1 / 68

Parceling: Benefits and Limitations

This workshop explores the controversy surrounding parceling in research methodology. Learn about the reasons for and conditions under which parcels can be beneficial or problematic.

tsalas
Download Presentation

Parceling: Benefits and Limitations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Why the Items versus Parcels Controversy Needn’t Be One Todd D. Little University of Kansas Director, Quantitative Training Program Director, Center for Research Methods and Data Analysis Director, Undergraduate Social and Behavioral Sciences Methodology Minor Member, Developmental Psychology Training Program crmda.KU.edu Workshop presented 05-23-2012 @ University of Turku Based on my Presidential Address presented 08-04-2011 @ American Psychological Association Meeting in Washington, DC crmda.KU.edu

  2. University of Kansas crmda.KU.edu

  3. University of Kansas crmda.KU.edu

  4. University of Kansas crmda.KU.edu

  5. University of Kansas crmda.KU.edu

  6. University of Kansas crmda.KU.edu

  7. Overview • Learn what parcels are and how to make them • Learn the reasons for and the conditions under which parcels are beneficial • Learn the conditions under which parcels can be problematic • Disclaimer: This talk reflects my view that parcels per se aren’t controversial if done thoughtfully. crmda.KU.edu 7

  8. Key Sources and Acknowledgements • Special thanks to: Mijke Rhemtulla, Kimberly Gibson, Alex Schoemann, Wil Cunningham, Golan Shahar, John Graham & Keith Widaman • Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M. (in press). Why the items versus parcels controversy needn’t be one. Psychological Methods, 00, 000-000. • 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, 151-173. • 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, 192-211. crmda.KU.edu 8

  9. Parceling: Averaging (or summing) two or more items to create more reliable indicators of a construct ≈ Packaging items, tying them together Data pre-processing strategy What is Parceling? crmda.KU.edu 9

  10. 1* 1* A CFA of Items -.29 Positive 1 Negative 2 .83 .81 .84 .77 .84 .71 .82 .82 .81 .69 .80 .76 Great Cheer ful Happy Good Glad Super Sad Down Un happy Blue Bad Terr ible .43 .31 .34 .30 .41 .30 .50 .32 .34 .35 .52 .35 Model Fit: χ2(53, n=759) = 181.2; RMSEA = .056(.048-.066); NNFI/TLI = .97; CFI = .98 crmda.KU.edu 10

  11. y21 Positive 1 Negative 2 y11 1* 1* y22 11 21 31 42 52 62 Great &Glad Cheerful & Good Happy & Super Terrible &Sad Down & Blue Unhappy & Bad q66 q11 q22 q55 q33 q44 CFA: Using Parcels (6.2.Parcels) Average 2 items to create 3 parcels per construct crmda.KU.edu 11

  12. -.27 Positive 1 Negative 2 1* 1* .89 .89 .91 .87 .87 .91 Great &Glad Cheerful & Good Happy & Super Terrible &Sad Down & Blue Unhappy & Bad .21 .21 .17 .18 .25 .25 CFA: Using Parcels • Similar solution • Similar factor correlation • Higher loadings, • more reliable info • Good model fit, • improved χ2 Model Fit: χ2(8, n=759) = 26.8; RMSEA = .056(.033-.079); NNFI/TLI = .99; CFI = .99 crmda.KU.edu 12

  13. Philosophical Issues To parcel, or not to parcel…? crmda.KU.edu 13

  14. Pragmatic View • “Given that measurement is a strict, rule-bound system that is defined, followed, and reported by the investigator, the level of aggregation used to represent the measurement process is a matter of choice and justification on the part of the investigator” • Preferred terms: remove unwanted, clean, reduce, minimize, strengthen, etc. From Little et al., 2002 crmda.KU.edu 14

  15. Empiricist / Conservative View • “Parceling is akin to cheating because modeled data should be as close to the response of the individual as possible in order to avoid the potential imposition, or arbitrary manufacturing of a false structure” • Preferred terms: mask, conceal, camouflage, hide, disguise, cover-up, etc. From Little et al., 2002 crmda.KU.edu 15

  16. Psuedo-Hobbesian View • Parcels should be avoided because researchers are ignorant (perhaps stupid) and prone to mistakes. And, because the unthoughtful or unaware application of parcels by unwitting researchers can lead to bias, they should be avoided. • Preferred terms: most (all) researchers are un___ as in … unaware, unable, unwitting, uninformed, unscrupulous, etc. crmda.KU.edu 16

  17. Other Issues I Classical school vs. Modeling School Objectivity versus Transparency Items vs. Indicators Factors vs. Constructs Self-correcting nature of science Suboptimal simulations Don’t include population misfit Emphasize the ‘straw conditions’ and proofing the obvious; sometimes over generalize crmda.KU.edu 17

  18. Other Issues II Focus of inquiry Question about the items/scale development? Avoid parcels Question about the constructs? Parcels are warranted but must be done thoughtfully! Question about factorial invariance? Parcels are OK if done thoughtfully. crmda.KU.edu 18

  19. Measurement Measurement starts with Operationalization Defining a concept with specific observable characteristics [Hitting and kicking ~ operational definition of Overt Aggression] Process of linking constructs to their manifest indicants(object/event that can be seen, touched, or otherwise recorded; cf. items vs. indicators) Rule-bound assignment of numbers to the indicants of that which exists [e,g., Never=1, Seldom=2, Often=3, Always=4] … although convention often ‘rules’, the rules should be chosen and defined by the investigator “Whatever exists at all exists in some amount. To know it thoroughly involves knowing its quantity as well as its quality” - E. L. Thorndike (1918) crmda.KU.edu 19

  20. “Indicators are our worldly window into the latent space” - John R. Nesselroade crmda.KU.edu 20

