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Computing Robust, Bootstrap-Adjusted Fit Indices for Use With Nonnormal Data
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2017
Year
EngineeringGeneralizability TheoryEducationPsychometricsClassical Test TheorySimultaneous Equation ModelingData ScienceRobust StatisticFactor AnalysisBiostatisticsEstimation TheoryStatisticsStructural Equation ModelingComparative Fit IndexMultidimensional AnalysisMultilevel ModelingMarginal Structural ModelsFunctional Data AnalysisBootstrap ResamplingHigh-dimensional MethodNonnormal DataStatistical InferenceStructural ModelingStructural EconometricsScaling Factor
Nonnormality of data presents unique challenges for researchers who wish to carry out structural equation modeling. The subsequent SPSS syntax program computes bootstrap-adjusted fit indices (comparative fit index, Tucker–Lewis index, incremental fit index, and root mean square error of approximation) that adjust for nonnormality, along with the Bollen–Stine bootstrap-adjusted χ2 equivalent statistic and associated scaling factor.