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Year 2016, Volume: 7 Issue: 1, 59 - 72, 30.06.2016
https://doi.org/10.21031/epod.88204

Abstract

References

  • 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).
  • 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). Greenwich, Connecticut: Information Age Publishing (2006).
  • 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). Mahwah, NJ: Lawrence Erlbaum Associates (2001).
  • 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).
  • 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, S.G., & Finch, J.F.: The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29 (1996). doi:10.1037/1082-989X.1.1.16
  • Davey, A., & Savla, J.: Issues in evaluating model fit with missing data. Structural Equation Modeling, 12(4), 578-597 (2005). doi:10.1207/s15328007sem1204_4
  • Enders, C. K.: The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352-370 (2001). doi:10.1037/1082-989X.6.4.352
  • Enders, C. K.: The impact of missing data on sample reliability estimates: Implications for reliability reporting practices. Educational and Psychological Measurement, 64, 419-436 (2004). doi:10.1177/0013164403261050
  • Enders, C. K., & Bandalos, D. L.: The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 3, 430-457 (2001). doi:10.1207/S15328007SEM0803_5
  • Finney, S. J., & DiStefano, C.: Non-Normal and Categorical Data in Structural Equation Modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 269-314). Greenwich, Connecticut: Information Age Publishing (2006).
  • Fleishman, A. I.: A method for simulating non-normal distributions. Psychometrika, 43, 521-532 (1978). doi:10.1007/BF02293811
  • Hoogland, J. J. & Boomsma, A.: Robustness Studies in Covariance Structure Modeling: An Overview and a Meta-Analysis. Sociological Methods & Research, 26, 329-367 (1998). doi:10.1177/0049124198026003003
  • Hu, L., & Bentler, P. M.: Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55 (1999). doi:10.1080/10705519909540118
  • Hu, L., Bentler, P.M., & Kano, Y.: Can test statistics in covariance structure analysis be trusted? Psychological Bulletin, 112, 351-362 (1992).
  • Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F.: To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151-173 (2002). doi:10.1207/S15328007SEM0902_1
  • Matsunaga, M.: Item parceling in structural equation modeling: A primer. Communication Methods and Measures, 2, 260-293 (2008). doi:10.1080/19312450802458935
  • Meade, A. W., & Kroustalis, C. M.: Problems with item parceling for confirmatory factor analysis tests of measurement invariance of factor loadings. Paper presented at the 20th Annual Conference of the Society for Industrial and Organizational Psychology, Los Angeles, CA (2005).
  • Muthén, B., & Kaplan, D.: A comparison of some methodologies for the factor-analysis of non-normal Likert variables: A note on the size of the model. British Journal of Mathematical and Statistical Psychology, 45, 19 –30 (1992).
  • Muthén, B., & Muthén, L.: Mplus user's guide (6th eedition). Los Angeles, CA: Muthén & Muthén (1998-2008).
  • Nasser, F., & Takahashi, T.: The effect of using item parcels on Ad Hoc goodness-of-fit indexes in confirmatory factor analysis : An example using Sarason’s reactions to tests. Applied Measurement in Education, 16, 75-97 (2003). doi:10.1207/S15324818AME1601_4
  • Nasser, F., & Wisenbaker, J.: A Monte Carlo study investigating the impact of item parceling on measures of fit in confirmatory factor analysis. Educational and Psychological Measurement, 63, 729–757 (2003). doi:10.1177/0013164403258228
  • Peugh, J. L., & Enders, C. K.: Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74, 525-556 (2004). doi:10.3102/00346543074004525
  • Plummer, B. A.: To parcel or not to parcel: The effects of item parceling in confirmatory factor analysis. Unpublished doctoral dissertation, University of Rhode Island (2000).
  • Sass, D. A., & Smith, P. L.: The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling, 13, 566-586 (2006). doi:10.1207/s15328007sem1304_4
  • Schafer, J. L., & Graham, J. W.: Missing data : Our view of the state of the art. Psychological Methods, 7, 147-177 (2002). doi:10.1037/1082-989X.7.2.147
  • Signorella, M. L., & Cooper, J. E.: Relationship suggestions from self-help books: Gender stereotyping, preferences, and context effects. Sex Roles, 65, 371-382 (2011). doi:10.1007/s11199-011-0023-4
  • Steiger, J. H., & Lind, J. C.: Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA (1980).
  • Sterba, S.K.: Implications of parcel-allocation variability for comparing fit of item-solutions and parcelsolutions. Structural Equation Modeling, 18, 554-577 (2011).
  • Sterba, S.K. & MacCallum, R.C. Variability in parameter estimates and model fit across repeated allocations of items to parcels. Multivariate Behavioral Research, 45, 322-358 (2010).
  • Yang, C., Nay, S., & Hoyle, R. H.: Three approaches to using lengthy ordinal scales in structural equation models: Parceling, latent scoring, and shortening scales. Applied Psychological Measurement, 43, 122-142 (2010). doi:10.1177/0146621609338592
  • Yoder, J. D., Snell, A. F., & Tobias, A.: Balancing multicultural competence with social justice: Feminist beliefs and optimal psychological functioning. The Counseling Psychologist, 40, 1101-1132 (2012). doi:10.1177/0011000011426296

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

Year 2016, Volume: 7 Issue: 1, 59 - 72, 30.06.2016
https://doi.org/10.21031/epod.88204

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.