  21. Classical Variants • a) Xi= Ti + Si + ei • b) X = T + S + e • c) X1= T1+ S1+ e1 • Xi : a person’s observed score on an item • Ti: 'true' score (i.e., what we hope to measure) • Si : item-specific, yet reliable, component • ei : random error or noise. • Assume: • Si and eiare normally distributed (with mean of zero) and uncorrelated with each other • Across all items in a domain, the Sis are uncorrelated with each other, as are the eis crmda.KU.edu 21

  22. Latent Variable Variants • a) X1= T+ S1+ e1 • b) X2 = T+ S2 + e2 • c) X3= T+ S3+ e3 • X1-X3: are multiple indicators of the same construct • T : common 'true' score across indicators • S1-S3: item-specific, yet reliable, component • e1-e3 : random error or noise. • Assume: • Ssand esare normally distributed (with mean of zero) and uncorrelated with each other • Across all items in a domain, the Ssare uncorrelated with each other, as are the es crmda.KU.edu 22

  23. Empirical Pros • Psychometric Characteristics of Parcels • (vs. Items) • Higher reliability, communality, & ratio of common-to-unique factor variance • Lower likelihood of distributional violations • More, Smaller, and more-equal intervals Never Seldom Often Always Happy 1 2 3 4 Glad 1 2 3 4 Mean 1 1.5 2 2.5 3 3.5 4 Sum 2 3 4 5 6 7 8 crmda.KU.edu 23

  24. More Empirical Pros • Model Estimation and Fit with Parcels • (vs. Items) • Fewer parameter estimates • Lower indicator-to-subject ratio • Reduces sources of parsimony error (population misfit of a model) • Lower likelihood of correlated residuals & dual factor loading • Reduces sources of sampling error • Makes large models tractable/estimable crmda.KU.edu 24

  25. Sources of Variance in Items crmda.KU.edu 25

  26. Simple Parcel T T T T + + Sx s3 S2 1/9 of their original size! S1 e3 e2 e1 3 crmda.KU.edu

  27. Correlated Residual T T T T + + Sy s9 Sy S7 S8 e9 e8 e7 3 crmda.KU.edu

  28. Cross-loading: Correlated Factors U1 T1 C U1 U1 U1 U2 C T2 C C + + C S4 S5 s6 e5 e4 e6 3 crmda.KU.edu

  29. Cross-loading: Uncorrelated Factors U1 T1 U1 U1 U1 C C C + + C U2 S4 S5 s6 e5 e4 e6 3 crmda.KU.edu

  30. Construct = Common Variance of Indicators crmda.KU.edu 30

  31. Construct = Common Variance of Indicators crmda.KU.edu 31

  32. Empirical Cautions T T T Construct + M M S1 S2 Specific E1 E2 ¼ of their original size! Error 2 crmda.KU.edu 32

  33. Construct = Common Variance of Indicators crmda.KU.edu 33

  34. Construct = Common Variance of Indicators crmda.KU.edu 34

  35. y21 Positive 1 Negative 2 y11 1* 1* y22 11 21 31 42 52 62 Great &Glad Cheerful & Good Happy & Super Terrible &Sad Down & Blue Unhappy & Bad q66 q11 q22 q55 q33 q44 Three is Ideal crmda.KU.edu 35

  36. Three is Ideal Matrix Algebra Formula: Σ = Λ Ψ Λ´ + Θ Down & Blue Great & Glad Cheerful & Good Happy & Super Terrible & Sad Unhappy & Bad P1 11y11 11+q11 P2 11y112121y11 21+q22 P3 11y113121y113131y11 31+q33 N1 11y214221y214231y214242y22 42+q44 N211y215221y215231y215242y225252y22 52+q55 N311y216221y216231y216242y2262 52y226262y22 62+q66 • Cross-construct item associations (in box) estimated only via Ψ21– the latent constructs’ correlation. • Degrees of freedom only arise from between construct relations crmda.KU.edu 36

  37. Empirical Cons • Multidimensionality • Constructs and relationships can be hard to interpret if done improperly • Model misspecification • Can get improved model fit, regardless of whether model is correctly specified • Increased Type II error rate if question is about the items • Parcel-allocation variability • Solutions depend on the parcel allocation combination (Sterba & McCallum, 2010; Sterba, in press) • Applicable when the conditions for sampling error are high crmda.KU.edu 37

  38. Psychometric Issues • Principles of Aggregation (e.g., Rushton et al.) • Any one item is less representative than the average of many items (selection rationale) • Aggregating items yields greater precision • Law of Large Numbers • More is better, yielding more precise estimates of parameters (and a person’s true score) • Normalizing tendency crmda.KU.edu 38

  39. Construct Space with Centroid crmda.KU.edu

  40. Potential Indicators of the Construct crmda.KU.edu

  41. Selecting Six (Three Pairs) crmda.KU.edu

  42. … take the mean crmda.KU.edu

  43. … and find the centroid crmda.KU.edu

  44. Selecting Six (Three Pairs) crmda.KU.edu

  45. … take the mean crmda.KU.edu

  46. … find the centroid crmda.KU.edu

  47. How about 3 sets of 3? crmda.KU.edu

  48. … taking the means crmda.KU.edu

  49. … yields more reliable & accurate indicators crmda.KU.edu

  50. Building Parcels • Theory – Know thy S and the nature of your items. • Random assignment of items to parcels (e.g., fMRI) • Use Sterba’s calculator to find allocation variability when sampling error is high. • Balancing technique • Combine items with higher loadings with items having smaller loadings [Reverse serpentine pattern] • Using a priori designs (e.g., CAMI) • Develop new tests or measures with parcels as the goal for use in research crmda.KU.edu 50

More Related