Dynamic fit index cutoffs for one-factor models

[1]  Lahnna I Catalino,et al.  The Psychometric Properties of the Prioritizing Positivity Scale , 2020, Journal of personality assessment.

[2]  J. Soland,et al.  Avoiding bias from sum scores in growth estimates: An examination of IRT-based approaches to scoring longitudinal survey responses. , 2020, Psychological methods.

[3]  Daniel M. McNeish,et al.  Dynamic fit index cutoffs for confirmatory factor analysis models. , 2020, Psychological methods.

[4]  M. C. Edwards,et al.  The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation , 2020, Educational and psychological measurement.

[5]  Albert Maydeu-Olivares,et al.  The Effect of Estimation Methods on SEM Fit Indices , 2020, Educational and psychological measurement.

[6]  W. H. Finch,et al.  Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis , 2020, Educational and psychological measurement.

[7]  David Goretzko,et al.  One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis. , 2020, Psychological methods.

[8]  Y. Rosseel,et al.  Assessing Fit in Ordinal Factor Analysis Models: SRMR vs. RMSEA , 2020, Structural Equation Modeling: A Multidisciplinary Journal.

[9]  Daniel M. McNeish,et al.  Thinking twice about sum scores , 2020, Behavior Research Methods.

[10]  Morten Moshagen,et al.  How to determine the number of factors to retain in exploratory factor analysis: A comparison of extraction methods under realistic conditions. , 2019, Psychological methods.

[11]  Alexander P. Christensen,et al.  Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. , 2018, Psychological methods.

[12]  Daniel M. McNeish.  Should We Use F-Tests for Model Fit Instead of Chi-Square in Overidentified Structural Equation Models? , 2018, Organizational Research Methods.

[13]  Albert Maydeu-Olivares,et al.  Understanding the Model Size Effect on SEM Fit Indices , 2018, Educational and psychological measurement.

[14]  R. Bowles,et al.  Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation , 2018, Multivariate Behavioral Research.

[15]  Daniel McNeish,et al.  The Thorny Relation Between Measurement Quality and Fit Index Cutoffs in Latent Variable Models , 2018, Journal of personality assessment.

[16]  Samuel Greiff,et al.  Why Psychological Assessment Needs to Start Worrying About Model Fit , 2017 .

[17]  M. V. van Assen,et al.  An Empirical Kaiser Criterion , 2017, Psychological methods.

[18]  Hudson F. Golino,et al.  Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research , 2016, PloS one.

[19]  Hao Wu,et al.  A Scaled F Distribution as an Approximation to the Distribution of Test Statistics in Covariance Structure Analysis , 2016 .

[20]  Samuel B Green,et al.  The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree , 2016, Multivariate behavioral research.

[21]  Francisco José Abad,et al.  Are fit indices really fit to estimate the number of factors with categorical variables? Some cautionary findings via Monte Carlo simulation. , 2016, Psychological methods.

[22]  Francis Tuerlinckx,et al.  Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression. , 2016, Psychological assessment.

[23]  D. Betsy McCoach,et al.  The Performance of RMSEA in Models With Small Degrees of Freedom , 2015 .

[24]  Hao Wu,et al.  Quantifying Adventitious Error in a Covariance Structure as a Random Effect , 2015, Psychometrika.

[25]  E. Fried,et al.  Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study. , 2015, Journal of affective disorders.

[26]  R. Millsap,et al.  Using the Bollen-Stine Bootstrapping Method for Evaluating Approximate Fit Indices , 2014, Multivariate behavioral research.

[27]  V. Savalei.  The Relationship Between Root Mean Square Error of Approximation and Model Misspecification in Confirmatory Factor Analysis Models , 2012 .

[28]  Yves Rosseel,et al.  lavaan: An R Package for Structural Equation Modeling , 2012 .

[29]  Christine E. DeMars.  Confirming Testlet Effects , 2012 .

[30]  Morten Moshagen,et al.  The Model Size Effect in SEM: Inflated Goodness-of-Fit Statistics Are Due to the Size of the Covariance Matrix , 2012 .

