscale in 85.1% of cases (range, 11-12). The number of questions answered on the Sports subscale was 6 (range, 5-6) in 66.4% of cases. The mean difference between the full FAAM ADL subscale and CAT was 0.63 of a point. The mean difference between the FAAM Sports subscale and CAT was 0.65 of a point. Conclusion: The FAAM CAT was able to reduce the number of responses a patient would need to answer by nearly 50%, while still providing a valid outcome score. This measure can therefore be directly correlated with previously obtained full FAAM scores in addition to providing a foot/ankle-specific measure, which previously reported CAT systems are not able to do. Level of Evidence: Level IV, case series.

Measuring the Impact of Psychological Trauma Change on Recidivism Among Juvenile Justice–Involved Youth

2016 ◽  
Vol 44 (3) ◽  
pp. 432-451 ◽  
Author(s):  
Krystel Tossone ◽  
Fredrick Butcher ◽  
Jeff Kretschmar
Keyword(s):  
Juvenile Justice ◽  
Latent Variables ◽  
Latent Variable ◽  
Psychological Trauma ◽  
Population Heterogeneity ◽  
Latent Variable Modeling ◽  
Trauma Symptoms ◽  
Post Traumatic Stress ◽  
Transition Analysis ◽  
The Impact

Population heterogeneity and intra-individual change are often overlooked in recidivism research. This study employs latent transition analysis of psychological trauma from intake into a juvenile justice diversion program until termination, followed by modeling of recidivism. A comparison model of a logistic regression without latent variables is also presented, to answer whether the same results would have been achieved without using latent variable modeling. Results indicate that juvenile justice–involved (JJI) youth are assigned into four psychological trauma classes at intake, and three at termination. Latent status membership predicts 6-month recidivism ( p = .03). Those who begin in classes that have Depression, Post-Traumatic Stress, and Anger have higher odds of recidivating than those who demonstrate generally high or low trauma symptoms at intake. The comparison regression model found no significant relationship between the five trauma symptom domains and recidivism. Implications for employing latent variable modeling and person-centered analyses for recidivism research are discussed.

The Impact of Traffic Information Acquisition on the Traffic Conditions of the Athens Greater Area

2015 ◽  
pp. 174-191
Author(s):  
Athena Tsirimpa ◽  
Amalia Polydoropoulou
Keyword(s):  
Information Acquisition ◽  
Latent Variable ◽  
Traffic Information ◽  
Activity Scheduling ◽  
Variable Model ◽  
Travel Information ◽  
Traffic Conditions ◽  
Real Time Information ◽  
The Impact ◽  
Time Information

The main objective of this article is to gain fundamental understanding on the effect of real time information acquisition, on the traffic conditions of the Athens greater area. Activity scheduling is a dynamic process, where individuals often need to modify their schedule, as a result of new insights. Research so far hasn't analyzed the effect of traffic information acquisition, in activity scheduling, although several studies have been conducted to capture the factors that influence the rescheduling of activities. An integrated latent variable model has been estimated, that predicts the probability of rescheduling activities as a function of flexibility, mode choice constraints and travel information. The analysis of the results indicates that one of the biggest impacts of traffic information acquisition is reflected in the rescheduling of activities. Therefore, traffic information not only can significantly improve the travel experience of individuals but may directly affect the performance of the transportation system.

scholarly journals role of perceived satisfaction and the built environment on the frequency of cycle-commuting

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tomás Echiburú ◽  
Ricardo Hurtubia ◽  
Juan Carlos Muñoz
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Latent Variable ◽  
Structural Equation ◽  
Subjective Experience ◽  
Central Business District ◽  
Variable Model ◽  
Perceived Satisfaction ◽  
New Findings ◽  
The Impact

Understanding how several street attributes influence the frequency of cycle commuting is relevant for policymaking in urban planning. However, to better understand the impact of the built environment on people's choices, we must understand the subjective experience of individuals while cycling. This study examines the relationship between perceived satisfaction and the attributes of the built environment along the route. Data was collected from a survey carried out within one district of Santiago’s central business district (N=2,545). It included socio-demographic information, origin-destination and route, travel behavior habits, and psychometric indicators. Two models were estimated. The first, a satisfaction latent variable model by mode, confirms previous findings in the literature, such as the correlation between cycling and a more enjoyable experience, while adding some new findings. For instance, satisfaction increases with distance and the number of trips per week. The second is a hybrid ordered logit model for cycle commuting frequency that includes satisfaction, through a structural equation, that shows this latent variable plays a significant role in travel behavior. The presence of buses along the route decreases cycling satisfaction and frequency, while the trip length and the availability of cycle paths has the opposite effect for male and female cyclists. These results allow us to understand the main factors that deliver satisfaction to cyclists and therefore induce frequent cycle commuting. Overall, our study provides evidence of the need for policymakers to focus their strategies so as to effectively promote cycling among different types of commuters.

scholarly journals Optimizing Detection of True Within-Person Effects for Intensive Measurement Designs: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores

2020 ◽  
Author(s):  
Jonathan Rush ◽  
Philippe Rast ◽  
Scott Michael Hofer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Variable Model ◽  
Alternative Approach ◽  
Scale Scores ◽  
Weighted Model ◽  
The Impact

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs rely on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and MSEM models were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEM models than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

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