SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

Analyzing Data from Small N Designs with Multilevel Models

jcross avatar
jcross
20 diapositives · 244 Views · Jan 01, 2020

Explore the application of Hierarchical Linear Modeling (HLM) to small N designs using datasets from overeating behavioral control and toddler play studies. Understand linear and quadratic model estimations, predictions, and simple models in HLM analysis.

Analyzing Data from Small N Designs with Multilevel Models - slide 1
01 / 20Analyzing Data from Small N Designs with Multilevel Models
1 / 17

Télécharger la présentation

Analyzing Data from Small N Designs with Multilevel Models
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation

  1. Analyzing Data from Small N Designs using Multilevel Models Eden Nagler The Graduate Center, CUNY David Rindskopf, Ph.D The Graduate Center, CUNY

  2. Overview/Intro • What is our current work? • Where did we start? • How does HLM fit into this framework?

  3. 2 Initial Datasets: Stuart, R.B. (1967). Behavioral control of overeating. Behavior Research & Therapy, 5, (357-365). Dicarlo, C.F. & Reid, D.H. (2004). Increasing pretend toy play of toddlers with disabilities in an inclusive setting. Journal of Applied Behavior Analysis, 37(2), (197-207).

  4. Stuart (1967):

  5. Stuart (1967):Procedures for Getting data into HLM

  6. Stuart (1967): Procedures for Getting data into HLM

  7. Stuart (1967): Level-1 dataset

  8. Stuart (1967): Level-2 dataset

  9. Stuart (1967): HLM (Linear model) Linear Model: POUNDS = π0 + π1*(MONTHS12) + e

  10. Stuart (1967): HLM – Linear Model Estimates Final estimation of fixed effects: Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------- For INTRCPT1,P0 INTRCPT2, B00 156.439560 5.053645 30.956 7 0.000 For MONTHS12 slope, P1 INTRCPT2, B10 -3.078984 0.233772 13.171 7 0.000 ---------------------------------------------------------- The outcome variable is POUNDS ---------------------------------------------------------- POUNDSij ≈ 156.4 – 3.1*(MONTHS12) + eij

  11. Stuart (1967): HLM – Quadratic Model Quadratic Model: POUNDS = π0+ π1*(MONTHS12)+ π2*(MON12SQ)+e

  12. Stuart (1967): HLM – Quadratic Model Estimates Final estimation of fixed effects: Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ----------------------------------------------------------- For INTRCPT1, P0 INTRCPT2, B00 158.833791 5.321806 29.846 7 0.000 For MONTHS12 slope, P1 INTRCPT2, B10 -1.773039 0.358651 -4.944 7 0.001 For MON12SQ slope, P2 INTRCPT2, B20 0.108829 0.021467 5.070 7 0.001 ----------------------------------------------------------- The outcome variable is POUNDS ----------------------------------------------------------- POUNDSij ≈ 158.8 – 1.8(MONTHS12) + 0.1*(MON12SQ) + eij

  13. Stuart (1967): HLM – Linear vs. Quadratic Model Stuart (1967) – Actual Data Linear Model Prediction Quadratic Model Prediction

  14. Dicarlo & Reid (2004):

  15. Dicarlo & Reid (2004): Level-1 dataset

  16. Dicarlo & Reid (2004): Level-2 dataset

  17. Dicarlo & Reid (2004): HLM – Simple Model Simple Model: FREQRND = π 0 + π1*(PHASE) + e

  18. Dicarlo & Reid (2004): HLM – Simple Model Estimates Level-1 Model Level-2 Model log[L] = P0 + P1*(PHASE) P0 = B00 + R0 P1 = B10 + R1 ---------------------------------------------------------- Final estimation of fixed effects: (Unit-specific model) Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------- For INTRCPT1,P0 INTRCPT2, B00 -0.769384 0.634548 -1.212 4 0.292 For PHASE slope,P1 INTRCPT2, B10 2.516446 0.278095 9.049 4 0.000 ---------------------------------------------------------- LN(FREQRNDij) = -0.77 + 2.52*(PHASE) + eij

  19. Dicarlo & Reid (2004): HLM – Simple Model Estimates LOG(FREQRNDij) = B00 + B10*(PHASE) + eij For PHASE=0 (BASELINE): LOG(FREQRNDij) = B00 FREQRNDij= exp(B00) For PHASE=1 (TREATMENT): LOG(FREQRNDij) = B00 + B10 FREQRNDij= exp(B00+B10) = exp(B00)*exp(B10) Estimates: B00 = -0.77; B10 = 2.52 For PHASE=0 (BASELINE): FREQRNDij= exp(B00) = exp(-0.77) = 0.46 For PHASE=1 (TREATMENT): FREQRNDij= exp(B00+B10) = exp(-0.77+2.52) = exp(1.75) = 5.75

  20. In conclusion… • Other issues we’ve encountered and explored • Issues we’ve encountered, but not yet explored • Issues we’ve not yet encountered nor explored