SavedAdd your journal

Journals / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2016 / Cilt: 7 - Sayı: 1

A Note on the Use of Item Parceling in Structural Equation Modeling with Missing Data

Fatih ORÇAN
Yanyun YANG
Pages
59–72
Publication Date
2016-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results showed that analysis conducted at the parcel level yielded lower model rejection rates than analysis based on the individual items, and the patterns were consistent across missing mechanism, percentage of missing, and distribution of item data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Item parceling procedure may be applied to alleviate some difficulties in analysis with missing data and/or nonnormal data in structural equation modeling.
📊
Key Findings & Data:Design factors included missing mechanism, percentage of missingness, distribution of item data, and sample size.
💡
Conclusion & Impact:Results showed that analysis conducted at the parcel level yielded lower model rejection rates than analysis based on the individual items, and the patterns were consistent across missing mechanism, percentage of missing, and distribution of item data.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Item parceling procedure may be applied to alleviate some difficulties in analysis with missing data and/or nonnormal data in structural equation modeling. A simulation study was conducted to investigate how item parceling behaves under various conditions in structural equation model with missing and nonnormal distributed data. Design factors included missing mechanism, percentage of missingness, distribution of item data, and sample size. Results showed that analysis conducted at the parcel level yielded lower model rejection rates than analysis based on the individual items, and the patterns were consistent across missing mechanism, percentage of missing, and distribution of item data. In addition, parcel-level analyses resulted in comparable parameter estimates to item-level analyses.

🔖

Cite This Article

ORÇAN, F., YANG, Y. (2016). A Note on the Use of Item Parceling in Structural Equation Modeling with Missing Data. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Cilt: 7 - Sayı: 1, 59–72. https://ajindex.com/articles/137402

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (7)

  1. Achenbach, T. M., Bernstein, A. & Dumenci, L.: DSM-oriented scales and statistically based syndromes for ages 18 to 59: Linking taxonomic paradigms to facilitate multitaxonomic approaches. Journal of personality assessment. 84, 49-63 (2005). doi:10.1207/s15327752jpa8401_10 Acock, A. C.: Working with missing values. Journal of Marriage and Family, 67, 1012-1028 (2005). doi:10.1111/j.1741-3737.2005.00191.x Allison, P. D.: Missing data. California: Sage Publications (2002).
  2. Allison, P. D.: Missing data techniques for structural equation modeling. Journal of Abnormal Psychology, 112, 545-557 (2003). doi:10.1037/0021-843X.112.4.545 Bandalos, D. L.: The effects of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling, 9, 78-102 (2002). doi:10.1207/S15328007SEM0901_5 Bandalos, D. L.: The Use of Monte Carlo Studies in Structural Equation Modeling Research. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 385-426).
  3. Greenwich, Connecticut: Information Age Publishing (2006).
  4. Bandalos, D. L.: Is parceling really necessary ? A comparison of results from item parceling and categorical variable methodology. Structural Equation Modeling, 15, 211-240 (2008). doi:10.1080/10705510801922340 Bandalos, D.L., & Finney, S. J.: Item parceling issues in structural equation modeling. In G.A. Marcoulides & R.E. Schumacker (Eds.). New developments and techniques in structural equation modeling (pp.269-293).
  5. Mahwah, NJ: Lawrence Erlbaum Associates (2001).
  6. Bentler, P.M.: Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238-246 (1990). doi:10.1037/0033-2909.107.2.238 Bernstein, I. H., & Teng, G.: Factoring items and factoring scales are different: Spurious evidence for multidimensionality due to item categorization. Psychological Bulletin, 105, 467-477 (1989). doi:10.1037/0033-2909.105.3.467 Bollen, K.A.: Structural equations with latent variables, Wiley, New York (1989).
  7. Chen, G., & Astebro, T.: How to deal with missing categorical data: Test of a simple Bayesian method. Organizational Research Methods, 6, 309-327 (2003). doi:10.1177/1094428103254672 Chou, C.P., Bentler, P.M., & Satorra, A.: Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: a Monte Carlo study. British Journal of Mathematical and Statistical Psychology, 44, 347–357 (1991). doi:10.1111/j.2044-8317.1991.tb00966.x Curran, P.J., West