References
Achenbach, T. M. (1991). Manual for the youth self-report form and 1991 profile. Burlington, VT: Department of Psychaiatry, University of Vermont.
Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49(2), 155–173. http://doi.org/10.1007/bf02294170 Bandalos, D. L. (2002). The effects of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling, 9(1), 78–102. http://doi.org/10.1207/s15328007sem0901_5 Bandalos, D. L., & Finney, S. J. (2001). Item parceling issues in structural equation modeling. In Marcoulides, G. A. & Schumacker, R. E. (Eds.), New developments and techniques in structural equation modeling (pp. 269–296). Mahwah, NJ: Lawrence Erlbaum Associates.
Barrett, P. T., & Kline, P. (1981). The observation to variable ratio in factor analysis. Personality Study and Group Behavior, 1(1), 23–33.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238.
Bodin, D., Pardini, D. A., Burns, T. G., & Stevens, A. B. (2009). Higher order factor structure of the WISC-IV in a clinical neuropsychological sample. Child Neuropsychology, 15(5), 417–424. http://doi.org/10.1080/09297040802603661 Bollen, K. A. (1989). Structural equations with latent variables (Vol. 210). John Wiley & Sons.
Bollen, K. A., & Noble, M. D. (2011). Structural equation models and the quantification of behavior. Proceedings of the National Academy of Sciences of the United States of America, 108, 15639–15646. http://doi.org/10.1073/pnas.1010661108 Boyle, G. J. (1991). Does item homogeneity indicate internal consistency or item redundancy in psychometric scales? Personality and Individual Differences, 12(3), 291–294. http://doi.org/10.1016/0191-8869(91)90115-r Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In Bollen, K. A. & Long, J. S. (Eds.), Testing structural equation modeling (pp. 136–162). Thousand Oaks, CA: Sage.
Byrne, B. M. (2005). Factor analytic models viewing the structure of an assessment instrument from three perspectives. Journal of Personality Assessment, 85(1), 17–32. http://doi.org/10.1207/s15327752jpa8501_02 Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological bulletin, 56(2), 81.
Castellanos-Ryan, N., & Conrod, P. (2011). Personality correlates of the common and unique variance across conduct disorder and substance misuse symptoms in adolescence. Journal of Abnormal Child Psychology, 39(4), 563–576. http://doi.org/10.1007/s10802-010-9481-3 Castro, J., dePablo, J., Gomez, J., Arrindell, W. A., & Toro, J. (1997). Assessing rearing behaviour from the perspective of the parents: A new form of the EMBU. Social Psychiatry and Psychiatric Epidemiology, 32(4), 230–235. http://doi.org/10.1007/bf00788243 Cella, D., Yount, S., Rothrock, N. et al. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194. http://doi.org/10.1016/j.jclinepi.2010.04.011 Cheung, G. W., & Rensvold, R. B. (2001). The effects of model parsimony and sampling error on the fit of structural equation models. Organizational Research Methods, 4(3), 236–264. http://doi.org/10.1177/109442810143004 Coffman, D. L., & MacCallum, R. C. (2005). Using parcels to convert path analysis models into latent variable models. Multivariate Behavioral Research, 40, 235–259. http://doi.org/10.1207/s15327906mbr4002_4 Collins, L. M., Schafer, J. L., & Kam, C. M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6(4), 330–351. http://doi.org/10.1037//1082-989x.6.4.330 Comrey, A. L. (1961). Factored homogeneous item dimensions in personality research. Educational and Psychological Measurement, 21(2), 417–431.