[31]  Moritz Heene,et al.  Masking misfit in confirmatory factor analysis by increasing unique variances: a cautionary note on the usefulness of cutoff values of fit indices. , 2011, Psychological methods.

[32]  Urbano Lorenzo-Seva,et al.  The Hull Method for Selecting the Number of Common Factors , 2011, Multivariate behavioral research.

[33]  Gregory R. Hancock,et al.  The Reliability Paradox in Assessing Structural Relations Within Covariance Structure Models , 2011 .

[34]  R. P. McDonald,et al.  Structural Models and the Art of Approximation , 2010, Perspectives on psychological science : a journal of the Association for Psychological Science.

[35]  Edward Haksing Ip,et al.  Empirically indistinguishable multidimensional IRT and locally dependent unidimensional item response models. , 2010, The British journal of mathematical and statistical psychology.

[36]  D. Boomsma,et al.  Sex Differences in Sum Scores May Be Hard to Interpret , 2009, Assessment.

[37]  M. C. Edwards,et al.  Measurement and the Study of Change , 2009 .

[38]  R. Purc-Stephenson,et al.  Reporting practices in confirmatory factor analysis: an overview and some recommendations. , 2009, Psychological methods.

[39]  K. Bollen,et al.  An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models , 2008, Sociological methods & research.

[40]  J. H. Steiger,et al.  The insidious effects of failing to include design-driven correlated residuals in latent-variable covariance structure analysis. , 2007, Psychological methods.

[41]  Xitao Fan,et al.  Sensitivity of Fit Indices to Model Misspecification and Model Types , 2007 .

[42]  Roger E. Millsap,et al.  Structural equation modeling made difficult , 2007 .

[43]  W. R. Dillon,et al.  A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models , 2005 .

[44]  Xitao Fan,et al.  Sensitivity of Fit Indexes to Misspecified Structural or Measurement Model Components: Rationale of Two-Index Strategy Revisited , 2005 .

[45]  H. Marsh,et al.  In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings , 2004 .

[46]  D. A. Kenny,et al.  Effect of the Number of Variables on Measures of Fit in Structural Equation Modeling , 2003 .

[47]  R. MacCallum,et al.  When fit indices and residuals are incompatible. , 2002, Psychological methods.

[48]  Edward H. Ip,et al.  Locally dependent latent trait model and the dutch identity revisited , 2002 .

[49]  Kenneth A. Bollen,et al.  Monte Carlo Experiments: Design and Implementation , 2001 .

[50]  Ke-Hai Yuan,et al.  F Tests for Mean and Covariance Structure Analysis , 1999 .

[51]  R. MacCallum,et al.  Power analysis and determination of sample size for covariance structure modeling. , 1996 .

[52]  R. Stine,et al.  Bootstrapping Goodness-of-Fit Measures in Structural Equation Models , 1992 .

[53]  Robert Cudeck,et al.  Constructing a covariance matrix that yields a specified minimizer and a specified minimum discrepancy function value , 1992 .

[54]  A. Satorra,et al.  Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: a Monte Carlo study. , 1991, The British journal of mathematical and statistical psychology.

[55]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[56]  Karl G. Jöreskog,et al.  Recent Developments in Structural Equation Modeling , 1982 .

[57]  W. Velicer.  Determining the number of components from the matrix of partial correlations , 1976 .

[58]  L. Tucker,et al.  A reliability coefficient for maximum likelihood factor analysis , 1973 .

[59]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[60]  J. Horn.  A rationale and test for the number of factors in factor analysis , 1965, Psychometrika.

[61]  H. Kaiser.  The Application of Electronic Computers to Factor Analysis , 1960 .

[62]  T. Brown.  Confirmatory factor analysis for applied research, 2nd ed. , 2015 .

[63]  K. Bollen.  Latent variables in psychology and the social sciences. , 2002, Annual review of psychology.

[64]  D. N.,et al.  DYNAMIC GENERALIZATION OF THE RASCH MODEL , 2000 .

[65]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[66]  P. Bentler,et al.  Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification , 1998 .