Using structural equation modeling to detect response shifts and true change

The assessment of change in patient-reported outcomes is hindered by the fact that there are different types of change. Besides ‘true’ change, different types of response shift, such as recalibration, reprioritization, and reconceptualization, may occur. We describe how structural equation modeling can be used to detect response shifts and to measure true change.

[1]  D. Karnofsky.  The clinical evaluation of chemotherapeutic agents in cancer , 1949 .

[2]  M. R. Novick,et al.  Statistical Theories of Mental Test Scores. , 1971 .

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

[4]  Anders Christoffersson,et al.  Factor analysis of dichotomized variables , 1975 .

[5]  Robert T. Golembiewski,et al.  Measuring Change and Persistence in Human Affairs: Types of Change Generated by OD Designs , 1976 .

[6]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[7]  B. Muthén.  Contributions to factor analysis of dichotomous variables , 1978 .

[8]  U. Olsson.  On The Robustness Of Factor Analysis Against Crude Classification Of The Observations. , 1979, Multivariate behavioral research.

[9]  N. Schmitt,et al.  The Use Of Analysis Of Covariance Structures To Assess Beta And Gamma Change. , 1982, Multivariate behavioral research.

[10]  B. Muthén.  Latent variable structural equation modeling with categorical data , 1983 .

[11]  Neal Schmitt,et al.  Comparison of Three Techniques to Assess Group-Level Beta and Gamma Change , 1984 .

[12]  B. Muthén.  A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators , 1984 .

[13]  M. Browne.  Asymptotically distribution-free methods for the analysis of covariance structures. , 1984, The British journal of mathematical and statistical psychology.

[14]  A. Satorra,et al.  Power of the likelihood ratio test in covariance structure analysis , 1985 .

[15]  B. Muthén,et al.  A comparison of some methodologies for the factor analysis of non‐normal Likert variables , 1985 .

[16]  Jan de Leeuw,et al.  On the relationship between item response theory and factor analysis of discretized variables , 1987 .

[17]  Sandra B. Hartog,et al.  Alpha, beta, and gamma change in evaluation research: A structural equation approach. , 1988 .

[18]  M. Browne,et al.  Single Sample Cross-Validation Indices for Covariance Structures. , 1989, Multivariate behavioral research.

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

[20]  Karl G. Jöreskog,et al.  New developments in LISREL: analysis of ordinal variables using polychoric correlations and weighted least squares , 1990 .

[21]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[22]  B. Muthén,et al.  A comparison of some methodologies for the factor analysis of non‐normal Likert variables: A note on the size of the model , 1992 .

[23]  Karl G. Jöreskog,et al.  On the estimation of polychoric correlations and their asymptotic covariance matrix , 1994 .

[24]  Jeri Benson,et al.  The robustness of maximum likelihood and distribution-free estimators to non-normality in confirmatory factor analysis , 1994 .

[25]  Conor V. Dolan,et al.  Factor analysis of variables with 2, 3, 5, and 7 response categories: A comparison of categorical variable estimators using simulated data , 1994 .

[26]  S. West,et al.  The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. , 1996 .

[27]  James G. Hunt,et al.  Inside the Black Box of Alpha, Beta, and Gamma Change: Using a Cognitive-Processing Model to Assess Attitude Structure , 1996 .

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

[29]  N. Aaronson,et al.  Translation, validation, and norming of the Dutch language version of the SF-36 Health Survey in community and chronic disease populations. , 1998, Journal of clinical epidemiology.

[30]  Albert Satorra,et al.  A scaled difference chi-square test statistic for moment structure analysis , 1999 .

[31]  C. Schwartz,et al.  Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research. , 1999, Social science & medicine.

[32]  M. Sprangers,et al.  Integrating response shift into health-related quality of life research: a theoretical model. , 1999, Social science & medicine.

[33]  A. Boomsma,et al.  The robustness of LISREL modeling revisted. , 2001 .

[34]  F. Oort,et al.  Three-mode models for multivariate longitudinal data. , 2001, The British journal of mathematical and statistical psychology.

[35]  J. Ware.  SF-36 health survey: Manual and interpretation guide , 2003 .

[36]  M. Sprangers,et al.  An application of structural equation modeling to detect response shifts and true change in quality of life data from cancer patients undergoing invasive surgery , 2005, Quality of Life Research.

[37]  M. Sprangers,et al.  Methods to detect response shift in quality of life data: A convergent validity study , 2005, Quality of Life Research.

[38]  D. Flora,et al.  An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. , 2004, Psychological methods.

[39]  K. Jöreskog,et al.  Structural Equation Modeling with Ordinal Variables using LISREL , 2004 .

[40]  B. van Hilten,et al.  Recent Developments and Applications in Social Research Methodology , 2004 .

[41]  C. Schwartz,et al.  Reconsidering the psychometrics of quality of life assessment in light of response shift and appraisal , 2004, Health and quality of life outcomes.

[42]  F. Oort,et al.  Types of change in self-report data: definition, interpretation, and operationalization , 2004 .

[43]  C. Schwartz,et al.  Toward a theoretical model of quality-of-life appraisal: Implications of findings from studies of response shift , 2004, Health and quality of life outcomes.

[44]  Roger E. Millsap,et al.  Assessing Factorial Invariance in Ordered-Categorical Measures , 2004 .