Cortes Hidalgo, A. P., Neumann, A., Bakermans-Kranenburg, M. J. et al. (2020). Prenatal maternal stress and child IQ. Child Development, 91(2), 347–365. http://doi.org/10.1111/cdev.13177 Costa, P. T. Jr., & McCrae, R. R. (2008). The Revised NEO Personality Inventory (NEO-PI-R). In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The SAGE handbook of personality theory and assessment, Vol. 2. Personality measurement and testing (pp. 179–198). Sage Publications, Inc. https://doi.org/10.4135/9781849200479.n9 Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434–449. http://doi.org/10.1007/s11747-011-0300-3 Eekhout, I., de Vet, H. C. W., de Boer, M. R., Twisk, J. W. R., & Heymans, M. W. (2018). Passive imputation and parcel summaries are both valid to handle missing items in studies with many multi-item scales. Statistical Methods in Medical Research, 27(4), 1128–1140. http://doi.org/10.1177/0962280216654511 Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling: A Multidisciplinary Journal, 8(1), 128–141. http://doi.org/10.1207/S15328007SEM0801_7 Enders, C. K. (2008). A note on the use of missing auxiliary variables in full information maximum likelihood-based structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 15(3), 434–448. http://doi.org/10.1080/10705510802154307 Enders, C. K. (2010). Applied missing data analysis. New York: Guilford Press.
Field, A. (2016). An adventure in statistics: The reality enigma. Thousand Oaks, CA: Sage.
Galambos, J., & Kotz, S. (1978). Characterizations of probability distributions: A unified approach with an emphasis on exponential and related models. New York: Springer.
Gorsuch, R. L. (1988). Exploratory factor analysis. In Nesselroade, J. R. & Cattell, R. B. (Eds.), Handbook of multivariate experimental psychology (pp. 231–258). Boston, MA: Springer.
Gottschall, A. C., West, S. G., & Enders, C. K. (2012). A comparison of item-level and scale-level multiple imputation for questionnaire batteries. Multivariate Behavioral Research, 47(1), 1–25. http://doi.org/10.1080/00273171.2012.640589 Graham, J. W., Tatterson, J. W., & Widaman, K. F. (2000). Creating parcels for multi-dimensional constructs in structural equation modeling. In annual meeting of the Society of Multivariate Experimental Psychology, Saratoga Springs, NY.
Gruner, K., Muris, P., & Merckelbach, H. (1999). The relationship between anxious rearing behaviours and anxiety disorders symptomatology in normal children. Journal of Behavior Therapy and Experimental Psychiatry, 30(1), 27–35. http://doi.org/10.1016/s0005-7916(99)00004-x Hall, R. J., Snell, A. F., & Foust, M. S. (1999). Item parceling strategies in SEM: Investigating the subtle effects of unmodeled secondary constructs. Organizational Research Methods, 2, 233–256. http://doi.org/10.1177/109442819923002 Hau, K. T., & Marsh, H. W. (2004). The use of item parcels in structural equation modelling: Non-normal data and small sample sizes. British Journal of Mathematical & Statistical Psychology, 57, 327–351. http://doi.org/10.1111/j.2044-8317.2004.tb00142.x Herman, K. C., Hodgson, C. G., Eddy, C. L. et al. (2020). Does child likeability mediate the link between academic competence and depressive symptoms in early elementary school? Child Development, 91(2), e331–e344. http://doi.org/10.1111/cdev.13214 Holzinger, K. J., & Swineford, F. (1939). A study in factor analysis: The stability of a bi-factor solution. Supplementary Educational Monographs
Hopkins, J., Gouze, K. R., Lavigne, J. V., & Bryant, F. B. (2020). Multidomain risk factors in early childhood and depression symptoms in 6-year-olds: A longitudinal pathway model. Development and Psychopathology, 32(1), 57–71. http://doi.org/10.1017/S0954579418001426 Howard, W. J., Rhemtulla, M., & Little, T. D. (2015). Using principal components as auxiliary variables in missing data estimation. Multivariate Behavioral Research, 50(3), 285–299. http://doi.org/10.1080/00273171.2014.999267 Hoyle, R. H. (2012). Handbook of structural equation modeling. New York: Guilford Press.
Iliceto, P., & Fino, E. (2017). The Italian version of the Wong-Law Emotional Intelligence Scale (WLEIS-I): A second-order factor analysis. Personality and Individual Differences, 116, 274–280. http://doi.org/10.1016/j.paid.2017.05.006 Jambon, M., & Smetana, J. G. (2020). Self-reported moral emotions and physical and relational aggression in early childhood: A social domain approach. Child Development, 91(1), e92–e107. http://doi.org/10.1111/cdev.13174 Joo, H., Aguinis, H., & Bradley, K. J. (2017). Not all nonnormal distributions are created equal: Improved theoretical and measurement precision. Journal of Applied Psychology, 102(7), 1022–1053. http://doi.org/10.1037/apl0000214 Keith, T. Z., Fine, J. G., Taub, G. E., Reynolds, M. R., & Kranzler, J. H. (2006). Higher order, multisample, confirmatory factor analysis of the Wechsler intelligence scale for children-fourth edition: What does it measure? School Psychology Review, 35(1), 108–127. ://WOS: 000202998000008.
