Network Psychometrics.

This chapter provides a general introduction of network modeling in psychometrics. The chapter starts with an introduction to the statistical model formulation of pairwise Markov random fields (PMRF), followed by an introduction of the PMRF suitable for binary data: the Ising model. The Ising model is a model used in ferromagnetism to explain phase transitions in a field of particles. Following the description of the Ising model in statistical physics, the chapter continues to show that the Ising model is closely related to models used in psychometrics. The Ising model can be shown to be equivalent to certain kinds of logistic regression models, loglinear models and multi-dimensional item response theory (MIRT) models. The equivalence between the Ising model and the MIRT model puts standard psychometrics in a new light and leads to a strikingly different interpretation of well-known latent variable models. The chapter gives an overview of methods that can be used to estimate the Ising model, and concludes with a discussion on the interpretation of latent variables given the equivalence between the Ising model and MIRT.

[1]  A. Martin-Löf On the spontaneous magnetization in the Ising model , 1972 .

[2]  Steven P Reise,et al.  Item response theory and clinical measurement. , 2009, Annual review of clinical psychology.

[3]  L. Elton,et al.  THE DIRECTION OF TIME , 1978 .

[4]  A. Willsky,et al.  Latent variable graphical model selection via convex optimization , 2010, 1008.1290.

[5]  R. P. McDonald,et al.  The effect of additional variables on factor indeterminacy in models with a single common factor , 1978 .

[6]  P. Costa,et al.  Empirical and theoretical status of the five-factor model of personality traits , 2008 .

[7]  Brian W. Junker,et al.  Tail-measurability in monotone latent variable models , 1997 .

[8]  Jeroen K. Vermunt,et al.  Estimation of Models in a Rasch Family for Polytomous Items and Multiple Latent Variables , 2007 .

[9]  D. Borsboom,et al.  Network analysis: an integrative approach to the structure of psychopathology. , 2013, Annual review of clinical psychology.

[10]  H.L.J. van der Maas,et al.  A dynamical model of general intelligence: the positive manifold of intelligence by mutualism. , 2006, Psychological review.

[11]  D. Borsboom,et al.  State of the aRt personality research: A tutorial on network analysis of personality data in R , 2015 .

[12]  Paul W. Holland,et al.  The Dutch Identity: A New Tool for the Study of Item Response Models. , 1990 .

[13]  Zoubin Ghahramani,et al.  MCMC for Doubly-intractable Distributions , 2006, UAI.

[14]  Peter Buhlmann Statistical significance in high-dimensional linear models , 2012, 1202.1377.

[15]  D. Borsboom,et al.  Critical slowing down as early warning for the onset and termination of depression , 2013, Proceedings of the National Academy of Sciences.

[16]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[17]  O. Penrose The Direction of Time , 1962 .

[18]  Larry A. Wasserman,et al.  The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs , 2009, J. Mach. Learn. Res..

[19]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[20]  Mark Kac,et al.  MATHEMATICAL MECHANISMS OF PHASE TRANSITIONS. , 1969 .

[21]  T. Wickens,et al.  Multiway Contingency Tables Analysis for the Social Sciences , 1992 .

[22]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[23]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[24]  Jiahua Chen,et al.  Extended Bayesian information criteria for model selection with large model spaces , 2008 .

[25]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[26]  Mathias Drton,et al.  High-dimensional Ising model selection with Bayesian information criteria , 2014, 1403.3374.

[27]  Melvin R. Novick,et al.  Some latent train models and their use in inferring an examinee's ability , 1966 .

[28]  Verena D. Schmittmann,et al.  The Small World of Psychopathology , 2011, PloS one.

[29]  Sara van de Geer,et al.  Statistics for High-Dimensional Data , 2011 .

[30]  K. Kendler,et al.  DSM criteria for major depression: evaluating symptom patterns using latent-trait item response models , 2004, Psychological Medicine.

[31]  J. Lafferty,et al.  High-dimensional Ising model selection using ℓ1-regularized logistic regression , 2010, 1010.0311.

[32]  N. Meinshausen,et al.  High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.

[33]  L. Zhao,et al.  Correlated binary regression using a quadratic exponential model , 1990 .

[34]  Andrea Rotnitzky,et al.  Regression Models for Discrete Longitudinal Responses , 1993 .

[35]  R. P. McDonald,et al.  Behavior Domains in Theory and in Practice , 2003 .

[36]  Steffen L. Lauritzen,et al.  Graphical models in R , 1996 .

[37]  Ingram Olkin,et al.  Multivariate Correlation Models with Mixed Discrete and Continuous Variables , 1961 .

[38]  P. Green,et al.  Hidden Markov Models and Disease Mapping , 2002 .

[39]  J. Laurie Snell,et al.  Markov Random Fields and Their Applications , 1980 .

[40]  A. Jensen,et al.  The g factor , 1996, Nature.

