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Effects of parceling on model selection: Parcel-allocation variability in model ranking.

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Parcel allocation variability (PAV) can impact structural model selection, even with large samples. Researchers should use the across-allocation modal ranking strategy to ensure robust model selection when PAV occurs.

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Area of Science:

  • Psychometrics
  • Structural Equation Modeling
  • Statistical Modeling

Background:

  • Structural Equation Modeling (SEM) often involves comparing models with different latent factor relationships.
  • Parceling, grouping items into scale scores, is common in SEM, assuming known item structure and unidimensionality.
  • The impact of parceling on model selection and potential variability in rankings across item-to-parcel allocations is not well understood.

Purpose of the Study:

  • To develop a theoretical framework predicting parcel-allocation variability (PAV) in model selection indices and structural model rankings.
  • To investigate the consequences of PAV for model selection within-sample using simulation.
  • To propose an empirically supported strategy for model selection when PAV in ranking is present.

Main Methods:

  • Developed a theoretical framework for parcel-allocation variability (PAV).
  • Conducted simulation studies to examine PAV in model selection indices and rankings.
  • Investigated the relationship between structural model rankings in parcel- and item-solutions.
  • Proposed and illustrated the across-allocation modal ranking strategy with software tools.

Main Results:

  • Parcel-allocation variability (PAV) can affect model selection index values and the ranking of competing structural models.
  • Conditions causing PAV in absolute model fit do not necessarily lead to PAV in model ranking, and vice versa.
  • PAV in ranking can occur under various conditions, including in large samples.
  • The across-allocation modal ranking strategy was shown to be effective for model selection amidst PAV.

Conclusions:

  • Parceling can introduce variability in structural model rankings, necessitating careful consideration.
  • The proposed across-allocation modal ranking strategy provides a robust method for selecting models when PAV is present.
  • Investigating within-sample PAV in ranking serves as an important sensitivity analysis for structural model comparisons.