Continuous and discrete latent structure models for item response data

Psychometrika ◽  
1990 ◽  
Vol 55 (3) ◽  
pp. 477-494 ◽  
Author(s):  
Edward H. Haertel
Keyword(s):  
Item Response ◽  
Latent Structure ◽  
Response Data ◽  
Latent Structure Models

scholarly journals Reanalysis of an Allocentric Navigation Strategy Scale based on Item Response Theory

2020 ◽  
Vol 2 (1) ◽  
pp. 90-105
Author(s):  
Jimmy Y. Zhong
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Large Scale ◽  
Relative Magnitude ◽  
Latent Structure ◽  
Response Theory ◽  
Future Studies ◽  
Bidimensional Model ◽  
Navigation Strategy ◽  
Component Loading

AbstractFocusing on 12 allocentric/survey-based strategy items of the Navigation Strategy Questionnaire (Zhong & Kozhevnikov, 2016), the current study applied item response theory-based analysis to determine whether a bidimensional model could better describe the latent structure of the survey-based strategy. Results from item and model fit diagnostics, categorical response and item information curves showed that an item with the lowest rotated component loading (.27) [SURVEY12], could be considered for exclusion in future studies; and that a bidimensional model with three preference-related items constituting a content factor offered a better representation of the latent structure than a unidimensional model per se. Mean scores from these three items also correlated significantly with a pointing-to-landmarks task to the same relative magnitude as the mean scores from all items, and all items excluding SURVEY12. These findings gave early evidence suggesting that the three preference-related items could constitute a subscale for deriving quick estimates of large-scale allocentric spatial processing in healthy adults in both experimental and clinical settings. Potential cognitive and brain mechanisms were discussed, followed by calls for future studies to gather greater evidence confirming the predictive validity of the full and sub scales, along with the design of new items focusing on environmental familiarity.

Latent Structure Models for Relationally Defined Social Classes

1985 ◽  
Vol 90 (5) ◽  
pp. 1002-1021 ◽  
Author(s):  
Peter V. Marsden
Keyword(s):  
Social Classes ◽  
Latent Structure ◽  
Latent Structure Models

scholarly journals Robustness of Projective IRT to Misspecification of the Underlying Multidimensional Model

2020 ◽  
Vol 44 (5) ◽  
pp. 362-375
Author(s):  
Tyler Strachan ◽  
Edward Ip ◽  
Yanyan Fu ◽  
Terry Ackerman ◽  
Shyh-Huei Chen ◽  
...  
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Real Data ◽  
Model Parameters ◽  
Simulation Studies ◽  
Response Theory ◽  
Computational Stability ◽  
Data Set ◽  
Response Data ◽  
Higher Dimensional

As a method to derive a “purified” measure along a dimension of interest from response data that are potentially multidimensional in nature, the projective item response theory (PIRT) approach requires first fitting a multidimensional item response theory (MIRT) model to the data before projecting onto a dimension of interest. This study aims to explore how accurate the PIRT results are when the estimated MIRT model is misspecified. Specifically, we focus on using a (potentially misspecified) two-dimensional (2D)-MIRT for projection because of its advantages, including interpretability, identifiability, and computational stability, over higher dimensional models. Two large simulation studies (I and II) were conducted. Both studies examined whether the fitting of a 2D-MIRT is sufficient to recover the PIRT parameters when multiple nuisance dimensions exist in the test items, which were generated, respectively, under compensatory MIRT and bifactor models. Various factors were manipulated, including sample size, test length, latent factor correlation, and number of nuisance dimensions. The results from simulation studies I and II showed that the PIRT was overall robust to a misspecified 2D-MIRT. Smaller third and fourth simulation studies were done to evaluate recovery of the PIRT model parameters when the correctly specified higher dimensional MIRT or bifactor model was fitted with the response data. In addition, a real data set was used to illustrate the robustness of PIRT.

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