Kishton, J. M., & Widaman, K. F. (1994). Unidimensional versus domain representative parceling of questionnaire items: An empirical example. Educational and Psychological Measurement, 54(3), 757–765.
Landis, R. S., Beal, D. J., & Tesluk, P. E. (2000). A comparison of approaches to forming composite measures in structural equation models. Organizational Research Methods, 3(2), 186–207. http://doi.org/10.1177/109442810032003 Lee, M. R., Bartholow, B. D., McCarthy, D. M., Pedersen, S. L., & Sher, K. J. (2015). Two alternative approaches to conventional person-mean imputation scoring of the Self-Rating of the Effects of Alcohol Scale (SRE). Psychology of Addictive Behaviors, 29(1), 231–236. http://doi.org/10.1037/adb0000015 Lei, P. W., & Shiverdecker, L. K. (2020). Performance of estimators for confirmatory factor analysis of ordinal variables with missing data. Structural Equation Modeling: A Multidisciplinary Journal, 27(4), 584–601. http://doi.org/10.1080/10705511.2019.1680292 Li, C. H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48(3), 936–949. http://doi.org/10.3758/s13428-015-0619-7 Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1–55.
Little, T. D. (2013). Longitudinal structural equation modeling. New York: Guildford Press.
Little, T. D. (in press). Longitudinal structural equation modeling (2nd ed.). New York: Guildford Press.
Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9(2), 151–173. http://doi.org/10.1207/s15328007sem0902_1 Little, T. D., Lindenberger, U., & Nesselroade, J. R. (1999). On selecting indicators for multivariate measurement and modeling with latent variables: When “good” indicators are bad and “bad” indicators are good. Psychological Methods, 4(2), 192–211. http://doi.org/10.1037/1082-989x.4.2.192 Little, T. D., Oettingen, G., & Baltes, P. B. (1995). The revised control, agency, and means-ends interview (CAMI): A multi-cultural validity assessment using mean and covariance structures (MACS) analyses. Berlin: Max Planck Institute.
Little, T. D., & Rhemtulla, M. (2013). Planned missing data designs for developmental researchers. Child Development Perspectives, 7(4), 199–204. http://doi.org/10.1111/cdep.12043 Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M. (2013). Why the items versus parcels controversy needn’t be one. Psychological Methods, 18(3), 285–300. http://doi.org/10.1037/a0033266 Madley-Dowd, P., Hughes, R., Tilling, K., & Heron, J. (2019). The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of Clinical Epidemiology, 110, 63–73. http://doi.org/10.1016/j.jclinepi.2019.02.016 Marsh, H. W., Ludtke, O., Nagengast, B., Morin, A. J. S., & Von Davier, M. (2013). Why item parcels are (almost) never appropriate: Two wrongs do not make a right – camouflaging misspecification with item parcels in CFA models. Psychological Methods, 18(3), 257–284. http://doi.org/10.1037/a0032773 Massé, R., Poulin, C., Dassa, C. et al. (1998). The structure of mental health: Higher-order confirmatory factor analyses of psychological distress and well-being measures. Social Indicators Research, 45(1–3), 475–504. http://doi.org/10.1023/a:1006992032387 Mathieu, S. L., Conlon, E. G., Waters, A. M., & Farrell, L. J. (2020). Perceived parental rearing in paediatric obsessive-compulsive disorder: Examining the factor structure of the EMBU child and parent versions and associations with OCD symptoms. Child Psychiatry & Human Development, 51(6), 956–968. http://doi.org/10.1007/s10578-020-00979-6 Matsunaga, M. (2010). How to factor-analyze your data right: Do’s, don’ts, and how-to’s. International Journal of Psychological Research, 3(1), 97–110. http://doi.org/10.21500/20112084.854 Mazza, G. L., Enders, C. K., & Ruehlman, L. S. (2015). Addressing item-level missing data: A comparison of proration and full information maximum likelihood estimation. Multivariate Behavioral Research, 50(5), 504–519. http://doi.org/10.1080/00273171.2015.1068157 McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum Associates.