References

  • 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).
  • 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). Greenwich, Connecticut: Information Age Publishing (2006).
  • 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). Mahwah, NJ: Lawrence Erlbaum Associates (2001).
  • 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).
  • 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, S.G., & Finch, J.F.: The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29 (1996). doi:10.1037/1082-989X.1.1.16
  • Davey, A., & Savla, J.: Issues in evaluating model fit with missing data. Structural Equation Modeling, 12(4), 578-597 (2005). doi:10.1207/s15328007sem1204_4
  • Enders, C. K.: The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352-370 (2001). doi:10.1037/1082-989X.6.4.352
  • Enders, C. K.: The impact of missing data on sample reliability estimates: Implications for reliability reporting practices. Educational and Psychological Measurement, 64, 419-436 (2004). doi:10.1177/0013164403261050
  • Enders, C. K., & Bandalos, D. L.: The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 3, 430-457 (2001). doi:10.1207/S15328007SEM0803_5
  • Finney, S. J., & DiStefano, C.: Non-Normal and Categorical Data in Structural Equation Modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 269-314). Greenwich, Connecticut: Information Age Publishing (2006).
  • Fleishman, A. I.: A method for simulating non-normal distributions. Psychometrika, 43, 521-532 (1978). doi:10.1007/BF02293811
  • Hoogland, J. J. & Boomsma, A.: Robustness Studies in Covariance Structure Modeling: An Overview and a Meta-Analysis. Sociological Methods & Research, 26, 329-367 (1998). doi:10.1177/0049124198026003003
  • Hu, L., & Bentler, P. M.: Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55 (1999). doi:10.1080/10705519909540118
  • Hu, L., Bentler, P.M., & Kano, Y.: Can test statistics in covariance structure analysis be trusted? Psychological Bulletin, 112, 351-362 (1992).
  • Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F.: To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151-173 (2002). doi:10.1207/S15328007SEM0902_1
  • Matsunaga, M.: Item parceling in structural equation modeling: A primer. Communication Methods and Measures, 2, 260-293 (2008). doi:10.1080/19312450802458935
  • Meade, A. W., & Kroustalis, C. M.: Problems with item parceling for confirmatory factor analysis tests of measurement invariance of factor loadings. Paper presented at the 20th Annual Conference of the Society for Industrial and Organizational Psychology, Los Angeles, CA (2005).
  • Muthén, B., & Kaplan, D.: A comparison of some methodologies for the factor-analysis of non-normal Likert variables: A note on the size of the model. British Journal of Mathematical and Statistical Psychology, 45, 19 –30 (1992).
  • Muthén, B., & Muthén, L.: Mplus user's guide (6th eedition). Los Angeles, CA: Muthén & Muthén (1998-2008).
  • Nasser, F., & Takahashi, T.: The effect of using item parcels on Ad Hoc goodness-of-fit indexes in confirmatory factor analysis : An example using Sarason’s reactions to tests. Applied Measurement in Education, 16, 75-97 (2003). doi:10.1207/S15324818AME1601_4
  • Nasser, F., & Wisenbaker, J.: A Monte Carlo study investigating the impact of item parceling on measures of fit in confirmatory factor analysis. Educational and Psychological Measurement, 63, 729–757 (2003). doi:10.1177/0013164403258228
  • Peugh, J. L., & Enders, C. K.: Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74, 525-556 (2004). doi:10.3102/00346543074004525
  • Plummer, B. A.: To parcel or not to parcel: The effects of item parceling in confirmatory factor analysis. Unpublished doctoral dissertation, University of Rhode Island (2000).
  • Sass, D. A., & Smith, P. L.: The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling, 13, 566-586 (2006). doi:10.1207/s15328007sem1304_4
  • Schafer, J. L., & Graham, J. W.: Missing data : Our view of the state of the art. Psychological Methods, 7, 147-177 (2002). doi:10.1037/1082-989X.7.2.147
  • Signorella, M. L., & Cooper, J. E.: Relationship suggestions from self-help books: Gender stereotyping, preferences, and context effects. Sex Roles, 65, 371-382 (2011). doi:10.1007/s11199-011-0023-4
  • Steiger, J. H., & Lind, J. C.: Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA (1980).
  • Sterba, S.K.: Implications of parcel-allocation variability for comparing fit of item-solutions and parcelsolutions. Structural Equation Modeling, 18, 554-577 (2011).
  • Sterba, S.K. & MacCallum, R.C. Variability in parameter estimates and model fit across repeated allocations of items to parcels. Multivariate Behavioral Research, 45, 322-358 (2010).
  • Yang, C., Nay, S., & Hoyle, R. H.: Three approaches to using lengthy ordinal scales in structural equation models: Parceling, latent scoring, and shortening scales. Applied Psychological Measurement, 43, 122-142 (2010). doi:10.1177/0146621609338592
  • Yoder, J. D., Snell, A. F., & Tobias, A.: Balancing multicultural competence with social justice: Feminist beliefs and optimal psychological functioning. The Counseling Psychologist, 40, 1101-1132 (2012). doi:10.1177/0011000011426296
There are 40 citations in total.

Details

Journal Section Articles
Authors

Fatih Orçan

Yanyun Yang This is me

Publication Date June 30, 2016
Published in Issue Year 2016 Volume: 7 Issue: 1

Cite

APA Orçan, F., & Yang, Y. (2016). A Note on the Use of Item Parceling in Structural Equation Modeling with Missing Data. Journal of Measurement and Evaluation in Education and Psychology, 7(1), 59-72. https://doi.org/10.21031/epod.88204