[41]  E. Fried,et al.  Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors , 2013, Psychological Medicine.

[42]  S. Geer,et al.  On asymptotically optimal confidence regions and tests for high-dimensional models , 2013, 1303.0518.

[43]  Georg Rasch,et al.  Probabilistic Models for Some Intelligence and Attainment Tests , 1981, The SAGE Encyclopedia of Research Design.

[44]  D. Cox The Analysis of Multivariate Binary Data , 1972 .

[45]  C. Spearman General intelligence Objectively Determined and Measured , 1904 .

[46]  E. Ising Beitrag zur Theorie des Ferromagnetismus , 1925 .

[47]  R. Bagozzi,et al.  On the nature and direction of relationships between constructs and measures. , 2000, Psychological methods.

[48]  S. Carpenter,et al.  Early-warning signals for critical transitions , 2009, Nature.

[49]  J. Møller,et al.  An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants , 2006 .

[50]  W. Meredith Measurement invariance, factor analysis and factorial invariance , 1993 .

[51]  Honglak Lee,et al.  Efficient L1 Regularized Logistic Regression , 2006, AAAI.

[52]  Gideon J. Mellenbergh,et al.  Item bias and item response theory , 1989 .

[53]  R. Lennox,et al.  Conventional wisdom on measurement: A structural equation perspective. , 1991 .

[54]  Hsiu-Ting Yu,et al.  Log-Multiplicative Association Models as Item Response Models , 2007 .

[55]  Qiang Liu,et al.  Distributed Parameter Estimation via Pseudo-likelihood , 2012, ICML.

[56]  Rina Foygel,et al.  Extended Bayesian Information Criteria for Gaussian Graphical Models , 2010, NIPS.

[57]  Claudia D. van Borkulo,et al.  A new method for constructing networks from binary data , 2014, Scientific Reports.

[58]  A. Willsky,et al.  Latent variable graphical model selection via convex optimization , 2010 .

[59]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[60]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[61]  R. Philip Chalmers,et al.  mirt: A Multidimensional Item Response Theory Package for the R Environment , 2012 .

[62]  Giovanni Sebastiani,et al.  A Bayesian Method for Multispectral Image Data Classification , 2002 .

[63]  R. Tibshirani,et al.  Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.

[64]  Denny Borsboom,et al.  Generalized Network Psychometrics: Combining Network and Latent Variable Models , 2016, Psychometrika.

[65]  J. Besag Statistical Analysis of Non-Lattice Data , 1975 .

[66]  Keith A. Markus,et al.  Reflective measurement models, behavior domains, and common causes , 2013 .

[67]  Verena D. Schmittmann,et al.  Qgraph: Network visualizations of relationships in psychometric data , 2012 .

[68]  Jessika Weiss,et al.  Graphical Models In Applied Multivariate Statistics , 2016 .

[69]  D. Borsboom,et al.  The Theoretical Status of Latent Variables , 2003 .

[70]  Eric D. Kolaczyk,et al.  Statistical Analysis of Network Data , 2009 .

[71]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[72]  Peter C. M. Molenaar,et al.  State Space Techniques in Structural Equation Modeling Transformation of latent variables in and out of latent variable models , 2003 .

[73]  Denny Borsboom,et al.  Dimensions of Normal Personality as Networks in Search of Equilibrium: You Can't like Parties if you Don't like People , 2012 .

[74]  秀俊 松井,et al.  Statistics for High-Dimensional Data: Methods, Theory and Applications , 2014 .

[75]  Peter Bühlmann,et al.  p-Values for High-Dimensional Regression , 2008, 0811.2177.

[76]  M. Reckase Multidimensional Item Response Theory , 2009 .

[77]  D. Borsboom Measuring the mind: Conceptual issues in contemporary psychometrics , 2005 .

[78]  S. Haberman,et al.  Log‐Linear Fit for Contingency Tables , 1972 .

[79]  Iain Murray Advances in Markov chain Monte Carlo methods , 2007 .

[80]  J. Wilcox,et al.  Reconsidering formative measurement. , 2007, Psychological methods.

[81]  Jeroen K. Vermunt,et al.  3. Log-Multiplicative Association Models as Latent Variable Models for Nominal and/or Ordinal Data , 2000 .

[82]  Alan Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[83]  Charles C. Taylor,et al.  Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods , 2003 .

[84]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[85]  Gunter Maris,et al.  Bayesian inference for low-rank Ising networks , 2015, Scientific Reports.

[86]  Nanny Wermuth,et al.  A note on the quadratic exponential binary distribution , 1994 .

[87]  Larry A. Wasserman,et al.  High Dimensional Semiparametric Gaussian Copula Graphical Models. , 2012, ICML 2012.

[88]  Noel A Cressie,et al.  Characterizing the manifest probabilities of latent trait models , 1983 .