Motti-Stefanidi, F., Pavlopoulos, V., Mastrotheodoros, S., & Asendorpf, J. B. (2020). Longitudinal interplay between peer likeability and youth’s adaptation and psychological well-being: A study of immigrant and nonimmigrant adolescents in the school context. International Journal of Behavioral Development, 44(5), 393–403. http://doi.org/10.1177/0165025419894721 Murray, J. S. (2018). Multiple imputation: A review of practical and theoretical findings. Statistical Science, 33(2), 142–159. http://doi.org/10.1214/18-sts644 Nasser, F., & Wisenbaker, J. (2003). A Monte Carlo study investigating the impact of item parceling on measures of fit in confirmatory factor analysis. Educational and Psychological Measurement, 63(5), 729–757. http://doi.org/10.1177/0013164403258228 Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
Orçan, F., & Yanyun, 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. http://doi.org/10.21031/epod.88204 Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74(4), 525–556. http://doi.org/10.3102/00346543074004525 Pilkonis, P. A., Choi, S. W., Reise, S. P. et al. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS (R)): Depression, anxiety, and anger. Assessment, 18(3), 263–283. http://doi.org/10.1177/1073191111411667 R Core Team. (2020). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Rhemtulla, M. (2016). Population performance of SEM parceling srategies under measurement and structural model misspecification. Psychological Methods, 21(3), 348–368. http://doi.org/10.1037/met0000072 Rioux, C., Lewin, A., Odejimi, O. A., & Little, T. D. (2020). Reflection on modern methods: Planned missing data designs for epidemiological research. International Journal of Epidemiology, 49(5), 1702–1711. http://doi.org/10.1093/ije/dyaa042 Rioux, C., & Little, T. D. (2020). Underused methods in developmental science to inform policy and practice. Child Development Perspectives, 14(2), 97–103. http://doi.org/10.1111/cdep.12364 Rioux, C., & Little, T. D. (2021). Missing data treatments in intervention studies: What was, what is, and what should be. International Journal of Behavioral Development, 45(1), 51–58. http://doi.org/10.1177/0165025419880609 Rioux, C., Stickley, Z., Odejimi, O. A., & Little, T. D. (2020). Item parcels as indicators: Why, when, and how to use them in small sample research. In Van De Schoot, R. & Miočević, M. (Eds.), Small sample size solutions: A guide for applied researchers and practitioners (pp. 203–214). London: Routledge.
Rodriguez, J. H., Gregus, S. J., Craig, J. T., Pastrana, F. A., & Cavell, T. A. (2020). Anxiety sensitivity and children’s risk for both internalizing problems and peer victimization experiences. Child Psychiatry & Human Development, 51(2), 174–186. http://doi.org/10.1007/s10578-019-00919-z RStudio Team. (2020). RStudio: Integrated development environment for R. Boston, MA: RStudio.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.
Rushton, J. P., Brainerd, C. J., & Pressley, M. (1983). Behavioral development and construct validity: The principle of aggregation. Psychological Bulletin, 94(1), 18–38. http://doi.org/10.1037/0033-2909.94.1.18 Sass, D. A., & Smith, P. L. (2006). The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 13(4), 566–586. http://doi.org/10.1207/s15328007sem1304_4 Schlomer, G. L., Bauman, S., & Card, N. A. (2010). Best practices for missing data management in counseling psychology. Journal of Counseling Psychology, 57(1), 1–10. http://doi.org/10.1037/a0018082 Seaman, S., Galati, J., Jackson, D., & Carlin, J. (2013). What is meant by “missing at random”? Statistical Science, 28(2), 257–268. http://doi.org/10.1214/13-sts415 Stein, G. L., Mejia, Y., Gonzalez, L. M., Kiang, L., & Supple, A. J. (2020). Familism in action in an emerging immigrant community: An examination of indirect effects in early adolescence. Developmental Psychology, 56(8), 1475–1483. http://doi.org/10.1037/dev0000791 Sterba, S. K., & MacCallum, R. C. (2010). Variability in parameter estimates and model fit across repeated allocations of items to parcels. Multivariate Behavioral Research, 45(2), 322–358. http://doi.org/10.1080/00273171003680302 Sterba, S. K., & Rights, J. D. (2017). Effects of parceling on model selection: Parcel-allocation variability in model ranking. Psychological Methods, 22(1), 47–68. http://doi.org/10.1037/met0000067 Syed, M., Eriksson, P. L., Frisén, A., Hwang, C. P., & Lamb, M. E. (2020). Personality development from age 2 to 33: Stability and change in ego resiliency and ego control and associations with adult adaptation. Developmental Psychology, 56(4), 815–832. http://doi.org/10.1037/dev0000895 Takacs, L., Smolik, F., Kazmierczak, M., & Putnam, S. P. (2020). Early infant temperament shapes the nature of mother-infant bonding in the first postpartum year. Infant Behavior & Development, 58. http://doi.org/10.1016/j.infbeh.2020.101428 Tarka, P. (2018). An over view of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 52(1), 313–354. http://doi.org/10.1007/s11135-017-0469-8 Thompson, B., & Melancon, J. (1996). Using item “testlets/parcels” in confirmatory factor analysis: An example using the PPDP-78. Paper presented at the annual meeting of the Mid-South Educational Research Association, Tuscaloosa, AL. https://eric.ed.gov/?id=ED404349 Van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). Boca Raton, FL: CRC Press.
Van De Schoot, R., Schmidt, P., De Beuckelaer, A., Lek, K., & Zondervan-Zwijnenburg, M. (2015). Editorial: Measurement invariance. Frontiers in Psychology, 6(1064). http://doi.org/10.3389/fpsyg.2015.01064 Violato, C., & Hecker, K. G. (2007). How to use structural equation modeling in medical education research: A brief guide. Teaching and Learning in Medicine, 19(4), 362–371. http://doi.org/10.1080/10401330701542685 Wei, J., Sze, I. N.-L., Ng, F. F.-Y., & Pomerantz, E. M. (2020). Parents’ responses to their children’s performance: A process examination in the United States and China. Developmental Psychology, 56(12), 2331–2344. http://doi.org/10.1037/dev0001125 Weijters, B., & Baumgartner, H. (2022). On the use of balanced item parceling to counter acquiescence bias in structural equation models. Organizational Research Methods, 25(1), 170–180
Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 10–18. http://doi.org/10.1111/j.1750-8606.2009.00110.x Widaman, K. F., & Thompson, J. S. (2003). On specifying the null model for incremental fit indices in structural equation modeling. Psychological Methods, 8(1), 16–37. http://doi.org/10.1037/1082-989x.8.1.16 Williams, L. J., & O’Boyle, E. H. (2008). Measurement models for linking latent variables and indicators: A review of human resource management research using parcels. Human Resource Management Review, 18(4), 233–242. http://doi.org/10.1016/j.hrmr.2008.07.002 Yang, C. M., Nay, S., & Hoyle, R. H. (2010). Three approaches to using lengthy ordinal scales in structural equation models parceling, latent scoring, and shortening scales. Applied Psychological Measurement, 34(2), 122–142. http://doi.org/10.1177/0146621609338592 Yoo, J. E. (2009). The effect of auxiliary variables and multiple imputation on parameter estimation in confirmatory factor analysis. Educational and Psychological Measurement, 69(6), 929–947. http://doi.org/10.1177/0013164409332225 Yu, Y., & Kushnir, T. (2020). The ontogeny of cumulative culture: Individual toddlers vary in faithful imitation and goal emulation. Developmental Science, 23(1). http://doi.org/10.1111/desc.12862 Yuan, K.-H., Bentler, P. M., & Kano, Y. (1997). On averaging variables in a confirmatory factor analysis model. Behaviormetrika, 24(1), 71–83. http://doi.org/10.2333/bhmk.24.71